Deep learning is one of the most compelling new technologies available to businesses and is poised to be a game changer for many. Now is an ideal time for companies to consider leveraging deep learning because it is quickly moving from the realm of academic research to one in which new B2B software platforms can leverage the technology effectively.
Gartner recently reported that the use of artificial intelligence (of which deep learning is a specific category) among enterprises tripled in the past year, with 37 percent of organizations reporting that they use it.
Deep learning stands as one of AI’s most promising lines of research.
It’s a specific type of machine learning in which software algorithms ingest large amounts of data and then extract insights from patterns within it. That data can be customer behavior, images, sound files, video, maps, sales figures — provided the data is structured appropriately; the possibilities are endless.
Deep learning’s fundamental value — and particularly that of unsupervised deep learning — is that it’s flexible, it’s powerful, and it doesn’t require step-by-step algorithms or heuristics to tell it how to arrive at its conclusions.
Designed properly, it can reach human-level or better performance across a wide range of tasks without any specific programming instructions about how to do so. Knowing how to navigate the system itself, allows it to better manage novel situations that would confound traditional programs, such as when Google’s AlphaGo system used deep learning to beat world Go master Lee Sedol.
Getting Deep Learning Underway — What Does It Take?
Integrating deep learning into business isn’t as tricky as it sounds, but it’s helpful to have an overview of what it does and why it’s valuable before diving in.
The most common deterrent from implementing deep learning is the assumption that you need in-house expertise to leverage it. And that expertise isn’t cheap. Experienced deep learning engineers can command salaries upward of $1 million per year at the most competitive companies.
The good news, though, is that there is now technology that allows businesses to leverage the power of deep learning without in-house experts. Platforms like People Data Labs and BigML are helping users who are not experts in the field take advantage of the technology.
Another common deterrent is a lack of sufficient data. While deep learning does require a large amount of data to be effective, nowadays even smaller companies are tracking most interactions both internally and externally. So while they won’t have as many opportunities as larger companies to leverage deep learning tech, there are plenty of chances to do that as they scale up.
Given these two facts, there is now no legitimate excuse for not employing deep learning at your business.
3 Departments Where Deep Learning Can Make a Big Difference
To get some ideas for where you can deploy deep learning, let’s review a few critical areas in which deep learning is likely to shine.
Because modern marketing manages customer interactions at every step in the buying funnel, marketing departments typically have large data sets and are positioned to benefit from deep learning.
In the simplest case, deep learning can replace traditional heuristics-based lead scoring. But that’s just the start. Because it can consider many interconnected factors, deep learning can deliver significant returns across a variety of data points that are unique to your marketing needs.
I recently worked with a large technology firm that leveraged deep learning to create a “likelihood to convert into pipeline” model. After tuning, it was able to drive an 81 percent increase in performance over traditional technology. That kind of leap forward is consistent with predictions: Accenture foresees a 40 percent jump in labor productivity for companies that leverage AI effectively.
Sales teams can also exploit deep learning’s power for customer predictions. Because it can make use of unstructured data across a variety of sources, sales leaders can not only identify a good-fit potential customer, but also predict the possible deal size, deal cycles, and other insights.
Traditionally, unsupported human judgment — and some guesswork — was needed to decide how to manage customer interactions. Now, through deep learning, your team can match representatives to the deals they’re most likely to close; determine the time, day, or season that drives the most success; and evaluate the customer and seller interactions that are most likely to lead to a close. Overall, AI technology has the potential to increase worldwide sales by up to $2.6 trillion in value, according to one report.
Financial firms that take significant risks (e.g., credit card companies) build comprehensive models to determine how likely a person or company is to default on payment, how much they’re likely to spend, etc. While credit scores might be drivers at the consumer level, many factors determine how much of a credit line to extend that cannot be adequately captured by typical data analytics.
Because deep learning is designed to analyze complex multifactor scenarios effectively, it is a natural solution for creating highly predictive risk models. For this reason, big firms are beginning to employ large data science teams staffed with experts who leverage deep learning.
For instance, machine learning is being used by multinational insurance giant AXA to predict major traffic accidents with 78 percent accuracy. The accuracy of the predictions allows them to price optimally based on factors like each driver’s age, his or her address, and the car’s age.
These are just a few of the areas in which deep learning will be making strides in the near future, but we’re just beginning to explore the myriad uses for this technology. That’s why forward-thinking companies aren’t missing the opportunity to get their approach in place now.
Indeed, in a marketplace racing toward a future in which deep learning is critical to the success of all significant business operations, there will be few second chances for companies that don’t play ball.