SNA’s Failures Revealed – Getting it Right With Social Ripple Analysis

If you are using one of the market leader Social Network Analysis (SNA) tools, it is very likely that you are not seeing the forest for the trees. While SNA is an effective tool for identifying subscribers who are social leaders and followers, using it for churn prediction and propensity modeling can be dead wrong. As Forte, we recommend all operators take a leap towards using Social Ripple Analysis (SRA)…

What?

Social Network Analysis (SNA) is undoubtedly one of the rising stars of customer analytics (primarily in telecoms), bringing in the impact of social relations on customer decision-making into the picture, as well as improving various existing models (such as churn prediction and cross-sales modeling). While the benefits realized by most users are unquestionable, its traditional applications oversimplify the social paradigm and can guide companies towards making misguided / incorrect decisions.

Companies that have been using SNA should now take the next steps towards improving their understanding and accuracy in modeling of social interactions, by focusing on “social diffusion” models. Social Ripple Analysis (SRA) improves (moreover, complements) SNA solutions by leveraging this perspective.

Why?

It is a given that consumers listen more to each other than they listen to companies when it comes to making purchasing / company relationship decisions. According to a survey in 2007, the top source for product information was word-of-mouth, with a whopping 59% of customers referring to each other as the key method for making decisions. While SNA has jumped in with both legs to save the day, there are certain factors many SNA models do not address properly (despite a commonly held belief that they in fact do).

Below are three SNA myths that should not be taken 100% at their face values:

SNA Myth #1 – Leaders have “more influence” on members of the community

The Truth: Leaders have influence on “more members” of the community

How Come?

 

The leader of a social community is frequently the one with highest number of connections within the community. Although this means that a leader can affect “more” individuals with his / her decisions, it does not necessarily mean that every member of the community will be influenced equally with these decisions. Often, a single member’s top influencer can be different than the leader itself; in the above illustration, for example, John’s wife could have more influence on him rather than his former classmate James (whose phone number he does not even remember).

Most SNA users would argue against that their predictive models are improved by focusing on the status of community leaders. Although on a high level the math holds, it is not necessarily for the right reasons. Let’s assume, for the sake of above illustration, that a churning customer increases by 10% the risk that every other individual he / she is in contact with will churn as well. For the above example, this would mean that:

• James – Interacting with 6 different customers, this means that there is a 60% likelihood that there will be a churner in this community.

• Rachel – Interacting with only 2 different customers, this means that there is a 20% likelihood that there will be a churner in this community.

Although James’ churn will have a more drastic impact on the community, it is not due to the fact that each member will be affected more; rather, it is simply because he is in touch with more people. As a matter of fact, his departure would have zero direct effect on John, whereas Rachel’s would increase John’s churn risk significantly in this case.

Ask yourself…who would you take advice from, and be susceptible to being influenced by – your wife, or, the 18 year old cousin of your colleague whom you’ve never seen in your life but apparently is quite chatty with your colleague and their family?

SNA Myth #2 – How frequently you communicate with an individual dictates the level of influence you have over that individual

The Truth: How much you communicate with an individual means how much you communicate. Period.

How Come?

Every relationship between customers is unique, defined by the nature of the relationship, the demographics of the parties, as well as their personal needs and experience with the products and services they use. As a consequence, not every relationship has the potential to create the same influence. Although the strength of communications between two individuals is an important parameter of how much they can influence each other, it is very misleading when used as the sole indicator. Furthermore, the parameters which affect whether a customer would be influenced from another depends on the area of influence (i.e. churn, data bundle sales, tariff migration) itself. In above example, although John speaks most frequently with his mother, when it comes to product usage, he is more similar to his best friend, and is more influenced by his comments. Regarding business related VAS, however, he looks up to his boss, who is a more trendy mobile user.

Ask yourself…who could convince you that the new iPhone deal is a good one – your grandfather, who still uses his Nokia 2100 but you talk to everyday, or, your tech-savvy colleague, whom you don’t call so often since he sits in your next cubicle, but rely on greatly for advice around technology?

SNA Myth #3 – Individuals are influenced primarily by the core “social network” they belong to – each network contains numerous people who are sometimes strangers to each other.

The Truth: Individuals are influenced primarily by their “personal network” – each person has his or her own personal network comprised of people he or she is directly connected with.

How Come?

Although the social networks that subscribers belong to can have a certain influence on them, moreover it is the people that subscribers are directly / closely connected with that have greater influence. A subscriber of a given community comprised of 20 individuals may not have even heard of a product or service that 15 of that community’s members are using (as he or she is not directly connected with those people). On the other hand, just like John in the above example, a subscriber could be easily influenced by subscribers that are a part of his or her “personal network” but outside his or her core “social network.”

Opposition to this myth suggests that assigning customers to distinct communities may not be the best idea, after all. If John has separate relations with friends, family, co-workers, etc., why not put him at the center of his own community, and place his relations around him to get the true picture around who influences him? With such a perspective (which Social Ripple Analysis, the concept we will explain later in this article, takes into account), every individual becomes the center of his / her own personal community.

Ask yourself…would you switch operators because 2 of your and 6 of their not-so-close friends switched to some other operator or because your wife and child just did the same?

It is possible to expand this list further and debunk even more myths; what we have listed, however, should be enough to prove the point that SNA solutions (in and of themselves) are not what they are hyped up to be.

To summarize and simplify, relations are too complicated to be illustrated using simple graphs. The question of how to overcome the limitations of SNA can be answered by Social Ripple Analysis (SRA).

How?

SRA focuses on a true “social diffusion” view around individuals (instead of synthetically creating communities, the way SNA does), and models how much a given subscriber can influence another given subscriber directly around driving up the sales of a given product or service, or around driving the churn of that subscriber.

Deploying SRA involves conducting a six-step effort, which we have turned into our very own solution – Rippler©. If you are an operator and wish to learn about these six steps, or, would be interested in learning about Rippler©, please contact us via info@forteconsultancy.com.

What Next?

As Forte, we are challenging all existing SNA users to hit the pause button and test their current model performances against SRA. Having experienced its impact first hand, we strongly urge companies to close the gaps in their social view of customers using SRA or similar analyses.

Does this mean that the era of SNA is over? Of course not…when used properly, SNA can create significant value for companies. However, companies should start digging below the surface to gain such benefits, which we refer to as SNA 2.0, and includes analyzing and acting at the community level, rather than the individuals themselves. For more details on SNA 2.0 or our SRA services, please contact info@forteconsultancy.com.

Note: For what it’s worth, we still believe that every operator should experiment with SNA solutions. To facilitate this, and since at this point with the development of SRA that SNA is relatively a dead duck, we are now offering our proprietary SNA software for FREE.

Customer-Level Price Sensitivity Analysis – Maximizing Campaign ROI

While campaigns are a must for companies in B2C sectors seeking to drive up revenues, they also have an adverse affect on the bottom line, in that they partially cannibalize already guaranteed revenues (thus driving down profit margins). Price Sensitivity Analysis (PSA) is a sure-fire way to minimalize this loss…

At the very heart of B2C sector company marketing efforts are customer campaigns. A world without campaigns is at this point unimaginable, as a majority of companies are running one or several at the same time. Walk into a mall, check out a bank’s website, or stop by a mobile operator shop and take a look at the walls and windows; there are sure to be dozens of posters advertising a “latest and greatest” limited time campaign, with some sort of benefit being propositioned to potential clients to capture their business.

Campaigns have at this point become a de-facto standard in B2C, such that consumers constantly expect some type of benefit for giving their business to a company. Some facts from a variety of recent surveys:

• 74% of consumers believe they are entitled to a discount when buying consumer items, regardless of price.
• 20% of consumers refuse to pay full price for anything.
• 65% of women and 41% of men admit to searching for a discount offer before making a purchase.
• 69% of shoppers are disappointed and frustrated at having to pay full price.
• 22% will always choose a restaurant where they know there is an offer.
• 95% of consumers expect the number of discounts on offer to grow substantially in the next two years.

The impact on margins of campaigns can be significant, such that the bottom line can be negatively impacted. Without an x% increase in sales, an x% campaign discount on a given product or service cannot be offset. An illustration from an electronics store to illustrate this:

Despite a solid 25% increase in the number of laptops sold as well as around a 12% in revenues generated from the sale of laptops, the bottom line of this electronics store is negatively impacted, with around  a 40% decrease in profits vs. the period prior to the campaign.

Companies need to be very careful when conducting campaigns. We recommend that they conduct pilots prior to ramping them up if and when possible, as well as cancel those that appear to be headed in the wrong direction (this of course can be done only when there is an exit opportunity, a mass announced campaign can be near impossible to cancel before it runs for the stated duration). We also recommend that they conduct a customer level campaign price sensitivity analysis (PSA), so as to maximize the return on their investments.

The core objective of PSA is to prevent the cannibalization of revenues during campaigns, such that those customers who normally have and intend to pay full price for a given product or service do so. How? By not conducting any type of communications with them in regards to the campaign, as well as by keeping campaigns out of sales points that are disproportionately frequented by them.

