Big Data Analytics play an even bigger role in business decision making and today, companies have successfully leveraged better business insights with the right use cases. Big Data use cases are the foundation of Big Data Analytics. Big data use cases addresses business challenges to yield insights that lead to better decision making and generally are developed for specific business decisions that requires large datasets from different sources in different formats. Successful Big Data uses cases illustrate how businesses can get beyond the constraints that hold them back from being more attentive and responsive to customers.
Common big data use cases include operational analytics, supply chain analytics, behavioral analytics, customer segmentation, fraud detection and industry specific analytics, however customer analytics has become more successful in recent times and the the top five high impact customer analytics use cases include :-
Social media is increasingly being harvested and analyzed to help companies engage with customers and nurture relationships. Sentiment Analytics works by analyzing content drawn from publicly available websites in the form of fragments or “snippets” of text that contain the user’s search terms. The snippets are stored in a database that can be further searched and analyzed using dimensions such as date, region or keyword, the tone of the feedback, and other factors to provide insight into consumer attitudes toward brand, products and services.
For example, analyzing Twitter feeds and Facebook posts, a Telco was able to better understand the service provider’s customer service performance. When there were negative sentiments on social media pertaining to quality of service in specific regions or customer groups, the customer service and operations teams leverage this information to determine the next best action to assuage customer concerns.
Another telecommunication operator’s leverage sentiment analysis data from social media feeds to improve as well as defend their brand image and reputation. They gauge social media sentiment on newly released products, offers, and campaigns in a cost effective manner and proactively create service requests to improve brand perception
However, for companies to gain deeper & early insights with Big Data Sentiment Analytics, it`s important to not just analyze Facebook posts or Twitter feeds but across multiple channels such as blog posts, online reviews, and forums which are industry specific.
Best Next Offer Analytics
Best Next Offer analysis refers to the use of predictive analytics to identify the products or services customers are most likely to be interested in for their next purchase. Using Pattern analysis or market basket analysis, sophisticated algorithms are applied to predict the customer Next Best Offer.
Today banks are using Next Best Product offering to understand customer needs and provide suitable banking product offerings for the right customers at the right time-period to increase customer satisfaction, maximize profits and reduce risk.
Traditionally, the Next-Best-Offer for product offerings are determined by the customers buying history, but today banks are able to collect much more customer data through including interactions or events, to arrive at optimal next best actions in the form of real-time recommendations, or real-time automated actions and also the optimal channel like branch, contact centre, ATM or smart-phone. With big data analytics banks are able to analyze large amounts of customer-data in real-time to predict best suitable product offerings to increase share-of-wallet and maximize customer satisfaction at the point of decision.
Retailers are combining loyalty cards with social media information to detect and respond to changing buying patterns. For example, retailers now target newly pregnant women with baby-related goods promotions simply by predicting that a woman is pregnant based on changing buying patterns.
Customer Churn Analytics
There’s the old business adage that states “it’s cheaper to retain a current customer than it is to acquire a new one”
Today many businesses are turning to Big Data Analytics to understand why customers might leave and proactively address issues to reduce churn.
Using the vast amounts of customer data available throughout the organization and combining this with external customer observations & interactions across channels (CDR data, network data, emails, chats, tweets, surveys, weblogs, etc), telecommunication companies are building automated scorecards with multiple logistic regression models and decision trees to calculate and predict if the customer is most likely to churn in the near term. Upon identification of the churn root causes, telecommunication service provider’s act upon this insight by creating personalized offers across customer segments / regions to help reduce the customer churn, improve overall customer satisfaction and increase profitability.
Predicting Customer Wallet Share
A cargo airline achieved a 20 percent boost in wallet share by providing its sales team a dashboard with simple guidelines on flight capacity, corresponding pricing and competitor options. The complex big data model analyzed frequently changing capacity variance in time of the day, day of week, cargo space availability for the sales team to recommend capacity allocation to each customer optimally.
Perpetual Customer Loyalty with Analytics
Propelled by new, sophisticated Big Data techniques and tools, many organizations are gearing up for their next big data analytics push. One thing for sure, the proliferation of big data analytics use cases are only set to gain further momentum as organizations compete for greater customer loyalty across industries.