Advancing consumer segmentation during Covid-19 and beyond

The Drum 29 Jul 2020 01:31
By Andy Smith-29 July 2020 14:31pm

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Capgemini suggest ways for brands to adapt their segmentation approach to suit evolving consumer trends.

Research has found that on average, 71% of consumers express some level of frustration when their shopping experience is impersonal.

Poorly targeted offers, such as a vegan receiving coupons for meat products or mothers of teenagers being targeted with diaper advertisements, can lead to weakening brand perception.

Personalization for consumer brands

Consumers today increasingly expect personalised shopping experiences that make their lives easier. For consumer brands, this entails offering your consumers products and services that are relevant to their needs, communicating advertisements and promotions tailored to their context, interacting with them in the channels they prefer, and knowing when and how often to engage with them. Consumers now benchmark their experience with market leaders outside the consumer products (CP) sector, such as Apple and Amazon. Research from Econsultancy reveals that 93% of companies see an uplift in conversion rates from personalization.

In order to deliver meaningful personalised brand engagement, brands need to better understand their consumers at an individual level using the right data and analytics capabilities.

Traditionally, CP brands have either used segments of one or used static consumer segmentation models. These are usually updated across long time horizons to understand consumer behaviors, needs, and attitudes. Hence, they can have dated consumer understanding for brand marketing activities.

A deeper understanding of consumer preferences, pain points, and behaviors will enable companies to create more targeted offers and campaigns to which consumers are more responsive.

CP companies need to make a step change from traditional segmentation approaches to dynamic consumer segmentation to reflect ongoing changes in consumer behaviors and attitudes.

Dynamic consumer segmentation uses real-time consumer data to create fluid consumer segments that consist of individuals that move in and out of the segment based on a specific criterion. For example, emerging consumer insight for a food brand might show that its traditional ‘culinary enthusiast‘ or ‘cuisine connoisseur‘ consumer segment is starting to split into different cohorts of people based on a deeper understanding of behaviors, context, and needs. These emerging cohorts might be based on a rising presence of flexitarians, health-conscious vegetarians, vegans, passionate daily cooks, food activists, meal-kit spenders, and organic food spenders.

There are two key analytics capabilities required to operationalize an effective dynamic consumer segmentation capability:

A propensity model underpinning dynamic segmentation is used to predict consumer behavior. It surfaces insights which allows you to course-correct the definition and composition of the target audience groups. The target audience grouping adapts based on the characteristics which are most fruitful in your campaigns. The segments and the associated traits will change dynamically in real time based on feedback from consumers, retailers, media partners, and new data points representing context, behaviors, and attitudes.

AI for dynamic segmentation

Brands must adapt their segmentation approach to better adapt to changing consumer trends during the pandemic and beyond. In world where we’re getting used to what life looks like with Covid-19, companies can capitalize on better understanding who their audiences really are.

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