The jury is no longer out on whether Machine Learning will boost the efficiency and effectiveness of sales teams—it does.
We are now seeing concrete results from companies that employ Machine Learning (ML) and/or Artificial Intelligence (AI) to outsell their competition and close more deals. In fact, sales teams that adopt these tools are seeing an increase in leads and appointments of more than 50% and cost reductions of up to 60%, according to the Harvard Business Review.
While numbers like that are sure to please even the most stoic of sales managers, the question of how to actually use Machine Learning remains. Like most tools, there are right ways to leverage Machine Learning and there are wrong ways.
To start, it’s important to remember the goal of implementing an ML solution for sales.
Making Sellers Better
Here’s an understatement: Selling is hard. So how does ML make a salesperson’s job easier?
First off, it depends on how a company chooses to implement an ML solution. Every business is different, you need to decide on specific goals, yada yada yada.
Now that that’s out of the way, the motivation for bringing on a Machine Learning solution should be to leverage all the data your sales org is already generating in order to make better decisions, and to make reps more efficient and effective. Full stop.
Targeted Recommendations for Reps
When you feed an ML model lots of quality data, it can surface the actions that are moving the needle for your team (and the actions that are holding you back).
Machine Learning, data models, and the like may sound intimidating, but they shouldn’t. The process doesn’t have to be too difficult or science-y. A good ML solution will make the process of using it simple. The insights surfaced should be easy to understand and act on.
For example:
- Do your reps see more positive replies if they send emails at a certain time?
- Is there specific messaging that’s more effective?
- Is there information (phone number, title, etc.) in a prospect’s email reply that contradicts your contact record for that prospect in Salesforce?
A proper ML tool can answer those questions and give reps targeted recommendations that help them find and close more deals, faster.
That functionality is the baseline you should look for if you are in the market for a Machine Learning solution but quality tools can do so much more.
Automating the Mundane
According to a recent Forbes article, Machine Learning shows “the potential to reduce the most time-consuming, manual tasks that keep sales teams away from spending more time with customers.”
Salespeople want to engage prospects at the right time and in the right way, they don’t want to log activities, go back and forth to schedule a time to meet, or update Salesforce records with new contact information.
Wouldn’t it be great if there were solutions that could do all that for them? Machine Learning can take care of all of those mundane, yet necessary task for reps. ML algorithms can extract, process, and learn from massive amounts of sales data. Models can analyze sales activities and customer data at scale, generate deeper insights, and even take action on those insights automatically.
Summary
It’s time to move past the unfounded fear that Machine Learning will replace sales reps and agree that it enables reps to become better at their job. Power tools didn’t take the jobs of construction workers, they made those builders more productive and efficient. The same goes for Machine Learning and sellers. Managers can look to ML to not only surface actions and insights that will make their teams more effective, but also to improve the lives and livelihoods of their reps by automating administrative tasks. It’s cliche but also true: Machine Learning gives time back to sales teams so they can do what they do best—sell.
