Loyalty in the Age of Hyper-Personalization

Price, attention, delivery speed, quality, service – the parameters for brands to compete are many and to say it’s a battle out there to gain customer loyalty is an understatement; it’s pure savagery! Customers today openly scrutinize and share unforgiving opinions influencing a huge number of people, thanks to social media channels that have given a voice to the masses. No organization can now afford to ignore customer demands. Understanding them is therefore important and data takes the front seat in this scenario. Let’s look at the various options and approaches before brands to leverage data to their advantage.

Loyalty and Customer Data Platform (CDP)

There are no linear paths to purchases anymore. Customers take the curvy path of research before zeroing in on a purchase and this includes e-commerce websites and apps, physical stores, web forms, search engines, social media and so on. Brands need to keep up with this zigzagging of customers and that’s what customer data platforms facilitate 1. They gather relevant data and present it as a unified view, highly useful in hyper-personalization.

Data Science and Loyalty

While CDPs brings a wealth of data before brands, what to do with it is a question that data science answers. The power of data lies in being able to successfully use it to understand customer behavior at every step of the marketing funnel – from product awareness to activation. Data science brings forth quantifiable metrics that allow marketers to understand at which point a customer becomes disinterested in a product and drops off. It helps bring about personalization at this very point to enable sales conversion. Of course, when personalization is successful, loyalty naturally follows.

Understanding Customers Better through AI-Based Engagement Analysis

While data science brings customers the most effective stages to intervene, artificial intelligence aids in customizing and personalizing that engagement to bring about the desired results. Here’s how they help:

  • Making highly relevant purchase recommendations
  • Offering insights to make digital marketing efforts effective
  • Predicting the likelihood of lead conversion
  • Providing direction to sales agents
  • Optimizing the lifetime value of customers through predictive analytics

Personalization through Machine Learning Models

According to McKinsey Digital, organizations that successfully personalize customer experiences enjoy the optimization of marketing spends by up to 30% 2. Achieving this manually, however, is impossible and automation without intelligence can lead to ineffective personalization. Enter machine learning (ML) and the problem ceases to exist. ML algorithms allow for automatic identification of data (behavioral) patterns and suggest the best course of action without an explicit programming need for each scenario. The right mix of data and ML can help marketers build the perfect model for personalization.

Contextualization of Customer Journey

Customer lifecycle has different stages, and at each stage, the expectation is different. Moreover, the expectation differs with the nature of the business and its standing in the market. In order to be effective, the journey must be contextualized to the buyers’ preferences. It takes a dynamic combination of technologies discussed above and a practitioners’ understanding of the industry to achieve true personalization. Getting it right is only possible when you have a partner who adopts a consultative approach to offer your business a technology-led solution.

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