This content is republished with permission from Pandata, a Marketing AI Institute partner.
When many people think of AI, robot butlers and self-driving cars come to mind. These technologies are “stand alone” solutions that do not require much human interaction but do require a huge up-front cost. However, there is another branch of AI that augments human capabilities rather than replaces it, and often for a fraction of the cost.
Many businesses wait for customers to walk in the door, but in the world of advertising, salespeople are out talking with local businesses, trying to find a way to create a strategy that will move listeners to action.
Any business that has a sales team knows that simply calling on more potential customers, and asking for more business can be a great driver to increase sales. However, doing this “blindly” without a coherent strategy can also be a huge waste of time, or worse yet, backfire and end in a lost account.
Previously, navigating that nuance relied on experience and intuition, but now we can augment those skills with AI in sales, using machine learning and automated accountability.
To see how, let’s examine the sales cycle…
When managing a team, and especially when training new employees, being able to keep accurate measurements of how each person is doing, along with each account, is crucial. Being able to offer data-driven incentives on how changes to behavior can change outcomes takes it to a whole new level.
With state-of-the-art machine learning tools, an incoming email or customer review, or any bit of text, can be quickly analyzed around distinct topics like price, calendar, marketing strategy, etc. to extract sentiment or relevant facts from the conversation.
It starts with extracting named entities (aka: important nouns that likely have a large impact on the interpretation of the text). These automatically extracted entities can then be linked to each other in meaningful ways.
For example, a machine learning tool could suggest adding a calendar event on Tuesday morning at 10:30am (many email and calendar platforms can already do this). It could also be combined with sentiment scoring to better understand communication patterns of your team and their contacts.
Let's say a sentiment score from a coworker email breaks down as follows:
| % Negative | 4.4 |
| % Neutral | 73.3 |
| % Positive | 22.3 |
| Overall Score | 97.6 |
Now we’ll compare it to a follow-up email from a different coworker. Sentiment for this second email is noticeably different:
| % Negative | 9.5 |
| % Neutral | 76.4 |
| % Positive | 14.1 |
| Overall Score | 71.7 |
A manager could set a threshold for the “Overall Score” that would alert them when a conversation might have taken a bad turn, and swift intervention could address the issue. There are many more fine-tuned adjustments and automation tools that could be built in as well, especially if there is a large collection of documents to train the AI model for better future performance.
When designing an AI tool that is meant for augmenting human ability (especially if it will be imposed on people who were not part of the design process), it is very important to keep things simple and intuitive. A large draw for why you might use this kind of tool is because your sales staff barely have to change the way they interact with their technology and reporting system at all.
Having a “Guardian of Simplicity” in the design process will increase the likelihood of adoption when the tool is rolled out, and allow focusing on the distilled-down version of the data that contains the important parts, from the manager’s perspective, while still encouraging the human connection and relationship-building that roots so many good sales interactions.