AI-enabled sales technologies have made predictive selling a reality by combining data and analytics to drive sales success. AI can help salespeople prioritize leads and make relevant product or service recommendations using data science to provide guidance. This frees the sales team to focus on the art of sales and build relationships that lead to new opportunities.
But one of the most transformative facets of AI-driven predictive selling is its potential to help sales teams make the leap from a static action model to real-time action model. A real-time action model uses AI and machine learning to provide a path to success by examining buyer behavior and analyzing other data (such as social media) to identify signals playing out in real time. It’s an emerging capability that goes beyond delivering insight; it provides specific actions salespeople can take to improve productivity, and it evolves as the market changes.
Most companies that are currently engaged in predictive selling are using a sales playbook approach — a digitalized framework that defines objectives and next actions, and outlines performance metrics. This approach may be an improvement over strategies that don’t incorporate predictive solutions, but it is still basically a programmed sales playbook or set of playbooks.
A real-time action model is a superior approach to predictive selling. As AI-driven solutions evolve, it becomes possible for sales teams to react to changing factors in near real time. Real-time action means the solution is capable of capturing signals that indicate a change in buyer sentiment due to factors like competitor actions, a shift in the market, viral content and more. Whereas a static model would follow the programmed playbook without taking these changes into account, a real-time action model evolves.
To illustrate how this could work, think of a sporting goods equipment brand selling a whole fitness room to a large hotel chain. The hotel needs new workout rooms for over 30 facilities and is in pricing discussions on their mid-range line of equipment. The hotel buyer is engaged, but then there’s a marketplace shift: a competitor company releases a product line that synchs gym equipment with smartwatches. Now the hotel chain equipment purchaser is tempted to go with a competitor.
If the sales team is using a real-time predictive selling solution, they can take advantage of AI to analyze signals, recognize that buyer responsiveness has changed and detect that the competitive field has shifted. With a predictive selling solution that uses a real-time action model instead of a programmed playbook, the sales team can compensate for this change, jettisoning the strategy to push the mid-level line and offering their interactive high-level products instead. This keeps them in the hunt.
An AI-based solution that employs a real-time action model gives sales teams the holy grail of selling: a strategy for success that prescribes specific actions and is responsive to change. This goes far beyond sales automation. It also transcends solutions that deliver insight to help salespeople make better lead-scoring decisions.
A solution that incorporates AI to detect real-time signals from in-house and external data like social media, build comprehensive customer profiles and provide a path to success that changes as market conditions evolve is a truly transformative tool. By making the leap from a static to a real-time action model in predictive selling, sales teams can gain a bulletproof competitive advantage.
Anil Kaul is the Co-founder and CEO of Absolutdata. With over 22 years of experience in advanced analytics, market research, and management consulting, Kaul is passionate about analytics and leveraging technology to improve business decision-making. Prior to founding Absolutdata, Anil worked at McKinsey & Co. and Personify. He is also on the board of Edutopia, an innovative start-up in the language learning space.