A couple of years ago I had to buy a new fridge freezer. A rock and roll purchase, I know. Little did I know at the time, but that purchase would help me define a new social data analysis metric and methodology – decision-making heuristics.
Since then, I’ve used my decision-making heuristics metric countless times in projects. The insight from this metric has been used in conversion rate optimisation projects for eCommerce websites, to optimise the effectiveness of marketing campaigns, and used in new product development.
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Why Heuristics Work
When faced with making any decision, we use a set of heuristics, which are simply decision-making shortcuts to help us make an informed choice (without the brain drain).
If we find out the short-cuts people use to make decisions, we can start to place these cues in our communications to cut down on the processing time and complexity involved.
Consumers will also like your communication more because it is familiar to them (like reading their mind) and you don’t make them think too hard.
Essentially, we remove the friction in the purchase process by simply and quickly providing customers with the information that they need to confirm it is ok to make the purchase.
If we go back to my freezer example, we can see that the one I was thinking about buying was being sold by its super freezing and supercooling ability.
If I’m buying a fridge freezer then freezing and cooling is pretty much what I need it to do. It’s a given.
So, these marketing statements really aren’t helping me make a decision. And, this is where my heuristics metric works.
The Decision-Making Heuristics Metric
Decision-making heuristics is one of the simplest types of analysis that I run. It is also one of my favourite metrics.
I generally run the analysis with consumer reviews. No other data source is required, consumer reviews are sufficient for this analysis.
The tricky part with the analysis is the segmentation criteria as you are working with what I call the unknown, unknowns – you don’t know what your segmentation criteria will be before you start to run the analysis.
A starting point is to classify, and segment based upon the product attributes given in the description. So, for the fridge freezer, it could be the size, the brand, the cooling ability, the freezing ability, energy rating, holiday mode….
Then once, everything you know is segmented there will be a ‘bucket’ of data left over that doesn’t quite fit your segmentation criteria. This is where the unknown, unknowns are. I always say this is where the magic happens, you’ll find the golden nugget of insight you need in the unknown, unknowns.
To segment the remaining data, you need to read the reviews and understand what they are discussing – what’s the context of the review? Then, make any additional segmentation criteria needed.
It was the unknown, unknowns where I found that the noise of the fridge freezer is much-needed information before making a decision.
And, that insight completely changed the understanding of how people make decisions to buy a fridge freezer.
The funny thing is, the noise is one of the least discussed dimensions in brand and sales communications, but it is very important to the consumer. In fact, the most important attribute contained in 81% of all review I analysed about fridge freezers.
So, instead of supercooling and super freezing talking about the noise, or lack of noise, is what could help sell more fridge freezers online.
Your Customers Decision-Making Heuristics
It really is simple to find out what is important to customers when making a decision. All those consumer reviews have useful actionable insight for the sales journey.
Reviews are not just criticism or praise, they hold hidden gems in how to optimise your marketing campaigns and sales processes.
Take 20 minutes from your day and find out more about your customers’ preferences by analysing decision-making heuristics. You can download my step-by-step methodology by signing up for a FREE The SI Lab account HERE.
And, do let me know how you get on.
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