Not Your Father's CRM
Taking customer analytics beyond CRM and loyalty.
Taking customer analytics beyond CRM and loyalty.
Many organizations of all sizes have spent considerable time and expense collecting, cleaning, storing, analyzing and reporting on customer data, primarily for CRM and loyalty marketing applications. The most direct benefit path for a customer database is supporting CRM and loyalty programs, but an opportunity exists for a much broader enterprise benefit. By leveraging your CRM team’s customer data and analytic skill set, benefits can be delivered against a broader set of business objectives. The resulting increased visibility and credibility for the CRM team will lead to an increased role within the organization and will maximize the benefit resulting from the CRM investment in technology and people. All organizations have different analytic needs, and while the examples below are drawn from retailers, they can be applied to other businesses.
The path to business benefit for a customer analytic capability, at the highest level, relies on three elements: 1) data, 2) generating insight from data, and 3) communicating insight to business decision makers for action.
By considering broader business applications, it’s possible to extend or modify the scope of the data and insight components of this process to support action against a much more impactful set of business objectives.
Consider A Retail Framework:
Potential areas of analytic impact span the entire organization. Some of these will involve extending the customer view into existing analyses (e.g. pricing). However, others might not include a customer view at all (e.g. labor planning) and leverage the analytic skills of the CRM team.
The data structure required for CRM and loyalty can easily be extended to include information that will address the three elements described above (data, insight and action). Insight generation tools can be expanded beyond traditional analysis/modeling software (e.g. SAS) and Enterprise BI (e.g. Business Objects) to include the new class of exploratory visual analytics tools like Tableau. Tableau is the leader in Gartner’s Magic Quadrant for BI and Analytic Platforms. Its strengths are an ability to rapidly develop a visual representation of data and an ability to blend data sources together on the fly, resulting in much faster integration of new information.
A Hypothetical Retail Example
Assume that sales are in decline and that the CRM/Analytics team has been asked to provide insight and recommendations for action. If the CRM analytics function had been developed solely with CRM applications in mind, the data available would likely contain only identified transactions and customer-level data on CRM contacts.
A More Complete Data Model Enables Broader Analytic Application
A starting point is to include all transaction detail from POS and align metrics so that all analytics produced will foot to other KPI reporting within the organization. This is critical to gain a level of trust and collaboration with finance and other departments, which produce the “single source of truth.” Without a full view into the sales line, CRM/analytics loses all relevance for broader questions. The analytics team will be able to look for correlations between CRM and loyalty performance and overall business results, but there won’t be an ability to connect more deeply because of the lack of tie-out. By capturing a full view of sales, including transaction time stamps, it’s possible to drill into the year-over-year results by day of week/hour of day to determine if there are any potential day parts responsible for the overall decline in sales.
The Tableau chart below indicates Friday afternoons are underperforming (red dots). This opens up a larger set of questions:
Tableau’s ability to rapidly explore potential drivers can identify testing opportunities with the goal of reversing (in this case) a sales decline.
Producing slices by product category and customer segment can provide insight, but greater action can be achieved as additional layers of data are added. Some retailers possess a valuable data source which can truly add insight: customer satisfaction results. This data source isn’t always designed with data integration in mind. Creating a link to customer identifiers on the CRM database enables deeper insight into root causes of business decline. Overall satisfaction scores can aid the insight process, but any comments collected can really pinpoint the underlying issues via use of text mining tools—inventory, staffing, competitors, etc. By understanding what has happened and uncovering both operational drivers and customer experience issues, decisions can be made which are data-driven and have a high probability of moving the organization in the desired direction.
Ultimately, as the CRM database is enhanced with additional information, it will enable broader analytic opportunities—for example, media mix modeling. Media mix models focus on understanding all media activity (including CRM and loyalty) along with other business drivers (weather, labor, pricing, etc.) to understand which levers interact with each other and what the optimal mix should be going forward.
The example provided above is intended to illustrate the potential of broader applications for a mature customer analytic capability. By driving the customer dimension into business performance conversations of an organization, greater insight is achieved and more effective solutions can be brought forward. The benefits of this work come full circle back to CRM and loyalty.
Maximizing Organizational Benefit
As the customer analytics capability gains credibility within an organization, it will become easier to achieve approval for methodologies and resources. Ultimately, the capability will earn a “seat at the table” for strategic planning. This is the model for achieving maximum business benefit from a mature customer analytics capability.
Dave Scamehorn, VP Analytics, brings over 20 years of marketing analytics experience to his current role of leading the analytics function for Olson 1to1. The team is responsible for CRM test and measurement design, campaign management, CRM optimization through predictive modeling, segmentation, reporting, and ad hoc analytics support for Olson 1to1 clients. Prior to joining Olson, Dave led analytics teams at Fortune 500 retailers Best Buy and Advance Auto Parts, focused on transforming customer data into actionable insight and profitable recommendations. Dave has an MS in Statistics from the University of Minnesota.
Harnessing big data and machine learning to become more relevant in your customer touchpoints by identifying Share of Wallet opportunities in your customer base