I’ve written recently about the changes surrounding third-party cookies and data collection on the web, and at the end of my last piece I noted the likely rise of anonymized consumer intent signals for B2C marketing in this rapidly changing landscape. I want to dig in a little deeper on that topic today.
A B2C CONCEPT BORN FROM B2B PURCHASING
Over the years, the sales funnel has changed, shifting from direct connections with consumers at the top of the funnel—which enabled business to connect early and often in a purchase journey—to what companies such as 6sense refer to as a “dark funnel,” which leaves sales and marketing teams unaware of interest until a purchaser is on the brink of buying. In response to this shift, B2B businesses have turned to intent signals to help shine a light on who their prospects are earlier in the buying journey.
Intent data acts as a kind of bat signal, cutting through the darkness. By looking across an array of data points from a number of related spaces, intent data can tell vendors if a person is displaying a level of interest (i.e., active research) that would indicate a likely intent to purchase. It can be remarkably accurate, as the aggregated information paints a much fuller picture than third-party cookie tracking is able to.
For example, when taken together, a spike in content searches for durable carpet, “best office layouts,” and lumbar-supporting desk chairs could be an indication a business is moving offices and in the market for new office furniture. Armed with this insight, marketers and salespeople can engage with a prospect proactively, instead of passively waiting for them. In essence, it triangulates the data from a persona group to foretell their next actions.
Intent signals have become an important part of the B2B sales cycle, giving companies a significant leg up in connecting with the right audiences at the right time. With longer purchase cycles and highly specific markers of consumer interest, B2B sales and marketing teams have had the advantage when it comes to using intent signals. But the B2C market could finally be ready to take advantage of this powerful tool.
THE RISE OF CONSUMER INTENT DATA
B2B sales operate with a long-range view, which tends to rely on large data sets and a significant capital investment. That makes B2B a better match for the intent signal approach than B2C’s shorter-range timeframes. Plus, with strong third-party cookie options for B2C marketers, there was no impetus to shift.
But with increasing regulations regarding data and privacy, and increasing consumer scrutiny (witness the shift away from third-party cookies), an aggregated approach to using anonymized consumer intent data to determine purchasing patterns and customer wants seems like a natural evolution.
FROM RULES-BASED TO DYNAMIC INTENT
Up to now, businesses have relied on expressed desire (directly stated consumer information drawn from a CRM or loyalty program used to personalize future interactions), or rules-based intent or rules-based events. With the rules-based approaches, they manually select a sequence of events that signals consumer intent (e.g., landing at a URL, interacting with a comparison widget twice, and expanding a product description dialogue indicating a consumer in the research phase).
These are strong approaches, especially in B2B settings, and companies like Uniform or Dynamic Yield (a partner of Myplanet’s) have built performant tools to personalize experiences using these principles. But there are challenges with these approaches, too. First, humans are naturally prone to bias—we assume too much when setting the rules and miss important insights. And second, especially in the B2C context, the time and effort costs have been too high.
Today we’re seeing the rise of dynamic consumer intent signals from companies like Personify XP, where a data model evaluates and recommends the intent signals. Over a period of a few months, these models can be trained to cover intents we wouldn’t think of as humans. What’s more, they’re constantly evolving as a business changes.
The shift from a manual determination to one informed by data and powered by machine learning means B2C businesses can now access a more affordable, more flexible, and more powerful intent model than ever before.
With anonymized consumer intent signals, you don’t need to know that someone is in the market for a car. Based on a few key points of data and a couple of specific clicks, you can predict that, and begin responding to signals to tailor and enhance their experiences. And in this moment, when third-party tracking cookies are disappearing and anonymized data is an increasing imperative for marketers, the consumer space is primed for an anonymized intent data revolution.
A MORE RESILIENT APPROACH TO PERSONALIZATION
Some of these systems are meant to integrate with existing systems, making them smart investments for forward-thinking B2C retailers that want to stay ahead of the curve with modular technology solutions. And as a method, using intent signals is a resilient approach because as privacy standards increase, they’re not reliant on third-party cookies and can often be handled completely anonymously.
Already we’re seeing specialists in the field, companies like Miso.ai and Personify XP, carve out strong capabilities in anonymous pattern identification using small data sets, and I expect to see more businesses emerge that follow suit.
Online retail has long since graduated from just “being online” being enough for B2C businesses, and the pandemic put a definite squeeze on e-commerce on all fronts. Customer acquisition has always been an imperative, but the high cost of it in an increasingly competitive landscape is forcing a rethink of approaches to ones that have a higher success rate and place greater emphasis on customer retention. Consumer intent data means a business can get in front of customers with the products they’re looking for at the right time, minimizing the acquisition effort and elevating the overall experience for consumers to one that keeps them coming back in the future.