Marketers today are feeling the heat to show results while cutting costs, but they have a new weapon to drive results and demonstrate value to senior management – big data.
There is more information generated by and about consumers today than ever before, but with that comes an increasing risk of marketers making incorrect measurements or misinterpreting data analysis.
That’s the assertion of a trio of analysts from McKinsey & Co., in a recent Harvard Business Review blog post.
Marketers can increase ROI by 15% to 20% now that analytics techniques are available to corral big data and make marketing a more precise science, according to the post.
However, marketers must be cautious of the “blind spots” that inevitably arise from any analysis.
For example, one of McKinsey’s energy firm clients noticed that its customer losses correlated to the intensity of customer Google searches for energy suppliers. As a result, the company built a customer churn model where search was responsible for 65% of churn.
“However, in-depth analytics revealed that customers’ decisions to switch energy providers were driven by their and competitors’ prices, advertising and company’s position in social media, TV, print and other mass media,” according to the post. “When all these additional explanatory factors were included in the customer churn model, search was not the cause of customer churn since people had already made up their minds by the time they were searching.”
One major blind spot for marketers to avoid is what McKinsey calls “short-term-ism.” The majority of marketing activities have both a short-term impact and a long-term impact on sales; the short-term impact typically generates 10% to 30% of total sales while the long-term impact is one to three times greater than that.
“Big data-based analytical approaches, however . . . can detect only the short-term impact of marketing,” the post notes. “What this means in practice is that the majority of data and analytics provide marketers with a short-term picture and can lead to short-term decisions that are detrimental to the long-term sales performance.”
To avoid this potential pitfall, McKinsey advises marketers to overlay their big data models with analysis of the longer term brand equity effect that drives the remaining 70% to 90% of sales.
To do this, companies should first create baselines by estimating the potential decline in sales if all marketing activities were stopped. These estimates can determine the net present value of the long-term effect of marketing on future sales, the post notes.
A consumer food brand client of McKinsey’s teetered on the edge of “short-term-ism.” It launched a campaign using Facebook advertising, contests, sponsored blogs and photo-sharing initiatives.
The approach paid off with sales results similar to those of traditional marketing campaigns but at a much lower cost than TV or print advertising.
The company considered shifting a significant amount of its investment from TV and print advertising to digital and social media. But when the long-term effects were included in the analysis, the proportion of impact from digital decreased by half because most digital activities typically include short-term calls to action and don’t contribute much to the brand and consumer loyalty.
Cutting back significantly on TV advertising would have lowered the present value of the brand’s profit, McKinsey points out.
“Marketing analytics is far from a monolithic approach. It’s actually a collection of approaches and techniques that, when systematically applied across a specific set of issues, delivers useful insights for making marketing investments that pay off,” the post concludes. “The latest wave of data combined with the right models can illuminate a lot. But smart marketers will spend just as much time looking for their data’s blind spots.”