Analytics for Customer Engagement (Part 1 of 4)
New technologies make it easy to develop customer analytics, but a new platform doesn't create immediate improvement without work.
- By David Loshin
- April 5, 2016
The coincidence of four technological innovations has lowered the barrier for broad acceptance and adoption of customer analytics:
- The evolution of open source tools for managing and analyzing massive amounts of data
- Increased availability of data to be analyzed
- The maturation of reporting and machine learning/modeling technologies that simplify the application of advanced analytics
- The streamlined economic model of cloud computing
The combination of these technological advances enables organizations with limited resources to develop a customer analytics program and rapidly move it into productive use. The mechanics of devising a big-data customer analytics platform are straightforward: acquire the appropriate data sets, manage that data within a big-data system deployed on top of a cloud environment, and use predictive tools to analyze the data followed by prescriptive analytics to influence customer behavior.
Of course, that is the theory. However, in practice, there is one key ingredient that is frequently missing from this recipe: the problem that needs to be solved. Media case studies of organizations whose adoption of "analytics" has transformed the organization can hypnotize the reader into believing that the acquisition of an analytics tool will immediately result in improvements to the company's bottom line.
The reality is a bit different for two key reasons. First, although it is easy to understand the general benefits of using analytics, there is still a learning curve to be scaled before the team members understand both how the different techniques work and how they can be applied. Second, and perhaps more important, is that the technology's use is secondary to understanding core business challenges and determining which techniques are most effectively applied to address those business challenges.
There is a difference between creating a platform that can be used to solve a problem and solving the specific problems that need to be addressed. These problems are most likely going to be directly linked to one or more aspects of the organization's business processes. In particular, a customer analytics program would be designed to align with the different phases of the customer life cycle, which typically includes these stages:
1. Awareness, in which a prospect becomes aware that there is a potential need for a product or service
2. Knowledge acquisition, when the prospect investigates alternatives and narrows the scope to a set of preferred vendors
3. Evaluation and consideration, where the prospect sets up specific criteria for evaluation, reviews how the selected products meet their expectations, and performs a comparison of the product choices
4. Selection and purchase, in which the prospect selects one (or more) of the products and commits to buying it
5. Experience and satisfaction, where the vendor supports the customers in their use of the product
6. Retention and loyalty, where the vendor manages the relationship with the customer to ensure that the customer remains a customer and continues to use the product
7. Advocacy, where the level of customer satisfaction is great enough to warrant recommendation
As this sequence of stages is presented as a "life cycle," the process may continue again at Stage 1 when the customer realizes that there are other opportunities for acquiring different products or services to meet emerging needs. In turn, customer analytics must be linked to improving the company's performance associated with each one of these stages. That implies some pre-work to be done long before deciding to evaluate and acquire analytics tools.
In my upcoming columns, I will look at the opportunities that are presented at each of these stages and consider the techniques you can use for analysis.
Other articles in this series:
Part 2: Optimizing the Stages of the Customer Life Cycle
Part 3: Planning for Customer Analytics
Part 4: Managing Customer Data for Analytics
David Loshin is a recognized thought leader in the areas of data quality and governance, master data management, and business intelligence. David is a prolific author regarding BI best practices via the expert channel at BeyeNETWORK and numerous books on BI and data quality. His valuable MDM insights can be found in his book, Master Data Management, which has been endorsed by data management industry leaders.