Artificial intelligence (AI) is often identified as an innovation that will power the industries of the future. Less than a decade removed from IBM Watson competing on Jeopardy!, AI is now everywhere: AI for healthcare, AI for transportation, AI for manufacturing, and so on. Across business disciplines -- including sales and marketing -- companies are looking to invest in AI technology to upgrade their efficiency and effectiveness. According to Gartner, 30 percent of all B2B companies will adopt AI to enhance one of their primary sales processes by 2020.
For a term so universal, most people would be hard pressed to explain what it actually means. While broadly defined as a machine’s ability to think and work like a human, AI looks very different across applications. In the sales technology context, the term AI almost always refers to machine learning -- a method where statistical models learn from data, identify patterns, and make decisions without human instruction.
So how does Machine Learning for Sales work?
First, a model is built using a range of sales data: customer information, sales rep activity, voice/email communications, historical results, and more. The machine processes these large data sets to understand what drives both good and bad outcomes, allowing it to build predictive power. The model can then make predictions, such as how likely a deal is to be won based on content of communications between the sales rep and the customer. The more data the model ingests, the more it learns and improves.
The market for machine learning-based sales technology is growing as organizations look to get an edge in a highly competitive industry. The most established use cases for machine learning in sales break down into several key areas:
- Pipeline Insights: Empowers sales leaders and teams with more visibility into opportunity and pipeline health to drive smarter actions. An example would be a view into which deals are progressing, which are stalled, and why.
- Forecasting: Enables sales leaders to more accurately and consistently predict revenue across their organization, driving better decision-making and business planning.
- Lead Scoring: Prioritizes which customers to engage based on attributes that indicate opportunity size or relative value.
- Next-Best-Action: Guides sales reps with recommendations on where they should spend their time for maximum impact.
- Sales Enablement: Provides insight into “what works” by analyzing customer conversations. Learnings can be applied across sales reps.
SetSail is introducing a new category to the market: Machine Learning-Driven Incentives.
While traditional sales compensation systems are built around periodic (often quarterly) commission payouts, machine learning-driven incentives reward true progress in real-time. The underlying machine learning technology predicts the likelihood of a deal to close; as the deal moves forward and that likelihood increases, the sales rep is rewarded. This approach motivates sales reps to work on the longer-term, often riskier, deals that are highly valuable to the business, without the usual skew towards the safest or “closest to the money” deals.
Across applications, machine learning makes it possible to learn from tremendous amounts of data at lightspeed, a task better suited for computers than humans. Sales teams that can focus more of their time on what they do best -- sell to customers -- drive more revenue. In the modern sales team, technology and the human element come together symbiotically to accelerate performance and deliver outsized results.
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