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.
Similarly, methods to decode images in photos and videos create opportunities to engage customers in ways that incorporate and build on context, which can be leveraged to create a clearer picture of sentiment or emotional importance. Rather than optimizing every action to the intermittent feedback of purchases, these approaches build on the idea of relationships and work to deepen connections with key audiences.
Pinterest demonstrates a great example of this in action — it developed a visual search feature called Lens that integrates shoppable pins in the images they photograph, which let users search for items with photos. Pinterest’s resulting research showed 49% of users saying they developed a better relationship with brands through a visual search.
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. Even during the moments surrounding a purchase, a consumer’s focus is likely on the activity the purchase enables, or what will be pursued once the purchase is complete. To truly build relationships, brands need to better understand the values and priorities of the customer.
Building relationships through understanding
Using strategically focused AI, brands can identify what their audiences are doing when they are not engaged in a purchase or using a product or service. The secret here is understanding context.
In most circumstances, context is difficult to discern — it varies by industry, region, customer location, medium of contact and many other factors. This realm of mass complexity is the perfect setting for AI. AI can integrate and fine tune the brand’s response or outreach using streams of information such as previous customer interactions, current industry trends, regional conditions, weather and even factors specific to the customer. Beneath the surface, AI optimizes not on a future purchase, but on the effectiveness of interactions currently taking place.
This knowledge is power and catapults us away from the notion of basic intent. AI tools can help brands understand the meaning of the content people read, share and search for. Ultimately, this makes it possible to identify consumer actions or interest around a brand, product or category. From there, brands can develop a presence that positively engages audiences throughout their life — not just their purchase cycle.
A good example is how companies like Tasewise analyze data from social media, photo posts, restaurant menus and home recipes to pinpoint where consumers’ palates and preferences are headed, giving restaurants a leg up in providing dishes and ingredients. The real value comes from the ability to apply AI to first-party data sets as well as to key external sources. This data leads to richer behavioral insights that are beyond what a single brand could uncover from only first-party data.
A more inclusive and robust understanding of consumers enables brands to make correlations with other goods and services customers might want. It also gives brands unique data that lets them adjust their messaging to better resonate with consumers. By adopting a strategic AI-based approach, brands can create stronger and more impactful relationships and keep top of mind as a genuine resource.