It’s the new technique in town, and everyone’s jumping on board. This week, it’s sales’ turn to go high tech as marketing and sales analytics company Absolutdata launches an artificial intelligence-based sales guidance tool.
Launched Wednesday, NAVIK SalesAI provides each salesperson with a customized weekly game plan, allowing them to determine which leads to prioritize and which actions to take. The tool also provides the salesperson with the best channel of outreach for that week, based on a customer’s behavioral data.
What’s unique about NAVIK SalesAI is that it has the ability to learn from what has worked, and what has not worked as different types of salespeople take different daily actions, said Absolutdata CEO Anil Kaul.
“It’s this dynamic learning from the data that is the difference. SalesAI uses that learning to make recommendations,” he said.
The recommendations are not based on a static ‘playbook,’ a tool traditionally used by sales teams, but instead intelligent recommendations incorporate what is working now for that particular type of client.
The technologies and methodologies used in NAVIK SalesAI come from the AI and Machine Learning world, he said. “For example Deep Learning, Naïve Bayes, Random Forrest, Collaborative Filtering, and Gradient Boosted Models.”
According to Kaul, almost all organizations experience some version of the ‘80/20 rule,’ where a majority of sales are generated by a small subset of the salesforce. Here, we see that AI – in the form of a self-learning tool that reacts to each action taken by prospective buyers and by the sales team – provides power to empower every sales rep, helping predict buyer needs and ultimately allows sales reps to close the deal faster, he said.
Less than a year ago, studies were predicting that AI had another five to 10 years before it went mainstream, but Kaul said AI has gone mainstream faster than expected.
“Accelerated technology developments have happened for several reasons,” he said. “Companies like Google and Microsoft have released their solutions as open stack, so sophisticated solutions are available to a wider world of developers. Once people started experimenting with these new technologies, they quickly realized that the current level of development of AI tools can solve a lot of problems significantly better that the traditional analytical methodologies. Traditional analytics are dependent on a large army of data scientists who are expensive and difficult to find,” he said.
According to Kaul, AI overcomes many of these challenges.
“Once a solution is set-up, self-learning takes over,” he said. “So you don’t need a big team to constantly maintain and improve the models. They can focus on building more. This combination of accessibility, motivation and automation have accelerated AI adoption.”
Although the excitement over AI has seemingly bumped out Big Data, Kaul is quick to point out that this is not the case.
“Big Data has not gone anywhere,” he said. “In fact, it is a critical enabler of AI. Without Big Data, AI models cannot learn enough to be precise unless they have a lot of data to train upon and big data provides that environment.”
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