As revenue from ecommerce continues to rise, so too does the number of newcomers entering the market, insisting on a piece of the action. It means that, with every month that passes, you have to work harder for engagement and conversions. Predictive intent can bring you an edge over competitors, new and old – an edge that translates into huge dividends.
Advertising has always sought to predict the best way to pitch to consumers. But data in the pre-internet world was scarce, and marketing was much more ‘hopeful’ than ‘predictive.’ The internet age brought tremendous opportunities for brands to reach users with messaging that is tailored by way of two key methodologies.
The first is a process known as interest-based targeting. It’s a type of ‘cold’ audience targeting that uses demographics and interests to reach prospective customers who haven’t yet interacted with your brand and display personalized messaging to them.
The second method is called retargeting – a ’warm‘ audience strategy that seeks to re-engage people who have already interacted with your brand and then use that data to determine which product can inspire a conversion.
But both of these methods have limitations due to their reliance on third-party cookies. Even though there’s evidence that many of us actually prefer personalized messaging, privacy concerns continue. Add to this Google’s unknown future direction on cookies, and you have an environment in which the marketing strategies of many ecommerce brands are essentially built on rented ground.
Unlike retargeting and interest-based methods, which are by nature ‘reactive’ and based on past actions, predictive intent is ‘proactive.’ It uses machine-learning AI to anticipate the future needs of a user by analyzing complex data. It identifies intent through early behavioral signals that indicate future buying potential, and can lead to higher conversion rates. By matching messages to a user’s current state of purchase readiness, this improves accuracy, and all without reliance on third-party cookies.
Machine Learning hasn’t just made predictive intent viable; it has made it incredibly effective. Deep learning is a step further in machine learning. It can process enormous volumes of behavioral and contextual data to pinpoint likely buyers earlier than other systems.
It’s quicker, more flexible, and more accurate – the more you use it, the sharper it gets. This continuously self-optimizing next-generation AI, coupled with gen AI, such as IntentGPT by RTB House, combines deep analysis and contextual relevance to increase engagement.
But how can your brand leverage the power of predictive intent?
First-party data are your most durable signals. If leveraged to the fullest, they can bring you more accurate predictions on buying intent by building detailed customer profiles. Because they come from direct interactions, they are your most sturdy signals. With them, you can identify the patterns that push performance, especially when combined with contextual signals.
Assign a hierarchy to your signals (high intent v low intent). This way, you can precisely adjust your messaging to a given prospect’s intent stage. The right timing of messaging is essential to lifting marketing results and efficiency, and this is what predictive intent excels at – delivering the right messages on the right channel at the right time.
In contrast to retargeting and interest-based targeting, which are reactive and therefore require backward-looking metrics, predictive intent is proactive in nature and requires forward-looking metrics. This requires a shift in focus to predicting future outcomes and prioritizing the kind of metrics that open a wider window onto intent, such as cost per landed click, rather than cost per click, for example.
As with any type of online marketing, it’s a case of starting small to see the initial benefits, gauging the efficiencies across your funnel, and ramping up budget over time. Predictive intent is a complementary solution; it will bring an edge to your existing stack. Machine Learning, and particularly deep learning, works better the more data you expose it to, so it will optimize further going forward. You can then take stock and redirect funds to your most effective channels.
Marketers who embrace predictive models will have a clear advantage in both ROAS and brand experience. Not only does it ramp up quality traffic, it is, at its heart, a privacy-first approach. First-party advertising and predictive intent, far from being just an insurance against future headwinds around cookies, is a way to accurately anticipate buying intent and to precisely adjust messaging.
