We do our fair share of number crunching. The latest data set we are chewing over is well over half a million transactions. We are also aware of plenty of outfits who will crunch vast swathes of data to tell what your customers are up to and what they might do next.
However, looking at your sales history has a significant shortcoming: It is your data, not your competition’s data.
Sure, if you trade online, you can scrape your competitor’s pricing and a few other details, but you cannot tell when your customers bought from them.
The two things we look at most are recency and frequency. When did a customer last buy from you and how often do they buy a product. Frequency is an excellent indicator of price sensitivity… if they don’t buy it often they won’t remember how much they paid last time!
However, this kind of analysis only works if you have a strong assumption that the customer only buys from you. If they are fickle and flit off to the competition on whim, then your recency and frequency data tells you nothing about their buying pattern… unless of course you know their underlying usage.
Let’s take some grocery items to make things simple:
If you have a good measure of how much toilet paper the average person uses per week and the number of people in the household then you can tell how loyal they are to your supermarket and even a brand.
However, try doing the same analysis with baslamic vinegar of Modena and you won’t be able to tell the difference between someone who only buys it once in a blue moon because they saw Nigella use it in a recipe, and someone who puts it on their chips but buys a bottle a week from your competitor down the road.
You might get lucky and spot that their baslamic habit was fed by you before they went to the competitor, but these instances are tricky to pick out of the data.
The other common mistake made with simple data analytics, often done in house, is the substitution effect. Your sales data is great if you are never out of stock of anything. If someone is forced to buy an alternative, or worse still go to a competitor because you didn’t have the stock, you can stare at the data all you like and it will never tell you what was in the customers mind. In the old days when lead times were longer, we used to insist on looking at B2B order data rather than sales, because it was closer to what the customer wanted when they wanted it than when it was actually shipped.
Moral of the story:
Be wary of looking at what you sold to someone, because it often doesn’t tell you what you could have sold and didn’t.