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Supercharge Your Sales Efforts With Deep Learning

Deep learning, one of the most effective approaches to artificial intelligence, continues to gain traction in the business world. And AI is booming: Salesforce’s “2018 State of Sales” report found that sales leaders predict that the use of AI will grow 155 percent by 2020.

Gartner predicted that AI and deep learning would be commonplace by 2023, mainly as a means of accelerating data science. This will have enormous consequences for sales, so it’s critical for sales leaders to have a basic familiarity with these approaches.

Get to Know Deep Learning

Deep learning is an approach by which machines can analyze large amounts of data to find patterns that are difficult for humans to find unassisted. Deep knowledge is a subcategory of machine learning that looks for patterns in data by applying several layers of analysis, which can deliver hidden insights that can help humans make better decisions.

One strength of deep learning is that it doesn’t require anticipation of every contingency. Instead of trying to program specific attributes to look for, you allow the learning software to extract features from the data automatically.

For example, if you feed the system images of cats, deep learning software will, over time, decompose the images into particular components, such as color, edges, and contrasts. In the end, it makes a determination: Is this an image of a cat? Before approaches such as deep learning, this was nearly impossible for even our most influential computer programs.

While an image of a cat is not relevant for a sales organization, finding patterns in data is. For example, you could feed the deep learning algorithms all of the factors leading to a sale: leads that are converting, deal sizes, or the typical interactions of your sales team. From these data points, deep learning can extract key insights, such as recognizing a potentially good customer or salesperson.

Such insights matter to sales outcomes. Salesforce’s 2018 report showed that high performers prioritize leads based on data analysis 1.6 times more than less successful salespeople.

3 Considerations for Deep Learning in Sales

Although deep learning holds a lot of promise for sales, it is new enough that it seems difficult to implement and to know what issues to keep in mind. So let’s take a look at a few key points to consider when looking to achieve deep learning for your sales teams.

1. Build a predictable and value-driven pipeline.

Deep learning is most helpful when there are specific, measurable insights you are hoping to extract. An important one is the expected value of a customer, which is related to how likely a client — individual or institutional — is to become a customer and the revenue that will result. For example, clients who are 20 percent likely to make a $50,000 purchase would have an expected value of $10,000.

The challenge with traditional systems is that it’s often difficult to calculate expected value because there’s too much information to factor in an old-school heuristics-based approaches don’t cope well. Factors such as location, season, revenue, number of employees, company structure, purchase history, and other variables can play a role in determining expected value — often in complex ways.

But this kind of data set is where deep learning can succeed. It allows users to build models that take all these factors into account and calculate expected value much more accurately. With these actionable models, sales leaders can assign appropriate sales team members to the right clients and maximize revenue.

2. Create equitable, data-driven territories.

Deep learning is particularly good at building data-driven sales territories. Standard, geographic-based sales territories are convenient but are rarely well-informed by the data. But deep learning models can help sculpt a sales territory that makes the most sense in terms of expected value.

That’s because it’s much easier to apply your resources more effectively if you know how much a particular territory is likely to be worth to your business. You might find that your sales territory map doesn’t make as much sense geographically but has the highest value to your sales team. Knowing that insight is backed by in-depth learning analysis can also empower your salespeople and give them the confidence that they are seeking sales in the best locations.

3. Deliver ROI from your sales and martech stacks.

Many companies that are interested in deep learning are hesitant because it’s challenging to figure out the best ways to implement it. Fortunately, several vendors are offering AI-as-a-service solutions that can plug into your existing sales and marketing automation systems. This will save your data team the difficulties that come with building deep learning systems from scratch. Those include not only years spent in development and debugging, but also the high costs that go with paying for experienced deep learning engineers and managing these systems as your business grows. For these reasons, third-party options can be attractive to many companies.

In order to pick an appropriate solution, it’s important to understand what your data sets look like and how you are going to leverage that data effectively.

Deep learning works best with large data sets, and it’s essential that you have the resources to “clean” your data of any extraneous data points or significant inaccuracies.

To ensure success, you should expect some preparation before you begin to work with vendor solutions to develop your deep learning models.

Deep learning has the potential to transform sales, which can lead to more revenue, more satisfied customers, and an empowered sales team. But to get the full value from deep learning solutions, it’s important to prepare carefully and keep an open mind when the data points to necessary changes in how your sales team operates.

Companies that make this transition will be well-positioned to compete in tomorrow’s marketplace. And as your sales grow and your company scales in response, deep learning will continue to be highly adaptive and accelerate your trajectory.

About ReadWrite’s Editorial Process

The ReadWrite Editorial policy involves closely monitoring the tech industry for major developments, new product launches, AI breakthroughs, video game releases and other newsworthy events. Editors assign relevant stories to staff writers or freelance contributors with expertise in each particular topic area. Before publication, articles go through a rigorous round of editing for accuracy, clarity, and to ensure adherence to ReadWrite's style guidelines.

Greg McBeth
Head of Revenue at Node.io

Greg McBeth is head of revenue at Node.io, the first AI-infused discovery engine that identifies relevant, personalized opportunities for people and companies.

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