The AI for Sales Explosion

It used to be every decade or so a new technology appeared on the horizon. You could see its faint outlines, but it had to go through years of research, testing, and patent protections. Think about the telephone. It’s been only 144 years since Alexander Graham Bell spoke these historic words through a phone to the next room: “Mr. Watson, I need you.” And how many millennia in history did it take to get to that point? During the Napoleonic wars Nathan Rothschild famously used carrier pigeons to bring messages to London from the front. That was only in 1815. And look at us now!

We take all these technological innovations for granted. And eagerly await the next ones. Well, at least one group – artificial intelligence – is already here.

While sales reps and managers have gotten used to the explosion of digital tools designed to help them track and close deals, there’s no doubt these programs and devices make selling more efficient – or, at least, they make it easier to contact and track more prospects and customers. But sometimes the tools themselves can become, not technological marvels, but annoying distractions from the sales effort.

Is It Intelligent?

The latest tools family travels under the name of artificial intelligence (AI). So what exactly is AI? And will it help or burden salespeople? Some wary sales consultants think AI has become just a marketing buzzword software vendors use to sell solutions. But others look to reps’ and managers’ increasing ability to deal with closing deals and serving customers while technology handles the ho-hum administrative tasks instead. But wait: While sales is (and has always been) a people business, AI even promises to help with that.

AI certainly includes specific techniques, like natural language processing – hey, Siri does that! It also includes machine learning – and Siri does a bit of that too. Basically, though, AI is any software that “thinks” at least a bit like a human does, at least for a very specific and well-defined problem. And, if the problem happens to involve massive numbers or data, AI may actually think faster and better than humans.

This makes AI sound a bit like data analytics (and there is a fuzzy line between analytics and some AI tools). But, for sales leaders, AI’s true definition doesn’t really matter. What matters? What a specific tool can do, how well and accurately it can do it, and how much that will benefit sales performance. Some very respected neutral observers say certain AI tools already (or soon will) pass these tests.

For example, AI-assisted automation of many sales administrative processes and mundane communication tasks is already spreading. Smarter training recommendations enabled by AI are near. AI to remove at least some of the burdens of forecasting is coming. AI tools for territory decisions and helping set incentives could arrive in a few years. AI to feed pipelines, set priorities, and clear out the dead wood is on the near horizon.

Robots to the Rescue?

Tad Travis, research vice president at Gartner, thinks of AI as a natural outgrowth of the advanced analytics and robotic process automation (RPA) that has been adopted by B2C sellers and is now spreading through B2B. He argues AI will start with predictive analytics – essentially, scoring opportunities based on smart analysis of all the available data on them.

Then Travis sees AI moving into prescriptive analytics. “That will mean guided selling,” he explains. “All reps need help on what to do next with a prospect, deal, or account.” For companies willing to report to Gartner, a significant share are at least attempting to use prescriptive AI, and Travis thinks, “Generally, companies are very early on the path of adopting prescriptive analytics.”

One sign that all this is getting more real is that major CRM providers such as Salesforce.com, Microsoft, Oracle, and SAP are adding predictive analytics to their offers – scoring leads as a first step to prescribing the right moves for reps. And the same major software companies are developing RPA tools that can work with certain very standard data sets and processes common in sales.

For example, RPA is already automatically generating outbound emails tailored to the stage of the sales process, the prospect, past actions, and other data. “The app decides what message to send on what day,” Travis explains. “The recipient can click to schedule an appointment, and the message is very difficult to tell from one sent by a human.” Even after initial deployment, this AI approach gets better. Machine learning runs in the background tests and tweaks the automated emails according to phrasings that have been shown to achieve better results.

Message Me

Travis cites another AI tool that decides which of hundreds of inbound contacts need immediate qualifying contacts from reps or marketers, which message should be sent, and which channels should be used to send them. Gartner calls this kind of assistance “sales acceleration,” as it removes the manual analysis of leads that, in addition to requiring man-hours of effort, slow sales down.

Another near-term AI tool uses natural language processing to analyze reps’ conversations with prospects. These conversational analytic tools record conversations, convert them to text, and then analyze their effectiveness in advancing the opportunity.

Travis predicts major CRM providers will offer all these AI tools and more in the next three years.

Forrester Principal Analyst Mary Shea agrees with Travis that AI will soon enable better and faster sales decisions, suggest the best next actions, and automate manual tasks. She argues AI will thus help close the current gap between how prospects want to buy and how they are served. AI will enable reps to deliver a better customer experience. And it will do all this while slashing the 40 to 50 percent of rep time now devoted to low-value administrative tasks.

Predictive analytics will prioritize opportunities and recommend the right content to share with prospects. And AI can also be used to help train reps. Smart tools can suggest mini training lessons delivered in real time based on prospect characteristics, contacts to date, and where the prospect is in the sales process. Shea calls this “sales readiness AI.”

The Forrester analyst sees AI helping sales managers design their territories more fairly and effectively, set quotas and payouts for better performance, align reps with the right territories, and recommend regions and products on which lagging reps should focus. For sales leaders, Shea says, AI can even recommend the best spiffs to improve performance.

Shea argues that AI can also automate much of sales forecasting – saving reps and managers time. “Sophisticated organizations spend hours each week on forecasts.” That could turn into selling time or high-value coaching time.