Think about it – if a company has a specific group of satisfied customers who are willing to come back again and again and consistently pay full price for a given product or service, would it make sense for that company to offer discounts on those products or services to these customers? Not really. The objective of campaigns should be around moving an unsellable product or service, winning new customers, or winning back old customers who have drifted away, not cannibalizing guaranteed revenues. Just as a company that has a limited quantity on hand of a given product or service but is able to sell all of it at full price would never conduct a campaign, so too should companies avoid conducting campaigns in and around customers willing to pay full price.

PSA is an approach that will help companies identify these customers, with actions that can then be taken to ensure they continue to pay full price. To conduct and establish ongoing PSA efforts, the following six steps need to be conducted:

1. Normal Price Sensitivity Analysis: The first step is to go back historically (one to two years should suffice) and identify all of the products or services that customers have purchased at full price. For each customer, a score should be generated around their willingness to pay full price, both in terms of quantity as well as frequency. The idea here is to find those customers who give full share-of-wallet to the company at full price; that is, customers who show no decrease in their spending pattern, consistently pay full price during normal pricing periods, and purchase an amount of products or services that equate to the ideal expected amount a given customer would purchase over a period of time. For a gas station, this would be a customer who comes in at least once a week to fill his or her gas tank for a year. For a supermarket, this would be a single adult who comes in at least once a week and purchases at least $100 USD or so per week for a full year. For a mobile operator, this would be a subscriber who has an ARPU each month well above his peers for a full year.

2. Campaign Price Sensitivity Analysis: With the above completed, the next step is to identify those customers that are price sensitive; that is, customers who show an increase in their normal shopping patterns during campaigns, that are seeking out campaigns to ramp up their spend, that seem to give full share-of-wallet only when campaigns are running, when products or services can be procured for a discounted price. Just as in the above step, a score should be generated for each customer around their behavior during campaigns.

3. Gap Analysis: Bringing the above two together, the next step will be to identify “full-priced traditional loyalists (FPTLs),” those individuals who:

• Are full share-of-wallet shoppers when products and services are at full price
• Do not exhibit a disproportionate increase in spending when campaigns are running.
• Do not exhibit a tendency to purchase products or services they do not traditionally purchase just because a discount is being offered

4. Location Analysis: With a customer analysis complete, the next step will be to identify which sales points (or channels) are disproportionately utilized by FPTLs. By examining sales performance during and outside campaigns on a point-by-point basis, as well as the frequency of full-priced traditional loyalists to use one point or another, a score can be assigned to each sales point, as well as to each sales channel, indicating the overall willingness of the specific location’s customer base to pay full price, to not be swayed by campaigns.

5. Strategy Development: The next step is to bring it all together in designing a strategy around ensuring revenues are not needlessly cannibalized, by avoiding conducting and / or promoting campaigns to FPTLs. The company should examine each of the ways in which it can achieve this mission; some straightforward as well as more complex and creative strategies that can be considered here (relevant only to some companies in some sectors, not across-the-board):

• Not communicating campaigns on a one-to-one level, be it via e-mail, SMS or mailer, with FPTLs.
• Not putting up billboards regarding campaigns in areas where FPTLs live
• Not running commercials around campaigns on television channels or radio stations they listen to (this may require some market research be conducted with FPTLs)
• Not conducting campaigns at specific sales points that are overwhelmingly utilized by FPTLs.
• Not communicating campaigns during channel visits by FPTLS (i.e. no campaign communication when an FPTL logs in to their supermarket online delivery account, no campaign communication when an FPTL uses an ATM).

6. Piloting: The last but most important step is to conduct piloting around the above strategies before conducting a full-scale rollout. Companies have to be very careful to avoid being perceived as intentionally avoiding giving benefits to their best customers. The tactics undertaken have to be carefully tested, to assess the impact not only on the bottom line, but on the sensitivity of customers to being opted out of campaigns. By conducting pilots across a few locations and with a handful of customers, companies should be able to understand which strategies to deploy on a full-scale, which to scrap.

The above steps, once completed, should be conducted again and again on an ongoing basis, to determine if modifications need to be made to the designed strategies, to understand if specific customers are no FPTLs, or if specific locations are no longer disproportionately utilized by FPTLs.

We believe companies that deploy PSA can, in a very rapid manner, improve their bottom line significantly. To learn more about setting up and benefiting from PSA, please contact us at info@forteconsultancy.com.

Social Media Strategies – Twitter Follower Relationship Management

With a significant percentage of B2C companies well established at this point on Twitter, it’s due time they take steps to better understand and take actions around their followers – Follower Relationship Management (FRM) is a method for doing so…

What?

A recent survey found that consumers who engage with companies on social media spend 20% to 40% more with those companies than other consumers do; another survey found that consumers are 75% more likely to purchase from a brand they follow on Twitter. These facts combined with the fact that there are over 500 million active Twitter users necessitates that companies present on Twitter have a formal customer relationship management strategy in place for managing their followers. Follower Relationship Management (FRM) is a method for doing so; by engaging in FRM, companies will be able to not only understand their followers, but also take action on them.

Why?

Triggered and driven by globally ever-increasing internet / mobile internet penetration rates, Twitter has in a very short time become an indispensible communication channel for people and companies the world over. With over a half billion users tweeting around as many messages per day (on average), the power of Twitter is immense – it played an important role in the Arab Spring revolution, for example. It can and does play an important role in impacting the bottom line of companies as well, serving as a channel for many a company to provide sales and service through; more importantly, however, is the role it plays in allowing for the rapid spread of positive or negative word-of-mouth. Progressive Insurance is a prime example of a company that lost thousands of customers due to a spat with a customer on Twitter (and the subsequent firestorm of tweeting around the incident).

Despite its power, however, many a company is yet to effectively manage this channel; a recent study found that approximately two thirds of tweeters who have tweeted about a bad experience have never received a response from the offending company. Juxtapose this reality with the fact that two thirds of consumers expect a response the same day to an online complaint, it’s clear that there is a serious degree of channel mismanagement taking place.

Companies need to in a structured and ongoing manner manage this channel as if it were a traditional one, with a strategy in place that ensures effective client management across all spectrums. Just as in traditional channels customer relationship management (CRM) is practiced, so too should a similar concept be at play in the social media realm; follower relationship management (FRM) is this concept, a method for managing Twitter followers to ensure optimal customer-level actions are taken. Benefits of utilizing FRM include:

• Learning more and gaining deeper insights of consumers and audiences through a deep interest analysis of Twitter followers
• Improving the bottom line through grouping consumers according to their behaviors / interest patterns and addressing  potential advertising and promotion vehicles  to the right target audiences
• Driving up follower satisfaction with more relevant and targeted tweet content aligned to the interests of different follower groups

How?

FRM requires extensive use and analysis of social media data, to enable strategies to be developed that can be applied to followers. Three steps are required to be taken towards deploying an effective FRM practice…

1. Develop Tweet History Database: The first and most basic step towards building an FRM practice is to start recording tweets about the company, its products and services, its competitors, etc. Twitter limits the retrieval of tweets around a given subject (date and content limited; for example, only up to 1500 tweets old around a specific search term). As such, companies need to begin recording and logging all tweets that they may analyze and take action on in the future. The first insights from this data might be limited to a few major indicators (change in the number of followers, number of mentions, etc.) whereas deeper analysis (with social network analysis or text or data mining algorithms) will bring deeper insights such as identification of the influencer accounts in the follower base, monitoring the number of positive / negative mentions per product, or estimating the effect of a marketing campaign on Twitter. The resources required to set this up are minimal, with any decent size company’s IT department able to build such a database to ensure ongoing Twitter data retrieval.

2. Conduct Customer Segmentation: The next step is to conduct segmentation of Twitter followers, through identifying the varying needs, behavior, value, and relationship of each individual that is following the company Twitter page. Such segmentation can be conducted using data coming from / around three different areas:

• Account Information – Information pertaining to the individual follower’s twitter account, such as:
  • Brands / celebrities followed
  • Activity (number of tweets per day / tweet regularity / tweet diversity)
  • Account tenure
  • Number of positive / neutral / negative tweets regarding the company

• Network Information – Information pertaining to the individual follower and his or her Twitter network, to understand the status, role, potential impact, etc., better, information such as:
  • The number of followers the individual has
  • The brands the followers of the individual follow
  • The number of positive / neutral / negative tweets regarding the company tweeted inside the individual follower’s network
  • The total number of followers individuals inside the given network have

• Internal Information – Information pertaining to the individual that resides inside the company, such as:
  • Customer tenure
  • Customer value (revenue, profitability, etc.)
  • Customer product / service ownership
  • Customer status (active / passive)
  • Customer social network

Aside from “internal information,” the above analysis can be conducted in a relatively short amount of time in a simple manner. To match up Twitter accounts to internal customer records is a rather challenging task; it necessitates Twitter followers to identify themselves as customers, requiring the company to take some type of action to ensure this happens. Aside from self-identification that can happen through a customer care or sales support incident, a campaign will need to be conducted by the company wishing to link up internal information to Twitter accounts.

