Remember focus groups?
For decades, marketing has based its targeting strategy on the segmentation of consumers into behavioral clusters and the analysis of historical data. This systematic generalization, coupled with obsession over past purchase trends, may have indirectly contributed to self-perpetuating some social, cultural, and gender stereotypes.
In the end, chocolate has always been a treat for women, while men prefer steak, right? Of course, not – however, exposing audiences to standardized messaging not only conditions them to adhere to an idealized image, but also precludes brands from getting true visibility into actual consumer preferences.
Surprisingly, state-of-the-art marketing technology can act as an unexpected driver toward reducing this bias, while opening completely new markets for brands and organizations.
I am not a segment
For marketers, the enormous quantity of consumer information that has accompanied the digital revolution has been like manna from heaven. Suddenly, long-coveted data is here, it’s a lot, and now the challenge is, ironically, how not to drown in it.
This is exactly what the latest marketing tech, with the aid of machine learning, does today: Helps marketers turn this big data into actionable information by discovering hidden patterns and implicit purchase intents. In addition, information extracted from digital sources – be it browser cookies, social media, e-commerce purchase history, and other – records an actual behavior.
Brands can finally free themselves up from the exclusive reliance on projections of the past and concentrate on delivering a personalized customer experience.
Marketing to an audience of one
Today when marketers are supported by the right data and technology, they can precisely define the buying journey of every single customer they are targeting, shaping their offerings on an individual basis and delivering it on the channels, and in the moments, when it will most effectively drive a purchase.
Looking at a customer as part of a cluster may lead marketers to offer her/him a specific series of products, whereas looking at the individual behavior may lead to very different conclusions. As a beneficial side-effect, this, in turn, helps individuals eliminate the prejudice their specific cluster has locked them in when it comes to product preferences.
Is this why, in parallel with the rise of more sophisticated customer profiling techniques, male toiletries has been one the fastest growing subcategories of cosmetics just over the last three years? Or why personal finance apps are flourishing, targeting long-neglected younger customers who want to make the most out of their limited spending capacity?
Use customer data for the greater good
It is a fact that there is a subjective level of privacy under which customers will not go when it comes to sharing their personal preferences and data with companies. According to a 2017 Consumer Insights survey driven by SAP, 67% of global customers expect brands to protect customer interests with personal data.
And, as GDPR waits in the wings, establishing a trusted relationship with customers should now be the #1 concern for brands and organizations. Trust could begin by breaking the mold of old-fashioned, biased clusters, and, instead, respecting their customers’ individual personalities and preferences.