“It was the beer. That was what I told myself. I mean, you tasted it, we all tasted it. It was phenomenal. We had no idea that we’d end up with this.” My interviewee, ‘B’, pauses a moment to gesture around angrily.
We’re sat in one of London’s Samuel Smith’s pubs, Ye Olde Cheshire Cheese, but I know B was not referring to the pub itself. He’s gesturing north, east, south and west, above and below, at everything wrought in the world of beer by his former employer.
The choice of venue seems fitting. Like monuments made from older, stronger stone, Samuel Smith’s pubs have watched quicker-lived beer cultures rise around them, only to be eroded by time’s tide. They remain the time-machines-to-the-1970s they always have been, and feel like a place of true escapism now. Once a contrast to a beer culture with a faster metabolism, Sam Smith’s pubs now feel like a holiday to a place without any painful traces of ‘craft beer’ to be found.
B is incognito to avoid any chance of crossing the path of an angry beer geek. I barely recognised him as he sidled up to me at the bar. His beard shaved to extinction, his glasses replaced by contact lenses, he dresses in an unbranded grey zip-up hoodie and a trucker cap (one small concession to his former appearance). B exudes the same nervous energy of other Mainspring employees I have met, sorry for what happened but not sorry for what they made. “We knew what we were doing could end badly. We knew there was a plan. But we really thought that the beer we made was worth anything. In the end, it cost everything.”
There is a commonly held misconception that Mainspring was co-opted after it was founded, and had its noble aims blackened. To get the truth of Mainspring’s origin, I had to speak to one of the founding directors.
Throughout Mainspring’s short, Icarus-like history, its directors remained in the background, to the point of appearing illusive or non-existent. The directors preferred the brewery’s employees, drawn from high-profile breweries across the UK, and the world, to take the limelight.
Nevertheless, six very real men and women were certainly running Mainspring, each with extensive backgrounds in brewing, distribution, finance, marketing, sales and operations; but they were brought together by another party. B admits that the offer of a job with Mainspring felt Faustian, but it was nothing compared to the one offered to the company’s directors in the first place: create the best brewery in the country within a year, and have almost limitless resources. The price? Knowing that there is a very real chance that your brewery’s rise to fame could precipitate the gutting of the entire industry.
‘D’ had worked in distribution for a number of UK and European companies for the past ten years, eventually specialising in craft beer and seeking greater challenges. She was first approached about buying into a new venture in late 2016, by a man named Wes Cavendish, from a company calling itself Caistor Insights.
“He told me from the very beginning that he didn’t know anything about beer, and that he didn’t care to. He said he was only interested in proving what could be achieved.”
D has met me at a bar in Hoxton, East London. Here, bars and pubs seem to have been the quickest to moved on, though A-boards on the street still bare the faint, recently-erased lines of the words ‘CRAFT BEER’. Ghosts rendered in chalk.
“Cavendish talked a lot about Brexit, and Trump, and populism, and knowing what people are going to do before they do it. Knowing what people want. He said his people had done enough research to prove that, with enough data points [about someone’s age, gender, interests, social media activity etc] it was possible to make a ‘perfect brand’. He said that they [Caistor Insights and companies like them] had proven it could be done with democracy, so why not with a business?”
It was true that companies like Caistor Insights who specialised in ‘Big Data’, information willingly given by people in their use of online services and applications, had played a role in the success of the UK referendum on Brexit and the US presidential election. Even so, some felt this role was overstated, so if they had had such a hard time in the world of politics, why make the move to beer?
“They thought craft beer was the perfect test, as there would be so much data for them to extract from social media, blogs, apps and so on, and with relatively low risk of being… discovered.” But why would craft beer drinkers be more gullible or less aware than others of this kind of manipulation?
The answer was simple. “Arrogance,” says D. “Caistor’s earliest work on the project had proven that craft beer consumers, as well as those working in it, often thought they were immune to being deceived, which made it all the easier for their egos to be flattered. Mainspring was designed for them.”
So how did Cavendish, a man used to the world of politics and with no interest in beer, create ‘the perfect brewery’ that would set the entire industry alight?
