The first question on any CEO’s lips when we talk about using social data to understand customers is: ‘That’s all very well, but can social media really tell me anything about my sales figures?’
Put simply: yes, it absolutely can.
The majority of brands already have a perception of who their customers are and often enrich their customer data by paying for market research data that gives insight into that specific, predetermined group. Social data, on the other hand, is behavioural rather than attitudinal, making it very useful for highlighting customer groups that brands may not have previously considered, along with their differing motivations for shopping.
For example, social data indicates there are nine consumer segments within the UK beauty audience, each with their own motivations for choosing particular brands. The big industry players are embedded across a few of these segments, with differing levels of engagement. Superdrug has two clear audiences within the UK beauty industry, one motivated by value and one by convenience. While Superdrug often sells itself on its low prices, the convenience segment is twice the size of the value segment, indicating that Superdrug could be missing out on an opportunity to improve customer engagement and spark growth.
Expensive buys are very much driven by passions – and that’s where social media data can be very enlightening.
Clive Humby, Starcount
It’s true that social data can’t always shed light on the sale of long-engagement consumer goods, such as toothpaste and toilet roll. Much of the time, social media product associations in this area indicate nothing more than promotional promiscuity, as deal-savvy customers follow a host of brands in search of discounts. For FMCG brands, social data is much more useful as a tool for understanding and targeting consumers at key life stages, like students or mums.
When you look up the food chain, however, at pricier, less regular purchases, the story gets more interesting. Expensive buys are very much driven by passions – and that’s where social media data can be very enlightening.
Take London theatre-goers, for example: when comparing ticketing sales with social data, interesting patterns begin to emerge. Over 90% of venues sit within the same motivational segment for both social and transaction data, demonstrating that who you follow on social media can be predictive of future buying behaviour.
We can explore this theory further by diving deeper into the data. The online community that follows a large group of highbrow London theatres is roughly the same size as the group actually buying tickets to shows at those same theatres. However, a much broader group attend West End musicals and family shows, tending to reserve these trips for the occasional day out. For this group, there’s only a 20% match between the social audience and actual ticketing data.
This is indicative of their theatre-going behaviour: the first group are London-based theatre fans, who are happy to spend their disposable income on regular theatre attendance, while the latter group treat themselves to a night at the theatre once a year. Both groups are theatre-goers, but the motivations behind their ticket purchases are very different. By using social data to understand those motivations, you can start to predict how much and how often they are willing to purchase.
Social data can also assist in creating a complete customer view, enabling retailers to see the other areas in which their customers’ interests lie – as well as where they choose to spend their money. By overlapping customer data with social data, we can see patterns in consumer spending and illuminate which brands are competing for share of wallet.
Continuing with the theatreland example, social data not only shows which non-theatrical areas audiences are passionate about, but also highlights which brands, events and media they prefer in each of those areas. When it comes to travel and fashion, for example, regular theatre attendees love high-end London hotels like the Savoy and luxury department stores like Liberty, while annual theatre-goers prefer family-friendly resorts like Butlins and affordable clothing brands like George at Asda.
Looking at consumers through social data can also be very useful when predicting share of wallet across a particular sales period. While a particular supermarket’s overall number of Twitter followers did not necessarily relate to their Christmas 2015 sales figures, a boost in sales figures did correlate with an increase in followers from particular social media segments, such as foodies. Aldi, for example, experienced an 80% increase in followers amongst foodies and supermarket lovers, alongside a 13% increase in sales across the same period.
Despite the naysayers, the results are clear: by really drilling down into social data, you can use it to understand and predict consumers’ buying habits and brand preferences.
Clive Humby was founder of Dunnhumby and is now chief data scientist at Starcount.