machine-learning

How AI and Machine Learning Are Affecting Sales

By: Ankur Srivastava December 12th, 2017

Machine learning and AI technologies are affecting many industries, and that includes sales. It can be difficult to identify how Artificial Intelligence and Machine learning are affecting freelance sales and B2B sales. The complexity of technologies available for sales teams has quickly grown. Perhaps the best first step is to review what AI and Machine learning technologies can and cannot do for sales. Let’s discuss where these tools are effective, where they aren’t, and how companies are using them to their advantage.

Is AI, for instance, ushering a new age of business where every sales decision is predicated on the input of near-omniscient machines? Hardly. Doomsday scenarios wherein technological efficiency supplants human ability are still fiction. Although, in our current reality, tools like AI, machine learning, and data analytics are reshaping a wide range of sales tasks in tangible ways.

In sales, where innately human talents like “selling skills” are considered irreplaceable, these technologies are being used to refine the game. Staying abreast of and responding to the trends is critical to both individual sales professionals and sales organizations that want to stay competitive and grow. Employing some AI and Machine learning sales tools will eventually become commonplace in sales organizations that want to reduce the time it takes to close a deal.

Improving Efficiency Through Automation

One of the most immediate effects AI and machine learning are having on sales is reducing the number of mundane and repetitive tasks salespeople have to complete. AI can schedule meetings by analyzing schedules and determining the blocks of time that desired participants have free, or even adjust schedules to make a meeting happen. Considering the amount of time sales professionals spend on non-selling tasks, this is an area where AI and machine learning can have a big impact.

In a limited respect, AI can help craft cold emails, gathering information on a potential client from across the web, then using that to draft a message that speaks to their preferences. Machine learning tools deployed in sales organizations allow systems to grow by mimicking the edited versions of these drafts their human users send. The caveat, though, is that the need for human input is still vital here, and cold calling/emailing still requires the human touch to be effective.

The advantage of this kind of automation is obvious. When AI and other sales technologies cut through busy work that steals attention away from more pressing duties, it allows for a greater focus on critical sales matters. The combination of vast sums of data and AI capable of processing it, however, hasn’t limited itself to trying to take on mundane sales challenges.

Sales technology like Growbots has taken a stab at automating lead generation and client outreach efforts. However, they aren’t the only ones developing technology to capture leads for sales organizations. This article from Per Borgen details how he took on the task of developing a similar tool for Xeneta, with some initial success. There are also several other examples of machine learning stepping in an attempt to ease the sales workload.

What’s important to note, though, is that while there are a growing number of sales technologies focused on generating leads, they don’t actually solve the issue of needing a talented sales team to reach out to those leads and close a deal. Over-reliance solely on lead-gen bots, therefore, could result in billions of dollars of expense without a lick of return. Far from being a replacement for talented reps, the above examples underscore how much skilled sales teams are needed, and highlight how AI and machine learning can serve as tools to augment their success.

Perfection Through Analytics

In 2016, Chris Orlob, Senior Director of Product Marketing at Gong.io, wrote about the ways in which data analytics revolutionized the marketing trade, and how the sales profession was in for a similar transformation. Instead of relying on “what we think works,” data analytics now show “what is actually getting results in sales.” This is the type of fine-tuned detail that AI and machine learning technologies will need in order to impact sales effectively.

After analyzing 25,537 B2B sales conversations with their AI engine, they were able to determine the ideal “talking-to-listening ratio,” the best time (and how often) to talk price during a sales call, and specific language that sales representatives can use to assuage client fears and increase their win rates. The implications here are obvious: Data can take the guesswork out of the “sales dance” and optimize a rep’s ability to perform this core job function.

Marc Jacobs, VP of Sales at Greenhouse, used Gong’s insights to help coach his sales team.  “Every sales rep we deployed Gong to saw a marked performance increase within 45 days. They now pitch and sell sharper. My managers are much more attuned to what their reps need. Nothing else in the sales stack can do that,” said Jacobs. It is this type of disruptive sales technology that will have the biggest impact on sales.

Beyond perfecting the art of the sales call, though, AI is molding sales reps to make better and faster decisions throughout all aspects of their job. TechTarget, for instance, recently explored the concept of intelligent agents, or AI-enabled bots, and their capacity to “prompt sales reps to make decisions that are consistent with company goals.” This is based on the totality of data provided to them (a particularly useful feature for freelance sales, where reps might not be engrossed in the culture of the companies they represent). Fortune 500 companies are already embracing such ideas for their own sales teams, making use of sales technology systems like Tact.ai to augment what their reps are capable of achieving.

The Bottom Line

These AI-induced changes won’t totally upend the sales profession. Far from it, they stand to make sales more efficient by scanning data to find useful patterns, coaching reps to respond in the right way (and at the right time) to their leads, and eliminating draining busy work (among other things). The companies and individuals who adapt to these evolutions within the industry, though, will be those best positioned to grow in sales’ AI-enhanced, data-driven future.