I’m not a salesman. Yet I’ve fallen into sales, or at least, what I make of it, and it helps that I’ve fallen into the more consultative side of B2B selling where, actually, you’re not selling, people are just buying into you. That’s the easy bit. The hard bit comes in knowing who to sell to.
Or, more accurately, who not to sell to.
And instinctively, we all know who they are. We have a gut feel, or anecdotally we know who we should be avoiding. They’re the scope creepers, they’re the time-suckers, the tyre-kickers and the undecided. The time you spend handling these people is time that could be spent elsewhere – and there’s a lot of ‘elsewhere’ for you to deal with.
My advice is to chase the numbers.
Here’s a little context.
In horse racing, you don’t put money on every race. Or, at least, you shouldn’t. Anyone who knows anything about horse racing knows that not every race is equal and not every horse is equal. However, if you know that a horse has won in these conditions before, you’d probably back it.
That’s called chasing the numbers. Instead of backing a horse in every race, you put your money on the horse that has proved itself. It may not come off, but statistically, it has a much better chance, and if you keep doing that, your strike rate improves.
Now, in horse racing, you would most likely keep a record of where you’ve done well, and where you haven’t done well. You’d also have access to the data that shows whether that horse has a chance or not.
It figures that we should be doing the same with our sales performance.
So, in stall one, we have Smooth Talking Harry. His strike rate with small businesses is 38%, but with larger businesses it’s down at 12%. What’s more, he closes small business deals within 2 weeks, whereas with the larger deals, they can take several months.
In stall two, we have Clean Shaven Chris. He’s built up experience in manufacturing, and as a result, closes almost every time he speaks to a manufacturing company.
In stall three, we have Very Clever Sue. She takes a long time to close, but when she does, they’re usually pretty big deals and she has a great portfolio of retail clients.
Imagine, then, that this information is not anecdotal, it’s not somewhere in your mind, a hunch based on an idea you had.
It’s real-world, validated metrics that you’ve decided to monitor, and you can make decisions that drastically increase your strike rates. How on earth do you get to that stage?
- Decide what you’re going to measure. It’s nice to measure things, and you can measure everything in the world if you wanted to. The question is – what are you going to do with that measurement, and how are you going to implement your findings?
The first, and most important decision, is to decide what you’re going to measure. Choose enough metrics to make it interesting, but not too many to overwhelm you and leave you with data fatigue a few days later. Choose those that are appropriate to you.
For instance, you may want to measure industry, size of business, responsiveness, lead time, success rate, and value.
- Get everyone else’s buy-in to the data. Don’t be the only one measuring. If you’re measuring your sales team, get these metrics into the CRM and ask them to fill in this data. Insist on how important it is to measure this data so that you can make better decisions – and remember that this does not guarantee it will be filled in.
So once you’ve got buy-in, make sure you keep it. Reward those who’ve filled it in, and chase those who haven’t.
- Learn a little excel. It still surprises me how little people know about Excel, and also how easy it is. Just applying a filter to a row can turn a boring spreadsheet into something magical. And imagine what you can do with pivot tables.
In fact, the handiest thing I ever learned was the conditional formatting trick, which lets you shade data depending on its position within a range. At a glance, you’ll see underperformance and excellence.
- Let the data tell stories. The major failing of most data projects is that it’s just data, and people generally grow weary of constantly looking at data.
Let the data tell you a story – put things in context for other people to understand. You might find high sales values for certain industries – well, that’s a nice correlation, but what does it actually mean? Take the finding out of the data context and back into the real world. Only then can you understand what the data truly means, and validate it.
And this really is no different from how people use horse racing data. They wait for the right situation to click into place, and then they place their money. The difference is that you’re already spending your money on a sales team – so let the data be your guide. Chase the numbers, look for the high strike rates. The returns will follow.