From telecom to retail, hospitality to finance, customer analytics will continue to be a hot topic over the coming years. Keeping in mind that customer analytics is highly correlated with data (and most importantly, with the quality of it), any improvements in data quality will take companies a step ahead in this realm. This article summarizes eight recommendations for companies around how to increase the quality of customer data acquired from customer touch points.
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No matter how good your competent your company / employees are around data modeling, reporting, or business intelligence, an old saying will be said again and again in meetings when it comes time to make enhancements in these areas – “garbage in, garbage out”. Although there are several possible operational or systematic reasons why data becomes and is deemed “garbage,” the usual culprit is the customer-facing channel – the touch points where data around customers is collected (like stores, call centers, websites, etc.).
To get to the level where a company can be considered effective in managing the quality of its data, it must achieve three specific milestones first:
1. Defining Data Quality: Although as a phrase “data quality” is perceived as an open and well described one, the reality is almost always the opposite. There are numerous attributes that can be used in defining it – like accuracy, reliability, timeliness, relevance, completeness, consistency, sufficiency, and understandability. From company to company the final list of attributes and the importance of each one will differ when it comes to defining data quality.
2. Assigning Ownership: Without management support, internal parties solely responsible for it, and employees in various units supporting it, improving / managing the quality of data quality will never become a priority area, nor an important issue for the business. Some of the critical tasks needed to be completed to garner support include the designing of a data quality roadmap and the establishment of a governance structure.
3. Monitoring Systems Development: Once data quality is improved to desired levels, companies need to keep a constant eye on them. This requires the development of various monitoring tools, like employee / agent data quality scorecards, channel data quality report, recognition programs for data quality excellence, etc.
High quality data is a critical business enabler, touching many parts of the company. From marketing to operations, sales to R&D, having high quality data regarding the customer base is a must for ensuring the right decisions are made in day-to-day business. At a simpler higher level, high quality data can help increase revenues and decrease costs:
- Increase in Revenues
Improved data quality is able to create bottom-line benefits in the form of improved cross-sell/up-sell performance, optimized retention operations, targeted pricing practice, etc.
- Decrease in Costs
Poor data quality results in losses across a number of fronts – examples:
- $611bn per year is lost in the United States from poorly targeted direct mailings
- Poor data is the number one cause of CRM system failure
- 75% of organizations have identified costs stemming from poor data
- 88% of all data integration projects either fail completely or significantly over-run their budgets
Companies need to consider data quality management as a company-wide matter and tackle the issue with an inter-departmental perspective. If the company is in the early stages in data quality management (definitions are vague, monitoring systems are not in place, roles and responsibilities are not clearly set, etc.), a project team should be formed to design a data quality improvement roadmap:
We recommend companies undertaking this data quality improvement journey address the eight items listed below:
1. Clearly Define “Data Quality” and Share it Across the Company
The definition of what data quality is needs to be designed and shared across the company – of particular importance is ensuring front-line personnel are informed around the matter. Clear and simple definitions are crucial in these communications. Different level definitions are needed here for communicating with different internal stakeholders:
2. Introduce Data Quality Index
Introduce a data quality index that will not only ease the communication and reporting of data quality, but will monitor performance changes over time. An index can be developed by assigning weights to the data fields subject to data quality calculations based on the business priorities.
3. Set Data Quality Index Targets
The optimum level of data quality is rarely 100%, considering business realities and costs attached to it. Rather, a company must define specific, actionable, reachable and time specific targets for each data field, and communicate the current and targeted value of the data quality index with related parties.
4. Measure and Report Data Quality at Every Touch Point
If the company has several external parties within the channels (like outsourced call centers, franchise stores, or dealers), it’s even more critical to measure the quality levels of data flowing from those parties. Around this, companies should develop targets / agreements for each channel around each data field, so that data quality is maintained / increased over time.
5. Incentivize Front-Line Employees
Data collection initiatives strongly depend on front-line employees. Sales representatives, call center agents, and customer care representatives are the primary collectors of customer data. Assuring these individuals are motivated and armed with the right tools and information is of critical importance. Around this, incentives and recognition programs are two ways that can be used to drive up performance.
6. Demonstrate Bottom-Line Impact
Data quality improvement efforts are usually long-term initiatives that generate little in means of results in the short-term. It is important to gain some visible quick wins with improved data at the center of the success; as such a win will drive up performance across the company around improving data quality. As such, pilots should be conducted to create some level of bottom-line impact for the company (particularly around increasing sales).
7. Establish Data Quality Governance Structure
Accountability is vital to sustain desired levels of data quality. Companies should define roles and responsibilities within the organization and communicate it with the related parties. Together with data quality targets in place, different units should be responsible for certain aspects of data quality management linked to those data quality targets.
8. Prioritize Data Collection Efforts
For data collection or data update campaigns, companies should follow a segment-based approach. Having high value customer data on hand is of much higher importance than of having information regarding the mass segment. To that end, companies should prioritize their data collection efforts to focus on garnering information from the right clients.
With an ever-increasing importance placed on the utilization of data mining techniques for improving performance, the reliance on and importance of customer data is at an all-time high. Companies need to act to ensure they are armed with the right tools in winning their battles to get the most out of each customer – the development of a data quality improvement roadmap is an important step towards this.
To learn more about data quality management and how to operationalize the above, please contact us at email@example.com.