AI/ML in Sales: How to Get the Right Data to Succeed

Today's business environment is becoming increasingly competitive. As a result, sales organizations need deeper insights and access to data to stay ahead of the competition. One way sales teams are getting a competitive advantage is through artificial intelligence and machine learning (AI/ML).

However, while many companies are looking into and adopting AI/ML technologies, success relies on more than just the algorithms within the tools. Organizations need the right data in order for AI/ML to "learn" to be truly effective. To put it simply, machines can't learn and predict without the data to teach them. With that data, machines have a basis of information to build up from, continue to add additional data, and eventually derive learnings from it.

The Background on Machine Learning

In all honesty, machine learning (ML) is starting to feel a lot like a buzzword—high on hype and light on substance. Almost any technology article or release today mentions the use or benefits of the latest machine learning and artificial intelligence (AI) capabilities, but nobody really dives into the backend requirements to get the most out of it.

But the truth is that any company can add machine learning to its platform—there is nothing particularly new about the technology itself. In fact, the idea of machine learning dates back to 1959 when computer gaming and AI pioneer Arthur Lee Samuel coined the term. His first test bed was using ML algorithms so that computers could learn to predict the best moves in the game of checkers. What makes machine learning so revolutionary today is the vast amounts of data that is now available and can be analyzed to form predictions.

Today's AI/ML Capabilities for Sales Organizations

Advancements in cloud technologies and online systems have brought about the democratization of data processing, opening up a bevy of new opportunities for AI/ML learning and insight. For the first time in history, companies are benefitting from what I call a “data trifecta.” Innovations in data processing have empowered companies with:

1. The ability to capture data of all kinds—structured and unstructured
2. The affordability and vast computing availability for performance and scale
3. The readily available ML algorithm libraries to fuel data insights and predictions

These three factors together create an undeniable opportunity for any organization to transform not only its offerings, but also how it serves customers. Amazon and Netflix are great examples of effective AI/ML use.

Amazon Serves the Ultimate Buying Experience

Over the years, Amazon has learned about its customers, starting with the kinds of books they liked to read and later moving to merchandise, movies, and more. Its platform has learned user behavior patterns, enabling it to predict when and what customers might buy and provide recommendations for other products to consider. This is only possible because Amazon has the access and rights to a vast treasure trove of data.

Netflix Helps Subscribers Decide What to Watch

As Netflix has grown from mail-order tv and movies to a full-blown streaming service, it has gathered subscriber viewing behavior. It has used that data to serve users with related movies and tv series based on what viewers have previously watched and/or rated. Netflix has even gone so far as to provide a percentage rating system to show viewers the likelihood that they'll enjoy the suggested movie or tv show.

Reiterating the Need for Data

According to a McKinsey AI report, Netflix applied ML to their proprietary data to improve customer search results, avoiding what could have amounted to a potential $1B revenue loss annually due to canceled subscriptions. The same is happening in the enterprise world.

But unlike Amazon and Netflix, many companies still don’t have the data behind their systems to back their machine learning claims. While enterprise software systems have been around for ages, former on-premises models resulted in data that was locked away in silos. The cloud changed that paradigm, enabling the spread of data across thousands of companies and users to coexist. For machine learning algorithms, this kind of data is an educational panacea.

How Xactly Uses Data for AI/ML Sales Performance Management

Again, AI/ML technology and algorithms aren't effective without the right data. The sales performance management space, in particular, is one where the hype of machine learning has not yet delivered on the hope of providing truly meaningful, actionable intelligence to customers. Again, it all comes back first and foremost to the data, as well as access to vast compute power and readily available ML algorithms.

Xactly, however, was born in the cloud. Our sales performance AI platform is backed by more than 14 years of aggregated and anonymized sales pay and performance insights from hundreds of thousands of subscribers. No other data set of this magnitude exists in the SPM market today. And with great data comes great possibilities and predictions that can optimize sales performance.

The Power of Data with Xactly Insights

For the past two years, we have invested significant resources in putting our data to the test, applying different ML algorithms and features ensuring we can deliver the intelligence our customers need at scale.

Xactly's strongest use of AI/ML technology lives within Xactly Insights. Applying existing supervised ML algorithms to Xactly’s vast big data set, our customers will now be able to predict the probability of sales rep attrition.

The prediction is based on numerous factors such as historical sales performance, years in the company, a sudden drop off in their pipeline, finishing off a great year, etc. In fact, we used our aggregate data to analyze sales rep performance and were able to identify reps who were at risk for turnover (read the full story here).

When a potential red flag is identified, sales leaders receive an alert directly in their Insights dashboard. They can then drill down into that alert and the history of a particular rep to see what further steps can be taken to avoid turnover, such as identifying new growth and coaching opportunities, simplifying plans, etc.

Knowing not only the high price tag but also the opportunity cost of losing a rep (which on average is $155k), this level of proactive insight can be a game-changer. It can not only help companies maintain the health of their sales organizations, but also allow them to meet top-line revenue objectives and anticipate future staffing and hiring needs.

Taking the Next Steps with AI/ML in Sales Organizations

As we've said before, in any intelligent application, information is the key to learning, predicting and providing truly actionable insights. Unfortunately, for many applications in the market today, they are big on hype and light on information. Built on an AI platform and backed by more than a decade of real-world compensation data, Xactly’s solutions continue to drive the future of sales performance management with predictive analytics. The sales rep attrition algorithm is just the first of many to come.

Want to learn more about Xactly's data and AI/ML capabilities? View the on-demand webinar "A Toast to 2019: Cheers to Data-Driven Decision Making" to learn how to take advantage of your sales data to drive stronger sales planning.