A successful example of a company that has done this was the “sync with Twitter” campaign conducted by American Express, wherein Amex cardholders synced their Twitter accounts with their cards to automatically received savings on their card (when they tweet certain hashtags such as #AmexCoffee or #AmexBestBuy). The campaign not only managed to link up a significant number of cardholders to their Twitter accounts, but it also created a word-off mouth snowball that generates additional revenue for Amex and certain partner companies (like McDonald’s, Best Buy, and Whole Foods).

With the analysis complete, followers can now be segmented into actionable groups (with an individual possibly in several groups at once). Examples of groups that can be defined are as follows (the groups can and should be customized based on the company’s own set of circumstances):

Twitter Celebrity: Has more than x number of followers

Twit Addicts: Tweets more than x times a month

Disconnected: Has less than x number of followers or tweets

Company Friendly: Inside a network that has a high rate of individuals following companies

Competitor Friendly: Inside a network that has a high rate of individuals following competitor companies

Linked Circuit: Inside a network that has a high rate of individuals following the same companies

Loud Detractor: Has more than x number of negative tweets about the company

Company Advocate: Has more than x number of positive tweets about the company

Prestigious Ones: Has more than x number of followers, and, is above x value

Potential One: Has tweeted at least x number of times regarding the company and has been identified as definitely not a customer

Influencers: Has more than x percentage of his or her tweets retweeted

Reflectors: Retweets more than x percentage of other individuals tweets

Other segments may be defined based on relevant variables; one is sector – Avon, for example, segments its followers into four groups – beauty, fashion news & comedy, music & TV, and cutting edge beauty. They have gone as far as to develop a business plan for each of the segments, changing the way they target them; for example, by knowing that a certain segment’s individuals commonly follow a specific TV program (which is extracted from analyzing Twitter accounts), Avon customizes how it advertises to that segment (by having commercials run during that program, by using that program’s celebrities as spokespeople, by conducting product placement during that program, etc.).

3. Design & Take Action: Once the segments have been designed, the next step is to take action on those segments, aimed at driving up satisfaction rates as well as the bottom line, if possible. Some examples of actions that can taken on the various follower segments:

• Customized Treatment: Based on the behavior and needs of various important follower segments, companies can offer customized treatments that are aligned to their specific commonalities. An example of such a customized treatment can include ensuring tweets from certain important follower segments are responded to in a prioritized manner. Microsoft is an example of company that has developed another customized treatment; they have created a different Twitter account (Microsoft Student) to serve the needs of “student technologists around the world, providing software at no charge, resources, tech news & more.” Dell is another company that does customization very well, with dozens of Dell Twitter accounts supporting numerous different follower groups – some of the accounts:

Not only did the airline generate significant revenues, it donated $50,000 to Stand Up to Cancer, as well as increased its loyalty program sign-ups by 25%.

• Acquisition & Cross / Up-Sell Pitches: Targeting those follower groups identified as potential clients or susceptible to offers, companies can use Twitter for making direct pitches to followers. Numerous companies have already developed and use direct messaging applications to do this type of pitching, initiating the contact through Twitter, shifting to more traditional channels if the pitch is welcomed by the follower. Virgin America airlines did this recently, leading to the largest single day of sales in its history – teaming up with a charitable cause (Stand Up to Cancer), Virgin America targeted its followers with a campaign that donated a portion of the ticket sales to the charity.

• Targeted and Cost-Effective Advertising: The specific follower groups can be advertised to in a customized manner, seeking to maximize response rates, as well as enable positive word-of-mouth in a short period of time. Creativity is the key here, with Twitter an ideal channel for conducting unique and inspired campaigns for select follower groups. Volkswagen Spain, for example, created a campaign around an online game for its Polo brand called Polowers (a mash-up of Polo and followers); followers were able to play the game through Twitter, integrating the Twitter API into the Polowers micro-site, requiring users to become followers to play.

The campaign resulted in over 150,000 tweets being made within the first hours of the contest, and increased Volkswagen’s follower base by 50%.

The above are only a few examples of the wealth of actions that can be taken. What is critical is that companies go beyond taking mass actions on all of their followers; rather, they need to target specific follower segments, designing tactics that aim to acquire, grow, and retain niche pockets of individuals. Just as below-the-line micro-segment specific actions are taken, so too should they be taken with Twitter follower groups.

What Next?

FRM is a catch-all concept around client management that companies should begin using immediately within their marketing, sales, and customer care teams, striving to build a culture wherein Twitter relationships with followers are actively managed to maximize performance. Once such a culture is in place, then and only then can a company consider itself effective in and around managing its Twitter channel. To learn more about building a Twitter FRM culture, please contact info@forteconsultancy.com.

Grading Performance – The Automotive Industry BI Maturity Map

Automotive companies are increasingly placing emphasis on becoming customer centric, investing significant time and resources towards this endeavor. The effective utilization of business intelligence (BI) in any CRM enhancement effort is a must, necessitating that automotive companies assess where they are, as well as plan where they want to be, around this field…

What?

The investment by automotive companies into CRM (be it people or software, IT systems or loyalty programs) is on the up and up. Few (inside or outside the companies) would in general consider these investments “effective” to date; for every successful CRM initiative launched by an automotive company, there are dozens of apparent or hidden failures.

Behind most effective CRM efforts are people, analytical enough to understand the customer base, smart enough to capitalize on this understanding. At the very heart of most of these efforts is business intelligence (BI), data that through analysis allows for opportunities to be identified and acted upon. In order to assure and maximize the value add of CRM efforts, automotive companies should take a broader look at their BI capabilities, to identify if and how improvements need to be made. Without effective BI practices in place, many a CRM effort is doomed to fail, being guided not by data but by gut feel. To that end, we recommend companies assess how mature their practices are across a variety of BI areas.

Why?

Assessing the maturity of BI related efforts can help automotive companies in various ways:

• To ensure there is a clear definition of the baseline around company BI capabilities.
• To identify the current problem / improvement areas.
• To define paths in which employees and departments need to be aligned around to move forward.
• To define a list of milestones which can then be turned into targets.
• To set an objective scale which can be used in benchmarking.

How?

The “Automotive Industry BI Maturity Map” defines four different maturity levels that can be applied against five different areas under BI:

Maturity Level 1: Barely Basic

The “Barely Basic” level is the first stage of the business intelligence journey. At this level, the company has done almost nothing around developing its business intelligence efforts, nor around using data to take actions. Companies at this level are usually here due to lack of awareness about the potential benefits of BI, or absence of the mandatory enablers (such as capable workforce or proper data systems).

Data Management at this level is one in which companies have started to log sales and service data, but not always as related to each other. Basic demographic information (such as age or gender) is logged but usually significant data quality issues exist.  Automobile uniqueness is usually in place but customer uniqueness is questionable. Reference values required to give meaning to the records (such as car configuration or accessories reference table) are usually not up to date.

Technology at this level around supporting business intelligence efforts is none to minimal. Technologies are selected and managed according to current employee skill-sets, with technology investment decisions and budgets solely owned by IT. MS Office programs are used as the main platform for information exchange within the company.  There is no data mining tool in place or one being considered as of yet.

Reporting at this level is such that there is almost no reporting beyond that done for tracking basic financials. Ad-hoc reports are provided up to a certain degree, but consistency and efficiency are always in debate. Reports are not highly valued by management, thus little demand exists within the company for them.

Analytics is bare bones at this level, with little to no modeling in place, as well as no capabilities to conduct modeling. At the most there is basic segmentation around customer value, likely tied to the model of the automobile, or to total revenue generated by a customer over a given amount of time. This is significantly driven by the fact that there is minimal to no demand from the business units (marketing or sales) for customer analytics solutions.

Governance of BI is as well practically non-existent; BI-related efforts are managed haphazardly, with no formal business unit in place to oversee and handle them. Roles and responsibilities as such are not defined either, with no employees dedicated in a full-time manner to handle business intelligence-related tasks. Basic concepts have been defined (i.e. customer churn, profit margin, etc.), but are rarely relied on or utilized.

Maturity Level 2: On the Way

Companies at the “On the Way” level are those which realize the importance of BI and have started to tap into its benefits; they have yet to, however, overcome design related issues (i.e. data management structure design, analytical models design, roles / responsibilities design, etc.), or scale up BI operations such that it is sustainable on its own as a business unit.

Data Management at this level is one in which data is stored in a proper manner (i.e. in a data warehouse), though not in a complete manner (i.e. lacking prospect data). Data is both deeper (i.e. not just customer name, age, and address, but also occupation, education level, etc.), and richer (i.e. not just dealer visit information but also contact center interactions). Customer uniqueness is also assured, with a unified view of all customer sales and service data in place. Information can be historically tracked (such as a customer’s service pattern changes with a demographic stage change) as well. Data inventory and data quality is monitored regularly, but usually only from a company perspective.

Technology at this level is more advanced, with basic reporting tools in place, possibly a data mining tool as well (though not being used effectively or in an ongoing systematic manner). Alternative technologies for the main architectural components are known and reviewed regularly. Vendor evaluation scorecards are in place and SLA management structures have been established between the company and vendors.

