Customer Analytics

Focus on the lifeblood of your company, your customers.

Customer Segmentation

 

Customer Segmentation is the principal basis for allocating resources and extracting maximum value from high and low-profit customers. We can use simple demographic data and advanced clustering segmentation to:

  • Divide market into meaningful and measurable segments according to customers needs, behaviors, demographic and social profiles.

  • Determine the revenue potential of each segment and targeting segment according to their profit potential and your ability to serve them.

  • Tailor product, service, marketing and distribution strategies to match the needs of each segment.

  • Measure performance of each segment and optimize your segmentation approach over time.

Customer Churn

 

It doesn’t matter what type of business you run, retaining existing customers is cheaper and easier than attracting new customers. Monitoring churn is the first step in understanding how good you are in retaining customers. Companies need to identify the key drivers and triggers in the data that lead to churn, and detect early signs of intervention.

 

There is no “one-model fits all” solution to this problem and the approach has to be tailored to the data at hand. Our consultants employ a variety of modeling frameworks to score individual customer with a likelihood of attrition. The ultimate goal is to understand why the customer leaves your product or service so that you can adopt specific retention measures beforehand.

Basket Analysis
 

Understand which product combinations are being purchased together by your customers, and in what sequence. Understanding the product combinations and the strength of these relationships is valuable information that can be used to make recommendations, cross-sell and upsell, offer coupons and promotions. Just a few years ago, Netflix offered a $10 million prize to improve the efficiency of its recommendation engine.  Here are some of the things you can do for your business:

  • Assortment Optimization and Shelf Space Storage: CPG companies can plan and create market-based, customer-based, fashion-based, and price-based assortment mixes.

  • Build recommendation engines (like Amazon customers who bought this item also bought these items)

  • Targeted Marketing: Based on the past buying patterns, contact customers with product and service offerings that would likely interest them.

Customer Lifetime Value

 

To understand your customers’ lifetime value and cycle give you the tools to target them with the benefits they are looking, as well as to prioritize and budget your different initiatives. CLV ultimately helps you in forecasting. Some benefits:

  • Customer Acquisition and Retention: CLV gives you a better understanding of what you can spend to acquire customers and profitably retain them.

  • Targeted Marketing: Not all customers are equally important. CLV-based segmentation model allows the companies to predict the most profitable group of customers, understand those customers’ common characteristics, and focus more on them rather than on less profitable customers. 

Cross Sell and Up Sell

 

This is the classic use case we all experience at our favorite burger joint, would you take fries with that? Would you like to supersize it for only 99 cents? - Identify out of your customers who has the highest propensity to make another purchase. Analytical tools and techniques such as Acquisition Pattern Analysis analyze the patterns in the customer’s past behavior, correlate this information with similar customers, and then identify potential service or product opportunities at each contact with the customer. We can help you do the following: 

  • Identify prospective customers for cross selling

  • Determine products or service offerings and optimum price point for each customer.

  • Build a scorecard to calculate the propensity of the target customers to purchase various products.

Next Best Product

 

Through the use of a product recommendation engine understand the preferences and intent of each visitor and show the most relevant recommendation type and products in real time. Driven by models capturing life-event patterns, buying behavior, social media interactions, and other aspects, which customers need to be approached and on which channel. Here are some examples:

  • 360 View of the Customer: Having a complete picture of customers in terms of their profiles, needs and current behaviors is key when making a recommendation. This requires being able to collect data from all channels that customers use to interact with an organization. It is important that this data is merged into a single source and available at the customer touch point to obtain meaningful insights not only to describe customer’s past behavior, but also anticipate her future. 

  • Event Based Marketing: It identifies customer events that are key milestones to a customer. Events can take several forms, such as major life changes (getting married, having a baby) or interactions with the retailer (purchase of products).

 

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