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5 ways to optimize content based on customer context

December 19th 2022

By Scott Humphrey

Solutions Consultant

Our Contextual Automatic Optimize feature helps you get the maximum ROI from your content within the Fresh Relevance platform by optimizing based on customer context.

How does it work?

Contextual Auto Optimize automatically increases the show rate of your best performing content towards the goal that you have set, such as increasing conversion rates, as well as taking the user’s context, such as time of day, into consideration.

Keep reading to learn more about 5 types of user contexts you can optimize towards with Contextual Auto Optimize.

1) Auto

The default context within the Contextual Auto Optimize feature is ‘Auto’. For this, the system will select the context(s) that have the highest correlation to the outcome of the goal you select (such as increase conversion rates), based on all recent visitors to your website.

The system may select a second context if correlation is high for multiple contexts.

Auto can be a good context to start with. However, as it is based on all visitors, if you’re deploying content that is targeted for a specific customer segment, you may wish to manually select the context.

2) Technology

The technology a customer uses to access your content can have a big impact on their behavior.

The contexts available here include:

Operating system

This optimizes content based on the operating system the customer is using when the SmartBlock is rendered. For example, iOS users may behave differently to Android users, so different content may be chosen for each.

Device type

This optimizes based on which device type is being used, i.e. mobile, tablet or desktop. For example, customers often behave differently on mobile devices, therefore different content may be chosen for those customers.

3) Behavioral

Past behavior can be one of the strongest indicators of which content will perform best for each customer. For example, showing a customer who has never made a purchase more inspirational and social proof content may have a greater impact on converting them than it would for a customer who has made several purchases with you.

Behavioral contexts available include:

Number of purchases

This optimizes based on the number of purchases Fresh Relevance has recorded for your business. For example, businesses that sell large one-off purchases may find that showing add-on product recommendations to customers who have already made a purchase is a more successful approach than recommending the larger one-off products.

Number of sessions

This optimizes based on the number of total sessions recorded for each customer. For example, customers with a high session count may be more engaged than those with a lower session count, which means different content may have more of an impact on conversion.

Number of products browsed

This optimizes based on the total number of products browsed for each customer. For example, more products browsed would suggest a greater interest in the brand, so you could test different banner messaging tactics here.

Number of pages viewed

This optimizes based on the total number of pages viewed for each customer. You could use this context to test different data capture strategies, for example.

Previous user

This optimizes based on whether the visitor is new or returning. An example for a publishing business could be to display more personalized article recommendations to readers who have been on their site before.

Previous purchaser

This optimizes based on whether or not Fresh Relevance has recorded a purchase for the customer in the past. For example, existing customers are likely to already have established trust in your brand, so you might not need to show them as much social proof content, and instead use that website or email real-estate for different content.

Purchase price prediction

This optimizes based on which price prediction category the customer sits in, i.e. low, medium or high value. For example, recommendations for products significantly higher than the current product being viewed may work well with high value customers but not low value customers.

4) Time

Customer behavior may differ greatly depending on the time of day, week or month. For example, at the start of the month around payday your customers may be more inclined to purchase higher value products.

Time contexts available include:

  • Daylight / Dark (based on the most recent location available for the customer)
  • Weekend / Weekday
  • Day of the month
  • Day of the week
  • Hour of the day (based on the customer’s local time zone)
  • Period of the week (beginning middle or end)
  • Period of the month (beginning middle or end)

5) Location

Customer behavior can differ greatly between countries and continents due to cultural factors and regional trends. For example, recommending British-made products might boost conversions in the UK but actively hinder conversions in the EU.

Location contexts available include:

  • Country
  • Continent
  • UK

These contexts are based on the customer’s last known location.

Book a demo to learn more about how you could get maximum ROI from your content with Contextual Auto Optimize.

By Scott Humphrey

Solutions Consultant

As Solutions Consultant at Fresh Relevance, Scott manages the demonstration environment and helps the wider team to find the best possible product solutions for our clients.