Using Artificial intelligence to Sense Buyer Intent

Using Artificial intelligence to Sense Buyer Intent – Customer Intent AI

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.

Purchase Intent and AI

AI tools have the capability to evaluate thousands of website paths and hundreds of digital footprints, including page sequencing, referral URLs, time spent on pages, and page visits.

Later, these results can be compared against millions of past customer interactions and profiles.

Finally, you can see an increase in success rate from 5% to over 90%, and that’s possible because of AI and ML.

Purchase Intent AI Vs. Predictive Analytics

Monitoring users’ behavior and later applying rules to customize their journey is similar to predictive analytics.

Predictive analytics uses rule engines and hypothetical projections according to the small amount of data.

The hypothesis is the user, especially a marketer’s concept of what pathway a potential user will take.

Now, what’s different regarding purchase intent AI? ML. Machine learning enables you to analyze thousands of data points, even if you are not sure if they will be useful or related to each user’s behavior.

How AI Anticipates Customer Buyer Intent?

AI is a suitable tool to identify patterns in big data, which can then be used to develop product promotion and marketing approaches.

Tools including Natural Language Processing (NLP), AI, computer vision, and ML enable you to go through several thousands of pieces of data online to know the behavior of all individuals.

AI algorithms are trained in such a way that they can predict purchasing intent in real-time for millions of data points based on billions of familiar results.

Below mentioned are some ways that elaborate the process of buyer-intent prediction using AI:

Identifying the problems that customers face

Specific features of a product, numerous marketing strategies, or various factors that you need to modify according to the needs of customers can be understood by determining the problems of your customers.

By identifying problems before they arise further, you have to analyze the concept of buyer intent for particular products and figure out how to improve them further.  

You can do market research and analyze feedback received from customers to evaluate problems that can be improved further using AI. 

Clients Data can be collected from surveys, voice of customer analysis, social media and purchase ratings.

You can combine this data with CRM systems using customer feedback analysis tools. 

Buyer-intent AI can predict customers’ problems and is really helpful in situations where you have client feedback and market research data. 

Intent prediction using interest signs 

If direct data is unavailable, AI algorithms can gather data from a variety of sources.

You can find many 3rd-party service providers to gather data regarding client interaction to detect the factors that influence user buying intent.

AI can use only a few signals for buyer-intent prediction for products based on such data. These signs include:

1. Number of customers who visited your website
2. Duration of your webpage
3. Scroll speed
4. Products that were viewed and 
5. The amount of time spent to view every item

Predicting consumers buying-intentions is completely guesswork and a complicated task.

The use of AI in marketing can minimize this guesswork, ensure the whole process is more data-based, and make the predictions more accurate.

Popular Use Cases of AI in Understanding Buyer’s Intent

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:

Search

This is the most obvious touchpoint, besides, of course, social channels. A fav source of the intent signal is 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.

Editorial content

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.

Advertiser content

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.