How we helped a leading financial company use customer data to improve outbound marketing with a machine learning model
The most powerful asset a business can have is a deep knowledge of its target customer. But how do you attain is this kind of complex customer knowledge?
Centric Consulting had an existing partnership developing a comprehensive data strategy with a leading financial company that provides equipment financing for niche markets. This client specializes in flexible payment programs so businesses can obtain machinery, including tow trucks, cranes, screen septic pumpers and more.
To engage potential customers, they use traditional outbound marketing strategies such as email and phone outreach. While this method offered a way to open up new connections, our client was looking to boost those connections by more accurately identifying prospective customers and the likelihood of them answering a sales call or email and, ultimately, submit a lease application.
The company came to us with the desire to increase the frequency of “good” sales calls – ones potential customers would answer and be more likely to result in a conversion. After helping this company build out its data architecture, our team had a uniquely intimate understanding of the client’s capabilities and data.
Enter Centric: Using Machine Learning and Modern Data Analytics to Understand Customer Insights
The client partnered with our Chicago team because of our past successes providing them with comprehensive data strategies. Our team got to work helping the company better understand their customers by improving outbound marketing techniques so leads would more often become sales, and “cold calling” could become a much warmer experience. This process included:
- Focusing project scope and inventorying the client’s sales engagement data to determine its availability, quality and quantity for use in the predictive model.
- Creating a proof-of-value (POV) predictive model showcasing the potential success of an automated rating method for prospective customers. The model would draw on the company’s existing data on customer behavior to identify priority contracts before initial outreach and advise on marketing techniques and treatments.
- Tracking its performance and predictive capability against metrics, forecasting its potential impact on the overall business, and developing a plan to deploy into production.
- Working with sales managers to get feedback on customer engagement and collaborating with them on how to utilize the data provided to engage their target audience.
The Results: Warmer Calls and More Leads
Within a month, we worked together to create a robust, viable machine learning model that allowed our client to better understand their customers and the likelihood of them making a purchase based on engagement. Our solution generated significant value for the client during just the first week of implementation. During this time:
- The model drove a 50 percent increase in quality calls based on various outcomes, including conversion to an opportunity or call duration.
- Call durations increased, the frequency of calls going to voicemail decreased, and leads roughly doubled as a result.
In addition to the results mentioned above, our partnership with this financing company indirectly promoted internal cross-functional collaboration, breaking existing silos. Working across several teams allowed the data strategy to benefit all areas of the business instead of existing in a vacuum.
With the power of modern data analytics, we built a machine learning model that transforms clean, enriched data into profit-driving insights. The end-to-end data strategy, roadmap and training we implemented not only increased sales leads but ultimately empowered this company to drive more business value from their data.