A Deep-Dive into creating a Customer Single View

It is estimated that around 2.5 Quintilian bytes of data is created each day. This is an enormous number, which can be utilized by companies to figure out the behavioral patterns of its customers. Sorting through copious amounts of data to realize a customer’s needs and wants can be best achieved using a customer single view.

What is it?

A customer single view is a collection of all relevant data that a company has of its customers. It consists of a single record for each customer which gives a brief overview of that customer’s history with the brand, from transactions completed to their likes, preferences etc. The more detailed is the single view, the easier it is for the organization to offer personalized recommendations to its customers.

It is important to select only that data that is useful and provides essential insights about a customer. Adding irrelevant data takes up extra space and can be time-consuming to go through.

Why is a single view required?

The basic application of creating a customer single view is to give personalized offers to the customers. It helps to categorize customers in a better way and is an optimal way of choosing a target base. People appreciate offers that cater directly to their interests and provides them with a personalized touch. It makes them feel special and improves the bond between customers and organizations.

Example of creating a customer single view

The work done for a leading Entertainment industry of movie theaters is taken to illustrate the making of a customer single view. This industry receives a vast amount of data about its customers: details about movies watched, food purchased, the type of screen movie was watched in, and so on. All this information and more was taken to create a customer-wise data table.

Purpose:

To create a unified single view of movie-goers who have visited a theater and made transactions either on food or movies, or both.

Challenges:

Cleaning the data and verifying values. Choosing only relevant data to populate the single view from which useful segments can be made.

Process:

The first step done after receiving data was the cleansing of the data. Cleaning involved removing junk values and correcting inaccurate records. This was followed by the integration of the transaction-level data of the customers and the table containing information about movies. The table thus obtained had too many records since customers appeared multiple times depending on the number of transactions done. This is where the concept of using a single view became handy

In this case, each customer’s details were stored by their mobile numbers. Their mobile numbers were used as a unique identifier while creating a single view.

Brainstorming - This step involved preparing use cases of the customers wherein the needs of customers was realized and discussed upon. This is a very important step since correctly identifying the segments at this point makes it easier when updating the table for smaller changes in the future.

Removal of unwanted data - After deciding the important segments, the extra data was removed from the table mainly for the purpose of saving space.

Creating the table - Code was written in SQL where all the required data was selected from the consolidated table and grouped by the customer’s mobile numbers.

Some important columns selected were Sales, Movies watched, Recency (how recent was the last visit to the cinema), Food and Beverage Sales, Showtime (morning, afternoon and evening), Genres watched, Language preferences etc. A pictorial representation of all segments chosen is shown in the image below:

Necessary cuts were taken while selecting the data to get accurate results. Other segments such as Stores Visited, Director and Actor preference etc. were not taken but can be considered depending on the requirement.

A snapshot of how the single view would look like is shown below.

Conclusion

Not only does a single view give personalized offers to the customers, but it also provides benefits to the company in the form of increased customer loyalty, reduced risk of churn and the ability to generate better insights for up-selling, cross-selling and customer retention.

Further Work

Using the customer single view, a deep-dive can be done further into the behavior of users by dividing those using buckets and creating valid customer segments.