Analytics and Insight

Customer Analytics in an Insight-Driven Business

 

Tibco:

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In its report titled “The Insights-Driven Business”, Forrester explained that “an insights-driven business harnesses and applies data and analytics at every opportunity to differentiate its products and customer experiences”. Insights-driven businesses operate in a closed-loop learning process, where they identify metrics to measure business outcomes, gather data to develop insights, derive decisions based on these insights, run the decisions as insight experiments in software, measure the results, refine the insights, and repeat the cycle over and over again.

An Insight Platform is a set of software working together that allows businesses to function in such a continuous learning and improvement process. Through an Insight Platform, a business can gather the data it needs to understand and evaluate its operations. Its users can fashion the data into a form in which it can be analyzed visually in the platform. Analysts can derive insights from the data and data scientists can create models to predict how factors affecting the business may behave in the future. The business would then use these insights to prescribe decisions, automate these decisions, monitor the business outcomes, refine the insights, and further improve on the decisions.

 

The first step for companies to embark on this continuous improvement process is to use the insights they learn from the past to make decisions and turn these into actions in their business applications. Customer insights can be used to inform decisions on the next best thing to do for your customers to earn their loyalty and deliver an impeccable customer experience. TIBCO’s Chief Analytics Officer Michael O’Connell recently gave an excellent demo that shows how a retailer can use customer insights to figure out the best products to offer customers and turn these decisions into actions to make offers in real time.

In this demo, Michael has an analytics app that has data on customer purchases captured from events that happened in the past (e.g. checkouts from webstore or brick-and-mortar stores etc.). He used that information to predict how likely it is that each customer will buy products from a few categories for a promotional campaign. The analytics app shows him the top product category among those in the campaign to offer to each customer. It also shows him, customers who are likely to buy products from a second category, thereby presenting an opportunity to cross-sell. He then deploys the predictive model to make those offers to customers (e.g. as they shop in the webstore). The platform captures the customers’ responses to these offers and this information is monitored in real-time.

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