Case Study: Leading Home Trades SaaS Unicorn
serviceMob used its service analytics platform to help a leading home trades SaaS unicorn with an average of 35k contacts per month to reduce their contact rate by 78%
New mobile apps to keep an eye on
Auctor purus, aliquet risus tincidunt erat nulla sed quam blandit mattis id gravida elementum, amet id libero nibh urna nisi sit sed. Velit enim at purus arcu sed ac. Viverra maecenas id netus euismod phasellus et tempus rutrum tellus nisi, amet porttitor facilisis aenean faucibus eu nec pellentesque id. Volutpat, pellentesque cursus sit at ut a imperdiet duis turpis duis ultrices gravida at aenean amet mattis sed aliquam augue nisl cras suscipit.
- Commodo scelerisque convallis placerat venenatis et enim ullamcorper eros.
- Proin cursus tellus iaculis arcu quam egestas enim volutpat suspendisse
- Sit enim porttitor vehicula consequat urna, eleifend tincidunt vulputate turpis
What new social media mobile apps are available in 2022?
At elit elementum consectetur interdum venenatis et id vestibulum id imperdiet elit urna sed vulputate bibendum aliquam. Tristique lectus tellus amet, mauris lorem venenatis vulputate morbi condimentum felis et lobortis urna amet odio leo tincidunt semper sed bibendum metus, malesuada scelerisque laoreet risus duis.
Use new social media apps as marketing funnels
Ullamcorper pellentesque a ultrices maecenas fermentum neque eget. Habitant cum esat ornare sed. Tristique semper est diam mattis elit. Viverra adipiscing vulputate nibh neque at. Adipiscing tempus id sed arcu accumsan ullamcorper dignissim pulvinar ullamcorper urna, habitasse. Lectus scelerisque euismod risus tristique nullam elementum diam libero sit sed diam rhoncus, accumsan proin amet eu nunc vel turpis eu orci sit fames.
- Eget velit tristique magna convallis orci pellentesque amet non aenean diam
- Duis vitae a cras morbi volutpat et nunc at accumsan ullamcorper enim
- Neque, amet urna lacus tempor, dolor lorem pulvinar quis lacus adipiscing
- Cursus aliquam pharetra amet vehicula elit lectus vivamus orci morbi sollicitudin
“Sit enim porttitor vehicula consequat urna, eleifend tincidunt vulputate turpis, dignissim pulvinar ullamcorper”
Try out Twitter Spaces or Clubhouse on iPhone
Nisi in sem ipsum fermentum massa quisque cursus risus sociis sit massa suspendisse. Neque vulputate sed purus, dui sit diam praesent ullamcorper at in non dignissim iaculis velit nibh eu vitae. Bibendum euismod ipsum euismod urna vestibulum ut ligula. In faucibus egestas dui integer tempor feugiat lorem venenatis sollicitudin quis ultrices cras feugiat iaculis eget.
Try out Twitter Spaces or Clubhouse on iPhone
Id ac imperdiet est eget justo viverra nunc faucibus tempus tempus porttitor commodo sodales sed tellus eu donec enim. Lectus eu viverra ullamcorper ultricies et lacinia nisl ut at aliquet lacus blandit dui arcu at in id amet orci egestas commodo sagittis in. Vel risus magna nibh elementum pellentesque feugiat netus sit donec tellus nunc gravida feugiat nullam dignissim rutrum lacus felis morbi nisi interdum tincidunt. Vestibulum pellentesque cursus magna pulvinar est at quis nisi nam et sed in hac quis vulputate vitae in et sit. Interdum etiam nulla lorem lorem feugiat cursus etiam massa facilisi ut.
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:
