Nowadays with new big data platforms, companies are able to gather and retain huge volumes of data generated by users clicks/actions or even traced by devices such as mobile phones and tablets. Companies are looking for ways to utilize these massive volumes of data. The possibilities are endless! Below is one of the ways big data can be utilized to increase customer retention.   Marketers segment their customers by demographics (age, gender, location etc) and RFM (recency, frequency and monetary value) scores to create targeted segments for promotional offers. Recency means how recently did the customer purchase. Frequency means how often do they purchase. And Monetary Value means how much do they spend. But with big data, now we can store and analyze every single detail of every user's behavior beyond their RFM scores e.g. the time spent/engaged on the site, what areas of the site the user was engaged in, the depth of user engagement (products viewed, videos watched, mobile check-ins, interaction with activity feeds etc)   Marketers have used demographic segmentation and RFM scores for email/offline campaigns as well as real time personalized ads. However, demographics and RFM scores are static and/or slow changing indicators. Though those are powerful indicators, many researches have  shown that users current engagement is one of the most powerful indicators of user’s future behavior. In other words, user engagement goes down prior they drop off/stop visiting the site.   Drop in a user's engagement is an early predictor of her future attrition.   Now with big data, more than ever, it’s possible to identify the low engagement and intervene early, even in real time using machine learning. Don't’ wait for days for the recency or frequency metrics to show a drop in the scores. The best time to push engagement features in user flows or offer promotions is when user's engagement is going down but while they are still on the site! Many companies, including some of my clients, are leveraging big data and machine learning to improve customer retention.

Product development is driven by reaching out to internal and external users who provide feedback about the usage and value of each component of a proposed online product or service. Quite simply then, the web product analyst interprets this customer needs and/or wants input in order to deliver customer value.