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AI Comes to CRM Solutions: Page 2

By Drew Robb     Feedback
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Advisory versus Autonomy

Carl Landers, senior vice president, CMO, Conversica, thinks of AI in sales and marketing as acting in two main ways: advisory and autonomously. In the advisory role, AI is used to advise the salesperson or marketer on what to do next. For example, which account to call, which ad to run, which opportunity to advance next or which email to send. In the autonomous role, however, AI is offloading work from the salesperson or marketer completely.

This distinction is crucial for the success of AI. An application that turns salespeople into complete robots may quickly become unpopular. But they will welcome apps that save them time and help them zero in on the bullseye. An example of autonomous AI that falls into the latter category might be an intelligent assistant that engages and qualifies sales leads. When a "hot" one is identified, it sends it right to the salesperson for follow-up. Examples include SalesforceIQ, Aviso and Crystal.

Advisory AI, on the other hand, offers such features as predicting which leads to follow-up with first, as they're most likely to become customers, or scanning recent activities on an opportunity and suggesting to the salesperson that it's time to reach out.

“Autonomous AI is making its own decisions about what the conversation is about and what next steps to take as a result, just like a human would,” said Landers. “Having the AI do the hard task of lead engagement and follow-up frees up the sales team to work on more valuable activities, like proposing, negotiating and closing, which no AI is capable of doing today.”

Where AI is likely to excel in CRM is in those areas where sales and contact center people are likely to drop the ball. For example, if a customer is rude on the phone and hangs up, it’s possible that in the heat of the moment, the agent won’t record the details accurately. Thus a customer can fall through the cracks. AI will be able to automatically engage and communicate with every customer to ensure no one is forgotten. It will be able to predict when a customer is having a less-than-ideal experience and ensure proper follow-up and remedial action.

Further use cases include understanding external triggers that could impact a experience (like a change in management) and prompt the right level of outreach, and suggesting the most opportune times and methods to communicate with customers to match their styles and expectations.


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Chatbots are already in existence, though results can sometimes be hit or miss. Some are used in CRM to engage customers, provide responses to their queries and distribute content of interest. When the person is adjudged to be a hot prospect, he or she is turned over to the sales team.

But AI promises to take this technology to another level. [24]7’s chatbot deployments, for example, aim to empower agents to navigate and access information more quickly as well as accelerate learning cycles to helping improve sales teams' productivity and effectiveness.

“We will likely see more intelligent chatbots that can help service customers or support employees/agents (from answering questions to resolving issues and conducting transactions),” said Daniel Hong, senior director, product marketing at [24]7. “AI can allow us to anticipate/predict what the customer wants/needs and personalize that interaction.” In the area of spoken and text communications, [24]7 uses a unified natural language model combined with AI, across interactive voice response (IVR), mobile apps and virtual agents. This enables companies to engage customers in conversations that are contextually aware and consistent across touch points. It seeks to fuse omni-channel customer journey data (activity from prior interactions across different channels and devices) with data from enterprise systems of record (CRM, billing and profile) to identify the customer’s intent in real-time and provide the best treatment at that point in the journey, on that device and in that environment. Hong said the [24]7 platform processes and analyzes hundreds of billions of events each month in real-time and offline. Human-assisted (supervised and semi-supervised) machine learning is applied to optimize predictive models.

Scratching the Surface

As promising as these applications are, there is far more to come. Whether the applications will be annoying — a la Tom Cruise in Minority Report being inundated with special offers as he passes billboards in a mall — or useful — as in cold callers never contacting you unless you were a realistic prospect for a service — only time will tell.

“We’re at the very early stages of this revolution and AI is just starting to show its potential in the business world,” said Mayur Anadkat, vice president of product marketing, Five9.

Drew Robb is a freelance writer specializing in technology and engineering. Currently living in Florida, he is originally from Scotland, where he received a degree in geology and geography from the University of Strathclyde. He is the author of Server Disk Management in a Windows Environment (CRC Press).

This article was originally published on December 8, 2016
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