Reporting at this level is automated though standard and basic, with monthly reports issued around common and key KPIs, distributed across business units (with some customization at the business unit level).  One view of truth has been assured (such that all reported data has been reviewed and is free of discrepancies and inaccuracies), with ad-hoc reporting also in play but at a minimal level.

Customer Analytics is more advanced at this level, such that customer differentiation beyond value is in place (i.e. differentiation based on service usage behavior). Other basic models have been developed as well (such as churn prediction) but are not in use on a common basis; performance and accuracy of the models is questionable.

Governance has improved significantly at this point, with the establishment of a formal BI function within the company. BI personnel have assigned roles and responsibilities, with marketing and sales interaction common and frequent (though not systematic). Ad-hoc analysis are performed more frequently, with both BI and business units aware now of the value of tapping into data.  A data dictionary has been developed to serve as a reference for all stakeholders, with the entire set of customer definitions in place (i.e. “active service customer,” “sports car savvy,” etc.).

Maturity Level 3: Ahead of the Curve

Automotive companies that have made it to this level along the BI evolution journey have fully embraced the concept of tapping into data, viewing it as a main competitive advantage. Data is viewed as a critical asset, assessed constantly for completeness and accuracy, compiled and shared across all parts of the business.

Data Management at this level is one in which all data-related processes, measurement methods, controls, etc., are working smoothly. There exists a proper data strategy (which data to collect, where to collect, when to collect) at this point that is adhered to and realized. Data is comprehensively collected across internal channels (i.e. even around prospective customers), with data quality controls in place across all channels. Data privacy principles and guidelines are known and properly documented.

Technology is being tapped into and leveraged significantly at this level, with data mining and reporting tools used in an advanced manner. Experts around using the tools reside within the BI unit, with power users as well in the marketing and sales departments. The company is at the point where they are examining alternative solutions rather than a particular technology (with outsourcing, open source, or cloud solution options being reviewed and evaluated regularly). Demand and efficiency are headed in different directions, with requests from the BI unit constantly increasing, the turnaround time needed to fulfill requests constantly decreasing.

Reporting is fully automated at this level, customized for the various stakeholder groups in the company. More advanced reporting concepts (i.e. dashboards, scorecards, etc.) are in play, with KPIs being reported on a daily, even a real-time basis in some cases. Business units are power users as well, able to develop their own ad-hoc reports. Visual and graphic representations are fully utilized in reporting to allow for an increased ease of interpretation.

Analytics is in overdrive at this level, with models developed across a variety of spectrums allowing for strategies to be designed and carried out around acquiring, growing, or retaining the customer base. Segment management has been fully enabled, with an understanding of prospects and customers in and around their value, needs, and behaviors. Predictive sales propensity models have been developed and are being used to proactively re-sell to existing customers. Stock levels have been optimized thanks to sales / demand forecast models. Accuracy of the models is a given, as is automation; models are re-running each month with little to no manual support needed.

Governance at this level as well is almost an afterthought, with everything in and around BI running like a well-oiled machine. BI and business units clearly know their roles and responsibilities and adhere to them. KPIs are understood and adhered to. Collaboration between all stakeholders is significant, with all parties seeking to constantly improve BI-related performance and outputs. The BI unit is viewed by business units and executive management as a peer and not a support unit.

Maturity Level 4: Best-in-Class

The few automotive companies that have made it to this level are clear leaders in and around the BI domain. Most of the key decisions being made in such companies are not only being triggered by information, but also validated or dispelled by it. The utilization of intelligence in all activities across all business units is essentially a KPI, expected of all employees, reflected in their roles and responsibilities. Improvement opportunities for bettering BI-related efforts are almost at a minimal.

Data Management at this level is an after-thought; data is being collected in an accurate and complete manner from not just internal sources but from those interfacing with external parties as well (i.e. social media interactions, web behaviors, etc., are being tracked and logged). There is nary an interaction or event in and around the automotive company that is not monitored, noted, and stored in the data warehouse; data is deep (with records going back at least ten years), data is accurate (error-free), data is granular (down to the hour, down to the conversation details level).

Technology is being used in an efficient manner as is possible, with all hardware, software, processes, etc., optimized around the company’s BI practices. Certain efforts have been outsourced, allowing for the company to internally focus on value-added activities only. Almost everything works in a real-time manner, allowing for decisions to be made on the fly. There are essentially no opportunities for the company to improve how data is stored, accessed, manipulated, and disseminated; the company is, however, always on the lookout for such opportunities should they be made available thanks to new developments in technologies.

Reporting systems at this level are proactive; alerts have been built into reporting systems, notifying interested stakeholders in a real-time manner when and if needed (i.e. an email sent to a District Sales Manager when stock levels of a certain model hit a critical amount, ensuring action is taken immediately to drive replenishment). Standard reports are being updated in a real-time manner, as real-time as the data flowing into the company. They can be accessed now through mobile solutions (i.e. tablets and mobile phones). Tailored reports are produced and shared as well at this point with 3^rd parties (i.e. dealers and suppliers). Report usage intensity, diversity, and frequency is monitored to understand reports’ fit-for-need, with modifications made if needed to drive uptake.

Analytics is extremely advanced, with concepts like customer lifetime value being analyzed and modeled to allow for longer-term strategies to be deployed on a customer by customer level. Model outputs are being used across all channels to drive up the retention and growth of company value in a systematic and automated manner. Additional advanced concepts like GIS based analyses are also being conducted (i.e. potential analysis, new dealer or service location / site selection).

Governance at this level is a concept that is not needed or thought of on a regular basis. Stakeholders in and around the BI unit operate in an efficient, optimal, and ideal manner, requiring no procedures or policies be followed (as the operation flows completely smoothly).  The company culture revolves around the utilization of data in decision making; as such, the BI unit is at the heart of all company efforts.

What Next?

Once automotive companies identify what level they are at in each of the BI areas, a plan / strategy should be designed for moving up one or more levels in those areas the companies feel they need to be stronger in. With tangible targets defined, a BI development roadmap should then be designed, with the targets and relevant deadlines assigned to various stakeholders. Monitoring of the evolution across the areas then should be a common practice to ensure targets are being hit.

To learn more about business intelligence maturity levels and how you can assess where your company currently stands, please contact info@forteconsultancy.com.

Visitor Relationship Management – Optimizing the Online Lifecycle

As companies become more proactive and effective in targeting across traditional direct channels, it’s time to start focusing on customizing interactions online, seeking to gain a better understanding of each website visitor and making the most out of each visit. Website Visitor Relationship Management (VRM) follows the path of CRM, mimicking the use of similar analytical models and lifecycle management principles…

 

What?

According to a study by Forrester, 75% of companies consider web analytics vital for their business. Yet, most of these companies rely on standard and static reports, with minimal focus on proactive management of visitor relations and experience. To gain an optimal return on online investments, companies should start concentrating on Visitor Relationship Management (VRM), as they did around better managing customers via CRM back in the 1990s.

VRM has similar objectives as CRM, though applied from an online perspective – example objectives include the acquisition of high potential visitors to the site in a low cost manner, retention of visitors on the site until a conversion happens, having customers come back and visit new content on a regular basis, etc.

Why?

VRM provides the ability to customize online interactions at an individual level, increasing relevance for the visitor while maximizing return on each visit. At its most generic definition, VRM tackles business problems across the whole visitor lifecycle:

• Decreasing cost of visitor acquisition, by identifying the most effective traffic sources for each visitor segment
• Improving and customizing site design, by understanding behavior and preferences of different visitor segments
• Increasing site stickiness, by identifying content and issues causing site abandonment by segment
• Increasing pages per visit, by proactively promoting the most relevant content to individual visitors
• Maximizing online ROI, by identifying and acting on visitors with the highest conversion likelihood

How?

VRM requires extensive use and analysis of visitor data. For this purpose, we recommend companies conduct five key analyses (at the outset, to get things started – additional analyses can be conducted down the road), which would then be used for site content and experience customization. 

1. Acquisition Optimization: As the variety of traffic sources continues to increase, it is becoming more crucial and difficult to optimize investments in them. The value of a very fine-tuned Google search phrase or LinkedIn Ads target profile could be easily worth hundreds of times its cost, while the generic use of such traffic sources could bring in visitors for all the wrong reasons.

A common blunder in measuring effectiveness of traffic sources is blind focus on cost per acquired visitor, discarding whether that visitor could or can be converted, or whether he / she is in the target market to begin with. In order to get the most out of their digital marketing budgets, companies need to analyze the “lifetime value” generated from each unique visitor acquired (lifetime value is relatively straight-forward for e-commerce sites, with direct financial value from sales the determinant; off-line sales triggered through an initial unique visit may be very difficult to assess). 

An Example of a Traffic Profitability Analysis, by Search Phrase

2. Visitor Segmentation: To be able to customize for and take action on its website visitors, companies need to actually understand the differences between them (i.e. how frequently does each visitor come to the site, what is the depth and breadth of the visits, what type of content is the most interested in, etc.). As in traditional analytical CRM activities, this necessitates a segmentation of visitors, based on their profile (if registered) and visit behavior. 

