leaders to direct more calls to their best salespeople, or route specific types of calls to agents trained to specialize in those areas or products. While this approach was somewhat successful there were numerous drawbacks. Less successful agents ended up with fewer calls, which unintentionally prevented them from developing the needed expertise. This slowed the progress of new hires, referred to as “speed to green,” indicating the time it takes them to become proficient in their roles. These limitations in skill assignment also complicated compensation and bonus structures, as it became challenging to strike a balance between rewarding agents for their performance while also ensuring that the workload didn’t risk burnout or dissatisfaction leading to attrition.

When we step back, however, and consider the primary goal of skill-based routing for calls, at its core, we are looking to make the best personal connection between the agent and the person at the end of that call. New demographics append abilities, machine learning, and in-depth AI-assisted calculation allow a new level of sophistication in call center routing that not only assists in this matching but ultimately leads to a better customer experience, a better agent experience, higher Net Promoter Scores, and lower attrition.

While all callers have slightly different needs and all agents have slightly different personalities, understanding who the customer is, as a first step, allows for a much more nuanced call. Based on data points, that can now include such demographics as average age, number of members in a household, income, number of children in a household, home value, and other publicly available data points, individual personas, and product offerings can be developed.

To illustrate this further, let’s consider the example of a couple with young children. If the call is regarding internet and streaming services for example that person would be more interested in channels that not only give them buddled access to channels like Nick Jr. and Disney but also be able to ensure a high enough internet speed to allow the children to complete homework, game, and for the parents to online shop. This lies in stark contrast with an empty nest couple whose interests may lie more in leisure activities like travel, cooking, or golf, just to name a few.

By analyzing all historical data on how each agent handled the two examples above we can determine not only who has the highest sales success with each family but also allow us to prompt the agent with different offers that would be more enticing to each family.

The result is that the customer speaks with someone they feel understands them and their needs and finds offers that fit their lifestyle and the agent sees higher success, a quicker speed to green, and speaks with people that they too can best relate to. Ultimately this partnering leads to higher sales, better quality and compliance during the call, lower attrition among agents, and higher NPS scores creating a true win-win-win scenario for customers, agents, and the company.