A business recently showed me its numbers to prove how fast it was growing. Like any business, its aim was to demonstrate its health and vigor.
While the numbers looked impressive, they didn’t tell me the whole story.
- Having more customers is great. What is your retention rate? How many new customers did you need to gain for those you lost? How long do your customers typically stick around?
- Knowing how many of each product you sold is great. How many people bought more than one? What are the common intersections? What do those customers have in common?
- Demographic breakdowns of customers is interesting. How do they overlap each other? Are those international customers buying the same products as the domestic? Which demographics are growing the most?
- How has all of this changed year over year? How do I know if a customer retention of three years is good if I don’t know what it used to be? If retention was previously two years, three years is phenomenal. If it used to be four years, three tells a much sadder story.
Numbers by themselves open up a whole world of questions — now I want to play with the data and see what I can learn. What are the trends? How can they be capitalized upon? What other questions are not being asked?
Numbers are great, but to really get answers you have to dive into the data.
Baseball, Every Data Analyst's First Love
I love baseball. I love to relax at a game, chat with people and point out all the things that they've never noticed. Player shifts, the effect of the sun at different times of the day, how teammates communicate and how teams try to confuse each other.
I also love diving into baseball statistics to learn what is happening and discover if I am watching history in the making.
Fifty years ago, a small number of well-understood statistics defined great baseball players. Batting average, home runs and runs batted in (RBI) were the key metrics that measured the value of a hitter. Five hundred home runs marked a player as one of the greatest hitters of all time. Wins, strikeouts and earned run average (ERA) were the standards against which pitchers were measured, making 300 wins the hallmark of a pitching legend.
Then people began questioning the value of these statistics.
While they measured an aspect of a player’s game, did it really take everything into account? Getting on base by any method is a good thing. Being fast around the bases so you could get home is important. So is avoiding double plays. For pitchers, if your team scores a lot of runs, do wins really measure the pitcher or the team’s hitters? Aren’t the number of people the pitcher allows on base every inning a better indicator of reliable success than ERA over time? Where does defense play into all of this?
Today baseball fans follow a lot more numbers. While there have been attempts to coalesce them into a single number, even that approach has flaws. Context matters.
Who is more valuable, the player responsible for 100 runs on a team that scored only 600 runs or another player that created 100 runs on a team that scored 900 of them? I’d argue the former as runs were scarcer and thus more valuable.
Context is still important. Every time we think we have hit the ultimate number, we find another layer of data.
Layers of Research
Many larger organizations conduct some sort of customer research. They mine their data for numbers or conduct surveys. The more sophisticated companies take a blended approach and identify what differences there are between the two data sets.
These research efforts, for the most part, have a stated goal: they want to find an answer. Perhaps they want to measure growth of a new product area. Or determine where to focus their marketing dollars. Once they have that answer, they frequently begin to execute on what they think they learned.
I was recently at a BBQ place in Kansas City, Mo. for lunch when a beer distributor came in to talk to the manager. He was trying to convince them to start carrying "hard soda" — think root beer or orange cream soda with a 5-6 percent alcohol content. The pitch he gave was that hard sodas are very hot in the market right now.
He’s right. They are.
There was a problem though.
The distributor might have a lot of data on the increased sales of hard soda. What he clearly didn’t understand was the demographics of this establishment. This place had four beers on draft, three national legacy favorites and one local brew. This was a blue collar crowd that drank soda only when they couldn't have beer at lunch.
Hard soda is a more upscale beverage and this was not an upscale bar. The numbers the rep had didn’t go deep enough into demographics. Had he dug deeper, he would have understood he was wasting his time trying to sell hard soda in a place that barely sells normal soda.
When someone presents you with numbers, the first thing to do is determine what questions those numbers reveal. The numbers came from data. Find that data and start really digging.
Numbers only tell part of the story. To help your business you have to dig into the data and start asking questions. Only then can you move beyond the simple answers and gain true understanding.