���ϴ��¼�

The Hypothesis

Without intent data, retailers are limited in how accurately they can identify the most valuable segments using customer profiles.

Wherever you store your customer data, it will no doubt provide a comprehensive view of customer profiles based on past actions, attributes, and preferences. However, this retrospective data doesn’t always reflect the current context or intent of the person. 

For instance, a customer might have clicked on an email but does this really mean they have genuine intent to purchase? By adding intent data, retailers can see not just what customers have done, but what they are likely to do next—creating a powerful mix of retrospective and predictive insight.

This combination adds valuable context to customer profiles. It makes it possible to identify high opportunity segments, such as customers who last showed high intent to purchase but didn’t buy yet. Or the low intent customers who need reminding or nurturing, based on how high their peak purchase intent was in the current purchase journey.

This allows for more precise targeting and personalised engagement, but it all starts with finding the opportunity.

The Tech You’ll Need

• You will need a Customer Data Platform (CDP), Customer Relationship Management (CRM) or similar way of setting up, segmenting and engaging customer profiles
• You’ll need a way to get accurate intent data and to pass this through to your customer profiles. I’m not saying ���ϴ��¼� is the only way to do this and I’m clearly bias, but…it’s the best

Together, this technology enables retailers to to store real-time data, isolate target groups and create highly personalised customer experiences. Our intent metrics and segments enrich the retrospective customer data, allowing for more contextual targeting and better opportunity analysis.

Setting This Up

Using our Bloomreach integration as an example, here’s how ���ϴ��¼� can be set up to add intent data to customer profiles (and a peek at some actions too). 

The Target Segments

So, we’re focused on insights about your customers here. The right target segments for you will depend on what your data tells you.

That said, the quickest and immediate opportunity tends to be customers who have a high level of intent in some form. Perhaps they haven’t been seen on your site for some time, or they have very recently left the site with a low intent to return back to it. Perhaps they have had a peak high intent to purchase and that is now decreasing with a reduced momentum, suggesting you need to reengage them. 

Looking at our intent-based segmentation framework for ecommerce, there are some obvious potential contenders. Whatever you find in your list though, a good CDP or CRM will make it possible to build these segments in platform and target them accordingly. 

Last Chancers

This segment of customers is made up of people who have a high intent to purchase, a low intent to return and look likely to exit. If you’re seeing this attached to a customer profile, it’s likely they have already left. 

The best response for this segment is to think of ways to capitalise on their intent prior to exit, or increasing their chance to return. What offer, incentive or reason to buy now can you give them? As you’re seeing this at a customer profile level, you can also consider appropriate follow ups, but consider their low intent to return. You’ll have to work hard to persuade, but any engagement or conversion you can encourage is pure gain. 

Basket Abandoners or Abandoning Evaluators

Basket Abandoners are people who have added items to their basket but have not completed the purchase and look likely to leave. Meanwhile, Abandoning Evaluators have shown interest in a product but look to be about to exit.

Again, if you’re seeing these segments in your customer profiles the chances are they have already left. While the same onsite interventions mentioned for Last Chancers may be relevant to prevent more people matching this segment, the classic play here is in basket abandonment emails. Just consider their intent to purchase and ensure the message they receive is tailored based on their past and predicted behaviour. By all means incentivise if necessary, but maybe a simple reminder would reengage them.

The Impact

“���ϴ��¼� is the missing piece of the puzzle for Bloomreach and our customers. It brings real time intent predictions that feed perfectly into the real-time Bloomreach Engagement solution for shared customers to use in segmentation and personalisation to increase conversion rates across all channels. A quick and easy integration for massive business benefit, a perfect partnership of modern technologies.”

Max Wigley, Senior Solutions Consultant, Bloomreach

“We're able to prioritise how we engage our customers at the most appropriate time. Not only have we seen experiences improve conversion rates upwards of 10%, we're also seeing improved engagement metrics in our remarketing activities too.”
‍
Jo Homer, Head of UX and Product, Signet Group

How H. Samuel used intent data in their CRM to drive a £96k uplift in just 12 weeks

H. Samuel segmented their customers by those who have purchased in the last six months and have been on site in the last 30 days, but haven't converted. They combined this with ���ϴ��¼�’s intent metrics to add context on likelihood to purchase to their customers’ profiles.

From there, within Bloomreach, they were able to segment low, medium and high intent customers. They could then target them with different email campaigns based on their buying stage and try to secure a second purchase.

For example, a high intent customer received more of a sales-focused message enticing them to come back to site soon, based on what the user was looking at, with a reason to buy. Whereas low-to-medium customers received more of a nurture campaign, focused on “don't forget about us” messaging and brand USPs until they triggered a move into a higher likelihood to purchase.

This approach to email resulted in a 12% increase in CTR for H. Samuel’s more targeted, intent-based campaigns and a £96k uplift in just 12 weeks.

‍

By integrating predictive intent data with customer profiles, retailers can better identify high-opportunity customer segments based on their last known intent. But finding the opportunities is only part of it. This targeted approach allows for more effective and personalised marketing efforts, leading to higher conversion rates and improved customer retention.

Check out our other plays below for more examples of how you can apply intent, or dig deeper into how ���ϴ��¼� can help you reveal intent insights.