“If you’re competitor-focused, you have to wait until there is a competitor doing something. Being customer-focused allows you to be more pioneering.” – Jeff Bezos, CEO of Amazon
With over 80% of global consumers trying online shopping at least once, the greatest opportunity for e-commerce companies is to build a long-lasting and profitable relationship with this already existing audience. Such a strong relationship requires utmost focus on the customer as a whole and making sense of a flood of real-time information that goes well beyond demographics or shopping behavior.
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E-commerce is undeniably a huge market with solid current and future growth. B2C E-commerce sales amounted to $1.5 Trillion globally in 2014. With the continued proliferation of smartphones, it is expected to continue its solid growth at 18-20% in 2015 with a steady pace in mature markets such as North America and Europe, and even faster in evolving markets such as the Asia-Pacific, Middle East and Africa. This represents substantial opportunity, not only for retail giants, but also for many small businesses by offering easy-access to their audience as well as enhancing their global exposure.
There is, however, still a long way to go in terms of becoming the preferred purchase medium. E-commerce still accounts for about 6% of total retail purchases, even though over 80% of consumers say they shopped online at least once. Therefore, the biggest potential for the online retailer is not attracting new audiences, but being able to convert casual visitors to buyers before they go elsewhere. Customers are increasingly omnipresent where webroomers (customers purchasing in store after searching online) continue to dominate showroomers (customers purchasing online after going to the store). Gaining preference over the physical store experience is a key challenge that requires understanding the customer well enough to provide the most comprehensive and relevant value proposition.
Redefining Online Segmentation
Customer segmentation is hardly a new concept for online marketers. Market segmentation and storytelling based on key demographic and lifestyle attributes (e.g. age, gender, location, interests) is at the heart of content marketing. Many online stores also have multiple real-time triggers in place responding to recent online activities of a customer. Yet, integrated segmentation practices that establish the optimal balance among individual customer history, recent behavior, and market trends are still rare.
Maximizing online conversion requires a holistic understanding of customer behavior. While recognizing the recent surge in baby product purchases of a customer, for instance, the segmentation model should also remember the customer has long been a fan of high fashion deals. Similarly, it should adapt to customers’ changing schedules over time. A subset of customers may now have more time on their hands to browse through offers in the mornings via their tablets. Yet, they may still prefer to make the final purchase via their laptops in the evening after consulting their families.
A huge part of online conversion potential involves transforming anonymous browsers into loyal customers. By segmenting both anonymous and registered users in a consistent fashion, online retailers can capture valuable consumer impulses guiding registration, initial purchase, and loyalty decisions. By comparing anonymous and registered users with similar visit behavior, they can easily detect which customers have a higher potential, and which characteristics are prominent signs of such potential. Customers with the highest visit frequency or the highest browsing diversity in terms of products may have the highest conversion potential compared to low-frequency visitors or category loyals. At-work browsers may comprise recreational visitors or bargain hunters that prefer to shop primarily in physical stores on the weekend.
No matter the business objective (e.g. activate the inactives, transform anonymous browsers into shoppers), creating meaningful micro-segments that not only characterize the whole customer journey but also easily blend with one another is critical in understanding the whole story and predicting what is next.
Back in the day, most companies had only 1-2 segmentation models that were based on customer demographics, value, behavior, needs, or a mix of these. While these models served early marketing needs well, in response to the rapid growth in information availability, product and customer sophistication, more comprehensive approaches have emerged – such as micro-segmentation and segment of one.
Now, leading organizations usually maintain and manage up to 10-20 micro-segmentation models that each map onto a critical decision layer across the customer journey. By categorizing customers into distinct yet manageable groups, micro-segmentation is the first step in understanding the motivational drivers behind customer actions (e.g. searching for a product, abandoning their basket). The key success factor in effective micro-segment management is integrating different dimensions, such as device preference, visit time, and campaign responsiveness, to make predictions about customer behavior instead of managing them separately which inevitably results in information loss.
Especially in e-commerce, adopting a cross-device view of the customer in defining these micro-segments is crucial to obtain a comprehensive picture and leverage the ever-growing potential of mobile. From smartphone push notifications to “nudging” the customer via their smartwatch for flash sales, maximizing the benefits of anytime anywhere access to the customer requires understanding their habits and utilizing these insights to build meaningful relationships.
Even though every market and customer portfolio is unique, there are common micro-segment dimensions that address the requirements of most e-commerce companies. These micro-segments include both registered and anonymous users, where applicable, so that the aforementioned conversion opportunities can be fully realized. Nine of such frequently used models that utilize lifestyle, device, visit, purchase, and campaign data to full extent are listed below.
Use Cases across Business Models
Cross-targeting with solid micro-segments is a powerful tool for all businesses wishing to stay relevant to their customers. The more dimensions are integrated, the better marketers are able to understand where the customer is coming from, so that targeted offers resonate a lot more with the customer. If the customer is identified as at risk of leaving, for instance, constructing a retention offer that takes into account their past interests and shopping behavior is a lot more effective and feasible than any generic win-back activity.
