Challenge:
Our client was facing increased contact demand, decreased CSAT, and negative sentiment was being socialized online. Additionally, the business had no representation of customer effort to help prioritize the painful and prevalent issues their customers were facing when utilizing their software.
About:
Client: Home Trades with around 35,000 contacts (customer interactions) per month
Channels Supported: Voice | Chat | Email
Solution:
serviceMob designed and deployed our customer service analytics platform based on our customer’s industry/business model. We worked behind the scenes with our client’s data team, software vendors, and other cross-functional teams to ensure all of the necessary metrics, attributes, and indicators were captured across the entire service technology ecosystem.
After focusing on the data gaps of this client, we created optimized workflows, enhanced their data model in SORs (systems of record), and improved the data integrity for their service/support ecosystem. Once we rationalized the data model and deployed our customer effort-based analytics, we identified the most painful and prevalent issues customers faced when using the client’s software. This allowed engineering teams the ability to focus on the areas which caused more contacts into technical support, so they could deploy applicable solutions that reduce support contacts.
This allowed engineering teams the ability to focus on the areas which caused more contacts into technical support, so they could deploy applicable solutions that reduce support contacts.
Additionally, with visibility into contact level resolution, the business could see what issue types agents needed coaching on, to help reduce repeat contacts into the support center. Management teams are now able to quickly identify which cases/tickets should be reviewed, improving the speed at which coaching was delivered. This type of rigor improved the way the customer qualitatively assessed their agents’ performance and prioritized customer effort vs contact efficiency/traditional metrics.
We provided a view of resolution based on customer behaviors, meaning we could see when a customer comes in a channel, which agent they spoke with, and ultimately if their issue was solved; focused on the following: Did that specific user come back into support within x-number of days for that specific contact reason. This level of insight is not provided by any in-market solution. By reducing the number of repeat contacts and focusing on agent effectiveness, the business saw a significant reduction in rework across all of its support channels.
By reducing the number of repeat contacts and focusing on agent effectiveness, the business saw a significant reduction in rework across all of its support channels.
While visibility into customer behaviors, and rationalizing VOC priorities based on true contact data are extremely important, serviceMob still focuses on productivity/business efficiency. serviceMob deployed our proprietary forecasting model, allowing us the ability to have better precision on the number of agents required at the interval level.
While traditional forecasting patterns can perform at 85 to 90% accuracy, serviceMob can produce a forecast which is 95-97% accurate on the intraweek level.
While traditional forecasting patterns can perform at 85 to 90% accuracy, serviceMob can produce a forecast which is 95-97% accurate on the intraweek level.
In doing so, customers have better precision on the number of FTEs required. With this optimization, serviceMob furthered the efficacy of forecasting with our solution known as SHOP (Shift Optimization Pattern). This radical new way of looking at shift assignments that are based on the new forecast, allowed our customer to realize a lower cost to serve through more precise forecasting.
Results:
