How data science is driving customer journeys: Overview

How can companies get the most out of the intervention with their customer’s journey with the aid of data science

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Decades ago, McKinsey and Company, a consulting firm, had captured the traditional journey of a customer’s decision-making process through a somewhat linear model. You consider the products, shortlist and evaluate them at a reasonable length, before making a purchase. After the purchase, you start engaging with the product and decide whether to switch. Companies have been largely reactive, improving the efficiency of existing journeys or identifying and fixing pain points in them.


This has changed significantly. Today, customers have loads of information on products at their fingertips. Shoppers often jump in, from consideration to buying very quickly. And then the product does everything possible to elevate the experience and mesmerise the customer. Often, customers don’t go back to considering other products because they benefit from the journey itself. The ability to create such an irresistible and permanent engagement is driven by a combination of data science, technology, and creativity.

To appreciate this fully, we need to understand what data gets captured. 

Importance of data


The data you capture about a customer starts at home - what they watch, what social media platforms they interact with, how they use mobile while on the move, how they behave in a store, and so on. This consolidated view of a customer’s behavioural DNA is at the heart of making decisions on how to influence their buying journey. By building such a precise understanding of a customer’s behaviour, you can drive the customer's journey proactively.

How can companies get the most out of the intervention with their customer’s journey? 

The nuances can vary, but three central themes would give them a competitive advantage. These are- personalisation, contextualisation, and automation.



Every customer is unique and wants a distinct experience that is true for her. In the past, we didn’t have enough customer and transaction data to tell us what each person wants. Also, the cost of serving personalised offerings to each customer turned out to be prohibitively high. Today this is changing fast. Companies can personalise and create a huge differentiation across product offerings, pricing strategies, online experience, and other areas. Think of the recommendation engines from Amazon or LinkedIn, that make the experience of each customer distinct and unique.

Also, as online retail took the centre stage and expanded during the pandemic years, the competitive intensity in virtual marketplaces increased. With greater choices, customers also started becoming more demanding. Personalisation is no longer a nice-to-have. According to a recent Harvard Business Review study, 81% of executives believe personalization is an important driver of strategy. And 54% of them said that their organizations were investing heavily in personalization.



A customer’s journey to purchasing a product or experience could vary significantly depending on the context. For example, the behaviour of a relaxed weekend shopper is very different from the one picking up a few important things on the way to the office. Therefore, it is important to see where the customer is, physically or virtually.

In retail promotions, such contextualisation is increasingly becoming an important driver of success. Here’s what we see. At dunnhumby, we process 18 billion retail data records each week from 1 billion shoppers globally. Our work with retailers reveals that when promotions are contextualized using solid data science, the sales uplift could go up by 30% compared to a control group where no such contextualisation is done. Similarly, the engagement of the customers with the promotions on display goes up by 4X when they are contextualized. The returns could be substantial if contextualisation is carried out with meaningful data science.



Automation is essential if you want to provide a superior experience to the customer. People simply don’t have the patience to go through laborious and often redundant manual processes. You simply lose them if you make them spend more time than your competitor.

Chatbots are arguably the most common examples. Users feed in their messages through the messaging platform. A Natural Language Processing system brings some structure to it. Behind the scenes, a machine learning model is trained with extensive data obtained from real customer interactions. Chatbots can also be trained during the actual interaction with the customer.


What lies ahead

Beyond the proliferation of data and the advent of better data science tools and models, three core developments are redefining the customer journey.

  1. Products are getting connected. Channels are inter-connected. This enables companies to get a continuous stream of data from customers wherever they are.
  2. There are tools today that map and help optimize customer journeys. For example, customer journey analytics tools such as BryterCX, track customer behaviour across channels.
  3. APIs have become more important than ever. APIs help manage information across companies – so that Delta Airlines’ app can seamlessly connect its customers with Uber.

In the coming years, as technology matures, only human imagination will be the limit to making better customer journeys and experiences possible.

The article has been written by Manoj Madhusudanan, Head of dunnhumby India