Inactive customers can stay inactive for a long time since the majority of online purchases are not particularly out of necessity. The most common win-back practice involves sending customers regular discount reminders (e.g. “10$ off on your next purchase”). Without any customization in terms of product category or offer mechanics, these activities mostly attract the low-frequency, price-sensitive portion of the inactive base. With a solid segment foundation, however, e-commerce companies can refine the offer to include the customer’s favorite category (e.g. fashion) and to be sent at the customer’s preferred time (e.g. on the weekend) relying on lifestyle and visit timing segment insights from before they became inactive 6 months ago. In this sense, micro-segmentation also acts as a building block for predictive analytics, as well as for campaign management and monitoring activities, providing a granular view of different customer profiles that have the potential to act and react differently.
Segment definitions and impact areas are largely influenced by the strategic objectives and priorities of a business. Associated strategies for similar segments can vary significantly from one company or business model to another. For deal sites such as Groupon, assessing price sensitivity and driving volumes is a core challenge, whereas for subscription-based service providers such as Netflix, building customer loyalty is central. Online retailers and marketplaces like Etsy tend to focus more on early adopters and their preferences for product strategies, and exclusive brand stores such as Zara aim to unify customer experience throughout physical and digital channels.
Prioritization of segments for marketing investments should also be shaped by the strengths of each company. In selecting top priority lifestyle segments, one company can choose to focus on “Tech-Savvy” customers to build on their high basket value, whereas another can target “Fashionista”s for whom their existing value proposition and product portfolio are a better match.
What is common to all companies and business models is the ultimate goal of becoming the preferred, recommended and increasingly used purchase medium. In creating online enthusiasts out of the customer base, micro-segmentation forms the solid foundation for customer value and lifecycle management with major impact on the following areas:
- Increase frequency of shopping and basket size by understanding visit patterns and cross-sell potential
- Identify and delight the most valuable customers
- Retain an active base by accurately evaluating customer value and churn risk
- Maximize profitability of transactions by deploying optimal pricing and operation strategies
- Ensure long-term satisfaction and loyalty by increasing relevance in communications and offers
- Differentiate customer experience attributes in line with customer expectations (e.g. shipping or payment options)
- Refine ad targeting and product recommendations with extensive insights on customer interests and correlations between products
- Utilize segment evolution trends as guidance for decisions to expand into new product lines and territories
How to Make it Work
Customer segmentation, similar to other customer analytics activities, has to be a living mechanism rapidly adapting to changes in customer portfolio, business models, and data sources. Here are 5 tips we consider essential to building and maintaining an effective micro-segmentation framework:
1. Take the Time to Set the Scope: Define business expectations and use cases as well as target customers (e.g. active customers, new customers) first to ensure produced outputs are relevant and no duplicate work is required afterwards.
2. Automate Data Preparation: Make use of automation tools and techniques to accelerate the most time-consuming step, minimize human error, and dedicate resources to more value-added activities.
3. Detect and Fix the Cracks Before You Go In: Address data quality, availability and distribution issues prior to the segmentation exercise to avoid creating unreliable or unstable segments.
4. Mine for Business Value as much as Statistical Significance: Combine business inputs from end-users (e.g. strategic objectives, behavioral hypotheses, prioritized attributes) with sound algorithms (e.g. K-Means, SOM, Kohonen) to ensure early adoption and actionability.
5. Follow Up on Profiles to Discover and Guide Segment Evolution: Monitor key segment attributes and migrations to understand customer compositions, uncover revenue potentials, and refine segment strategies for proactive management of customer retention and value.
QIWare (Quick Insights Ware) is an agile analytics solution that delivers a complete suite of capabilities to support an end-to-end data mining cycle. QIWare simplifies data preparation and modeling while increasing business productivity, minimizing human error, and significantly improving usability. QIWare helps companies in:
– Effective segment management
– Proactive customer retention
– Customer value maximization
– Targeted cross-sales and up-sales
– Profitable risk prevention.
About E-Commerce ReadyWare
Built as an industry-specific plug-and-play extension to QIWare, the E-commerce ReadyWare provides 100% customizable models created based on Forte Consultancy Group’s experience with industry leaders around the world. With the ReadyWare, e-commerce companies almost instantly obtain a complete view of their customer portfolio ready for targeting (e.g. tech-savvy, special-occasion, big-ticket purchasers with 80% likelihood of becoming inactive over the next 3 months).
From segment management to value retention and growth, E-commerce ReadyWare addresses the most common challenges in customer lifecycle management. By automating the end-to-end process, e-commerce companies are able to dedicate their efforts to acting on insights rather than building models from scratch.
For more information on how our ReadyWare can help you with segmenting your cardholders, with additional customer analytics challenges, or discuss credit card customer segmentation case studies please visit www.fortewares.com or email us at email@example.com.