Leveraging Context to Improve Self-Service
StepOne’s new service uses context, machine learning and predictive analytics to boost self-service and the customer experience.
Context seems to be getting more consideration these days in customer service, as a topic of discussion at Enterprise Connect Orlando 2014, and making its way into many contact center conversations. A new company, StepOne, launches today, with their flagship product Contextual Care aimed at improving self-service for both the customers and the businesses serving them.
StepOne is a worth taking note of for a number of reasons. I had the opportunity to talk about the new company with co-founders Alex Mitchell and Bill Gravette, who pointed out that 70% of customers prefer to help themselves if they can when an issue arises, rather than calling a help desk. Seeing this preference toward self-help, the pair recognized the opportunity to provide better seamless cross-channel customer experience by leveraging the information a business knows about its customers.
How It Works
StepOne's Contextual Care system, a SaaS solution, uses unique identifiers to deliver support content to individual customers through a mobile application - essentially a support recommendation engine similar to how Netflix suggests which movies you might like. Companies with complex products can attach QR codes that the customer will then scan with their smartphone when there is an issue or question that needs to be addressed. The code directs the user to the self-help portal, and both predictive analytics and context are used to predict the question that a customer has. Hundreds of attributes such as what services the customer has purchased, the technical performance of the product, and the state of the billing cycle are used to predict the customer's inquiry.
The customer is then directed to appropriate content to help answer the predicted question. The really interesting part of the process to me, however, is what comes next. Once content recommendations are made to the customer, machine learning algorithms are used to measure the effectiveness of the interaction, which makes this contextual system truly adaptive. Such areas include which suggested items were clicked on and viewed, and patterns like which items are commonly being viewed together. Traditional KPIs that are used by call centers in day-to-day dealings, such as contact rate per customer and first contact resolution rates, are also combined with this data so that the customer experience is constantly improving.
Many times, when an enterprise starts working with new customers, they don't have much information to go on, and those first few interactions can be a bit of a "cold start." However, StepOne's software can be primed according to customer types so that the enterprise is better prepared to assist going into that first interaction, and cold starts become a thing of the past.
Improving the Business
While it is certainly valuable for the clientele to have a better customer experience, StepOne also aims to help the business improve. This sort of analysis can actually unearth which content is not working well, where there may be gaps in certain subject matter and highlight which areas are proving to be the most useful to the customer base.
In fact, Mitchell and Gravette explained that the bigger value proposition of their offerings may actually be providing the info that allows insight into how the business is running. These insights are delivered to the business so that appropriate adjustments can be made to its self-help architecture. While StepOne does not produce support content itself, it has partnered with other companies to help a business develop better information pages.
Currently, StepOne has deployments with two global communication service providers, Telstra and a Tier 1 U.S. cable company. The company additionally received $4 million in Series A financing that will facilitate its growth moving forward.