Predictive Analytics For Dummies
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Predictive analytics can help websites with personalization. You may have noticed that websites remember what you did or which pages you looked at on their website last week or last month. Such websites are tracking your behavior, from clicks on certain parts of the page to the order of the pages you viewed for a session, to offer you the most relevant advertisements, products, or news articles.

Online customer experience

These websites are attempting to personalize your online experience to influence you and make it easier for you to take the action the companies that operate the websites want. The desired outcomes or goals these companies are usually looking for include
  • Filling out a registration or an appointment form
  • Clicking on a product link
  • Reading another news article
  • Watching a video
  • Buying a product
After the company has collected enough data for customers that successfully meet the goal, they can try to learn from the patterns those customers did before the action was taken, then apply the patterns to similar customers. Finding customers that are similar is by no means a simple task. Sure, an analyst can easily identify a single to a few customer attributes to manually segment the data by and see whether there are any patterns. But this process becomes exponentially more difficult as the number of transactions gets bigger and the customer attributes get wider.

Using machine learning algorithms, the machine can find micro-segments of similar customers so companies can target a group of customers more precisely with personalized content and offers.


Your web surfing behavior tracking isn't limited to the current website you're on. It can be tracked across multiple websites. This technology is possible through browser cookies and advertising networks that share data with its affiliates. When you visit one website and view a product page, then move on to another new website and see an advertisement for the product on the banner or side rail, this is retargeting.

By itself, retargeting isn’t predictive analytics. But this gives you the knowledge that the data is out there and can be shared across websites. The ad networks share the data with their affiliates, and much of this data is available through data management platforms. By combining this third-party data with the company's own data, they can create more advanced predictive models.


Personalized websites can be created in several ways. It really depends on which types of data that are available and whether the customer is logged in or not. Having profile data on the customer adds many more attributes to create a predictive model.

These are some data types and sources that can be used to create personalization models:

  • Customer's profile data, when she is logged in.
  • Profile data can include such attributes as age and gender.
  • Content on the page.
  • Using text mining techniques like TF-IDF, you can find important keywords.
  • The referring webpage.
  • The referring webpage may have keywords in its website address (URL).
  • The websites and pages you visited before.
  • When this data is available, it can show interest in a product or subject.
  • The geolocation of the web browser.
  • The physical location of the browser accessing the website, using its Internet Protocol address.
  • Temporal data, such as time of day.
  • Time of day and day of week are common temporal segmentation attributes.

Optimizing using personalization

Optimizing an e-commerce site using personalization is a great example of improving customer experience and satisfaction. By providing individualized content, customers see relevant offers, encounter fewer distractions, and build trust in the system, which will ultimately drive sales. Here’s an illustration of how personalization increases sales.

Suppose you are operating a travel booking website. You want to optimize the experience for our two biggest markets: California and Florida travelers. Our analysts know that Californians like to travel to Nevada and Floridians like to travel to Georgia. So you use a simple rule to personalize the home page by geolocation.

You can show a hero image of Las Vegas to IP addresses belonging to California; for Floridians, you show a hero image of downtown Atlanta. You use the hero image to personalize, inspire, and influence the site visitor to make a booking. Some visitors will make the booking regardless of the personalized image, because that was their original intention. Others may be inspired and influenced into further researching and planning a trip to Las Vegas or Atlanta, thus increasing your booking rate.

Using predictive modeling, you can use every available attribute to create sophisticated models that target specific segments of the California and Florida markets. For example, the model could have discovered that northern Californians with children like to travel to Lake Tahoe, while the ones without children like to fly to Waikiki Beach in Hawaii.

Predictive modeling allows you to be as granular as you like in segmenting data to make personalized offers. It can detect patterns in micro-segments that would normally be very difficult. A rule-based system may start with a few simple rules, but eventually turn into a complex and convoluted set of rules. The constant manual updating and deploying may make a rule-based system unmanageable. Using machine learning to algorithmically produce content presentation for personalization is a scalable and cost-effective solution. Personalization has great potential to increase ROI.

Similarities of Personalization and Recommendations

Personalization is similar to recommendations, and they are often used together. In addition, some websites use the term personalized recommendations. Recommendation implementations are easy to identify because they are often called out specifically with “Recommended for you” or “Customers that bought this also bought that.”

One type of personalization can refer to how you like the content of the website to be presented to you:

  • The website’s arrangement:
    • Main navigation on the top or the left panel?
    • Content groupings by vertical columns or horizontal rows?
    • Text lists or thumbnail images?
  • The colors of CTA (call to action) buttons and links:
    • Colors matter, and they often mean different things and exude different feelings to different people. Some research has shown orange to be the color that converts the best, because orange contrasts well with typical websites.
    • Most people are accustomed to Google search links being blue for unvisited and red for visited. But the default colors on browsers could be different. People with color blindness may prefer different colors for links.
  • The flow of the checkout process.
    • High-frequency customers may prefer one-click ordering from the product page, while standard customers may prefer a one-page checkout process after clicking the product to review their order.
    • Another type of personalization refers to which type of content on the website should be presented to the customer according to their profile. This can be in the form of personalized recommendations.
  • Relevant ads and offerings.
    • For example, it may not make sense for an apparel company to show ads for men’s clothes when the customer is female and have no history of purchasing men’s clothes. They should be showing ads for women’s clothes in similar price point categories as their purchase history.
  • Relevant items.
    • Based on your profile, purchase history, reading history, and similarity to other like-minded customers, show relevant products or articles on your personalized home page.

About This Article

This article is from the book:

About the book authors:

Anasse Bari, Ph.D. is data science expert and a university professor who has many years of predictive modeling and data analytics experience.

Mohamed Chaouchi is a veteran software engineer who has conducted extensive research using data mining methods.

Tommy Jung is a software engineer with expertise in enterprise web applications and analytics.

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