Wouldn’t it be nice to have a tool that could study your sales data, then use algorithms to help understand your customer base and predict their behavior? What if this technology only improved its accuracy as time went on?
You’re in luck. That already exists, and it’s called machine learning.
If properly used, machine learning technology can be applied to help improve your team’s sales funnels in areas such as optimizing campaigns and prioritizing leads.
Grzegorz Mrukwa, Data Science Manager at Netguru and Moritz Spangenberg, Client Partner at Netguru recently sat down to discuss four areas where machine learning could improve your company’s sales.
1. Operations discovery
When it comes to finding ways to optimize your sales team’s operational flow, machine learning can be a great resource.
Discovery of the spots in your processes that drive unnecessary costs. You are trying to figure out which part of the marketing funnel or which part of the sales funnel is driving this specific cost. To know where to focus further on.
Before you can make improvements to your sales funnel, you’ll need to learn where the problems exist in the first place, and that can be a challenge in itself—especially if you already have a dynamic and high-performing team.
By analyzing broad data and process mining, machine learning software can help to spot which part of a sales funnel is driving up costs, and furthermore, explain which part of the sales funnel to focus on after eliminating that.
In many cases, costs can go on unnoticed and fly under the radar, causing companies to lose a lot of money that could have been better utilized elsewhere.
Unnoticed on one hand, but on the other hand, some costs that may not necessarily be relevant to your actual performance.
So, for example, in a large-scale analysis of the keywords you are running in your Google Ad campaigns, there may be a lot of keywords that are performing poorly, but you still need to pay for them. So, this is that area. To discover the keywords which perform poorly on a large scale, and to be able to identify that there is something happening there.
2. Customer discovery
Getting a better understanding of your customers, through segmentation and analyzing the customer journey, can help to bring in more sales, which in time can lead to more profit.
Machine learning can help your sales team find your ideal customers in the right areas, including those which you may not have been looking at initially.
Another possible angle would be, I want to sell more or want to sell in a more profitable way.
That could mean I need to do a better job in understanding my customers or maybe by delivering better. To deliver better would mean meeting them in other channels. Maybe I’m not meeting my customers in the channels where they’re hanging out.
This is not to say you were completely wrong with where you were looking for customers previously. It could perhaps signal that your customers have moved on and went elsewhere, and you weren’t aware and still trying to serve that market.
“Maybe your customers are not hanging out on Facebook anymore, and also on Instagram,” Moritz Spangenberg adds. “No, they’re on TikTok right now, but maybe you didn’t notice that and that’s why you should take a lot of care to think about where your customers are. Machine learning can give you the opportunity to optimize your distribution channels, your marketing channels.”
Ultimately, by discovering where you can find your customers online, and creating the ideal customer profile, you can more accurately target these users, thereby bringing customers who have a higher lifetime value.
3. Operations efficacy
Now that you have identified which segments of your sales operations need to be improved, you can get more granular and work on efficacy and efficiency from a personnel standpoint.
“You want to see how efficient you are with your own resources, when it comes to people,” Spangenberg says.
We saw with a couple of our clients and partners that they got overwhelmed with sales opportunities, but their sales forces or the number of their salespeople did not grow.
As you optimize your keywords and generate more quality leads, it’s quite possible for your team to get flooded with more tasks and work. It’s important from a growth perspective that as you get more quality leads and opportunities, the framework of your operations is buttoned up so that you don’t have to hire more people. Machine learning can help you identify and use the resources already at your disposal in the most efficient way possible.
“How can you, in the best way, make use of the resources that you already have, right?” Spangenberg asks. “So, that would mean you don’t even have to save on cost. You want to grow, or you need to grow without growing, right? So, growing in terms of you needing to handle more workload, and handle more incoming opportunities. But you don’t want to grow from a sales and general administration expense perspective. You do not want to put more salespeople on your payroll.”
Use machine learning to help find the areas where you can improve upon the speed and accuracy of your internal processes. By reducing the amount of time it takes to work on a specific task or process, you create more time for team members to work on something more valuable.
Also, if you’re able to accurately predict the demand for your services in the future, you can reduce the response time to client inquiries, thereby avoiding the excessive costs of personnel without a project.
Machine learning can help in all these areas of operations efficacy.
4. Customer efficacy
Once you have gathered all the new data from the customer profiles you have built, improved upon the operations flow, and optimized your campaigns, machine learning can now help you improve your conversion rates and customer loyalty.
Here you already know what the bottlenecks are, and now you can focus on a very specific, narrow area that you want to optimize.
Machine learning can help with prospecting customers and predicting which ones are more likely to buy your product or service. It can also help by automatically prioritizing leads based on the customer profile and their intent.
And most importantly, it can assist with closing.
Machine learning can predict the next best action to take when trying to close a sale based on that customer’s journey. It also looks at similar cases that have closed and others that were lost. By collecting all the data from these cases, it will expose any signals that there is an indication to buy.
In the end, although artificial intelligence and machine learning are innovative and resourceful tools, it’s important to acknowledge beforehand that they’re not the answer to all of your problems. They’re merely sophisticated technology that can help play a role in improving your sales performance.
You need to remember that machine learning is not the goal here.
Machine learning is just one of the ways to go. But what you need to do is understand your customers at scale, and this is where machine learning can help you.
You need to understand what makes customers come to you, what are their struggles, and how to position yourselves the best.