• 10:25:08 am on October 22, 2009 | 0
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    The Ants Go Marching

    If ants have to choose between two unequal length paths from a source of food back to their colony, they ultimately always choose the shortest, easiest one. Here’s how it works:

    1. Ants run around the colony, more or less randomly, looking for food.

    2. If an ant finds food, it returns back to the colony, and leaves on the ground a scent trail.

    3. This scent trail attract nearby ants, which will follow this path and strengthen the scent of the trail, attracting more ants, strengthening the trail…

    Ant colony optimization can be analogous to customer service. Think of how customer service agents answer customers’ questions. They each have their own style of interacting with the information at hand. Typically, they hunt and peck through disparate, unintegrated data, knowledge and back-end systems in a way that is unique to each of them. This often results in inconsistent, inefficient or, worse, incorrect service.

    To avoid such consequences, companies must leverage technology to create process flows that guide agents through the same method of discovery. Think back to how ants find food – no single ant finds the best route back to the colony alone, yet a collection of ants are successful in doing so. And positive (stronger scent trail) and negative (weaker scent trail) feedback throughout this quest is used to guide this optimization.

    Imagine if you had the ability to design not only a single customer service process that led agents to the correct answer to a customer question, but several variations of this process. Take, for example, varying the step in a service process in which the identity of a customer is verified, or when a specific knowledge article is presented to the agent.

    Ideally, companies measure KPIs at each leg of this process. These steps are akin to the resistance that ants experience at each leg of their journey. By uncovering the strongest segments of each process, companies essentially rely on basic ant colony optimization techniques to determine the best customer service processes.

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