Artificial intelligence-driven sales tools help customers forecast sales and close more business.
Artificial intelligence-driven sales tools help customers forecast sales and close more business.
Given a choice of superpowers, salespeople might well choose the ability to predict what customers will want to buy tomorrow. Predictive sales and lead scoring enabled by artificial Intelligence (AI) brings them as close as they can get to gaining this ability. The technology uses current and historical data to prioritize sales efforts and focus on the most promising leads.
Greg Plum, senior vice president of strategic alliances at Markee and chair of CompTIA’s Emerging Technology Community, discusses the application and outcome of AI-driven predictive sales and lead scoring with Jason Juliano, director of digital transformation at EisnerAmper Digital and member of CompTIA’s AI Advisory Council, in this episode of CompTIA’s From Promise to Profit series.
Sales forecasting is critical to a profitable business. Doing it right requires a 360-degree view from a single pane of glass of marketing, sales and customer data. It’s an easy way for sales reps to identify the most promising customer opportunities based on historical buying patterns and current habits. One of Juliano’s customers, a regional HVAC company, was struggling to close deals because they lacked this kind of visibility.
“The inside sales reps were hyper-focused on getting existing customers to renew their contracts before they expired,” Juliano said. To prioritize sales opportunities, sales reps had to wade through multiple databases containing customer sales information. There was no integration between systems, complicating the workflow and making it difficult for reps to gain solid, actionable insights. “This is actually a problem that many companies have with sales, forecasting and lead scoring.”
To enable efficient, accurate lead scoring, EisnerAmper Digital integrated the client’s Salesforce platform with machine learning (ML) tools and automation software. This enabled “more intelligent, efficient workflows for the reps to easily send contract renewal reminders to existing customers,” Juliano said.
Using AI for sales forecasting and lead scoring made it possible to implement business rules based on insights from customer data. The insights enable predictive recommendations that guide sales reps on the right time to approach customers for contract renewal. The data also alerts reps to additional potential sales opportunities, so they can close more business, Juliano said.
The implementation produced substantial results for the customer, boosting the company’s opportunity-to-win ratio by more than 30 percent, Juliano says. “The solution sends the sales reps actionable and dynamic recommendations based on customer predictions and agile business rules that the sales leaders were able to tweak from a day-to-day perspective.”
From EisnerAmper’s perspective, the implementation enabled its bag of tricks. “Any of the tools that we create—you know, APIs, best practices—we actually create these tools and put them in as part of our tool chest,” Juliano said.
The tool chest has grown to include AI-enabled tools leveraging IBM’s Watson, Google and Amazon AWS that EisnerAmper integrates with the Salesforce and ServiceNow platforms to build predictive sales algorithms, digital assistants and service management applications.
“We looked at the opportunity-to-win ratio, and that actually increased over 30 percent.” – Jason Juliano
Before developing a solution for a customer, EisnerAmper typically does a deep dive to identify key performance indicators and pain points. In this case, the provider leveraged an innovative technology—artificial intelligence—and integrated it with existing systems to deliver a welcome business outcome.
From an MSP perspective, Juliano says, it’s important to leverage innovative technologies such as AI, ML, predictive analytics and RPA (robotic process automation) to develop self-service offerings that boost value for customers. These experiences can lead to other opportunities to leverage and integrate AI tools with other systems—not only at client sites but also for the MSP itself, he said.
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