Ryan asked at Social Media Marketing World for cases where data goes against intuition:
In the time since this post was written (2015), so much has changed. Data is more counterintuitive than ever. Why? We have so much more of it, and that complexity is what creates counterintuitive situations.
Intuition is, in many ways, our brain’s perception of correlation. When we see things happen together, we associate them together, and intuition is more or less our back-of-the-envelope calculation that things belong together.
What happens when we exceed our brains’ abilities to correlate at massive scale, or see interactions that involve many variables? We stop perceiving correlation. This is the situation marketers find themselves in more than ever.
Consider just some of the data you have access to:
- Web analytics data
- CRM data
- ERP/customer data
- Social media data
- Advertising data
- Public relations data
- Survey data
- Macroeconomic data
This list could go on for quite some time, and highlights just how overwhelmingly complex marketing data is today. If you consider even just two of the variables in this data gumbo, it would be difficult to see a correlation – such as the Baltic Dry Index (macroeconomic data) and customer lifetime value (ERP data).
Only the powers of machine computation can overcome this massive amount of data and find relationships among variables that we simply could not. How do different variables interact? What variables predict the outcome we care about?
Here’s a simple example: we’ve long said, as marketers, that obsessively counting the number of followers you have in social media is pointless vanity. We tell people to focus on real business metrics instead. However, in a recent Trust Insights client engagement, we built a machine learning model to calculate what variables predicted revenue and we were shocked to see social media audience size as one of the variables for this particular customer. Talk about counterintuitive!
Yet, when we used the exact same technique on a different customer, social media audience size dropped to be a completely irrelevant predictor of the business outcomes the customer cared about.
That’s the great challenge for marketers: not only is data counterintuitive, but what works for one company is almost certainly not likely to work for another company. Best practices are a great place to start (and certainly better than just making things up) but once you have enough data, only true analysis will help you take your marketing beyond the basics.
Disclosure: this post was written in 2015 and updated since then. It was completely rewritten in 2019.
You might also enjoy:
- Marketing Data Science: Introduction to Data Blending
- How to Measure the Marketing Impact of Public Speaking
- Is Social Listening Useful?
- Transformer les personnes, les processus et la technologie - Christopher S. Penn - Conférencier principal sur la science des données marketing
- Retiring Old Email Marketing Strategies
Want to read more like this from Christopher Penn? Get updates here:
Get your copy of AI For Marketers