How Data Science Can Help Growing Businesses Boost Conversion Rate

May 4, 2018 | Ecommerce

Growth is your mandate, yet mismanaging that growth can cost people their jobs.

As your customer base grows, processing increasing number of leads can become a burden on your limited resources. Overloaded contact centers become inefficient and sloppy. This issue is compounded as the focus shifts to new acquisitions and yet your team must maintain the same level of service quality for existing customers.

All growing companies face this situation. But we caution you to choose one of these standard quick fixes:

• Hire additional workforce
• Overtime with existing workforce
• Reduce the lead inflow

Being quick fixes, these solutions can only help manage customer satisfaction and conversion rate for a while. As the number of leads continues to pile up, each lead gets lesser attention and time from the staff. And a lot of time is wasted just in their categorization alone. The result is, many prospects’ queries remain unanswered, opportunities are missed, and customer satisfaction & conversion rate again begins to nosedive.

Data Science Comes to Rescue

Instead of incorporating more resources to handle more queries, improving operational efficiency sounds like a better approach. Using data-science based techniques, such as Random Forest Model to automate the categorization of leads can help save lots of time for actually addressing queries rather than sorting them. If that sounded a bit too technical, let’s bring more clarity on that through a case study.

Case Study

The Problem
We have a client (a US-based financial service provider) for whom we had implemented Salesforce to better manage their growing customer base, and thousands of new leads that they receive on daily basis. For a couple of years, Salesforce CRM enabled them to handle leads rather effectively, with the following workflow:

However, as the volume of leads continued to grow, they hit a roadblock. The continuously increasing number of leads burdened the sales managers to triage them properly. The result was flagging customer satisfaction that impacted the conversion rates.

The Solution

Trantor worked with the in-house team to improve operational efficiency. We came up with a novel idea of using random forest model to classify the incoming leads.
Following steps were involved in the development and deployment of the solution:

The Results

• Operational efficiency was improved by 45%
• Conversion rate was improved by 2% (whereas the industry average lingers
  around only 3%)

Other benefits include being able to retrain the model to improve the accuracy (we train it every quarter). In addition, it helps identify the most critical variables in classifying a lead’s status, which further helps in improving the operational efficiency.

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