‘Big data’ specialists like Caistor Insights use psychometrics (also known as psychographics) to measure and determine people’s personalities, based on the ‘Big Five’ or ‘O.C.E.A.N.’ personality categories (Openness, Conscientiousness, Extroversion, Agreeableness, Neuroticism). To use these accurately once required extensive questionnaires, but in the age of social media, big data companies were simply able to mine people’s social media, particularly Facebook likes, to determine personalities and behaviour (and you can test it out for yourself on the website of the Psychometrics Centre of the University of Cambridge). Those with the right tools and resources could potentially use psychometrics to make lists of voters to target with specific ads, or, as Caistor envisioned, drinkers to target with specific beers.
Craft beer proved to be fertile ground for Caistor – a beer culture that gave its opinions, feelings and desires willingly, and lengthily, across a whole range of social media and applications. But these drinkers were no long people, or even consumers, they became ‘datasets’. Based on all the information they happily gave to websites like RateBeer and BeerAdvocate, apps like Untappd, and the usual social media, the ranges of beers to best meet the needs of the datasets were composed and constructed. But these were not recipes, or even beer styles. More work was needed.
The overlapping and occasionally conflicting interests from different datasets, such as, for example, being obsessive fans of a particular cask ale, but generally preferring keg beer and shunning most other cask beers, presented ‘refreshing and exciting challenges’ for Caistor, which spent a great deal of time trying to ‘understand’ beer geeks. This was done by dedicating server capacity to unravelling common factors between blogs, RateBeer scores, Untappd data and more. Resources were spent interviewing attendees at various UK beer festivals, both traditional and modern, under the guise of a young couple starting a Dutch YouTube channel, ‘Bier Extreem!’. The answers helped Caistor Insights make sense of the flood of data.
D describes meeting another Caistor Insights employee, much later in the development of Mainspring. He resembled “a drill sergeant with marketeer’s fashion sense”. More rugged, less detail-oriented language was spoken. Data ranges were ‘thrown against the wall to see what sticks’, the raw results from their market research went through a ‘bullshit-strainer’ and was pulled apart by ‘number fuckers’ to extrude the most salient information. You imagine some Caistor Insights employees work far longer hours than others.
The result was based on the boiled down data from all this work. When it was all presented to D and her fellow potential directors, Caistor described the process as being ‘simple’, applying binary answers and continuing to filter the results as necessary, until they had formula for creating enough beers to please every beer drinker.
A set of four spectra were developed: light/amber/dark; strong/session/special; experimental/accessible; and modern/traditional. With these alone, Caistor believed it could create the range of beers to suit the specific demands and tastes of the vast majority of the UK craft beer scene. It could create a brewery with output to satisfy anyone.
Caistor called these combinations ‘brand desires’, each of which correlated to identified interests from thousands of the datasets. The results became Mainspring’s tantalising opening line-up.
Brand Desire 1: Light-Traditional-Accessible-Session (Zentrum Lager series)
Brand Desire 2: Light-Modern-Experimental-Special (Nucleus mixed fermentation project)
Brand Desire 3: Amber-Traditional-Accessible-Session (Heart cask beer range)
Brand Desire 4: Amber-Modern-Experimental-Strong (Alpha IPA series)
But why were there no big dark beers in this line-up, especially considering their popularity among the RateBeerians and Instagrammers? As ever, there was cold, straightforward reasoning behind it.
Caistor had identified some ‘intriguing’ correlations between consumers being ‘surprised’ or ‘shocked’ into purchasing unannounced beers or special releases, and the kind of heavy mega-stouts and flavoured big beers that breweries like to showcase at festivals and events. A ‘brand desire’ to match this had been defined, but deliberately held back from the initial line-up:
Brand Desire 5: Dark-Modern-Experimental-Special (Foundation)
So, when festival season rolled around, and Mainspring famously announced it would release a new member of the Foundation series at every festival it attended for the next twelve months, we all, of course, lapped it up.
We, the beer drinkers, had been identified, analysed, interpreted and understood. And we were going to like what we got. We just needed to be told what to do next.
To be continued, in Part 3.
3 thoughts on “Mainspring, Part 2 – Brand Desires”
That reminds me, I had a terrific 10% stout at the Manchester B&CF the other week. What was the name of the brewery, bit like ‘Mainspring’ actually… ended in ‘water’….
Great story – I’m looking forward to part 3. But I’m not convinced their regression analysis was all that, if “traditional” always went with “accessible” and “modern” always went with “experimental” (and “traditional”+”accessible” always went with “session”)!
I take your point on the analysis. I’d considered mixing them up more, but realised the results should not be wholly perfect. Also, this does come up in the next part of the story. 🙂
The temerity of identity.