Hidden Data

And AI can also automate the entry of data into CRM tools – saving time and aggravation. “It can extract data from emails, chats, and in-person meetings, loading it into Salesforce.com. That is happening now.”

Shea thinks prescriptive analytics’ potential to guide selling is substantial. “AI can advise sellers on where to take conversations based on customer talk in real time. This is nascent now, but will move forward as technology develops.”

Like Travis, the Forrester analyst sees AI tools not as replacements for current sales tools but as enhancements to be embedded in these tools to make them smarter and more useful. That is why major sales IT vendors are seeking to buy or partner with AI specialists.

Dan Cilley, CEO of VendorNeutral, says AI is already blurring the lines between sales and marketing. New leads from website visits or other contacts used to pass to marketing for qualification and addition to CRM systems. Now AI can take at least some new leads and pass them directly to reps based on automated analysis of lead characteristics.

For managers, AI can also remove, from pipelines, those prospects who are not going anywhere – based on the lack of any favorable activity. “Reps can no longer hide behind big opportunities, thinking there is hope when there is no real hope.” AI algorithms can hunt for prospect actions or status and spot the hopeless ones.

Like Shea, Cilley says AI call monitoring and transcribing is happening today. And he predicts AI tools will also analyze transcripts – looking for tips on next steps. “It’s heading toward providing the right questions to ask in the near future.”

In addition to analyzing customer contacts with a selling company, AI tools can put cookies in competitors’ websites and learn what a prospect does and asks for on these websites. That is true competitive intelligence. “That is where the market is heading,” Cilley predicts.

All about Priorities

Again like Shea, Cilley sees AI setting smart priorities for which prospects reps should pursue based on their likelihood of buying and the likely amount of purchase.

Even when well along in the process of a complex sale, AI can identify all the stakeholders in the prospect’s buying process and alert a rep on whether any of these have been missed in personal contacts already made.

For distributors that sell thousands of products to hundreds or thousands of customers, AI tools can do what reps cannot practically do. It can help optimize prices based on each customer’s ability to spend and other massive data sets sales reps could never effectively analyze without AI tools.

Cilley stresses the importance of AI’s machine learning capabilities. These will constantly improve the performance of all AI processes. In a sense, machine learning is like embedding AI in AI itself.

Jason Eatmon, chief sales officer of the sales consultancy Sales Gravy, is skeptical about whether some AI tools really go far beyond analytics. But Eatmon says one true AI solution is already here – often associated with B2C selling. The AI used on websites like Amazon to guide the acquisition of simple products is highly effective. And websites can use AI algorithms to sell simple products to businesses and governments as well. And Eatmon agrees that effective and accurate prescriptive analytics to guide sales reps on effective selling will, when it finally comes, be a major advance.

Hurdles and Benefits

When will the best tools really arrive? And what are the hurdles to implementation? Cilley insists, “You’ve got to have the data.” And AI works best when it has all the data on prospects – from whatever channel the data comes in. That is one reason Cilley foresees the integration of various platforms that now support different sales channels.

Apart from tapping all the data, Cilley says AI tools will usually require about 24 months of this data to develop truly valuable insights.

Cilley argues that some AI tools can be used by companies of any size, while others may not be. AI to monitor and analyze sales contacts works for small as well as large companies. Software that monitors one channel and enters results in CRM is available for as little as $15 per month, while a half-dozen channels can be monitored for $60 per month. Another easily affordable tool is software that converts relevant blogs, podcasts, and articles into audio recordings reps can listen to in cars.

On the other hand, AI that requires the integration of a company’s own data with third-party data may be affordable only for large companies that can buy costly third-party data.

Gartner’s Travis agrees on the importance of data. “The best benefits are for companies that have already cleaned historical data in their CRMs.” And implementing AI also requires managers to have a clear understanding of their current end-to-end sales processes.

Once these AI requirements are met, large companies have a choice when seeking to implement prescriptive analytic AI to guide salespeople. They can either buy commercial off-the-shelf solutions or build their own algorithms. Travis says companies with very complex and sophisticated sales processes may have to invest in in-house development. Fortunately, “very large corporations are capable of doing that.”

Real Benefits

But benefits are achievable even with simpler AI tools. For example, one company purchased an AI tool that recorded voice contacts and entered relevant data in its CRM system. Travis says some reps reported a 25 percent increase in revenue during the pilot. The new tool was not only saving rep data-entry time, “it was capturing better data – deeper, more relevant information on what was happening – and giving them a better set of signals,” Travis says.

Shea notes that much sales AI software is now available as a service and can be rented for a per-rep fee with credit cards – making it highly affordable for smaller sales forces. But she urges that companies seeking to deploy AI tools learn from the mistakes made in implementing CRM. “Bring in key reps and managers, expose them to use cases, help them understand how to use them and how they fit in workflow, and get their buy-in.”

The benefits can be significant. A recent Forrester survey reported that implementing just three core sales tools with AI – for engagement, automated enablement, and sales readiness – yielded a 20 percent increase in revenue in a year, a 25 to 50 percent reduction in ramp-up time for new reps, and a 666 percent ROI in three years.

That’s not artificial – that’s a real result of intelligent sales.

Recommended AI Solution Providers

Each of the AI solution providers listed in this section earned a spot on Selling Power’s 2020 Top AI Solutions for Sales List. Please visit each company’s Website to learn more about their services and solutions.