International Institute for Analytics

This is the second in a series of three research briefs on Customer Event Analytics. Part 1 set a business context for customer event and response analytics and explored types of events in detail. This brief focuses on the techniques used by analytics practitioners to transform transactional and other data into signals for how the firm should respond. Part 3 will discuss the data storage and retrieval technologies enabling advanced customer event analytics.

Business leaders continually face the high stakes challenge of understanding and responding to customer needs. However, the emergence of high-velocity, high-density business transaction data adds to the challenge while also providing a vast opportunity to profile the behavior of customers, to understand their unmet needs, and to predict their likely next actions.

In Robin Way’s presentation, he discusses the opportunities and value organizations may achieve with customer event and response analytics. Robin discusses how you can build and apply these capabilities - data, tools, analytics, decisions, and delivery of resources to customer interactions - in your own firm.

We look at questions you can ask your business partners, to get them thinking about enterprise analytics and what they are missing without it.

Rather than striving for “perfect data” in all supply chain systems, my research suggests that primary efforts should be focused on the most fundamental yet most critical form of supply chain systems output: the spend analysis.

A client recently asked us, “What are the benefits of having an Enterprise Analytics strategy?” In this inquiry response, we examine three specific reasons to take an enterprise-level approach to analytics and to maintain an enterprise analytics (EA) strategy.

There is an important analytics and business intelligence annual prediction that was largely overlooked for 2015. This noteworthy and developing trend is grounded in the healthcare industry. There is tremendous potential in the accelerated growth and proliferation of genomics, which is enabled by data accessibility, accompanied by clinician and researcher cooperation and collaboration.

Visions evolve over time, and are never implemented quite as envisioned. But the IoT course is long and complex enough so that it can’t be accomplished without a vision. To contradict Alice of “Looking Glass” fame and George Harrison, if you don’t know where you’re going, any road will not take you there.

Every technical project involves some sort of analytics, ranging from simply reporting key facts, to predicting new events. Here, we define ten increasingly sophisticated levels of analytics so that teams can assess where they stand and to what they aspire. Along the way, we clarify definitions of three types of analytic inquiry and four categories of modeling technology.

As your organization continues down the path of being data-driven, consider a survey of how wide the gap is between your CEO, senior management, and the broader employee base when it comes to the use of data and analytics in the organization.

A roadmap for Analytics 3.0 not only addresses your current and target state in terms of talent, but also should address the broader capabilities including technical infrastructure, business processes, data ecosystem, and governance structures to drive it.