You’ve got the sale. Or at least you think you do. Then, without warning, it all goes south. Experienced sales reps know what’s coming next from their manager:
“So what happened?”
Answering that question has traditionally been based on scant information. Usually it comes down to the rep’s best guess based on what the customer or prospect said, implied or (sometimes most importantly) didn’t say. Managers, in turn, have had little choice but to accept whatever the rep tells them.
This, in a nutshell, is the uncertainty artificial intelligence (AI) was designed to end.
In our last post we discussed the role of AI in lead generation, which is obviously one of the most important — and challenging — parts of the sales process. But let’s face it: even with all the nurture-worthy leads in the world, technologies like AI won’t matter much if they don’t turn into a series of closed deals.
That’s why we’re going to spend the second part of this three-part series looking at the role of AI in getting over the big hoops that reps almost always have to jump through.
“Sounds Good, But . . .”
The best reps always walk into a pitch meeting prepared to deal with potential objections. Unfortunately, though, sometimes the objections are highly generic, based on what they’ve been trained on when they first joined a company or feedback they received from their last, unsuccessful pitch.
Tools like Salesforce Einstein, combined with the power of Sales Cloud, can factor in much more than what the average rep might remember to look at more specific kinds of potential objections, such as:
Timing: Not all purchases are based on an annual budget cycle. A particular organization may have cash on hand based on when they’re launching a new product, when they sign a big new customer of their own or myriad other reasons.
Vertical: Selling to a bank is a lot different than selling to a retailer or a consulting firm. Each may have specific regulatory issues, approvals and other details that could derail a pitch before a rep has gotten through the “Agenda” slide.
Integration issues: Sometimes bringing on a new product means having to work with an old, clunky piece of technology, machinery or process is nonetheless well-understood by the prospect.
Pitch Post-Mortems And Follow-Ups
Reps usually walk out of a pitch meeting with some additional information they need to dig up, a quote they need to put together or other tasks. And all of these things contain data about the customer which, when woven with what is already in the CRM solution, can help AI to give a report card of sorts about the meeting. This could include:
Content considerations: If there hasn’t been a firm timeline on when a decision will be made, AI can look at what sorts of assets (case studies, competitor comparisons, etc.) will nudge things along.
Win likelihood: By comparing the buying cycle and other details of similar clients, AI may be able to help a team give a more reasonable estimate of their success, contributing to a more trustworthy sales forecast.
Follow-up sentiment: Whether you e-mail, call or use a different communications channel, AI can pick up on signals in what prospects say to suggest what they are feeling and how it may affect getting a deal closed.
Some of these details will not only be important to the rep but managers who want to improve their own ability to de-brief after a critical pitch and possibly even apply some of the insights from AI in their next coaching session.
Tunnel Vision Correction
Sales reps tend to pay close attention to their customers and prospects when they’re dealing with them in real time. The rest of the time? They’re probably juggling many other things, and can’t always be expected to study each company in a truly holistic way.
In between those pitch meetings, though, a lot more may be going on in a company behind the scenes — or even on the front lines — that a rep might miss. AI is a big help here because of its ability to monitor news, social media and even public databases for details that could have a trickle-down effect on a particular customer’s propensity to purchase. These are just a few of the curveballs:
Business performance: Are reps really going to sit on every customer’s quarterly earnings calls? Probably not. They might miss the story in the business section of their favourite publication. When a customer’s revenue tanks or is in jeopardy, though, a deal that once seemed like a sure thing is suddenly anything but.
Personnel changes: Reps may get an e-mail when the key decision maker they’ve been talking to is laid off or gets another job. But what about the person’s boss — or their boss’s boss? Depending on the size of the organization, staff shakeups of any kind can reset priorities (and purchase decisions) overnight.
New competition: Who would have predicted how the “sharing economy” would threaten once-dominant players in sectors such as transportation, hospitality and financial services? These are trends that can take time to play out, which means getting any early warning signs could ensure a customer acts more quickly to defend their turf — and is grateful to the vendor who helps them do so.
Product Problems: In B2B, everyone is selling something, just like you are. The other common trait is that lots of things can go wrong with a company’s products, such as a delay in launching, a safety recall, an unexpected surge in demand or supplier issues. It’s not always easy to understand what these things could mean for selling to a particular customer — unless you have AI up your sleeve.
Much like lead generation, the earlier and more often AI is introduced into the sales closing process the more likely it will prove effective for reps and their employers. That’s still not all the value this technology brings to the table, though. In our third and final post in this series, we’ll showcase what AI means to sales management.
Are you prepared for an AI world? A cutting-edge CRM solution can help. Learn more in our ebook, “AI for CRM: Everything You Need to Know.”