A lot of organizations are still investing to achieve the elusive goal of a 360 degree view of their customers. It seems obvious that such a view would enable many roles in the organization – sales, field marketing, customer service, professional services, and many others – to be more productive. But the reality is that many of these projects take years to complete (if they are completed at all) and the results are often quite unsatisfying to their constituencies. Most organizations have plenty of tools, capable people, and data, so why can’t they seem to hit the mark with the customer data systems?
For one thing, the 360 degree view of the customer has come to mean a comprehensive integration of enterprise data. While this framing has worked well for the numerous consultants and vendors who work on these projects, it often misses the point about where data can be leveraged in the business to have impact. The projects have morphed from solving a specific business problem – which is hard to do – to adding more and more data – which is a known solvable problem. And so the goal becomes on-boarding data source after data source from internal systems.
In addition, framing these projects as a data integration problem for enterprise sources takes the attention away from the business processes that generate really powerful and useful data (e.g., five things that every sales person needs to know about their customers). Instead of working with incomplete data in the system, it may make sense to tighten up the process and collect the data you actually need to solve the business problem. It also tends to lead to an implicit assumption that all data should be treated the same way when, in fact, information about certain segments tends be far more valuable than others. (e.g., 20% of my customers might account for 80% of profits) These efforts often miss out on many prioritization opportunities and end up delivering too much mediocre data. Because these projects leverage the techniques for application data integration, there is a tendency to overemphasize detailed data elements and to shy away from the deeper syntheses needed to truly understand a customer. It is harder to extract requirements for how separate data elements should be combined into a more meaningful synthesis. The alternative, however, is to turn all of your non-technical consumers of data into analysts – an even harder problem.
Numerous sources and data user groups often lead to a customer model that doesn’t fit well with any specific business use-case. At one extreme, the ‘customer’ devolves into either a restatement of abstract entities designed to make applications configurable, a very operationally-based definition (e.g., bill to customer vs. ship to customer vs. sold to customer), or an overly aggregated view (i.e., who has actually sold anything to a domestic ultimate DUNS?). Finally, with so much emphasis on internal systems, these efforts often overlook readily available external sources that can add new dimensions to understanding a customer.
What, then, is the alternative? Start by identifying the consumers of this new customer view in the organization. In what business processes will they use the new view? What specific decisions and behaviors could change within these business processes? For example, could you improve the targeting of your outbound telemarketing efforts with enhanced visibility into which customers had a larger available wallet? Would you change your sales engagement model if you knew that there were 20, not two, potential buyers in an organization?
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Having identified the users, processes and decisions that the new customer view will support, your next step is to assess data quality. You will have to ensure that it can both have an impact on the decisions it influences and that it will be accepted as reliable and accurate data by the end users. Maybe it turns out that the data could work for some important segments but not all. Assuming you can source the data, do you have a culture that accepts data-driven decision making for the target business process areas? Are the users willing to support a data-driven process by committing to collect other information along the way? For example, will they record the renewal dates for a competitor’s product? You can develop the a culture of data driven decision making by starting with modest objectives, working with thought leaders among the data consumers, and developing a robust measurement capability to enable the ongoing demonstration of value. Once you develop a culture, it is much easier to build on successes and have the consumer guide you to the right problems.
True 360 degree customer visibility is a valuable asset for any organization. Realizing the value of that asset involves more than just compiling all the data in one place. It involves aligning to real business decisions, sourcing data internally and externally, segmenting the customer base to ensure sufficient data quality, and synthesizing the data to enable its use by non-analysts. In short, it is time to focus that 360 degree view and get on with solving real business problems.