Research and Analytics: Time to Unite!
For many years, the customer research and analytic business functions have operated as largely independent practices within organizations. This was understandable as new analytic techniques and approaches were just starting to be implemented in early adopter departments such as sales, marketing and CRM. But now that analytics has reached a similar level of maturity as research, change is necessary. Continuing to operate as two independent business functions is suboptimal. Before I explain why, let's first define the two practices.
According to Interaction Design Foundation, customer research is conducted so as to identify customer segments, needs, and behaviors. It can be carried out as part of market research, user research, or design research. Even so, it always focuses on researching current or potential customers of a specific brand or product in order to identify unmet customer needs and/or opportunities for business growth. Customer research may be conducted via a variety of quantitative and qualitative methods such as interviews, surveys, focus groups, and ethnographic field studies. It also commonly involves doing desk research of online reviews, forums, and social media to explore what customers are saying about a product.
In contrast, according to Gartner Research, customer analytics is the use of data to understand the composition, needs and satisfaction of the customer. Also, the enabling technology used to segment buyers into groupings based on behavior, to determine general trends, or to develop targeted marketing and sales activities.
Based on these definitions there appears to be a great deal of overlapping responsibilities between the two groups, which would further support greater integration of these practices. That said, there is a key difference that is important to highlight:
Source Data: The data used in customer research is typically collected via primary or secondary research using many of the methods described above, whereas the data used to perform customer analytics is most often generated from direct online and offline interactions with customers, such as sales transactions, product returns, customer support calls and website chats that are then stored in business information systems.
What this means is that we could get a far more holistic and deeper understanding of customers if we were to combine the source data used in research and analytics.
So back to why things must change and why there should be greater collaboration and alignment between the research and analytic business functions. In today's business environment, many forward-thinking companies have made the delivery of superior customer experiences a top business imperative, believing this will to lead to greater brand loyalty and help differentiate themselves from their competitors. Sounds like a win for businesses and consumers! However, in order to improve the customer experience (CX) you must understand how customers behave and also feel about their interactions with your brand across the entire decision-making and purchase journey.
The best way to generate this insight is by combining the source data from research and analytics. Here's the most important take-away from this post. Evaluation of top of the purchase funnel interactions (customer awareness, consideration, preferences) rely heavily on customer research methods, while bottom of the purchase funnel interactions (customer purchases, returns, product support, repeat purchases) are best evaluated with customer analytics and supporting big data management platforms.
Therefore, the most efficient and optimal way to deliver on this CX improvement corporate mandate is to combine the strengths and expertise of these two critical customer insight functions. Of course there will be organizational resistance and corporate politics to navigate, but these should not be a barrier to changes that enable a more complete understanding of your customers and inspires insight-driven customer experience improvements!
I welcome your thoughts and comments.
Please check out www.mcguirkanalytics.com/blog for additional blog posts on a variety of analytic topics.