Yes, you can take the guesswork out of lead gen
How AI helped transform an old-school sales pipeline from stale to hale.
BECAUSE YOU CAN'T LAND FRESH BUSINESS WITH STALE LEADS.
The Situation
Using digital technology to improve your business is pretty much table stakes right now, yet many workers—even at major technology companies—are finding it tough to keep up with these trends and still do things “the old way.” The future is already here, the saying goes, it’s just not very evenly distributed.
Case in point: The cloud services group at just such a technology company, where the sales reps were getting customer leads from marketing picked through traditional methods—heuristically. The thinking behind these methods had always made sense: If you held a senior position at a big enterprise, and your enterprise could benefit from cloud services, then you’d be a good candidate for a sales call. Standard procedure really, and besides the company had historically enjoyed tremendous growth.
But that growth wasn’t evenly distributed either. The company’s early success came from hyper-scaled consumer products that seemed simple but were powered by massive background data sets—and that stuff sold itself.
Enterprise cloud services were another beast entirely—selling those meant, well, selling them: connecting, person-to-person, to make the pitch. Establishing a relationship. And since relationship-building can’t be hyper-scaled, growing the cloud sales pipeline meant the company would need to focus its superpower—data science—where it would have the most impact: improving sales leads.
THE SOLVE
To do this, Deloitte collaborated with the company to implement the latest artificial intelligence techniques. This meant tapping the company’s prodigious supply of data to build a variety of machine learning models—if-this-then-that decision trees—thus increasing the number of considerations for qualifying a sales lead from two … to as many as possible.
We worked with the company’s stakeholders to choose and fine-tune the most effective model, then used that model to put their machines to work learning patterns in the customer data. Was there a sweet spot, in terms of a buyer’s title? How often they’d been contacted? Their email domain? Time of day? The patterns uncovered by the machines helped the marketing team better qualify and prioritize leads. They also helped the sales team get specific on the needs they’d be addressing with each lead, as well as the speed and frequency of contact.
If fed new data, the model could also be used to predict likely outcomes. What if we change how a lead is initially greeted? Or explain a different product benefit? With digital heavy lifting like this happening in the background, sales reps can now focus on what they do best (and what no machine can do): make human connections.
WHAT'S BETTER THAN AN EDUCATED GUESS? DATA-DRIVEN DECISIONS.
The Impact
Artificial intelligence has redefined what it means to make an educated guess about customers. With a continually updated—and still learning—customer model in place, marketers in the cloud group are managing demand generation at greater scale and precision, meaning sales reps aren’t wasting time on stale leads anymore. Instead, reps are managing their time and pipelines more efficiently—and enjoying greater success with leads.
This kind of operational efficiency has led to real results. Within six months, the company’s cloud services group enjoyed an increase in its sales pipeline overall, with lead-to-opportunity conversions up 20%.
Sales of cloud services increased in that same period, with no increased costs for the sales team. Marketers are contributing to growth of the sales pipeline more than ever before, while the sales team is closing more deals.
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