Diving Deep into Big Data with Predictive Analytics to Optimize Customer Acquisition & Lifetime Value
If customer acquisition is the primary source of your business growth, predictive analytics is your ultimate way out.
By diving deep into big data with predictive analytics, organizations can come out with the most effective customer acquisition and retention strategies. Big data is constantly growing bigger at an overwhelming rate with high-volume, variety, and velocity of data sets coming from different sources. When combined with predictive analytics, big data can unleash new possibilities for customer acquisition and lifetime value enhancement.
For the same marketing cost, data-driven marketing campaigns can bring in more customers by targeting the prospects that are most likely to respond. Predictive analytics can help identity the right prospects, as well as the right offer, the right channel and the right time for maximum sales conversations. By targeting high potential and value prospects through their preferred channels, and at the time when they are more responsive or open to an offer, data-driven marketing campaigns can help not only acquire new customers, but also maximize the ROI.
Predictive analytics involves identifying patterns in big data to predict future outcomes. By analyzing huge and diverse sets of data, predictive models evaluate patterns in data to reveal hidden opportunities and risks for organizations. Considering multiple factors, these predictive models forecast future events with a high degree of accuracy.
customer-table
With predictive analytics, the entire process of optimizing customer acquisition and retention will involve the following:
  • Identifying the prospects who are more likely to purchase certain products, services or deals
  • Targeting these prospects with suitable offerings
  • Identifying factors or key events leading to customer churn
  • Detecting high-churn probability customers
  • Targeting customers who are at-risk of attrition with customized offerings to effectively retain them
Acquiring New Customers
Neither all customers are the same, nor are their needs. So, organizations need to understand that all customers cannot be satisfied with the same set of services or products. In order to reach out to customers and understand their preferences and needs, segmenting them into right groups holds great significance. Combined with big data, predictive analytics can reveal which customers are more likely to buy if special deals are offered to them. For instance, predictive analytics helps determine what kind of advertising or marketing campaign will reach best to a particular segment of customers.
The whole process of customer acquisition with predictive analytics will involve grouping like customers together, analyzing their overall buying behavior, and forecasting their future buying behavior. Leveraging this valuable intelligence, marketing campaigns, based on a targeted response modeling, are devised to efficiently acquire new customers.
1. Segmenting Customers into Groups: Customer segmentation into different groups is based on group similarities, considering variables like age, gender, marital status, place of employment, geographic location, purchase and product usage history, service preferences, needs, attitudes, etc.
2. Predicting Future Outcomes: Combining these variables with transactional and other external data, predictive models score prospects based on their propensity to respond to particular marketing efforts. Predictive models, based on regression analysis, unveil the relationship between input (customer data) and output (customer spending). Leveraging these insights, organizations can come out with more targeted services and products by customer segments.
customer-table
Furthermore, by determining most profitable customers, appropriate customer acquisition costs and contribution margin, the customer lifetime value can be optimized.
Retaining Existing Customers
One very effective technique to retain existing customers is the next-best product or service recommendation. Customers’ future buying actions can be predicted by evaluating their historical data and current actions like what items they are looking at on the website or the keywords they are typing on search engines.
Utilizing this intelligence, next-best product recommendations can be determined to retain existing customers. Further, exploring the customer database can help recommend special offers that can turn out to be lucrative for the existing customers. Equipped with this knowledge, organizations can build higher levels of engagement with their existing customers and influence their buying decisions through targeted retention campaigns.
Reactivating Old Customers
Predictive Analytics is equally helpful in reactivating old customers who have stopped buying for some reasons. A study of the customer database always reveals figures around active customers and customers who have not purchased anything, over a period of time. But before organizations decide to reactive old customers, they must compare the costs between reactivating an old customer and acquiring a new one.
Predictive analytics can help organizations easily get over this challenge by providing comparative insights on whether reactivating an old customer will be costlier than acquiring a new one and vice-versa.
Moving forward, predictive analytics can help maximize ROI by predicting who will or who will not come back, and thus help organizations prioritize their customer reactivation efforts.
Conclusion
Predictive analytics has emerged as a true game-changer. It has radically transformed the way organizations engage, acquire, and retain customers. By enabling organizations to look forward in terms of what’s next or what they should do to meet their business goals, it’s paving the way for unprecedented business growth and success.
However, to truly exploit the potential of predictive analytics, organizations need to ensure they pull in quality data from a variety of data sources including external and internal. . Predictive models, driven by relevant and quality data, can help build and run more effective customer acquisition and retention campaigns and realize best possible outcomes.
About Sheetal Kumari
Sheetal is an MBA with 6.5 years of experience in the publishing and the IT industry. She is currently working as an Assistant Manager in the RSI Marcom department.

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