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Audiences

Metrics

Personalize pages based on audience segmentation

Learn how to boost your conversion rates by personalizing web pages based on your visitor’s traits, behaviors, purchase histories, and more.

One of the most effective ways to improve conversion rates is to personalize your website’s content based on the user’s segment membership.

When a visitor arrives on your website, they do so with different levels of intent. They could be anywhere on the scale of “just browsing” to “ready to purchase.”

Let’s use the example of an online grocery shop with two different users and very different needs:

  • User A has never purchased a grocery subscription plan before. They’ve never even heard of such a service until a friend brought it up. They are cautious but interested in the idea.
  • User B had a weekly grocery subscription service before, and now he is looking to extend it.

Your homepage and product pages can dynamically address those different needs. Maybe you adapt your product description to new visitors. Or perhaps you show a welcome back message to returning visitors.

Here are the steps to create specific audiences that will be directed to personalized pages.

Step 1: Connect your data source

First, you will need to make sure that your customer interaction data is properly sent to Intempt Platform. Go to Sources -> Create a source and set up an Intempt JS tracker on your website.

Step 2: Create event collections

To start tracking all your transactions, you will need to create specific event collections linking to these actions.

Step 3: Build your metric

To specify “Big spender” users, you will need to calculate the average order value of every customer’s purchase history.

Go to “Metrics” -> Create a new metric, define the period you want to analyze, select the format and choose the required metric creation options from the dropdown.

Formula: [A] Total revenue / [B] Total count of users

[A] Total revenue:

Type -> Collection

Source -> your JS tracked website

Collection -> purchases

Field -> purchase amount

Aggregation -> Total

[B] Total count of users:

Type -> [B] Users

Filter -> [B] Total User Count

Concept: Custom metrics

image

Step 4: Create customer segments

Go to “Segments” -> “Create a segment” and build an audience based on the metric created.

“Big spender” segment definition: A and B

A = Has done -> Event “Purchase” within last 30 days

[and]

B = Has done -> Average Order Value -> Greater or equal to $50

image

“Passive customer” segment definition: (A and B) or (C and D and E)

A = Has done -> Event “Register” within last 30 days

[and]

B = Has not done -> Event Purchase” withing last 30 days

[or]

C = Has done -> Event “Added to cart”

[and]

D = Has done -> Event “Visited Checkout”

[and]

E = Has not done -> Event “Purchase”

Step 5: Create web pages for your segments

Once you’ve created your segments, you can create personalized web pages or dynamic page tweaks.

In this case, let’s focus on two web pages - one for the “Big spender” segment and another for the “Passive customer.” Design and content should be arranged based on audience tendencies. “Big spenders” will be more interested in new products and special subscription offers while the “Passive customer” should be presented with a clear value proposition like seasonal sales offers.

Once you create the 2 different page designs, you can set up the connection with Intempt Platform to activate the web pages based on the users. Here is the process:

  1. Your server receives a request to render a webpage for a visitor.
  2. Before rendering the page, the server pings Intempt’s Segmentation API to find if he belongs to any segment (In our example, we’ll identify if the user is a big spender from the segment we set up in the previous section.)
  3. Intempt saves that visitor information in a cookie on the front-end.
  4. The server reads that cookie then takes over to deliver a tailored web page based on the webpage created for the segment.
  5. Using Intempt, you can trigger personalized pages only to run when a cookie variable is identified.

Step 6: Test and experiment

Once you’ve shipped your personalized web pages, it’s time to iterate.

Use the segment comparison graph to analyze how two different segments (“Passive customer” and “Big Spenders”) compare against the “Revenue” metric before and after the dynamic web pages were implemented.

image

If the revenue (after the test period) is rising - it means that the change was successful, and your users are more likely to buy once presented with personalized content.

If you don’t see any significant change in the revenue line, you should experiment further by applying different web page variations modifications.

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