An Example of a Basic Visit Behavior Segmentation Output

Such visitor segmentation is enabled through the unique identification of each visitor over a longer period of time than just one visit, necessitating the use of Cookies, IP tracking, or other methods for monitoring.

These segments can then be used for monitoring site performance in a more specific manner, as well as defining strategies and actions specific to each, such as:

• Identifying whether your loyal visitors are losing their interest, and if so, ensuring that new content that is in line with their needs is available and visible to them
• Understanding why frequent visitors do not become paying customers on an e-commerce site (i.e. whether they are landing from competitor sites for only price comparison) and taking actions to uplift the conversion rate
• Analyzing the triggers which make an offer seeker land on the page for the first time, and how to utilize those traffic sources better

3. Next Best Content: As companies invest more in microsites, blogs and content in general, the chance of visitors landing on content irrelevant for them or not accessing the most valuable pages from their perspective at all increases significantly (not to mention a diminishing rate of return on content). Although a well-designed site hierarchy and menu system can be of assistance here, visitors need more refined guidance in larger sites. “Next Best Content” analysis identifies the pages or microsites that would be the most interesting for each visitor and visit, facilitating targeted promotion of that content. In other words, it is the application of Next Best Activity analysis in VRM. 

An Example of a Next Best Content Promotion

Such analysis requires the use of clickstream and path data along with visitor profile and visit statistics, building a predictive model for the page that would have the highest click rate for each visit. Then, companies may choose to optimize time spent on the site by promoting the most relevant content or optimize conversion likelihood by promoting the content that would be of interest and take the visitor to a conversion page via the shortest path.

4. Conversion Propensity: Especially for those sites with highly specific conversion targets (i.e. customer registration, online sale, newsletter subscription, etc.), conversion propensity models predict which of the visitors and visits are most likely to serve these targets. Predicting the likelihood of conversion for each visitor, companies can proactively and efficiently target visitors while they are on the website, via the utilization of online chat tools, customized pop-up offers, etc.

An Example of Conversion Propensity Modeling

5. Site Abandonment Prediction: Another application of common analytical CRM models in VRM is prediction of site abandonment during each visit. Based on the visitor profile, as well as the current visit experience (i.e. 404 errors experienced, searched content not available, delayed page loads), each visited page can become the last for a visitor. Identification of what causes site abandonment and which visitor is likely to leave the site can provide valuable insights in site design, performance management and content driving visitor loyalty.

An Example of Site Abandonment Prediction Based on Experience Analysis

Through the utilization of site abandonment prediction, targeted visitors can be caught before they abandon the site, through customized offers being made immediately, or through a representative intervening via a chat tool. Such actions can help in increasing conversion rates around the desired action.

What Next?

Once companies turn their websites into relationship management platforms through the use of VRM, the next step would be the use of web data for additional purposes (i.e. as an early indicator in demand forecasting based on traffic to specific product or promotion pages). E-commerce sites, on the other hand, need to bring VRM and CRM together, incorporating visitor and customer analytics to enable a more comprehensive understanding of their portfolio.

To learn more about making the most out of your online assets, please contact info@forteconsultancy.com.

How Much is Too Much? Getting it Right With One-to-One Communications

Overwhelmed by how often companies you have a relationship with get in touch with you? You’re not alone – companies have lost all control around how frequently they are communicating with their customers. It’s due time they take a look at their communications practices and make amends…

Something we as consumers can all universally agree on is that the proliferation of channels over the past several decades has been a good thing. It used to be that the retail shop and contact center were the only ways we could engage with companies; now, from the convenience of our couch, we can buy clothing, pay a bill, lodge a complaint, etc., without moving more than a muscle or two. There are now more than half a dozen unique channels we can use for engaging with our service providers through (contact centers, shops, company website, social media sites, email, mobile apps, kiosks), with new channels sure to be developed and launched over the coming years as well.

Proliferating as well during the same time has been the number of products and services offered by companies (regardless of sector); this, in turn, has led to the skyrocketing of the number of campaigns ongoing at any given time by any given company. From new products to holiday-specific sales, partnership promotions to bundle offers, marketers are more active now than they have ever been inside companies.

Take these two trends together and it’s obvious why things are out of control when it comes to customer communications. Companies are bombarding their customer base with emails, SMS, phone-calls, auto-dials, pop-up ads, etc., at a record clip. According to Retail Mail Blog, in 2012, top retailers sent out an average of 210 promotional emails to their customers. That’s translates to roughly four emails a week, and that’s only one channel of communication! Accordingly, a survey in January, 2013 (conducted by YouGov) found that 75% of respondents said that they are overwhelmed by retailer emails.

We suspect that if executives of companies had any idea of the amount of communications taking place with their customers, they would be shocked; essentially many have become spammers, without even realizing it. One-to-one communications with consumers has gone unchecked, and with the proliferation of channels and campaigns, has become a serious issue companies need to address.

We believe that an effort consisting of six steps can help companies set the right level of communications with their customer base.

1. Segment Communications: The first step is to identify the various types of communications that are made with the consumer base, and categorize them based on type. The purpose here is to separate between those types of contacts that are essential and have to be made, and those that are optional. The focus of this effort is to ultimately set the right level of optional communications with customers, and as such, they need to be segmented and separated. Generally speaking, there will likely be two types of communications identified:

2. Segment Consumer Base & Assess Current Volumes: Once the above has been completed, the next step is to understand the volume of communications taking place with different segments of consumers. Segments here does not imply those that are being used inside the company only (i.e. high-value, mass, youth, etc.); rather, it means around products, channels, etc. – the number of segments that can be examined are only limited by the number of combinations that can be defined based on the variables used (i.e. high-value multiple-service subscribed newcomer that is SMS opted-in, as one example of a segment that can be examined). The goal here is to first understand the existing level of communications with various segments, then, identify outliers (those relatively being over-communicated with or under-communicated with).  An example of what this output would look like (with red over-communicated segments, yellow under-communicated segments):

3. Set Targets For Communication Levels: The next step is to determine the right level of communications that should be taking place with the customer base (in general) and various segments. One size does not fit all, each segment should have a level set for it – for example, newcomers are relatively more open to accepting offers, thus, over-communicating with this segment may be a wise decision. On the flip-side, over-communicating with high-value customers may cause more harm than good, pushing consumers in this segment to start ignoring the communications. A minimum barrier should also be set (i.e. at least one contact a month) for the entire base, such that some type of pitch is made with every customer – many may fall through the cracks based on the campaign filters and channels used. Though consumers prefer minimal communications in general from their service providers, they nonetheless need to be “touched” at least once in a promotional sense.

Focus groups and surveys can be conducted with consumers here to set these levels. Understanding their preferred volume of contacts per month, their channel of contact preferences, the point at which they start ignoring communications, etc., can be tested with the various segments, helping to guide this level-setting effort.

4. Design Communications Rules & Processes: The next step is to set up the mechanism for determining which communications are to be made with what consumers, via what channel, and how the communications are to be monitored. If, for example, a decision has been made to reduce communications with newcomers to 6 a month, and the current number of contacts is 12, 6 need to be eliminated.

A framework needs to be designed that prioritizes communications for the various segments, and, as well, a complementary process that ensures communications are capped and stopped if monthly communication target thresholds are hit (this can be done manually or through built-in solutions in various campaign management systems). A general rule of thumb to follow would be to prioritize those communications that have had the highest positive impact in the past (in terms of generating revenues, increasing customer satisfaction, etc.); aside from this, a segment manager or an assigned employee should be the last word here around what should or shouldn’t be communicated that month with the given segment.

5. Test Segment-Specific Communications Strategies: Before rolling out the effort and applying to the entire customer base, we recommend tests be conducted with each of the segments to determine the effectiveness of the designed approach; the desired outcome is a positive impact on the bottom line as well as a more receptive / less-irritated client base. As such, those communicated with using the new rule-sets should be examined from an ROI perspective; ideally, they will have a higher offer acceptance rate vs. those that have been communicated with in an un-regulated manner. Modifications should be made based on the findings, if necessary (i.e. ramping up or down communications, changing the channel of communications, etc., until the optimal level and channel of communications per segment is realized.

6. Conduct Relevancy Analysis (optional): An optional step is to conduct a relevancy analysis, filtering out the junk that gets sent to customers; this will also help with reducing the level of communications being conducted. We all as consumers have and continue to receive irrelevant and wrongly targeted SMS, emails, etc., communications which serve minimal purpose, which ultimately may have a detrimental impact on the bottom line as they desensitize us to relevant pitches.

By conducting a historical analysis on response rates by various segments to the numerous types of offers they have received in the past, many communications can be banned from being conducted in the future. Segments examined here for responsiveness to offers should be more than just those listed in the examples above – for example, responsiveness by gender is worth examining, as it may vary significantly based on offer type (i.e. pitches around discounts being offered by retail clothing partners of a certain bank / credit card may have an extremely low response rate from men historically, so low that the communication should not be sent in the future, as it dilutes and diminishes the potential response to offers that are more relevant for this segment).

