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.
Capabilities required for personalised brand engagement
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.
Covid-19 has shown us that consumer motivations and purchasing patterns can evolve. Consumer behaviors have adapted due to the pandemic. We have seen consumers buying in larger quantities, pantry stocking, increasing online orders, trying new brands in the face of stock outs, educating children online, conducting workouts virtually, and more. Many consumers have also moved to new channels for shopping, such as online or touchless channels, during the lockdown. The pandemic has changed the way consumers research, select, and buy products. As a result, consumer engagement touchpoints have also changed.
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.
However, in order to deliver personalized consumer experiences and address current consumer needs during and beyond the pandemic, CP brands need to think of consumers in a more dynamic and current context – addressing a changing cohort of people with a set of adapting attitudes, context, behaviors, and motivations as consumers respond to future waves of the pandemic and the changes to their life, post pandemic.
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.
Hence, such companies can expect to improve their ROI through more efficient and effective sales and marketing initiatives.
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 for personalization
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.
The definition of consumer segments can adapt over time as a brand builds up more data and starts to understand people on an individual level. People will move in and out of consumer segments and the boundaries the brand uses to characterize these segments can change depending on the traits that have the most positive outcome on the desired action – for example conversion, repeat purchase, advocacy, traffic, sign-ups, or sales.
There are two key analytics capabilities required to operationalize an effective dynamic consumer segmentation capability:
Propensity modelling for dynamic segmentation
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.
However, a key driver of success of such models is the quality of data used to create them. Hence CP companies must pay special attention to their data acquisition strategy when creating dynamic segments.
AI for dynamic segmentation
Today, it is critical to ensure you have embedded AI to make the shift from static to dynamic segmentation. Using machine learning capability, you can train your algorithm to become more predictive over time, showing which traits are most productive in converting, driving repeat purchase, and consumer engagement while assessing the contribution of each trait and characteristic to the potency of a specific audience – thereby, helping companies better predict demand, plan supply to avoid stock outs, and deploy effective consumer campaigns to drive consumer interest in the brand.
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.