LONDON: If measurement is crucial to understanding marketing effectiveness, then choosing the right metric is no less so and marketers can ask five questions to help decide what metric is appropriate.
In a Warc Best Practice paper, How to set marketing metrics effectively, Neil Bendle, Assistant Professor in Marketing at the Ivey Business School, Western University, London, Ontario, Canada, outlines the benefits of the WAITA model – Who, Assumptions, Ingredients, Theory, Action – when assessing which metrics to use.
The first question to ask, he says, is "who uses the metric?" Metrics that relate to a firm's published financial accounts or stock market valuation, for example, will reflect many things that individual marketing managers can't influence much.
"An effective metric should connect to something that the metric's user influences," he states.
It's also important that marketers understand the assumptions behind any given metric. When considering customer lifetime value, for example, the standard formula assumes an infinite life of customers – something that is clearly impossible, but which Bendle observes isn't as divorced from reality as it at first sounds.
"The formula's usefulness comes from appreciating that, providing retention rates are not very high (that relatively few customers survive for a very long time) and discount rates are not very low (that the value of future cash is considerable less than today's cash), the impact of the far future on CLV becomes trivial."
Subsequent use of the chosen metric will require an understanding of the source of the data that is going into it; a survey reporting aided brand awareness typically produces very different results from an unaided one.
And a grasp of the theory behind a metric is advisable, says Bendle, otherwise marketers can find themselves mistaking correlations in data for casual links. "Along with insight there is a lot of nonsense to be found in big data if you look hard enough," he cautions.
Finally, "marketers should always have a clear idea of why they want to calculate a metric before doing so" – and know what action they intend to take as a result.
Establishing a historic customer's value over their lifetime, for example, is useful only if one can find new potential customers similar to the prior customers.
Bendle cites Netflix as an example of a brand that has extended customer lifetime value by using a full range of data to track customers' viewing habits and determine how to better engage each individual customer; by doing so it has reduced churn to 4%, he notes.
Data sourced from Warc