We believe that by taking the above steps, companies will realize in a very low cost manner a positive increase in ROI around their below-the-line marketing efforts. At a minimum, they will go a long way towards shifting customers towards listeners again, such that they will review the communication they receive, rather than discard it as spam.

To learn more about optimizing below-the-line customer communications, please contact info@forteconsultancy.com.

Product Network Analysis – The Next Big Thing in Retail Data Mining

One of the biggest challenges retailers have is the depth of data available for decision making, especially if they don’t have a loyalty program. Though limited, are retailers nonetheless maximizing use of their existing data today? The answer is no. Product Network Analysis opens a new range of insights which can maximize return on category investments.

According to a 2011 study by Kantar Retail, 72% of retailers consider category management very / extremely important, with 88% believing that use of category insights will differentiate companies in the future, putting it on top of the list. Yet, for years, category management analytics in most retailers has not gone beyond using standard market research, voice of the customer, and market basket analysis tools, barely scratching the surface. Today, thanks to advancements in data mining techniques, retailers can do more.

Social Network Analysis (SNA) is a relatively new commercialized analysis method, automatically identifying social relations between individuals, based on their telecommunications, finance or social media interactions. Today, most leading telecoms operators in the world are using some SNA solution.

Product Network Analysis (PNA) is the application of SNA algorithms in the category management domain, in order to automatically identify:

• Which products naturally belong to the same micro category
• Which products are most important in terms of creating category loyalty
• Which products are most likely to trigger cross-category sales
• Where category rationalization opportunities exist

Why?

Product Network Analysis presents significant advantages over the most common insights / analysis methods used in category management, complementing them for a 360 degree view of the product portfolio. Three general methods are used in retail to conduct product-related analysis, each with their own shortcomings.

Categorization Based on Product Features

The simplest and most common form of product segmentation is based on the product features entered by purchasing, category management, or supplier teams. While this is a necessary evil, it has a number of shortcomings when compared to PNA. To name a few:

• It Is Subjective: As a simple example, whether a product is luxury or not depends solely on the perception of the person labeling it as such during the segmentation effort.
• It Is Open To Human Errors: Missing and low quality product data is very common (especially in retailers with tens of thousands of products), as keeping product data accurate and up to date becomes a secondary objective against the fast processing of items.
• It Is Constrained By The Human Mind: One of the most important shortcomings of manual categorization is its lack of ability to reveal hidden relations between products (an example of which is the famous business intelligence correlation between beer and diapers, whereby young fathers were found to be buying beer when buying diapers on weekends, a highly unpredictable bundle to say the least).

Categorization Based on Customer / Shopper Insights

A relatively more sophisticated form of product segmentation uses the profiles of customers who are buying these products. For example, products more frequently purchased by health-conscious young families are identified and managed as a lifestyle / life stage category. Though this approach allows for a deeper understanding of products, it has its shortcomings:

• It Is Limited By The Depth Of Customer Data: There are generally significant issues around data availability and quality of customer-related data in retailers. In order to do a proper product categorization driven by customer insights, detailed demographics, socio-economics and psychographics data is required, information which is a serious challenge for most retailers to obtain.
• It Is Not Micro Enough: Although customer insights driven product portfolio analyses are highly useful in macro level decisions (i.e. whether to enter a category or not), they can’t provide enough detail (on their own) to allow for more tactical decisions to be made.

Correlation Based on Market Basket Analysis

Last, but not the least, most leading retailers make use of market basket analysis to understand correlations between products, which then becomes a critical input to bundling and promotion decisions. Like the prior two analysis methods, although this is a necessary activity, it cannot replace PNA due to its shortcomings:

• It Is Not Complete: Market basket analysis identifies pairs and groups of products sold together most frequently, but it does not categorize or provide a complete view of the product portfolio. Knowing that Snickers is most frequently sold with Mars is not enough to optimize the whole confectionary section.
• It Is Biased Against Substitutes: Since competitor products which can substitute each other are rarely seen in the same basket, market basket analysis fails to identify the correlation between them, which is, again, a major drawback in organizing or managing a complete category.

Although there exist other analyses which can support category management decisions, none provide insights as deep and complete as PNA. Without needing to conduct external research or invest heavily in technology or human resources, retailers can easily perform PNA and start benefiting from the insights gained in a matter of days.

How?

A simple five step approach translates raw POS data into category optimization actions:

1. Compile POS Data for Analysis: The only requirement for a basic PNA is the simple POS transaction data, with barcodes listed for each sales transaction. Based on the level of analysis required (which can be at SKU, product, brand, or product group level based on business objective), this data can be summarized as well. Next, this transaction should be transformed into a format, representing pairwise frequencies of products – i.e. product A, product B, number / % of transactions with both products, etc.

2. Run a Network Clustering Algorithm: Once the data is ready, an existing Social Network Analysis solution (such as SNA Forte, our own open-source solution for the same) or graph clustering solution can be used to perform the analysis (for a demonstration or recommendation on alternative tools, please contact us at info@forteconsultancy.com). The outcome from these tools would present the product networks – i.e. micro categories – as well as products playing key roles in these networks. For example, for a hypermarket, the below partial sample represents identification of two micro categories:

• Healthy Baby Products: With organic baby food and skincare products linked together
• Parenting Books: With baby and motherhood related books linked together

 

 

3. Review Networks and Key Products: All network clustering solutions require a certain level of fine-tuning of parameters to identify ideal networks – meaning that this is a cyclical process, going back and forth a couple of times before selecting the ideal results. After each cycle, identified micro categories, as well as products playing key roles in them, should be reviewed and evaluated. The typical product roles we define in PNA are as follows:

• The Core: These are the most commonly purchased products of each micro category. They appear very frequently in baskets containing products within a micro category, meaning customers buying from a certain micro category are highly likely to be interested in or purchase them. In the example above, ‘organic baby oil’ is the core product for healthy baby products. These products are ideal as aisle centers.
• The Hook: Also referred to as category crossers and category connectors, these products are those which are most likely the first bought when a customer who traditionally purchases in one category begins purchasing from another. In the example above, ‘Organic Baby Book’ is the link product between the healthy baby products and parenting books categories, meaning that promoting this product to customers already buying from healthy baby products can trigger sales from the parenting book micro category. These products are ideal to place as danglers inside their correlated micro-category display areas (i.e. placing several of the above-stated books in the healthy baby products area).
• The Expandable: These products do not relate well to any of the micro categories in the product portfolio. They create very low cross-sales opportunities and do not create synergies in the existing portfolio. These products are ideal for category rationalization activities (i.e. discontinuation), unless new micro categories are built around them.
• The Staples: These products exist very frequently in baskets, independent of the micro categories. Their purchase is mostly driven by basic needs – examples of such products include bread, milk and water.
• The Add-on: These products are almost always sold with a set of other products as they address a certain need. Their purchase is mostly driven by the purchase of the main product. Examples include purchases inside categories (like ice cream cones being purchased when ice cream is purchased), or across categories (like when a lighter is purchased when cigarettes are purchased).

4. Develop Micro Category & Key Product Strategies: Once all the micro categories and product roles are identified, these should be used in category management and marketing activities, including but not limited to:

1. Sourcing & Product Assortment: By focusing on more lucrative micro categories and critical products, retailers can increase their product portfolio effectiveness while keeping variety at a manageable and rational level.
2. Layout & Shelf Optimization: As PNA provides deeper insights into sub-categories and product group relations, it enables micro management of shelf space and store layout. Using PNA results, retailers can decide on which 20 products should appear in a section, which one should become the center of attention, which section should follow them, and which products should be connecting them.
3. Product Pricing: By pricing products in specific roles and micro categories more attractively, retailers can selectively increase customer loyalty and price perception while not decreasing overall profitability significantly.
4. Targeted Promotions: By identifying micro categories, as well as core and hook products, PNA enables retailers to identify the right list of products to promote to the right list of customers.  For example, while loyalty and category share of wallet of customers can be increased by promoting core products, cross-category sales can be triggered by the hooks.

5. Pilot Strategies and Deploy: As in all successful commercial initiatives, defined strategies should first be put to the test through pilots, and deployed in stages after fine-tunings based on findings.

What Next?

Once overall category management is optimized using PNA, the next step for retailers is to take a top-down approach, localizing product portfolio by store. Especially for retailers with stores in districts with dissimilar demographics and socio-economics profile, aligning product portfolio with local needs is a must have.

To learn more about making the most out of your product or channel portfolio, please contact info@forteconsultancy.com.

Effective Channel Management Strategies – Segmenting the Channels

Channel analytics, the often neglected sibling of customer analytics, presents significant opportunities for companies seeking to better their channel-related performance, especially those in industries with large distribution networks (such as financial institutions, telecoms operators, retailers, automotive distributors, etc.)…

What?

