Wizards behind the curtain: The role of data scientists in securing your customers’ loyalty

Daniel Cantorna, Vice President, Data, Insights and Technology, Asia Pacific, Collinson
15 Mar 2022

The role of data scientists in securing your customers’ loyalty

In today’s competitive business environment, you need every advantage you can get. Data science can give you that edge by providing the insights you need to truly understand your most valuable customers and retain their loyalty.

In previous Collinson articles, we outlined some of the most important steps in transforming your loyalty strategy, including streamlining your customer data to achieve a single customer view and leveraging segmentation tactics to easily identify your most valuable customers. We also looked at how to personalise your interactions with customers, create a seamless loyalty ecosystem, and build customers’ trust in your use of their personal data.

Data science unlocks the deep insights needed to make these steps more effective. Read on for insights on how to get the most from this activity, and to learn why it’s an integral part of leading brands’ loyalty activity.

Why data science is vital to brands today

Traditionally, businesses have relied on business intelligence – the discipline of managing and analysing historical and current data – to support their data-driven decision-making.

In the 2020s, successful companies are also embracing the sophisticated insights offered by data science, which draws on data visualisation, artificial intelligence (AI) and machine learning to perform predictive analyses based on specific hypotheses.

Combining the descriptive insights of business intelligence and the predictive insights of data science can be invaluable in terms of driving strategic business decisions.

Predictive analytics

Data scientists use advanced analytics to create models that predict customer behaviours and suggest how best to meet customer needs.

They build models that anticipate the lifetime value of customers, who will be the most valuable customers in the future, and the types of experiences and content that will be highly valued by these customers.

A data scientist will typically create a ‘product recommendation model’ or algorithm that can predict and suggest the products or services that a business’s most valuable customers are likely to purchase. For example, a comprehensive recommendation system is important to Netflix’s business. It enables Netflix to offer personalised suggestions that reduce the time and effort a customer needs to spend finding relevant content to view. This, in turn, positively impacts long-term customer loyalty. It would not be possible without the input of skilled data scientists.

Another model – the ‘churn prediction model’ – can foresee when a customer may be likely to end their relationship with a brand. The business can then try to prevent this defection, perhaps by offering a better deal. The Churn prediction model is particularly valued by businesses in the telecommunication industry and other sectors with highly competitive or commoditised services. By predicting which customers are likely to leave a company, and their reasons for doing so, it can create strategies and programs to minimise churn and improve loyalty. 

A data scientist can use a churn prediction model to identify different customers’ value to the business, calculate the probability of them leaving, and determine which customers the business should prioritise. 

This, in turn, can ultimately increase the number of valuable customers who remain satisfied and loyal.

Advanced analytics as a service

A difficulty for many businesses is that building a data science capability invariably requires a large financial investment in people, processes and technology.

Even if your business has the capital and resources, skilled data scientists are notoriously difficult to find and recruit, and even more difficult to retain. They may also not have the domain expertise required to immediately apply their skills to your business.

Many organisations are choosing the cost-effective alternative of engaging a partner that delivers analytics as a service (AaaS). Your business can access the expertise of data scientists through a subscription model for data mining, predictive analytics, AI and machine learning that is tailored to your customer retention and loyalty challenges.

Collinson has been recognised by Gartner in its list of reputable data science and machine learning service providers. Contact us for insights and advice on how best to leverage data science in your Loyalty programmes.

WRITTEN BY
WRITTEN BY
Daniel Cantorna, Vice President, Data, Insights and Technology, Asia Pacific, Collinson

Daniel holds over 15 years of experience in consulting, product development, system integration, automation and gamification experience and is passionate about delivering customer-centric solutions and services that help Collinson clients build meaningful, enduring and increasingly valuable relationships with their customers.

Daniel has worked extensively with marketing, automation, integration, business intelligence and advanced analytics for global enterprise organisations across sectors including technology, aviation, hospitality, luxury and retail. 

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