Customer Analytics focusses on the improvement of an Organization’s overall business development and its results serve as an effective measure for comparing performance and growth. Customers are the fuel for running an organization. Understanding them better will help serve them better. Serving and gaining their trust leads to better sales – a good sign for business. Read more to know about some effective ways to boost performance and what makes them achievable.

What approaches are in focus?

Let’s look at some of the prevalent approaches in Customer Analytics.


customer view

Customer 7200, theoretically speaking, is the availability of complete access to a customer from all aspects (online + in-store). Though complete access to all the channels is hard to obtain, a selection of channels comprising of the major touch points that bridges the silos allows an organization to represent customer attributes under a single entity and form a virtual connection. This selective subset comprises of internal data generated through the application, forming the first 3600 view, and the availability of enriched user information from social networks paves way for the other 3600. Combining structured, semi-structured and unstructured data available through CRM applications, Orders and Bills, Customer Service records and call logs, ratings and feedbacks provided on purchase, posts and tweets through social networks, demographic location and census details presents an overall picture about the customer. Expansion of each channel gives better insight.

A connected customer can be reached through applications currently signed into, which promotes strategies like Ad-placements which are common in social networking sites. This connectedness also increases the possibility for cross-selling products of interest expressed by the same customer through other media. Using Natural Language Processing (NLP) techniques help in identification of key terms and their similarity scores with the organization’s focus. Sentiment analysis gives an opinion of the customer’s expression of interest. It may be through reviews, write-ups or even a social post after purchase.



Customer segmentation aims to treat each customer separately and provide a custom view of offerings. One of the most common approaches includes clustering the customer population into different tiers based on their actions.

In a Big Data environment where there are billions of customers. Propensity models are preferred because they can handle large amounts of data as well as give good accuracy. A high level sense of categorization is achieved by grouping the data into smaller subsets. Product recommendations (based on Collaborative/Content filtering or a hybrid approach) are more effective when a clear picture on segmentation is known. Using the information obtained a better choice of preferences can be provided.

There are also motives to identify customers as they arrive at the doorstep and keep track of new customers to achieve real-time segmentation.


“If you addict a customer, you have a customer for life” – Jon Gabriel, Hungry for Change

The quote’s origin is a health-related one, but another way to put it is to genuinely understand the customer’s needs and cater to it with the highest relevancy and lowest latency possible.

The best example of engagement comes from games. Boston Retail Partners’ Survey suggests around 87% plan on employing gamification for better customer engagement. [1] Dynamic product comparisons will also make customers achieve a sense of satisfaction on purchases.

Just like propensity models, uplift models help in determining whether a customer will likely visit again if discount coupons or special offers (the typical buy-3-get-1 free) are sent. Analysis from the population and selection of customers who have maximum likelihood to be interested are targeted. This can also aid retailers boost customer relationship if they launch unique schemes a week prior to special events (such as birthdays, anniversaries, festivals etc.)

Another factor which is crucial to business is the calculation of churn. Churn/Attrition prediction determines whether a customer’s is likely to churn. (i.e. leave) Successive decisions taken by a customer (action/inaction) are modelled and the next step can be predicted as to whether the customer is likely to be retained or lost.


Almost every organization aims at maximizing its profits for which a direct factor is positive customer attention. A prominent way of achieving this is ensuring continuous increase in Customer Engagement which in turn causes the Attrition rate to go down, as represented above.


Linking the data generated through customer-product interactions (orders, reviews, clicks to page etc.) and transactions generated by suppliers can be used by management systems to control ordering and distribution of products throughout a company’s extended supply chain. Through predictive analytics it is possible to observe the correlations and relationships among data elements and supply chain decisions and make certain decisions like the current demand of a product, the locality of frequent purchases and so on. Models that are built by supervised training typically predict the uncertainty of future demand. The models are improved iteratively by reducing the error rates between the predicted and the actual realizations of customer demands. Frequent Itemset & Association Rule Mining techniques lead to better inventory control.

What makes the whole idea feasible?

Data already collected over the years (or even in seconds nowadays) is waiting to be analyzed.

  1. The availability of frameworks and packages (proprietary as well as open-source, whose focus is increasing nowadays) provide efficient management and analysis of massive amounts of data.
  2. Simplification and rapid development of advanced analytics solutions using modern reporting & machine learning/modeling technologies.
  3. Elastic scalability on the cloud overcoming the limits of computation and storage.
  4. The need for more data analysts and scientists has pushed many individuals to take up data science courses and are now sufficiently equipped for the task.
  5. Evolution in the next major phase of mobile telecommunications standards.

Up to a decade back, time was primarily spent on the development of the product, setup and maintenance of infrastructure for application deployment and collection of data. Modern times are such that infrastructure is made available in a minute (thanks to cloud based services), development and deployment incur minimal time, and data constantly generated by customers is now securely stored. Expanding storage for accommodating terabytes of data is no more a worry for huge costs because of decreasing storage prices. Many organizations prefer migrating to the distributed architecture for speed has overtaken storage.

The next step, therefore, is to tap into this massive source and gain insights that may well decide your organization’s next course of action.