A Fairy Tale About Data-Driven Sales

Do you know this one? Once upon a time, beyond nine mountains and nine rivers, there was a kingdom. It was ruled jointly by the handsome King Jigme Khesar Namgyel Wangchuck and the wise Queen Jetsun Pema Wangchuck. In that kingdom, nearly everyone sold something. It was full of small traders, salespeople, sales representatives, relationship managers, key account managers, telemarketers, and so on and so forth. For centuries, they sold the same way and were very happy. The royal couple was pleased, because the happiness of their subjects lay very close to their hearts.

But in a neighboring kingdom, an evil sorcerer ruled. He envied the happy salespeople their way of life and decided to take it from them. He built a “Machine” that he named Salesforce, walked through his land and shouted incredible things like:

People feared the “Machine” wanted to take their jobs, to replace them. To be fair to the sorcerer, that’s not entirely the case, as his next quote shows: “All of ‘data science’ is about software taking on the role of the expert and enabling the average user to understand the situation.”

The goal of using data in sales is to eliminate human errors (e.g., forgetting to respond) and increase productivity through prioritization and automation of sales processes. The goal, on the other hand, is NOT to replace contextual understanding, intuition, and the human art of selling.

We’ll have to wait a bit longer to see how this fairy tale ends. What’s certain, though, is that “data-driven sales” ranks among today’s buzzwords, similar to AI, ML, or carbon neutral. At BizMachine, we deeply engage with both data and sales, and we always try to extract what’s practical and usable from trendy concepts.

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So how do we think you can recognize a company that drives its sales with data?

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1. It knows the defining characteristics of its good customers
2. It knows the identity of potential customers in the market
3. It prioritizes effort, sets KPIs, and measures results by potential
4. It can forecast results, and when it spots deviations from forecasts, it can identify market changes in advance and react to them
5. It has sales channels set up based on measured margins, conversions, and unit sales acquisition costs (SAC)

In the real-life examples below, we’ll focus on “B2B sales”—companies that sell their products and services to other companies.

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It knows the defining characteristics of its good customers

NOT like this: “A good customer is one whose management will soon start thinking about capital investments in buildings, because they’ll be open to some of our dozens of services.” [Utility]YES, like this: “A good customer manufactures or refurbishes machine tools and has the freedom to locally decide which control system to install in them.” [Industrial conglomerate]

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Characteristics must: (i) be present in nearly all good customers and nearly none of the bad customers/companies without potential (ii) be reliably identifiable (ideally from the outside, but at the latest during a conversation or visit) (iii) be discoverable at reasonable cost (iv) be understandable to salespeople and especially to the customers themselves.

What are the characteristics of your good customers? Would they pass the test above?

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It knows the identity of potential customers in the market

NOT like this: “I know the market because my salespeople fill up at gas stations and they hear everything there.” [Beverages]

YES, like this: “We start from a database of all companies in the market. We’re only interested in the healthy ones with at least five years of history, at least 10 employees, and a willingness to invest in their employees. We determine that by whether they offer any benefits and by the quality of their office.” [Employee benefits]

Market knowledge should be centralized (if you lose a salesperson, you don’t lose their knowledge) and systematic (you store discovered information so it doesn’t need to be collected again). The exclusion of companies that don’t meet the characteristics of good customers should be centralized to the maximum extent possible (that’s why it’s important to know them). Sales channels can then focus on deepening knowledge about promising companies and not waste time on unpromising ones. If you leave the decision about whether a company is promising to salespeople’s gut feelings, you’re completely at their mercy (more on that another time). Today, there are countless public data sources (for the Czech Republic alone, we process over a hundred) and tools for mining them that can be acquired for an entire company at a cost less than a quarter of one salesperson’s expense.

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End of Part 1. Next time, we’ll discuss how to calculate the attractiveness of individual companies, when it makes sense, how seemingly great salespeople can turn out to be terrifyingly bad when viewed correctly, and that properly set goals and their measurement can mean the difference between whether your company survives or not.