In today’s marketplace, where the number of channels being utilized for providing service or conducting sales through has proliferated, companies are still managing them broadly – generally, we observe retail management (i.e. “own stores”, “partner stores”, “franchises”, etc.) and alternative channels management (i.e. web, app, etc.) concepts in use. This, however, is not enough, barely scratching the surface in regards to truly identifying the differences between and within channels, allowing for dynamic and unique management methods to be utilized. With ever-increasing pressures around managing the cost of sales, around increasing the level of service provided to customers as well as around competing fiercely with rivals for a dwindling number of potential clients, companies need to dive head-first into channel segmentation.

Why?

Channel segmentation can do wonders on the bottom line of companies. A few years ago, a prime example of this was realized when an international software vendor utilized channel segmentation as the basis for boosting its partner program. The objective was to optimize the performance of its over 250,000 partners – the result? The company realized an over 300% increase in return on marketing investments (compared to pre-program performance).

More recently, even brands known for more standard format stores have started using segmentation to customize their channels to match local needs better. One example is Target, which has recently started evaluating smaller urban-format stores (City Targets); another, is Marks & Spencer, which has been piloting with store segmentation based on the profile of customers they will attract (like ‘family first’ stores with more emphasis on kids wear). The approach is not limited to retailers, with companies that have a reliance on brick and mortar investing in channel segmentation (banks, for example, 64% of which, according to a recent Asian Banker survey, have plans for segmenting branches) as well.

Channel segmentation relies on the same principles as customer segmentation – just as with customers, all your channel partners / branches are not the same, so why treat them the same? A telecoms dealer located in a commercial district with a sophisticated customer base that is interested in data products and devices cannot be supported in the same manner as another dealer located in a sub-urban area, with a more traditional customer base that is interested in money transfer services and recharge cards. While the first would require more advanced training materials, marketing collateral focused on more sophisticated products, and higher rewarding around data sales target realization, the second dealer’s needs would be totally different. Without proper channel segmentation, recognizing these differences and customizing strategies and actions would be impossible or incomplete and inconsistent at best.

Channel segmentation can allow for improvement to be made in and around:

• Lead Management – Better matching leads with channels which could serve their needs more effectively and efficiently, so that cost of acquisition is minimized while conversion rates are maximized
• Product Management – Optimization of assortment by channel, based on location potential and channel capabilities, decreasing cost of stock and logistics while increasing local revenues
• Price Management – Facilitating local price differentiation based on channel and market specifics
• Promotion Management – Better localization of promotions as well as marketing messages and materials based on channel capabilities and customer portfolio
• Operational Efficiency – Avoiding waste of time and resources on channels with low performance and limited potential, while addressing the exact needs of each channel (in terms of training, support, etc.) separately
• Performance Management – More realistic and fair evaluation, with increased ability to differentiate local market conditions and compare more similar channels with each other

How?

In order to get the most out of the channel segmentation effort, we recommend following our actionable segmentation approach, analyzing channel partners across various dimensions:

This effort should be conducted via the following four steps:

1. Collecting the Data: As in all analytics activities, channel segmentation relies heavily on availability and accuracy of the right data (about channel partners and their customers) as well as the socio-demographic profile of the regions they serve. Hence, companies need to first define a partner data strategy, enlisting and prioritizing data elements to be collected about and from each channel partner, and design / implement various methods for collecting this data. Various organizations with independent sales channels (such as sky) already do this, utilizing platforms such as partner portals.

2. Segmenting & Profiling Partners: Once the data is available for segmentation, using various data mining algorithms, partners can be clustered into micro and macro segments. The key success factor for this segmentation is to utilize various dimensions describing different characteristics of channels separately, each dimension serving a different strategic or tactical purpose. For example, in order to align product portfolio with customer needs, the company needs a micro segmentation focusing primarily on the product stocks and sales of its channels…

…whereas to optimize training efforts, the same company would require a micro segmentation based on the employee profile of its channels. The output from this approach is a 360 degree view of each channel, enabling customization of management across all required dimensions.

3. Matching Partner & Customer Segments: For companies with an already existing customer segmentation output, channel segmentation provides an immediate opportunity to identify and address mismatches between the needs of customer segments and what each channel has to offer. If the location where certain customer segments shop can be traced, then opportunities around migrating them can be identified as well – the example below shows such a situation, whereby “Tech Savvy” customers are shopping at “Minimalist” partner shops (a strong mismatch).

4. Strategizing & Optimizing Channel Mix: Once the segmentation effort has been completed, the next step is to design micro and macro segment-specific strategies; the objective here is to customize how each segment is being treated, supported, enhanced, etc. This must translate into specific actions and initiatives across the channel lifecycle, as well as customizations in product, promotions, and price mix for each segment.

An example of the kind of enhancements we are talking about here is being utilized by Bank Audi – in some of their branches, a video-conferencing solution is in place with tellers connecting to customers remotely; this allows the bank to virtually manage inconsistent demand levels in certain branches (such would be a concept that can be tapped into for a retail bank branch that is in the “Irregular Peaks” operations segment).

What Next?

The natural next step would be to pilot the various designed initiatives before rolling out across all channels / all locations. An assessment around the realized benefits should be conducted here, to ensure the changes will have a positive impact on the bottom line. Additionally, changes should be rolled out gradually; a radical revamp of channels is not recommended all at once.

To learn more about making most out of your channels using analytics, please contact info@forteconsultancy.com.

Bridging the Chasm – The Collaboration of Consumer & Business Marketing

Rare is the company that within has its consumer & business units working hand in hand to improve the bottom line of the company. Bundling across business units is one area which holds significant potential for those companies that can pull it off…

Competition is a healthy thing for companies; it’s thanks to competition that companies strive to innovate, to better their service offerings, to improve how they operate (across the board). It’s definitely a good thing for end users as well; with no competition, prices remain steep, product alternatives remain limited, service remains poor.

Competition within companies is also a healthy thing; thanks to competition between sales & marketing departments, as well as consumer & business units, companies are propelled forward in various ways. Ever struggling to get a larger budget and be recognized as the keystone of a company, departments and units strive to generate more revenues, operate more efficiently and innovate better than their counterparts in other areas of the company.

For the most part, this internal competition is healthy. Where it causes harm, however, is around collaboration – to a strong degree it kills it. Rare is the instance where sales and marketing departments work together inside a company in a fruitful and objective manner; even rarer is the instance where consumer and business units work together towards the greater good. We believe this internal competition is the key reason few (if any) companies have tapped into the benefits of consumer & business bundling, via collaboration between the consumer & business marketing units of companies.

What is consumer & business bundling? It’s a simple concept that can best be illustrated through a few examples:

Example 1 (Telecommunications): Such a bundle at a mobile operator that would target small businesses would consist of several consumer as well as several business prepaid or postpaid numbers, with a few handsets thrown in as an option. The business numbers would naturally be used by the owner and employees of the small business, the consumer numbers would be used by members of the owner’s or employees’ households. The incentive would be some type of discount or added benefit (rewarding the business owner for giving both his business and personal share-of-wallet to the operator).

Example #2 (Banking): Such a bundle at a bank would consist of a package of current accounts, business credit cards, and a line of credit for a large company, with all consumer products extended to the employees (and their family members) at a discounted rate or with added benefits (i.e. no annual fee on credit cards, lower interest rate on credit products, etc.).

Example #3 (Hotels, Airlines, Retailers): Such a bundle here would be in a simpler form, through the extending of corporate discount rates to the employees (and their family members) of business clients.

The potential this concept holds is significant for those companies that can tap into it. Through consumer & business bundling, companies can turn a handful of clients into thousands, tapping into a relatively easy to acquire pool of potential clients at a minimal cost – an illustration of this:

The opposite direction can also be tried here; individuals identified in the consumer base as business owners / executives can be targeted for their business share-of-wallet as well. Bundles would not so much be needed here as would a strong pitch to lure the consumer to bring his or her business share-of-wallet over to the company.

No doubt there are several issues to be resolved here; how revenue should be credited (to business or consumer units), how the client should be serviced, how billing should be handled, etc.; these are all issues, however, that can be overcome and addressed. No single issue is large enough to be a roadblock to the launch of such benefit-generating bundles.

To get such an initiative up and running, we recommend companies follow the below four steps:

1. Hypothesizing & Preliminary Testing: The first step here is to brainstorm about the possible ways in which the company can capitalize on this concept, pushing business clients towards becoming consumer clients as well. The focus here is to not only identify what groups of products and services from the business and consumer side can be bundled together, but also how and with what benefit to the end users (there needs to be an incentive to make this concept work). The side that stands to gain most from this collaboration is the consumer business unit; as such, incentives need to be put forth by them, to benefit the end-user consumers that are being targeted.

Other details need to be hypothesized about here as well – how the bundle will be communicated / marketed, how the proposition will be sustainable vs. competitors, how the packaging will be priced / discounted / benefits applied, etc. The designed concepts should be tested before taking the next step, to identify if there will be demand. Focus groups can be used here as well as surveys with business clients, to understand if the planned bundles would be welcome and utilized.

