Murray Calder wrote a blog post for Mediacom titled Meaningful Measurement. “As much as we each become the stories we tell ourselves, so businesses become what they measure.”
“Perhaps the most overlooked dimension of measurement is that of time. We’ve become obsessed with short term data.”
“Of course, short-term data has it’s uses but a long-term outlook is important because of what we know about the multiplier effect of highly creative, emotionally engaging campaigns. As Binet & Field have shown, the sales effects of rational campaigns are quite immediate and they tend to outperform those of emotional campaigns for around the first 6 months. However, because rational messages are forgotten more quickly than feelings, emotional campaigns are seen to be much more effective over the longer term. Short-term effects can, therefore, be quite different from long-term effects and ignoring this fact can lead to poor decisions being made about future strategy.”
“Indiscriminantly collecting that data just because it’s there creates no value. Data has no intrinsic value. It only has value when it is turned into insight which can help answer specific questions.”
“As Byron Sharp pointed out in this piece from 2017, much of the investment in marketing metrics can seem wasted because of this short-term obsession. Do you really need monthly (I’ve even seen weekly) tracker data? How much are those metrics really changing over the long term? Beyond statistical sampling fluctuations?”
“Knowing what you’re measuring and why will give you far more confidence that you know what you’re doing.”
“There are most likely three buckets of questions you need to concern yourself with:
- Input Indicators: What is contextually different from last time? How does this activity differ from what we did before? What were our competitors doing that might impact on our results?
- Leading Indicators: Have enough people seen what we’re doing? How do they feel about it? Is that different from how they felt before? Different how?
- Lagging Indicators: Has what we’ve done changed behaviours? Have we sold more? Where did those extra sales come from? Is that where we expected them to come from? If not, why not?
Knowing the questions you’re trying to answer defines the type and volume of data you need to collect.”