Bridging the Gap in Competitor Audience Analysis

The rich combination of Gener8’s datasets, interconnected by 1 user ID, enables advanced  audience analysis of target brands and competitors

Today brands sit on extensive first-party data and possess comprehensive research on their customers. This enables them to map out customer preferences, behaviours, and purchasing patterns with remarkable precision, which in turn helps them to make highly impactful data-driven advertising, UX and product decisions.

However, when it comes to competitor audiences a significant gap in understanding often exists, making the task of benchmarking and opportunity analysis highly challenging.

At Gener8 we have a solution to bridge this competitor audience understanding gap and have conducted research across Boots to demonstrate the depth of audience insight available.

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Get an advanced understanding of your target audience, fast

At Gener8, we have a panel of over 100,000 UK users that share multiple datasets linked together through one user ID.

This enables us to quickly identify and size up a target audience cohort based on a number of different data connections.

For example, to better understand the audience of Boots, a UK beauty, healthcare and pharmacy retailer, we used a variety of datasets:

• Consumer Browsing to identify 36.2k users who visited Boots.com
• Purchase Intelligence to identity 9.8k users who shared a Boots eReceipt
• App Usage to identify 4.6k users who opened a Boots app

This resulted in a deduplicated audience of 45.4k users who have engaged with the Boots brand over the past 12 months.

After creating this audience cohort, we can start to analyse the Boots audience through both demographic and psychographic lenses.

Across demographics, we found that the Boots audience indexed highly in being female (i152) and younger, with both Gen Z and Millennials indexing the highest. Furthermore, we were able to dive deeper into psychographics and understand the attitudes, with the Boots audience indexing as being highly trusting in brands they buy from.

These are just a taster of what's possible within our broader audience segmentation framework, which offers countless ways to analyse and interpret data for a deeper understanding of your target audience.

So what are the perks of this audience analysis approach?

1. Much larger panel. Securing a large number of survey respondents is both challenging and expensive. However, by analysing past validated user behaviour, we were able to identify over 45,000 users who interacted with the Boots brand in the last 12 months.
2. Immediate audience insight. We don’t need to launch a new survey and wait for respondents to come in to understand the Boots audience. Since our platform users answer key demographic and psychographic questions during onboarding (and every 6 months after), we can simply filter by Boots site, app and purchase behaviours to get rich audience insights.
3. Validated audiences. We know that users have actually engaged with the Boots brand and performed those behaviours in the past, instead of just claiming to have done so in a survey, where inevitable biases affect the accuracy of the results.
4. Surveys are available for deeper insights. If you still want to launch a survey to the validated Boots audience, there's no need to repeat the core demographic questions to which answers are already known. Instead, you can focus on asking project-specific questions that align to your objectives, ensuring a more seamless and engaging experience for the respondent.

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Enhance your audience understanding with behavioural insight

Brands often face challenges in understanding how their audiences vary across different platforms.

For Boots, we were able to uncover meaningful differences between the overall Boots audience (those who either visited the website, purchased from and/ or used the app) and those who had purchased from Boots or used the App. 

Users who had purchased from Boots were much more likely to be women (i198) and Millennials (i112). Meanwhile, those who had used the Boots app were even more likely to be women at i208, and less likely to be male at i48. Notably, Boots app users were also much more likely to be part of older generations compared to the overall audience, with Gen Z being much less likely to use the app at i50.

And while data source serves as a useful filter, we can also break out the Boots audience into sub-cohorts based on:

• Activity time. How does the audience differ between those making purchases in Q4 and those buying during the rest of the year?
• Activity frequency. How do the top 10% of users (based on website visits, app opens, or purchases) compare to the overall audience?
• Activity depth. This can take various forms:
  
  • Consumer Browsing: Did the user visit a target page (e.g. new product/ offer), the basket or the purchase confirmation page?
  • Purchase Intelligence: Did the user purchase a specific item or brand?
• Close competitor activity. Did the user also visit competitors’ websites and/ or use their apps?
• Other target site/ app activity. What other activities did the user engage in? (e.g., visiting review websites, watching relevant YouTube videos, etc.)

Identifying and benchmarking your target audience activity across close competitor sites and apps is a crucial behavioural insight.

For instance, we were able to confirm that those who had purchased from Boots were also more likely to have visited the Boots website than those of close competitors of Superdrug, Lookfantastic, Holland & Barret and LloydsPharmacy.

The findings were similar when we looked at app usage. Those who had bought from Boots were much more likely to have also used the Boots app than those of close competitors.

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Uncover what your audience’s online behaviour

It can be valuable to look beyond the demographic and psychographic profiles of your target audience and ask a broader question: What other websites do they engage with the most? While we can assess the behaviour overlap between specific sites, as we did with close competitors earlier, we can also take a step back for a macro view.

Given that users can engage with millions of different websites, this is a difficult challenge to tackle. However, by focusing on the top 50,000 most popular sites based on UK visits and categorising them according to IAB-standard categories, we can reveal valuable insights into online audience behaviour.

Focusing on those who had purchased from Boots, we observed that Style and Fashion (i240), Family & Parenting (i222), and Food & Drink (i182) site categories indexed the highest. As had been found above, women are key Boots buyers (i196), and some of these browsing habits can be seen to align with wider female browsing habits.

But how does this compare to close competitors of Boots?

When comparing Boot’s audience web browsing patterns against those of close competitors, we identified a number of themes. Firstly, web consumption behaviour largely overlapped, highlighting that the audiences are very similar in nature. However, it was also useful to look for the outliers, such as the Lloyds pharmacy audience, which comparatively indexed much higher across Health & Fitness. 

These online behaviour insights can both help to confirm existing pen portrait audience understanding and, importantly, challenge past findings through a validated behaviour lens.

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Identify which YouTube categories are watched by your target audience

We can take it a step further by exploring YouTube browsing behaviour across the web as well. By connecting the Gener8 Consumer Browsing dataset to the YouTube API, we can delve even deeper into YouTube consumption patterns among our extensive panel of users.

Customers who made purchases from Boots showed a strong preference for watching videos in the Pets & Animals (i155), How-to & Style (i137), and Travel & Events (i135) categories. This aligns closely with the content preferences typically seen in audiences with a higher female demographic.

But what about close competitors?

When we look at close competitor YouTube browsing habits some notable trends emerge. Notably, the top three consumption categories for Boots also show a high index among its competitors. However, differences also arise, such as the fact that content related to Nonprofits & Activism resonates more strongly with the audiences of Holland & Barrett and Superdrug.

Should we want to dive even deeper from here, we can also explore specific YouTube videos for content themes, as we tackled in our past YouTube audience research deep dive.

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How can I access this data?

We utilised Gener8’s Consumer Browsing, Purchase Intelligence and App Usage datasets alongside Domain and YouTube video classification to learn more about the Boots audience and those of its close competitors. 

Gener8 Labs’ complete data and insights solution empowers media and marketing businesses to find actionable consumer and market insights, using our unique, consented, first party panel data sets that are all connected around one user ID.

Discover how you can power your decisions and gain a competitive edge from our behavioural truth set by contacting us today!

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