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Predictive Personalization

What Is Web Content Personalization?

Web content personalization is the process of creating and delivering customized content and experiences for website visitors – and it’s a must in today’s world.

Based on their experiences with the likes of Amazon and Netflix, even business prospects and customers expect all their digital experiences to be tailored to their preferences, geography, interests, and more. Simply put, they want the most relevant experience possible to streamline their purchase process.

In fact, prospective buyers decide whether to trust a company based on their web experience. If that site (i.e., company) is not delivering a relevant experience for a potential buyer at that point in their journey, it is falling short on its value promise. In frustration and disappointment, the visitor is quite likely to leave the site, and quite possibly never return. As a result, the company never gets a chance to explain the value of its offering and convert a potential buyer into a customer.

Marketers spend billions to get prospects to the website, only to provide a low-conversion, one-size-fits-all experience. This is the biggest squandered opportunity in all of marketing.

To drive conversion rates up, marketers need websites that perform differently, ones that deliver highly responsive, highly personalized website experiences in the moment. These are websites optimized for every single visitor, not lowest common denominator sites set in concrete.

Two Key Web Content Personalization Options

Marketers typically call upon one of two methods to personalize web content and experiences: rules-based personalization and predictive personalization.

Rules-based personalization

Rules-based personalization avoids the winner-take-all approaches of A/B testing and multivariate testing to show a different experience to each site visitor.

With rules-based personalization, marketers set up a series of rules to determine what each site visitor sees and experiences on their websites. The personalization in this case is prescriptive: the marketer chooses in advance what each segment sees. Some refer to rules-based personalization as real-time personalization because what each visitor sees is determined in real time as the page is rendered. The important distinction is that marketers must set the rules up far in advance.

Marketers need to know in advance which segments are going to come to their site, and must be able to identify each visitor by their segment when they first touch the site. This often means enriching what is known about each visitor, perhaps using third-party data from advertising efforts, or registered data for people who are visiting the site again.

Rules for rules-based personalization often include three components:

  1. The audience the rule applies to.
  2. The content or experience they’re going to see on the site.
  3. The page or pages and context the rule applies to.

Marketers can think about these logically as ‘Show this content to these people at these times under these conditions.’

Upside of rules-based personalization

  • Marketers can drive meaningfully higher conversion rates by showing each audience segment the right content and experiences.

Potential downsides to rules-based personalization

  • Marketers can fall down a rabbit hole with rules, creating a practically endless number for each individual sub-segment or account.
  • Rules won’t accommodate changes in audience segments and behaviors until the marketer updates them.
  • Lack of testing behind the rules makes it unclear if the optimal rules have been created.
  • Marketing usually calls upon engineering to help implement the tests, and requires design time to create the creative. As such, marketers become the bottleneck, needing the time to create, test, monitor and update a host of rules.

Predictive personalization

Predictive personalization is a more advanced approach than rules-based personalization. This form of personalization uses some system – often machine learning – to automatically decide what to show each site visitor as they show up on mobile and desktop websites.

Rather than use fixed rules like rules-based personalization, predictive personalization watches visitor behaviors and decides which variation to show each visitor. The system automatically figures out which creative to show to whom, and gives more traffic to the creative that performs well while “starving” traffic from the ones that perform poorly.

Like A/B testing, multivariate testing, and rules-based personalization, predictive personalization begins with marketers understanding site visitors, thinking through strategy, and coming up with ideas. Then marketers launch creative they think will help convert site visitors.

However, unlike rules-based personalization, predictive personalization frees marketers to focus more time on strategy ideation and creative development. Marketers can think of it as system-learned, rules-based personalization because they don’t need to come up with all the rules in advance. The system automatically allocates and reallocates traffic based on what actually works best, effectively putting marketers’ ideas on autopilot.

