Writing in the current issue of Admap, Leslie Wood argues that many marketers are underperforming because they continue to define consumer targets the old-fashioned way.
But, she points out, the dynamics of advertising to a loyal customer are – or should be – different to advertising to a person buying a brand only half of the time or 10% of the time; the same applies to heavy and light category buyers.
Based on these factors, there may be as many as 13 consumer segments in a single category. While this is not an entirely new idea, she concedes, what is different is that “we can fill that matrix with reliable, real-life behavioural data. At scale.
“That’s a gamechanger because we can finally set true performance benchmarks and draw real comparisons between campaigns.”
The nuanced use of purchase data can tease out a more direct correlation between the effects of an advertising campaign and subsequent consumer behaviour. (Read the article in full here: Peruse purchase data to optimise ad campaigns.)
“We don’t examine a campaign’s reach at the household level, but rather as coverage of purchase occasions,” Wood explains. “For every individual trip to the store, we determine whether an ad for the campaign was seen by the consumer before that trip – and recently enough to make an impact (typically 28 days).
“We don’t say ‘20% of the population was exposed to the campaign’, but rather ‘the campaign reached 20% of all purchase occasions for that product category in the purchase window’.”
Alongside this coverage metric, an incremental sales metric allows Nielsen Catalina Solutions to assess the effect of the creative in a campaign, while overlaying the campaign budget enables the calculation of a return on adspend (ROAS) which can be acted upon.
Ultimately, a marketer can align an advertising strategy to reach the most responsive buyers with creative suited to the audience, says Wood.
“This allows advertisers to optimise campaigns against the criteria that matters most: sales. But it can only work if the data that it feeds on is reliable, granular and representative.”
Sourced from Admap