Using Artificial intelligence to Sense Buyer Intent
Some months ago, booking.com joined the ginger group of brands to combine artificial intelligence with mobile to get a heads-up in anticipating a customer’s purchase intent.
Booking.com app users now not only receive instant booking access to a destination with a single QR code but also get personalized offers based on their earlier travel experiences, preferences, and interactions. That’s how it tackled the issue of buyer intent.
Over the last year or so, the process of anticipating a buyer’s intent has got even more scientific. We have seen brands like booking.com actively deploy the “cold and emotionless” instrument of AI in a field that is almost always centered around human emotions – intent. Instead of spending solely on advertising that could target perhaps the wrong demographics, or address audiences who may have no contextual relevance at all, brands are increasingly utilizing their funds to invest in AI-driven tech to “understand” a buyer’s intent. Ironical, isn’t it, that cold algorithms are being used to predict, accurately one must say, a human act which is a chain of events driven by a person’s knowledge and experience.
Purchaser intent is akin to a guessing game but AI is taking out some of the guesswork. Targeting buyer intent calls for using a combination of active and passive data to accurately deduce, with some degree of probability, whether a customer is in the market right now to buy or not. Data sources capture such “intent” signals that consumers emit and point the brand in the right direction.
AI, riding on a blazing fast computational power, lets brands quickly understand what the consumers are thinking to identify patterns, and also predict how quickly they will respond to advertising that touches an emotion.
AI and machine learning are linking the marketer with the “connected” individual. More and more marketers are relying on intent targeting to execute outbound demand generation campaigns.
It’s no longer only about grouping together like-minded customers and trying to second-guess what they want. The customer journey just got more sophisticated and granular with the deployment of AI in understanding what an individual buyer intends to do next in his journey. It’s like a race today; the first brand of the blocks who gets this also gets the customer as the prize.
There are a few brands in the hospitality business like bookings.com that have stepped up to the plate and integrated AI in their operations, giving consumers a plethora of AI-driven experiences.
Other brands are looking at using AI tech to do an even more effective predictive lead scoring, fine-tuning their marketing efforts, and predicting a purchaser’s intent near-automatically.
Popular use cases of AI in understanding a buyer’s intent are:
To predict why someone may want to contact your organization even before they do.
Send proactive notifications by identifying customer patterns and trends and contact them even before they call or contact your company.
Sources of data:
Earlier in this post, we spoke of sources of intent. Here are a few:
This is the most obvious touchpoint, besides, of course, social channels. A fav source of the intent signal is from what a customer has searched for online. So, if someone has just searched for red shoes, there’s a strong chance she is looking to buy them.
What a customer is currently reading is also a pointer. What’s more, articles read online can also indicate at what stage a buyer is. If he is simply browsing and looking up articles about smartwatches, he is still at the research stage. When he starts reading about specific watch brands, he is almost ready to buy.
Again, the very type of ads that people are clicking on can tell you what they are looking out for.
All of this monitoring and deduction can now be entrusted to AI-driven programs, to collect and collate, to take proactive action. All of which will elevate the customer’s experience and improves business results.
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