How data science is transforming customer loyalty

It is widely accepted that it is 5-25 times costlier to acquire a new customer than to retain an existing one. Hence, customer retention and engagement have become imperatives of the extant business strategies of firms in their attempt to strengthen their financial performance in a competitive environment. It is no longer adequate to satisfy the customers at a single point of time. Firms have to continue to strive and ensure that the customers remain loyal to the firm/brand through their repeated purchases and referrals and prevent them from moving away to other substitutes and vendors. Jeffrey Gitomer, in his famous book ‘Customer satisfaction is worthless, customer loyalty is priceless’ emphatically advocates customer loyalty over customer satisfaction. 

The key to achieving customer loyalty (CL) lies in providing ‘customized’ product/service to the customers. It is here that the data science has played a transformative role in the way the business, especially e-commerce, is conducted. Customization requires a 360-degree view of each customer. In the present digital age, this information is obtained through various platforms, viz., emails, websites, mobile Apps, social media, surveys, Voice of the Customer (VoC) initiatives, to name a few. The customers’ data so tracked continuously and stored from diverse sources is huge, but is of a heterogeneous nature and unstructured. The data on each customer obtained through various modes is merged into a unified framework and structured using data science tools, such as, AI and ML. 

The Data Science tools have enabled the corporates to segment and target customers using different criteria, such as, frequency of purchase, the amount of spend, age, gender, region, climate, profession, complaints, returns, mode of payment, etc. It is not out of place to mention that the ‘complaints and returning of products’ data also plays an important role in the design of Customer Loyalty Programme (CLP). ‘Hug Your Haters’ by Jay Baer (2016) demonstrates that the ‘negative’ information is equally useful as is the positive information about the product/service. The data science tools are useful in measuring customer loyalty  indicators, viz., Lifetime value (LTV),  Churn rate, Referrals, Net promoter score (NPS) etc. 

The analysis of the structured data is used to identify the drivers of customer loyalty which may be in the form of Product quality, Service Quality; and Brand image. A firm has to strive on all these three fronts for attaining customer loyalty for different groups of customers. The various types of Customer Loyalty Programmes (CLPs) suited for different category of customers are then designed. First, through Transactional Loyalty Programmes the repeat customers are offered rewards and discounts on their ‘next’ purchases. Second, Social Loyalty programmes are targeted for customers who are active in social media and they are rewarded through ‘redeemable points’ for posting the messages from the corporates to their social media contacts. The corporates aim for increase in their sales at a geometric rate by reach out to customers beyond their present clientele. However, these programmes are offered only for short period, so as to keep the financial costs of these programmes within limits. The third type of loyalty is Engagement Loyalty whereby the customers are rewarded with redeemable points for their ‘subscription to the information provided by the firm’. Through subscription, the firms get email and mobile contacts, which is useful for altering behaviour and nudging the customers. 

Fourth, the Emotional Loyalty, whereby the firms reach out to the customers on special occasions and make them feel important. Personal messages from the top management on special occasions, such as, birthdays, marriage anniversaries, etc. help in bonding between customer and the firm. Needless to say that devising this type of loyalty requires data and programming support. Fifth, firms try to devise programmes for Behavioural Loyalty, which implies making customers act as required by the corporates, such as, making shipping free for 5 purchases within a month. For example, get 500 bonus points if you make 3 purchases in one month. Lastly programmes are devised to achieve advocacy loyalty. It has been observed that people are likely to purchase 7 times more from a brand, if referred by their friends. Hence, referrals are rewarded with discounts. 

The market intelligence of yesteryears which was focussed mainly on consumer surveys did not have as much potential to get 360-degree view of the customers, without getting them fatigued answering the questionnaires. E-commerce with ever pouring data on social media has enabled firms to exploit the useful information needed for design of customer loyalty programmes and compete in the market by focussing on the non-price factors in their pursuit of profit and/or sales maximization. 

The article has been written by Dr. Pushpa Trivedi, Senior Professor and In-Charge Economics Group, Shiv Nadar University, Chennai