Optimizing the Call Center through Improved Targeted Data Analytics

Are you confident that your call center’s lead generation activities are targeted to reach out to the prospects that are more likely to respond positively? Often times, the answer turns out to be “What is targeting?” Let’s take a look a case study featuring call center lead generation efforts for commercial banking loan products.

In this case study, among the available prospect data records, only half were contacted each month, leaving the other half of the prospect data records untouched. The initial list selection was based on annual sales/revenue, which succeeded in eliminating the poorest performing prospects. However, those prospective customers were not further prioritized for their call center representatives to focus on the best prospects.

Adding marketing analytics to the mix improved lead generation results. Here’s a snapshot of the data analysis and recommendations made with the intent to increase the lead generation conversion rate:

Added filters to the prospect data to combine any call disposition history,

Created metrics that would track and measure lead conversion data,

Introduced third party demographics into the data to determine if prospect record prioritization
based on predictive modeling could improve their lead generation rates.

This analytical approach focused on leveraging important customer/prospect data history that the client maintains for each business. The historical data they were already capturing included: call outcome detail by month and lead disposition outcomes. As with any call center, leads could not be generated until a sales rep initiated a live discussion with a decision maker or buyer.

By incorporating an estimate (score) of each business’s likelihood to generate a live contact, the sales conversion model expected performance (aka “model lift”) to improve. The resulting scores enabled ranking that was not only reflective of the best prospective businesses but also of those most likely to generate a connection to a live person (instead of voicemail, ring/no answer, wrong number, and the like).

The initial results were quite encouraging, with a projected one-year increase in profits of $1.5+ million from the lead generation efforts. While maintaining consistent staffing and call activity levels, lead referrals for this client have increased 28%. In addition, the successful close rate of those leads has improved 10% and is expected to climb higher with additional time to book pending business. While a traditional method for building a customer look-alike model or a conversion model would have enhanced results beyond random calling, additional improvements were achieved by turning call disposition data into additional insights.

This is just one method of marketing analytics you can apply to your customer data to increase ROI through your call center or sales efforts. Optimizing your customer and prospect data before reaching out and scoring your prospects based on their interaction history and likelihood to respond can create efficiencies and enable your sales force to work more effectively on targeted lead generation efforts.

 

www.apexdm.net

www.twitter.com/apexdm

www.linkedin.com/in/tyler555

Paul Raca is the Vice President of Marketing Analytics at SIGMA
Marketing Group

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