2. Potential Analysis: Once possible bundles have been preliminarily designed, the next step is to estimate the potential they hold. This will require understanding existing penetration rates with potential clients (to understand the uplift in sales that can be realized – i.e. an estimated 85% of business clients’ employees do not have personal accounts), the expected response rate (to understand the acceptance of the offers – i.e. 5% of business clients believe their employee base will be interested in personal account-related offers), as well as the expected cost (of the benefit to lure consumers – i.e. 20% markdown on normal tariff rates).

With the above analysis conducted, a preliminary business case can be designed, allowing for a go / no-go decision to be made around specific bundles. Moreover, a prioritization can be made to determine which bundle to go-to-market with initially.

3. Operations Design: The first step towards the rollout of the chosen bundle will be to assign responsibilities around its detailed design (from pricing to process, FAQs to systems-related impact). As with the launch of any new campaign or product, there will a variety of tasks that need to be completed. Establishing a PMO to oversee the various streams around the effort is advised here. The other critical point is that both consumer and business units be heavily involved in the tasks, sharing the workload adequately. The natural lead around preparing the pitch and pitch planning will be the business sales and marketing teams, as it is the business clients that will be approached with the offer. The consumer marketing unit will have to take on the tasks around designing the bundle offer / benefits.

4. Piloting & Rollout: Once the bundles are ready to go and internal dry runs have been conducted, pilots should be conducted with a handful of business clients; tested here will be the scripts, collateral material, bundle content, bundle benefits, pricing points, processes, etc. Pilots should be conducted until no major kinks remain, until the effort of pitching and on-boarding is as smooth as with any traditional campaign. Also analyzed here should be the actual bottom-line impact of the bundles before going live (if negative ROI, then the bundles can be discarded). With the above actions taken and everything passing the test, full rollout can be conducted.

Once such bundles are launched, and success realized, companies can begin exploring making reserve pitches and bundles that drive consumers to bring their business share-of-wallet to the company as well. While this will be an effort of going from many (consumers) to fewer (businesses), it is nonetheless an unexplored additional way to acquire new business clients at a relatively minimal cost.

To learn more about designing and launching consumer & business bundles, please contact info@forteconsultancy.com.

Customer Analytics Gone Wrong – Eight Common Mistakes to Avoid When Deploying Customer Analytics Models

Designing customer analytics models is only half the battle. Equally, if not more difficult, is deploying them, such that actions triggered by the model outputs are being taken on a daily basis. In this follow-up article, we highlight some of the most commonly made mistakes that prevent companies from succeeding at deploying models…

As stated in a previous article, we found that more than 65% of customer analytics models companies in Turkey designed (or had a consulting company design for them) were never deployed. The amount of time and resources wasted on the design of such models is only one part of the loss here – more disappointing is that the wealth of opportunities such models would have presented to the companies was never tapped into. Millions of opportunities to cross-sell to customers, to up-sell to customers, to retain customers, all never acted on. Add to this that those companies which failed to deploy the models have likely lost confidence in the concept of customer analytics for good; all in all the negative impact of failing to deploy is significant.

When looking at the reasons why companies fail in deploying segmentation, retention, next best activity, and similar customer analytics models, we see a common set of mistakes they make, errors that provide ample reason for detractors to push the models onto a shelf. To help companies avoid making these mistakes, we highlight eight of the most commonly made ones in this article.

Around Ownership & Sponsorship

Often the single key reason why models fail to deploy is the lack of ownership around them, with no one group of individuals responsible for driving their uptake across the business. Moreover, a lack of a sponsor (ideally at the CMO level) creates a situation wherein no one feels responsible for utilizing the models (and no one is told to be responsible).

The Business Intelligence unit feels their area of responsibility is around designing and updating the models; the Marketing Department feels they are swamped with their day to day business – in such environments data mining models get ignored. With strong sponsorship at the C level along with a clear set of roles and responsibilities and targets around the models (explained below) such barriers that prevent models from being fully deployed can be overcome.

Around Target Setting

Failure to set targets around what is expected to be achieved from the utilization of the models is another commonly made mistake we see companies make. Proper deployment of marketing analytics models will impact revenues positively, be it via increase in acquisition, increase in cross-sales / share-of-wallet, or decrease in churn. This impact, once estimated, needs to be assigned to responsible teams and individuals, such that their focus area includes ensuring the impact is realized. To that end, model-related targets need to be linked to potential bonuses of employees; this is the single most successful way to ensure models are fully deployed.

Around the Offers

Lack of customized offers to drive consumers to take the desired action is another shortcoming we often see around model deployment. Simply knowing who is likely to churn, or who is likely to accept a given proposition is not enough – what is required is a customized offer to drive the desired action. In the case of churn at an operator, it’s free or discounted local & international minutes, SMS, data, handsets, privileges, etc., designed and available to offer to the customer when conducting proactive retention efforts. Around selling new products and services at a bank, it’s fee waivers, trial free periods, lower interest rates, repayment period extensions, etc., – sweeteners, if you will.

Just telling a customer to buy a new product or service or not to sever their relationship is not enough; the right offers need to be designed to match what the client wants or needs, requiring the Marketing business unit to actively support such development efforts.

Around the Channels

Another shortcoming around the deployment of models is around the utilization of all available channels for conducting the model-driven campaigns. What we often see is that companies rely too heavily on using SMS to cross-sell, up-sell, retain, etc., with a sprinkle of outbound calling thrown in. These are the channels most commonly used for conducting traditional mass campaign communications, and all too often marketers stick with what they know, what they are comfortable with.

Channels barely utilized include auto-dialer, inbound contact center, inbound dial-in, shops / dealers / branches, self-care portals, and auto-dialers. Rare is the opportunity to get to interact with a customer; a company should capitalize on each opportunity to make a pitch. As such, companies need to work on including some (if not all) of the above mentioned channels in their plans around contacting customers to take model-related actions.

Around the People

Selling is not easy; it is a skill that is traditionally obtained through lots of practice, with a great deal of falling and failing involved. Dealing with rejection over and over again is difficult, leading to many people who take a shot at selling to give up and walk away. Selling to irate people is even more so, such as is the case in terms of those contact center agents who try to retain disgruntled customers.

Companies often look for the low cost solution when it comes to building their retention and sales inbound and outbound desks, sometimes outsourcing it to offshore contact center agencies, sometimes building it themselves but hiring employees at minimum wage. In almost all cases, training is underutilized, guidance and monitoring is minimal, incentives are lacking, as are systems (with manual processes in play). The success (or lack thereof) of contact center efforts to grow or retain customers is tied at the hip to the value and importance placed on the contact center.

Around the Pitch

Even one key word said at the right time in the right manner can trigger a sale of a given product or service, can trigger a successful retention of a relationship. A great example from an engagement we had with an operator in the MENA region – letting the customer who intended to close his or her fixed line account know that in the case of a natural disaster (such as an earthquake), that the fixed line network almost always stays on line vs. the mobile network. This simple script being used alone resulted in a 10% decrease in reactive churn rates.

Companies too often fail to put the time and effort into making sure the right messages are used with the right customers, at the right time, via the right channel. Optimizing when a message is conveyed to a customer of a specific customer segment at what time with what words will significantly affect the outcome of the pitch, be it a success or failure. From contact center scripts to SMS, companies need to pilot all their communications across all channels to optimize performance, to maximize ROI.

Around Performance

Failure to measure performance is another short-coming we often see around the deployment of analytics models. Too often, growth and retention target lists are acted upon, but not measured, to understand not only the success around actions, but more importantly, around understanding the impact on the bottom line. Lack of measurement often results in campaigns being conducted that should in fact be cancelled (due to their failure to achieve a positive ROI), leads to a failure to recognize exceptional or poor performers (in the contact center, for example), as well as prevents efforts from being optimized (shifting customer contacts to the most successful contact channel, for example).

Companies need to measure a variety of aspects around analytical models when they are deployed – these areas include, but are not limited to model accuracy, channel conversion rate, agent performance, offer effectiveness, timing effectiveness, pitch effectiveness, etc.

Around Results

One final area to mention that companies often fail around is the sharing and celebration of results from the deployment of analytical models. Rather, the effort of companies to grow and retain customers often becomes one that takes place behind the scenes, below-the-line, not communicated or shared with employees of the company. As is the opening of a dealer or a branch often the cause for celebration in a company (with a formal communication made to the employee base, a ribbon-cutting ceremony, etc.), the success realized from analytical models being deployed should also be recognized.

Outstanding performers in the contact center should be rewarded for their performance; milestones around reducing churn should be announced via email; an uplift in revenues attributable to a specific campaign launched thanks to an analytical model should be shared – failure to give recognition to the impact the deployment of analytical models has on the bottom line will ensure they remain something that is an after-thought in the employees’ minds. Not everyone here has to be communicated with necessarily – key stakeholders who may be showing resistance to the models may be the primary targets (i.e. segment management that is yet to buy in to below-the-line methods).

We believe that if companies take notice of the areas we have advised around in this as well as the prior article in this series, they are likely to generate a significant ROI from the deployment of analytical models. To learn more about successfully deploying customer analytics models, please contact us at info@forteconsultancy.com.