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Training and Continual Learning
The Afiniti software takes from two weeks up to four months to train itself on agent performance after deployment (and it's always deployed on premises for privacy and security reasons). It then goes into production.
The self-training may seem like a long time, but consider that a single contact center agent may take about 700 calls/month, or approximately 2,800 calls over a four-month period. If a company has designated 100 different customer types, then that equates to 28 calls per customer type, which is really not a lot of data with which to train a machine learning system.
Consequently, once the system goes into production, daily agent performance continuously feeds into the regression engine, which in turn updates the system for the next day's calls. This continual learning mechanism is useful because agent performance and customer preferences may change over time, and events that temporarily skew the need for certain types of agents might pop up.
The constant feedback loop also helps train the system for new agents. The new agent's attributes are entered into the system immediately upon hiring, and the Afiniti software instantly begins computing that agent's likelihood of success when considering optimal pairings. Of course, with time and experience, that agent's pairing performance should improve.
Proving Afiniti's Own Performance: How Afiniti Makes Money
As I mentioned earlier, Afiniti is so confident of its technology that it will install its AI-based agent-customer pairing system in a contact center at no charge. The customer and Afiniti agree in advance on the system's performance measures, and Afiniti receives payment based on what it proves its effectiveness really is. For example, if an Afiniti customer wants higher sales conversion rates, then Afiniti gets paid based on some percentage of the higher sales numbers for which it can prove responsibility.
Afiniti proves its own performance using statistical sampling: every hour, the Afiniti system turns off customer-agent pairings for a given amount of time. It then compares agent performance while the Afiniti system is turned off with agent performance when the Afiniti system is turned on. In this way, both Afiniti and the customer can see the impact Afiniti's solution is having on the desired outcomes, and base payment on actual measured system performance.
Proof That It Works
In a case study on the Afiniti website, T-Mobile describes how it turned to Afiniti's solution when seeking a higher conversion rate for prospect to paying customer. According to T-Mobile, Afiniti's system is responsible for $70 million in additional new sales annually. In additional case studies, other multibillion-dollar companies show sales increases as percentages as opposed to dollar values. Nevertheless, in a large company, a small increase in sales conversation amounts to a lot of money.
Afiniti targets its software toward large, high-volume contact centers. The company claims that all of the major U.S. telcos use its technology and that it presently has 150 deployments.
CB Insights, a tech market intelligent platform provider, recently ranked Afiniti among the 100 most promising private AI companies in the world. And, based on its last round of funding, Afiniti is worth approximately $1.6 billion.
AI is clearly moving into the communications and collaboration domain, whether to add intelligence to contact center routing; provide intelligent chat bots and virtual digital assistants; or start meetings based on facial recognition. Afiniti's story resonates because it has real products in production using AI/machine learning, and its revenue model is so transparent and straightforward.
To learn more about how AI is being used in business communications, consider attending the session at Enterprise Connect 2018 titled "Understanding AI--Beyond the Hype." Enterprise Connect, will take place March 12 to 15, in Orlando, Fla. And, if you register now using the code NOJITTER, you can save an additional $200 off the Advance Rate or get a free Expo Plus pass.
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