What’s the Importance of Predicting The Behavior of the Unknown Visitor?

While the third-party cookie may be on the verge of collapse, companies that operate online cannot afford to return to the dark ages. Without detailed knowledge of your customers, you will struggle to provide them with the goods and services they want. You will also find it difficult to predict their individual and collective behavior, preventing accurate inventory forecasting along with the personalized offers that get conversions and subscriptions over the line.

Changes to the online privacy landscape mean you know less about each specific visitor

Third-party cookies were once the default mode of tracking user behavior, allowing advertisers to serve up highly personalized adverts while also delivering detailed insights into how users interacted with your websites and apps. Cookies are not ‘disappearing’ per se, but commitments by Safari, Google, and Firefox to stop supporting them mean that they will effectively vanish before 2025.

The decline of cookies has a long history. For years, users have increasingly employed ad blockers to avoid interacting with the targeted adverts that cookies help to produce. A 2022 study showed that 35.7% of internet users globally used these plugins, with the US at the forefront at 33.6%. A year earlier, cookies suffered a further blow when Apple blocked in-app tracking where users did not explicitly opt in. This means that, even now, these devices are of limited use to advertisers.

Google has recently pitched its Privacy Sandbox as an alternative that aims to eliminate the cross-site and cross-application tracking that presents ethical issues with cookies. However, the broad brush, advert-specific data offered by the Sandbox leaves much user behavior in darkness – essentially, relying on the Privacy Sandbox alone means you no longer know what your users are doing on your websites. This means you will no longer be able to predict their actions or provide personalized features or optimizations.

However, those who fail to predict user behavior because of these changes will lose out to those who find alternative means to do so. This is because prediction is essential for personalization, forecasting, and optimizing your development efforts. Let’s take a closer look at exactly what makes this kind of prediction so valuable, and how you can use first-party data to achieve the best results.

Predicting user behavior is key to effective personalization

Without prediction, personalization becomes arbitrary. You simply can’t serve relevant content to your users without some idea of which content they are likely to engage with. For this reason, we need to know how the personalized changes we make will cause users to act.

Using first-party data in conjunction with machine learning (ML), you can fill in the data gaps presented by the absence of cookies, use non-PII data securely to predict what a user will do, take the appropriate actions to convert visitors into subscribers, convince customers to sign up to a subscriber list or members’ features, or take any other action that it is important for your business.

Information such as whether a customer ‘favorites’ a page, their device model, location, and whether they use incognito mode, provides a vast data set that, when analyzed by ML alongside their interactions with your website, gifts you the insights required to hit them with personalized incentives to subscribe, at a time that is ideal for them. These measures ensure that they will be much more likely to return to your site to spend their time or money.

Predicting user behavior makes forecasting more accurate

Some behavioral predictions are short-term, and some are long. While predicting the behavior of an individual, anonymized customers allows you to better market your wares and services through personalization, collating individual behavioral data permits predictions regarding your entire audience, who you can divide into segments with similar traits.

For instance, if ‘cost-conscious’ users are 50% less likely to make a purchase, and ‘high-spenders’ are more likely to buy luxury items, you can target the former with bargains, and the latter with top-range products. This form of segmentation also allows you to accurately predict the long-term performance of your website, by determining which cohorts interact with your online spaces most frequently, and whether their numbers are rising and falling. You can then forecast inventory accordingly, supplying your most valuable or prominent segments with the products they need and avoiding losses on unwanted goods.

Focus your development efforts where it matters

Furthermore, obsolete functions of your websites cause unnecessary labor for your employees.

For instance, if your website offers features that are only applicable to over-55s, but the vast majority of your customer base are below thirty, you may make greater strides by focusing your attention elsewhere.

On other occasions, features of your websites can distract from those that provide your client base with more value or enjoyment. It is important to be able to track whether users are exiting the site when landing on a particular page or feature. If so, you can remove it, and redirect them to where they need to be. Using first-party data to predict the behavior of your client base allows you to drop neglected features just before interest in them falls, saving you time and money.

Don’t Just Replace Cookies, Improve Your Strategy

With cookies destined to return to the jar forever, Google’s Privacy Sandbox is set to replace some of their functions. However, the data this feature provides is only accessible to advertisers and tells you nothing about how users behave on your website. Collecting and collating first-party data in conjunction with machine learning not only lets you replace cookies but improves upon them. This strategy allows you to predict the behavior of anonymized individuals, facilitating effective personalized optimizations, the segmentation of cohorts for targeted marketing, accurate forecasting, and showing you which features to abandon and which to develop.

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