Katch Case Study
Company Size
200-1,000
Region
- America
Country
- United States
Product
- Katch Software
- Trumpia’s automation tools
Tech Stack
- SMS Technology
- Automation Tools
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Real Time Analytics
- Application Infrastructure & Middleware - API Integration & Management
Applicable Functions
- Sales & Marketing
Services
- System Integration
About The Customer
Katch is an award-winning software company known for enabling brands to engage and monetize visitors to their website, even those that do not transact. They offer a premium service for larger clients, working as a marketing agency and sending messages on their behalf. One of their clients is an insurance provider who was receiving a large volume of emails from insurance customers. Katch was responsible for handling these messages, which required a significant amount of time to organize, categorize, and respond to.
The Challenge
Katch, a software company, was handling messaging for one of their clients, an insurance provider. The insurance provider was receiving a large volume of emails from insurance customers. Due to the sheer number of messages, a significant amount of time was needed to organize, categorize, and respond to inbound messages. This led to low response times and missed opportunities. The challenge was to find a way to manage the high volume of messages in a more efficient and effective manner.
The Solution
Katch first tested the efficacy of texting by manually sending messages such as enrollment deadline reminders, insurance quotes, and agent information. After experiencing positive results, they decided to apply Trumpia’s automation tools. This allowed them to send the right messages to the right recipients at the right time, all automatically. Whenever their insurance client submitted a lead, they would have Trumpia’s software automatically sort based on their gender, DOB, zip code, state of residence, household size, and lead opt-in date, so they could send more relevant and targeted messages to them. These messages ranged from one-time messages, to multiple messages set on specific days to manage a real time lifecycle of a client.
Operational Impact
Quantitative Benefit
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