With AI, Sales is No Longer a Numbers Game

Early in my sales career, when I was employee no. 15 in a B2B startup, my fellow sales reps and I were issued strict marching orders to meet our quotas: write 250 emails and make 100 calls every day. That was it. That was the dictum from above.

The thinking — unassailable at the time — was that sales is a numbers game. If we just pumped out enough emails and phone calls, we’d generate enough discovery calls that would turn into inquiries that would turn into hits for targets. If you wanted more targets, you hired more reps. The company went from two sales reps to 40 in two years. My colleagues and I were pretty maniacal about hitting these activity goals.

Enter automation tech

When sales automation technologies like Outreach came along a decade or so ago, they lightened the logistical burden on sales reps. We no longer needed to juggle spreadsheets to keep track of cohorts, touchpoints, and so on. We made fewer mistakes. It was a game changer.

But automation had a downside. Sales reps who were hitting 200 people a day were now expected to hit 800 a day. Along the way, an interesting thing happened: despite this quadrupling of volume, we didn’t see additional lift. cohorts, touchpoints. In the flurry of activity, sales reps didn’t always know much about who they were calling or even why they were calling them.

Automation tech weaponized the sales-as-numbers-game idea, which created another casualty. An entire generation of sales reps — most of them recent college grads — missed out on learning key sales skills like customer research, rapport building, and discovery. The tech taught them to pump volume, nothing more.

Read More: SalesTechStar Interview with Kelli Hobbs, VP of Business Development, Valuedynamx

Spray-and-pray hits B2C

Meanwhile, a similar phenomenon was unfolding in the B2C space (which I entered when I joined a San Francisco sneaker company in 2013). But instead of tech being used to pump numbers, it was used to pump often poorly targeted ads, resulting in squandered ad budgets and spam. In both cases, the cracks in the sales-as-numbers-game mindset were starting to show.

In the B2C space, the problem was companies’ reliance on broad-brush demographics — such as age, gender, and zip code — to identify audience segments. These worked fine for identifying a general swath of the population that might be interested in your product, but if you wanted to target prospects more narrowly, they were of little help.

For example, if your company makes premium sneakers for kids, these demographic filters could help you identify moms in high-income households as a target audience (assuming moms typically call the shots in their kids’ footwear). But if a key benefit of the sneakers is that they’re also easy on the environment — for instance, because they’re made of high-quality materials that last longer and can be handed down to a second child — then you’d also want to target that subset of moms who actively care about the planet. For that message to resonate, advertisers needed to find a more granular way of segmenting their audiences.

The psychographics revolution

Innovations like psychographics and lookalike modeling from companies like Facebook and Google provided that more granular approach. Psychographics allows advertisers to understand people’s behavior based on their digital footprint, and lookalike modeling allows advertisers to reach new people who share characteristics with their existing customers.

These technologies sparked a revolution in B2C marketing. By sifting through millions of data points, they give companies new ways to build audience segments and to target consumers with more of a one-to-one message. Combined with social media and other digital channels, they enable companies to run multiple campaigns at once, test product features, and get rapid feedback. For example, companies can run micro tests with 100 or 1,000 consumers on product details — purple or green sneakers, Velcro straps instead of laces — then measure which option gets the best response, and rapidly iterate on and improve the product.

B2B embraces a true data-driven culture

Today, some B2B companies are embracing the same depth of quality that’s already taken off in the B2C space. With the help of AI, it’s a shift away from the numbers-driven culture of the past to a true data-driven culture.

Blunt demographic and firmographic tools are no match for machine data, which can identify the deep signals that help you hone-in on the best targets for your product or service at any given moment in time. After all, companies are always changing, so a company that was a hot prospect a month ago may no longer be a hot prospect today due to a change in CEO or CTO.

Sales today is no longer a numbers game and sales organizations are questioning the rote allegiance to activities versus a more surgical approach. After all, why make the additional 100 calls per day just to appease your sales manager, but have no results to show for it?

Read More: How Conversational AI Improves Retail Output