Building Effective Audiences for Digital Acquisition - Part 1

by George Collins on Wednesday 18 July 2018

At Forward3D our analytics team get very excited by anything and everything we can track on a website. Whether this is ecommerce sales, leads generated, videos watched or buttons clicked; there isn’t a challenge which the team won’t tackle head on. This leads to us having enormous amounts of data for our clients but know that most marketing teams only analyse and report on a few key metrics.

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A macro conversion is the primary conversion of a website, for example, the previously mentioned completed sale on ecommerce or a completed lead generation form. These are typically the metrics and figures that determine whether marketing activity has been successful or not and can be found in every weekly, monthly and quarterly reports agencies and marketers produce. 

 

However, all too often, the analysis of micro-conversions is bypassed despite these showing user interaction at a much more refined and detailed level.  A micro-conversion relates to smaller engagements such as a newsletter sign up or a user watching a product video on the website. These can but do not have to precede a macro-conversion, users can purchase a product without watching a video of how it works – it’s not compulsory. When a website is tagged and tracked to record this data it can be used to understand how campaigns have performed beyond the macro-conversions.

 

Similarly, this data can be used to segment the audiences on a website based on their behaviour.

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In the example above, tracking for this website could include all of the highlighted items:

 

  1. Engagement with sorting or viewing the products on offer
  2. Filtering through product options to narrow it down to their price and comfort range
  3. Engaging with extra information about specific products
  4. Sharing this information (in this case, most likely with a partner)

 

These can then be used to understand the user behaviour from campaigns and create personas and audiences for targeting.

 

Personas 


The purpose of personas is to create reliable and realistic representations of your key audience segments for reference. Effective personas:

 

  • Represent a major user group for your website
  • Express and focus on the major needs and expectations of the most important user groups
  • Give a clear picture of the user's expectations and how they're likely to use the site
  • Aid in uncovering universal features and functionality
  • Describe real people with backgrounds, goals, and values 

 

10 basic components of persona creation
 

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Examples of Personas

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Relating Personas to online activity


When analysing the impact of online campaigns, it can be difficult to take these research-based personas and apply them to the data. This can be down to the lack of digital touchpoints or on-site buying behaviour associated with them. Taking one particular example persona for ‘Connoisseurs’ , the below data conducted whilst researching the example personas highlights some of the buying and shopping habits.
 

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Understanding that high-end customers for this particular brand still shop around for high-quality deals gives insight into their likely path to purchase. As these users do sometimes engage in impulse purchases but are more likely to shop around for the best deal, we can start analysing pages/products viewed per session alongside transaction value to identify these users.

 

Creating/using these personas


Taking the example of the online travel agent shown earlier in this paper, our team was able to identify 3 simple but clear cut personas by analysing the micro-conversions on the website. Being able to analyse the following:

  • How users interact with multiple holidays
  • The value of the holidays
  • Whether they sort from price low to high or high to low on the results page
  • Filter the holidays by high/low quality
     
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The results of this confirmed the original hypothesis that users that are viewing the extremely high value holidays have the highest AOV. Similarly, users that view the most holidays before purchasing have a lower AOV. This is only a basic analysis but already shows how analysts can create segments and audiences to understand these particular.