Given how important customers are to any business, it’s not surprising to find customer metrics incorporated into the reporting of most departments. Customer service teams look at KPIs related to customer satisfaction and call resolution. Product teams reviewing target markets often define a typical customer (or set of customers) against whose needs they will build their product or service. Marketing teams look to understand what pain points customers have and find ways to position against these needs.
Despite the diligent customer focus displayed by every team, in aggregate these efforts rarely amount to more than a superficial understanding of your customer. Why this is the case?
Analyzing the same data will only uncover the same insight
Today, most cloud-based customer tools tout their customer analytics capabilities. CRMs, customer service and support tools and social media managers all provide options to analyze customer interactions based on the data stored in these systems. Naturally these tools should provide some form of customer analysis, after all, they include the word “customer” in their name.
The challenge with many of these tools is that they are limited to the data they were designed to collect. Customer analysis on a CRM will only be as good as the information entered by reluctant sales reps. Data gets better with customer service tools; those with IM capabilities or voice recording can at least transcribe a customer interaction. But the context that this data is collected in matters. CRM data will naturally be skewed towards the likelihood of a future customer purchase; the data being an interpretation of customer intent filtered through the eyes of the sales rep. It’s not impossible to imagine scenarios where this information is not going to be completely accurate. Similarly, customer service interactions often involve situations where the customer is not entirely happy with the product, hence the reason behind the call.
More analytics capability is always welcome, especially when it complements an existing service. But it’s important to realize that insights will naturally skew to the data that they are derived from.
Integrating data from other sources is difficult
Data analytics done right is not easy. It requires development of a formal data strategy incorporating data security, privacy and governance. Housing data from multiple sources involves designing a solid data architecture. And sharing insights across different teams requires opening appropriate channels of communication to ensure timely distribution.
Making this type of investment is a big commitment, especially for organizations that have legacy systems in place. Some people would argue that it’s not worth pursuing, especially if the company is just doing another “data project.”
If you’re willing to make the investment, the reward is huge
The reward for companies that chose to go beyond one-dimensional analysis of their customers is development of richer models that better predicts their behavior. The broader the data sources, the more nuanced the segmentation, which in turn drives differentiation across different customer groups.
Incorporating insights from as many different teams as possible, including sales, marketing, customer service and finance allows your organization to make much more intelligent decisions with a higher probability of success. For example, feedback from customer care might show that most customers value 1:1 support. Integrating data from finance might show that only customers purchasing high-end products value that 1:1 support. And integrating data from marketing might show that this is true of only a few demographics, who happen to also show strong interest in another product line.
The point here is not that your company should deploy micro-segmentation, rather you should deploy rich data analytics from multiple sources to segment your customers to the level that your company can reasonably support. Integration of data from non-traditional sources, like finance or operations will help your company determine when you’ve reached this point.
Don’t sell yourself short
Many companies are satisfied to passively adopt new analytics capabilities as they get integrated into their tools. There’s nothing wrong with this approach – more insight is always good. But if your company is looking to know every possible detail about your customers, diverse data is always better.