Posted In: Strategy by
Jenn Ferrier,
April 26, 2023
WHY RICH DATA MATTERS WHEN ITS COMES TO UNDERSTANDING AUDIENCES
Sophie Cork , Boutiques’ DISCOtiquer
Helping brands to understand their audiences is a vital part of what we do as a channel-agnostic agency. Unlike a single-service agency, we can’t jump to a single channel solution and instead we must take a holistic approach to a brief. This always starts with stepping back to assess the data a client uses to understand their business performance. Brands typically prioritise collecting data on their business performance; often we see a robust sales reporting process, a clear view of the competitor market, a strong growth tracker. Where brands can fall down is with the depth of data they have available on their audience – and how that has shaped their understanding of who their customers are.
Not having a robust audience data portfolio can often lead brands to one of two scenarios: either they end up with single-source insights which lack context, or they have no insights at all. For example, a brand could base their targeting strategy on the belief that they have a ‘male-leaning’ audience, just because their site analytics shows a 55% male session split. Or they know their audience only within the confines of a generic Mosaic profile. And for brands who have no real data on who their audience are, they can end up making assumptions and building ‘hunch’ audiences – AKA, who we think our customers probably are. These are often entirely subjective down to the individual.
What changemaking, strategic decisions can a brand really make if the Marketing Manager and the CEO both have fundamentally different ideas of who their core customer is?
The problem comes from siloed data – using only one data point to form what is actually a very complex thing: human understanding. How can we fully explore the complexities of audiences – their thoughts, behaviours, attitudes, with just one single data view?
Siloed data only paints one picture, and it’s usually the equivalent of a picture of a square house with a line of green grass with a circular sun in the top corner, done in crayon. It can’t be trusted to give us the full view of the depth of an audience, how they’re changing and what the opportunities are for growth. The alternative, rich data, where we merge and layer a range of different sources across the data spectrum, provides a masterpiece watercolour of who a brand’s customers are and how we can recruit more.
Why Should We Use Multiple Data Sources When Building Audience Insights?
Comprehensive View
No one data source can tell us everything we need to know about consumers. Sure, site activity data is great at telling us how long users spend on site and what page they exit on, but what can it tell us about how they heard about the brand, or which other brands are they considering at the same time? By layering data – ideally from both quantitative and qualitative sources – we can learn more nuanced details about the people we’re reaching and create a far more complex story about their motivations. When segmenting customer types for clients, we strive to obtain a mix of 1st and 3rd party data; the former gives us a view of a brand’s actual real customers and how they interact with this brand specifically, and the latter giving us a wider market view and point of context. By blending the sources together creatively through the output of pen portraits, we generate new, bespoke and ownable insights.
Deeper Insights
With multiple sources comes the ability to draw comparisons, to identify trends and spot patterns that might not be apparent from a single data source. In a recent piece of custom 1st party audience research conducted for a client, we surveyed customers and indexed the applicable answers against a UK base taken from GlobalWebIndex. This allowed us to index customer responses against the general population and put their audience context on a bigger scale. This revealed insights about how their audiences’ behaviour differs to that of the norm, and lead us to a strategy of how to incorporate this into their targeting.
Improved Accuracy
With comparison also comes cross-referencing and validation of data, improving the accuracy and reliability of audience insights. Some sources are inherently more reliable than others, particularly those with a large sample size or those with little to no self-reporting is involved. This is why site behaviour data is invaluable; it’s available in large quantities and is automatically tracked with the right tagging, leaving little room for manipulation. It can be used to test more open, qualitative sources or those based on likelihood as opposed to undeniable fact. For example, a brand’s consumer Acorn profile report may suggest that their core audience are most likely to be aged 65+, however their site analytics shows a consistent sessions and sales skew towards those aged 35-44. When comparing the two sources, naturally we would look to the site data as the ‘true’ picture of who their most frequent customer is, as Acorn uses geodemographic indexing (ie. suggesting who someone is most likely to be based on the area in which they live). This is not to say the Acorn findings are invalid, but instead we would look to how the data is collected in each case to assess what is most relevant and look to where we can collate the findings for richer insights. In this example, it is likely that their core customer base of those aged 35-44 live in areas most heavily populated by those aged 65+ – therefore, most typically living in larger homes in suburban or semi-rural areas, and therefore of above-average affluence for the 35-44 age group.
Future-Proofing
Relying on a single data source can be risky when it comes to a long-term strategy. Changes to or dissolvement of that source can have significant impact on insights. As data tracking becomes more and more unpredictable, it’s important for brands to have a robust backdrop of sources to fall back on, and be prepared to adjust to changes. The impending switchover from Universal Analytics to GA4 for example has the potential to wreak havoc on unprepared analysts; those who embraced the change early and have been gathering data from both sources will find themselves in a much better position than those who haven’t – with a bank of historic data to measure trends from and two sources of site data to compare and draw conclusions from.
Building, analysing and ultimately blending data sets is the first step we take when approaching a new brief. This powerful tool starts our DISCO model: our process for delivering objective-driven outcomes for brands who want to see noticeable growth – following the cycle of Data and Insights, to Strategy and Channel, which leads to delivering Outcomes, which we then utilise as a data source and so on. By delving into who the audience actually is first, it can drive invaluable insights to inform and build out a refined strategy based on a rich understanding of who their customers actually are, and what opportunities there are to find more of them.
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Sophie is our resident strategy genius and master of our DISCO model and an all round bloody good human.
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