Why Real-time Predictions Are Critical for Modern AI Systems

Nowhere does the phrase ‘Time is of the essence’ ring truer than in the hypercompetitive field of online business. Split second predictions and personalizations enabled by first-party data solutions are fast transforming the arena, and mean that those who don’t act in haste will repent at leisure. 

With user anonymity increasingly the norm, these predictions are often the only chance you have of targeting a customer with promotions or personalization. 

Let’s delve into exactly what makes real-time predictions so essential, and how to harness their power for your business.

What are Real-Time Predictions?

The term ‘real-time predictions’ is largely self-explanatory. Real-time predictions are essentially inferences made while a user is onsite, about what they are likely to do, or how they might react to promotions or prompts. 

These predictions can rely partially on the information you have already garnered from personal profiles (which is getting hard to collect due to data privacy) but are more often based on first-party data accrued as the user browses your site – including behavioral signals such as which page the visitor is viewing right now. The crucial thing is that these predictions are triggered and adjusted milliseconds after the user takes an action. This is what makes it ‘real-time’. Real-time predictions can be made about almost any aspect of online user behavior but typically revolves around a few key concerns, including:

  • When is a customer likely to leave the site or abandon a cart?
  • How likely is a customer to make a purchase, and what purchase are they likely to make?
  • How likely is a user to sign up to email, SMS, or create an account?
  • What would it take to change the way the user is likely to behave? For instance, if a user is 90% likely to abandon a cart, what kind of promotion could secure a conversion? If a user is 60% likely to book a standard suite, what deal could convince them to upgrade to deluxe? 

How Are Real-Time Predictions Calculated?

Real-time predictions, by their nature, are made in response to ongoing developments relating to a visitor browsing a site. This means that, although they may refer to preexisting user data and PII if available, these forms of data are not necessary. Instead, these predictions utilize the thousands of data points generated by a user when they visit a site. These include whether a user browses with coupon shopping extensions, uses incognito mode, favorites a page, what browser or device they use, along with countless other factors. 

Adaptive AI solutions collect all of this data as the user browses and compares the information to cataloged records of the behavior of all other site visitors. This allows the segmentation of the visitor into a detailed cohort of users whose data points are most similar to them, and to whom the user will likely display similar behavior. 

For instance (although this is an oversimplification), incognito mode, older devices, and weak internet connection all correlate with a lower likelihood of buying. A new user who exhibits these traits can be placed in a cohort with past users who did so. The AI model might then scan this cohort’s prior behavior and find that its users abandoned their carts 70% of the time, indicating that the new user is similarly likely to abandon their cart. It might also detect that these users buy 55% of the time when offered a particular deal. The model could then predict that the best way to keep this customer onsite and encourage a purchase is to offer them this promotion. 

However, the segmentation process is extraordinarily complex, and constantly developing. If a user navigates to a particular page, adds an item to their basket, hovers their cursor over an image, or opens a new tab, the AI model might use this signal to adjust its prediction in real-time and recategorize the visitor as belonging to a different segment. A specific prediction might consider both the cohort that the user belongs to, and their specific actions on the site, as the basis for a recommended ‘next step’. These predictions update with every new action and microaction, facilitating a degree of accuracy impossible to attain by simply using details from user profiles or cookie-generated PII.

Why Are Real-Time Predictions Essential for Online Business Success?

Before recent technological advancements, real-time predictions used to be nearly impossible to aggregate and analyze. Now, it is nearly impossible to get by without doing so. In the words of Senior Walmart Analyst, Naveen Peddamail, “If you can’t get insights until you’ve analyzed your sales for a week or a month, then you’ve lost sales.” Let’s look at a few crucial areas where real-time prediction is the difference between success and failure. 

Personalizing content

Real-time insights into each user’s behavior and data points enable businesses to provide personalized product recommendations, tailored content, and customized experiences. This builds stronger customer relationships, improves conversion rates, and prevents users from feeling overwhelmed by generic suggestions. In fact, research shows that 89% of businesses see improved ROI after introducing personalization features. However, with 86% of users now anonymous, the only way to reach the vast majority of your visitors is by personalizing based on first-party data.  

Tailored promotions 

Another benefit of real-time predictions is the ability to craft promotions tailored to every individual customers’ needs and desires. For example, if a user belongs to a cohort of ‘impulse buyers’, they can be targeted with a smaller reduction than one who belongs to a low-spending cohort. Modern predictive AI solutions drive growth by locating price point sweet spots that maximize both profits and the likelihood of buying. 

Dynamic pricing

Travel booking sites track searches, browse rates, and booking abandonment levels to gauge shifts in travel demand and intent in real-time. This facilitates the rapid adjustment of prices and availability for hotels, flights, and vacation packages. When widespread changes indicate emerging or declining destinations, they tweak inventory allotments, surface relevant offerings, and set competitive pricing to maximize revenue.

Churn reduction

Analyzing individual user behavior patterns in real time helps identify those at risk of canceling subscriptions or abandoning carts. Metrics indicating a risk of churn include declining frequency or duration spent using the app, inability to find desired content, low ratings for recommendations, and higher repeat viewing of the same material rather than discovering new content. Targeted retention incentives and offers can then prevent churn by addressing user issues promptly.

Real-time is Real-Money

Larger, long-lasting decisions should take time and ‘slow data’, but as any brick-and-mortar salesman will tell you, individual sales are won or lost in an instant. Real-time predictions facilitated by first-party data can act like virtual salespeople, enabling responses to minute hesitations and purchasing decisions within split-seconds, and with pinpoint accuracy. However, rather than simply encouraging sales, real-time predictions craft tailored promotions, recommend personalized content, automatically adjust prices, and reduce churn, saving your business real-time and real money.

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