One of my first consulting engagements involving “insight” rather than operational execution, years ago, was for a large brewing company that people in Golden, Colorado would recognize immediately. We’ll call them Brewer C (at that time, the domestic brewer’s alphabet went A,B,M,C…). Brewer C at that time was third in domestic market share, trailing by double digits against the second largest in market share, in a business where selling involved spiffing large distributors so they would push more of your product. Think boondoggles like taking a customer to the boxing title fight at Ceasar’s Palace; the bigger the spiff, the better you did…
This brewing company figured out, trailing as they did in market share, that the two largest domestic competitors could afford a whole lot more spiffs than they could, so they needed a better approach. Considering this problem, they came to two conclusions: 1) beer distributors really like spiffs; and 2) while spiffs are nice, they don’t sell more beer, and beer distributors like selling more beer a lot more than they like spiffs. And so this brewer launched their “Go to Retail” initiative: pull more consumer demand for core product through distributors by helping them sell more beer.
It’s a great goal, but this brewer had a huge problem: they knew only at the aggregate level who was buying what, when, and where, because the only data THEY had was what distributors were ordering and paying for. They had NO data on what customers were buying, because this brewer like most brewers sells through non-captive distributors; they don’t sell any beer directly to you or I (their end consumers), and so the only data they have internally is their sales data to distributors.
They needed better data – they needed to know what retail outlets (bars, restaurants, liquor stores, chain stores) were selling to the end consumer (as an aside, it’s important to influence bar and restaurant demand, because most people try new brews by ordering a drink in a restaurant or a bar before they invest in a six pack at the local liquor store). So they negotiated for, and mostly got, invoice data of what retail outlets were ordering from distributors. Lots of data. Big data. But only data – this distributor customer number, this product number, this quantity, this price; hundreds of thousands of rows of data every week.
Data doesn’t inform, it only describes. Brewer C needed to be informed; the information they needed was which store (7/11 number 123, part of the 7/11 chain) was buying which product and packaging combination (e.g. Original in 6 pack cans), where was the store located, and what volume were they buying (and presumably selling) in a given period of time? So we overlaid multiple sources of data onto the core invoice data, to create this information. This informed Brewer C, but it didn’t tell them what they wanted to know – it wasn’t knowledge.
What Brewer C wanted to know – the knowledge they sought – was “how do we make the case for each retail outlet to carry more of our product: more volume, more varieties, more packages (some people prefer six packs of bottles, some prefer 12 packs of cans).” The data they had described a lot of transactions, and the information they now had told them which retail outlets were buying which products. But that didn’t help them with what they wanted to know.
“Knowledge” is created by experience bumping up against information to glean insights into the true nature of a thing (a goal, a challenge, an issue, a problem). That’s my definition, at least. So we worked with experienced beer salesman, mined the information we had to gain insights into patterns that mattered, then put that information and insight into the hands of a field sales force that worked with distributors to help promote Brewer C product. And that sales force did exactly what you would expect, now that they were armed with knowledge:
- They went into local liquor stores and said “you are selling 10 cases of product XYZ per week; your competitor down the street is selling 100 cases per week. What do you want to do?”
- They went into restaurants and said “you don’t carry Original, but liquor stores in this area are selling hundreds of cases of original every week. What do you want to do?”
- They went to national accounts and said “your average store sells $200 of our product every month, but your best selling stores average $750 every month. If we pushed this product and raised your average store sales to even $350 per week, you would gross an additional $6.75M. What do you want to do?”
Not to be trite, but “knowledge is power.” In this case, it had the power to change the sales model for this brewer to focus on results rather than spiffs (although they still provide lots of spiffs), and generate more revenue by making the business case for carrying more Brewer C product. As pointed out by the CEO, this led to a significant increase in revenue for the company, and a greatly elevated stock price.
Data, and even information, are means to an end. The end itself is knowledge. What do you need to know to grow your business?