Over the course of recent years more and more operators have been talking about segmentation than ever before. There’s certainly been an increase in the amount of references to segmentation in briefs to elliotts.
For instance, most major pubcos segment their estate. Why? Every site deserves an offer relevant to their locale, their customer, their circumstance. Providing that bespoke proposition to 500 locations would be nigh on impossible (certainly from a central support), so classifying them into groups of 5,10,15 based on common principles they share means it’s only necessary to devote resource to 15 propositions, and they will be significantly close to satisfying each site’s requirements.
Customer segmentation is equally important, not least for those brands who, by their very nature, have to keep their offer super consistent nationally, most pertinently casual dining brands and branded pubcos.
Where businesses have less freedom to develop different types of site, they can obtain empowerment from understanding and segmenting their various customer types. A thorough customer understanding process typically unearths a handful of customer types, such as the millennial who comes in with friends and cares most about the atmosphere, the young parents who need extra ordinary value and a good children’s menu, etc etc- you get the picture.
By understanding their customer groups and nuances, an operator can tweak their overall offer to ensure that there’s something of appeal for each target consumer. But is this where segmentation should end? Site segmentation and customer segmentation are now almost always part of a marketer’s arsenal, but what about product segmentation?
A member of our insights team showed me a TED Talk earlier this week by Canadian author Malcolm Gladwell. It was quite an old one, filmed in 2004, but still incredibly interestingly and arguably more relevant than ever.
He spoke about the history of the food industry’s pursuit of the perfect spaghetti sauce, conducting research upon research in the process.
It’s often the case, when conducting food testing, to receive varied, mixed feedback, with no clear winner amongst the products trialled. If, for instance, a food trial is looking to identity the optimum product out of 9 or 10 potential variants, it’s highly likely that the most popular product will only have been the favourite with 15 – 20% of participants. Hardly a convincing statistic, and hardly likely to win over the decision-maker. With this in mind, it’s always tempting for researchers to minimise the products tested in order to ensure there is a clear ‘winner’.
This was likely the case for legendary US researcher Howard Moskowitz throughout his early career. But when he was approached by Campbell’s Soup to help develop their Prego pasta sauce product into a rival to the United States’ dominant Ragù, he looked at things differently. Instead of finding the optimum sauce, he intended to find the optimum sauces.
45 sauces were cooked up by Campbell’s food development kitchen, varied in every conceivable way. By the level of sweetness, by level of garlic in it, acidity, sourness, amount of tomato: you name it, they tried it. Consumers across the US were asked to try 10 of them over a 2-hour period, and then score each of them out of 100.
When the results were in, rather than look for the highest scoring variant, Moskowitz clustered the variants according to what was common between them. The end result? Rather than identifying that Americans had an appetite for a specific sauce, there were three groups: those who like it plain, those who prefer spicy sauce, those who prefer it chunky. The latter was the most significant, as no product satisfied this need. A line of extra chunky sauce was added to their repertoire, and made $600m in the next ten years.
It took the food industry by storm: for decades and decades they had known that consumer perception is highly subjective, and finally there was a way to take this into account and still inform decision-making. Ragù also took notice, calling upon Moskowitz’s services, and now stock well over 30 variants in the US. There is no perfect pasta, only pastas to suit different preference niches. In the same way, there is no perfect cola drink (think about the number of variances there are today!), no perfect chocolate bar, no perfect coffee.
It’s not enough to ask consumers what they want.
You have to think in depth about the right question to ask. Effective research doesn’t necessarily have to analyse in depth what consumers say they want, but rather analyse in depth what that means they actually want. Consumers can’t always explain what they want, and critically consumers often don’t know what they want because it was never an option for them. Henry Ford of course famously said that if people were asked what they had wanted prior to the invention of the automobile, they’d have asked for faster horses.
Horizontal segmentation, grouping variants like the aforementioned pasta example, is one way of identifying a hidden need. This doesn’t have to be complex. One of our researchers explained it really well recently: if you think about the last time you filled out a survey online, you were probably asked for your age, and you were probably given brackets to choose from (the classic 25-34, 35-44 etc). Each of these age bands is horizontally segmenting the individual ages within it.
But in 2017 and the age of information the possibilities are so much vaster, particularly for the hospitality sector. Why wouldn’t a brand do this with likes, dislikes and of course food preferences to empower their data?
For instance, traditionally we’d look at sales data of a menu and remove anything less than a certain percentage of the mix. But does this give a full sense of what customers want? Not necessarily: all of a menu’s spicy dishes (even across different sections) might account for 30% of mix when grouped together, but all be low mix items when looked at individually. This would be a customer base saying they want spicy options, but this could get missed if only individual dish scores are examined.
Moving away from product again, horizontal segmentation has uses elsewhere. Another opportunity perhaps comes via single customer view, and having countless pieces of insight on a customer base. A dissection of data to identify points of commonality beyond the obvious could throw up a surprise opportunity. Do a noteworthy proportion of a restaurant’s customer base follow parenting blogs/ tweeters? It might be time to revisit just how family friendly the venue is. Do a significant number of a bar’s customers like some form of quiz show on Facebook? Maybe a quiz event is in order.
These are really cut and dry examples, but the truth is that a lot of brands probably have such insight lying dormant in spreadsheets somewhere! There are almost certainly brands out there looking at data in this way already that have even better examples, and I’d love to hear about them. For now I’m trying to address my newfound TED Talk addiction.