With predictive personalization, marketers can:

  • Specify segments in advance with a set of rules, or have the system do it
  • Set the system to look at all possible combinations of data to describe an individual visitor
  • Run many, if not all, ideas at once. It’s akin to a massive multivariate test or running numerous A/B tests simultaneously

In addition, a predictive personalization system should keep learning. Because it’s deciding the traffic allocation, it should be able to adjust to changes in audience, the market, products, promotions, and visitor behavior over time. By learning and adapting, predictive personalization helps ensure marketers harness every opportunity to drive meaningful, valuable experiences for each site visitor – increasing the likelihood of conversion.

Upsides to predictive personalization

  • Rather than spend time managing large sets of rules, marketers can easily personalize their sites for each visitor, showing whatever is most likely to get them to convert.
  • Even with many rules, marketers can usually only address a handful of segments through rules-based personalization, while predictive personalization unlocks the ability to personalize for individual site visitors.
  • Marketers can react to (inevitable) changes in visitor behavior automatically.

Potential downsides to predictive personalization

  • Marketers must spend time understanding their prospects, and developing creative ideas to engage them. The machine needs more personalization ideas available to discover combinations that deliver the largest gains.  
  • Several versions of the website are active at one time. For those in regulated industries where marketing and marketing pitches are regulated, this may not be accepted. This can be allayed by ensuring legal approves all possible combinations developed by marketing.

The Essential Building Blocks of Web Content Personalization

No matter which approach marketers take to enable personalization, the following steps are foundational:

  1. Know the audience and segments (such as by industry, geography, age cohorts, gender, company size, existing technologies)
  2. Understand pain points by audience/segment
  3. Map audiences to solutions by identifying the intersection between pain points and value prop
  4. Determine how to differentiate for each segment/cohort to develop messages and offers that resonate and drive action
  5. Build web pages for each audience and determine rules for serving content
  6. Deliver relevant content to the right audience at the right time by identifying the site visitor (e.g., by sending emails to contacts within a certain industry, by deducing on website through reverse IP lookup, using third-party data enrichment, based on behavioral data)
  7. Monitor and analyze performance of personalized content compared to global web experience
  8. Combine personalization with A/B testing to fine-tune the approach and impact

Predictive Personalization Delivers Better, Faster Results

Intellimize uses predictive personalization to replace static, low-converting websites with high-conversion pages. The Intellimize platform delivers a new kind of experience, where visitors get a bespoke, optimized page in the moment driven by a marketer’s best, most creative ideas and almost always without the need to code.

Calling upon machine learning, Intellimize delivers a uniquely crafted web page to each and every visitor, assembled from the dozens or hundreds of variations envisioned by insightful marketers. Using powerful algorithms, Intellimize’s predictive personalization selects each specific element of content based on all of the available known data – including the user’s behavior.

Then Intellimize beautifully presents the best version of the website to each visitor, delivering a world-class experience for them – and game-changing results for the marketer and their company in the form of high conversion rates.

Predictive Personalization in Action

With Intellimize’s predictive personalization, you can transform your website into a conversion machine that drives results 25x faster than traditional A/B testing. Check out these case studies explaining how Intellimize has worked for other marketers:

“Intellimize massively accelerated our website conversion optimization, enabling my team to optimize across 15M+ versions of our website in parallel. Through a joint continuous testing approach, we drove 24% more marketing qualified leads across our site and 87% more pricing page leads in two months. It would’ve taken my team decades to run these tests using traditional methods. Intellimize enabled us to optimize our website experience for each unique prospect at the moment.”

Udi Ledergor, CMO of B2B technology leader Gong

“Intellimize married the best of marketing’s creative thinking with machine learning to deliver 1,500 more leads in the last 90 days, and the pace of improvement is accelerating. Their [AI Optimize] also delivered machine learned insights about user behavior, leading to more creative ideas to test. By turning a static website into a Learning Website, Intellimize has been a great partner and enabler to the marketing team.”

Ryan Carlson, former CMO at Okta now working on a new special project at Okta

To see Intellimize for yourself, request a demo here.

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