Customer Understanding and Digital Analytics Integration

As we wrap up our customer understanding blog series, we look at how to keep the customer in mind when using modern digital analytics.

Digital analytics no longer pertain only to public-facing experiences like websites. Modern organizations are deploying an integrated approach to all interactions that ensure they can enrich both customer profiles as well as aggregated behavioral analysis.

Almost every organization tracks and measures the performance of any public digital experience using an analytics platform like Google Analytics. However, where more mature organizations thrive is when they extend the same thinking into different realms of the overall user experience. Companies like American Airlines and Amazon, for example, are looking across different interaction models to gain a more robust understanding of what people are doing, why, when and how.

This strategy does two things. First, it helps these companies understand how large numbers of customers use all the platforms and experiences to support their overall business objectives. This information can inform decisioning and prioritization. Second, the strategy helps modern organizations understand how to group individual users so businesses can tailor experiences based on behavioral data and attributes.

Planning for Prescriptive Digital Analytics

These two ways of using digital data, while not necessarily new, are more prominent now as enterprise-wide machine learning (ML) and artificial intelligence (AI) platform installations are outpacing the digital platform integration of these models. ML and AI provide vast opportunities for many organizations to mature with their data as they acquire it.

But with the growth of ML and AI comes a change in how to prepare your analytics practice. Too many organizations focus on measures and metrics as opposed to what they want to learn. In other words, they are more descriptive than prescriptive. We prefer to reverse the planning process to focus on what questions you want to answer before creating a relational model based on categorizing those questions. For example, consider the following questions:

• What features do customers most frequently use?
• Are customers likely to use any combinations of features together? What are they?
• How often do customers use each feature?
• What are the most highly used attributes in each primary functional flow?

How Customer Understanding and Digital Analytics Support Your Business

These questions all address overall engagement with a product, but they specifically focus on the features used within the product. This level of planning shifts the paradigm from “What did the features do?” to “How are these features driving our business?” The answers to each question can support a variety of business teams across the enterprise, which promotes sharing of intelligence and normalization of data to support the organization as a whole.

Most importantly, you can use this more dynamic way of planning, collecting and using modern digital analytics throughout your organization:

• User Experience Teams will gain a better idea of the overall use-flow correlated to each goal rather than a single point in time. How does someone shop for an airline ticket across website and mobile experiences? Is it different whether they are logged in or logged out? How about whether they are using the app on their phone versus a laptop or tablet?
• Data Architects will have more data to inform models and adjust levers as needed to increase profitable outcomes. For example, automatically adjusting prices of a product in real time can induce purchase behaviors and the likelihood of a sale.
• Merchandise Teams will understand more about inventory controls, helping them plan for buys better with vendors, capture margin impacts to dynamic pricing, and identify other purchase signals. These capabilities help merchants better forecast sales and work with leadership to ensure investors of business success.
• Technical Leaders will be better able to prioritize work efforts tied to value-based outcomes while executing releases to maximize team utilization. They will also begin to prepare enhancements related to increasing sales potential at a category or functional level, removing waste from the experience and making it easier for customers to buy your products or services.

Conclusion

In these examples, the teams can use the same normalized data to help them make decisions and move forward together. This modernizes how they use new data facets based on what they want to learn, which will transform how they operate together.

If you truly want to be a modern analytics organization that closes the gap between customer expectation and business execution, you need to understand how to transition from being reactive to predictive. You can then evolve from an organization that provides insights to one that provides action.

This level of sophistication will increase customer satisfaction as you start automating relevant experiences internally and across channels, reducing your cost to acquire and serve customers. However, you can only accomplish this by advancing the role that analytics and their relevant technologies play in acquiring, analyzing and distributing behavioral information.

Over the course of this series on customer understanding, we have looked at how to gather customer information tactically with in-depth interviews, observational research and journey mapping. Then, we stressed the need to bring all that data together by investing in Customer Experience Management before considering more advanced use cases of process design and integration.

We look forward to sharing even more information that will help you wherever you are on your journey toward building empathy and understanding for all the people you serve, from traditional customers to employees, partners and other stakeholders.