Marketing automation has usually focused on driving sales, mainly using past purchase or late funnel behavior (e.g., paid search) as a predictor of an imminent purchase. While effective at boosting sales numbers, this widely implemented strategy can result in a disservice to brands and industries that adopt it, as it promotes the perpetual devaluation of goods or services. Narrowing a brand’s focus only to aspects linked to conversions risks stripping the customer experience of key components that lay the groundwork for long-term success.
We live in a world rich with data, and insights are growing more vibrant every day. With this in mind, companies and advertisers can strategically weave together all the data they collect during the customer experience. This enables them to understand every inference available during customer interactions and learn what benefits the customer most at a given time.
But focusing exclusively on data collected from customers, brands risk falling subject to the law of diminishing returns. Even companies with meaningful consumer interactions or rich service offerings struggle to gain impactful contextual insights. Only by harnessing a broader dataset can we understand how people become customers in the first place, what makes them more or less likely to purchase again and how developments in society impact the growth or struggle a brand will experience.
Here’s a look at how we can achieve a more complete picture of current and future customers.
A critical component in re-imagining customer experience as a relationship is recognizing that brands often don’t focus enough on consumers’ wider needs and concerns.Leverage AI to unlock new perspectives
Over the past several years, almost every industry has capitalized on the opportunity data-driven marketing presents, inching closer to the “holy grail” of real-time, direct and personalized engagements. Yet, the evolving toolset encouraged brands to focus on end-of-the-funnel initiatives, jeopardizing what really impacts a business’ longevity: relationships.
While past purchase or late-funnel behavior data does provide value and is useful in identifying habit changes or actual needs, it is relatively surface level and doesn’t offer insight into consumers’ future behavior or what led them to a specific purchase in the first place.
By incorporating AI, brands can successfully engage with their audiences in a more holistic, helpful and genuine way. Technologies to discern not just the content of language (e.g., the keywords) but its meaning as well, open up possibilities to better infer consumer interest and intentions. In turn, brands can tune consumer interactions to generate satisfaction and delight, and ultimately accrue stronger insights for future use.