Data does not have a great deal of intrinsic value in isolation. Said differently—it’s what you do with data that matters. Collecting it is of little direct benefit when it’s simply stored to no end. Conversely, when it’s put to use in service of your business goals–and chief among those should be delivering the kind of personal engagement that makes customers more loyal and more profitable–companies can be transformed.
The three keys to leveraging your customer data to maximum effect are:
You need a platform that captures user interactions in real time. These interactions could be updates to the user profile, purchase activity, logging-in to a mobile application or a website, etc. With this information, you can take action based on a pre-defined rules engine, journey mapping, or behavior building algorithms.
Additionally, all events/interactions captured should be time stamped and indexed against location/venue information where applicable. Also, press for the ability to capture third party data sources, including external events, into the platform. All of this information should be cataloged and normalized to provide robust customer-level analytics.
With these foundational pieces in place, compare multiple data points by utilizing your analytic dashboards to aggregate event data into time-series graphs. There are many ways to do this, including feeding your existing external-facing data marts and third-party tools of your choice for data visualization and exploration (e.g. Tableau, Qlik, etc.). With this information, time and offer correlations are more easily made for in-flight adjustments based on sensitivity and receptivity analysis.
Most B2C brands focus on positively impacting these three critical metrics:
To move the needle on these key indicators, you need a rock-solid mastery of your data in real time. Sending a “We Miss You” note to someone who was in the store yesterday is a bad look and antithetical to increasing RFM. A modern customer data management capability helps marketers increase customer loyalty (which is essentially a roll-up of RFM) by predicting customer behavior/value based on purchase history and other latent factors that influence affinity, motivation, and intent to purchase. Through this analysis, marketers are able to calculate backwards-looking scores such as recency, frequency, and monetary spend for multiple activity types. At the same time, they will be able to calculate forward-looking metrics such as probability of churn and customer lifetime value. A robust product recommendation engine can then act on those metrics, using historical purchase behavior to predict the probability of “liking” a specific product or group of products. Next best product offerings are ranked by probabilities from highest to lowest. Where applicable, marketers can add “weights” to the results to ensure a single outcome is raised or lowered in the ranking. This is useful for promoting new products, where there is not enough history to provide strong probabilities for individual user/product pairings.
With liberated data out from behind silo walls and into the hands of skilled practitioners across your organization, you can use your customer data to its fullest effect (after preparing data, cleansing it, and applying filters get it ready for action). Along with user-level analytics, an integrated platform allows for audience generation, campaign creation and execution, customer messaging, and loyalty program management.
Broken free from organizational silos (POS, social, mobile, web, customer service, and so on) and joined with other data, transformative business results can occur. You have all the ingredients, but they don’t do any good on the shelf.
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