Speech Technology Magazine

 

The 2016 Speech Industry Implementation Awards: Open English

By Steve Chirokas - Posted Aug 1, 2016
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Customer: Open English

Vendor: CallMiner

Product: CallMiner Eureka 9.4

Open English is a Miami-based organization that teaches English via online live classes with groups of students and private lessons. Since 2007, Open English has helped more than 400,000 students achieve English fluency in Latin America and beyond.

Open English contact center agents located in Colombia, Brazil, and the United States enroll new students by outbound dialing to prospects who have expressed interest though a website or campaign inquiry. Increasing enrollment has made the ability to monitor agent performance or gain insight from calls challenging, as manual efforts limit the volume of calls that can be reviewed.

Over a year ago Open English reached out to CallMiner to explore how automated monitoring using speech analytics could help improve the acquisition of new students. Open English suspected that top performing agents tended to adhere more closely to an established script. To evaluate this hypothesis, CallMiner Eureka was deployed to automatically track the progress of dialogues for more than 200 agents distributed across multiple contact centers.

CallMiner Eureka provided a framework that made it easy for Open English to break down components of interactions to consider the importance of specific words and phrases. A quality monitoring form (QMF) was established to set a mark that would have meaning for agents, supervisors, and Open English leadership. The QMF took advantage of CallMiner’s ability to evaluate dialogue elements, enabling Open English to add emphasis to more important script components.

Supervisors were then able to use QMF scores as a comparison against conversion rates. What they found was a strong disparity between script followers, who performed well, and agents who tended to deviate from the script, who did not. Supervisors then used the evidence available within CallMiner for training by gathering examples from the top 10 and bottom 10 performers to modify behavior.

Agent performance consequently improved, but that was not the sole benefit. Esam Rhman, business intelligence manager for Open English, maintains that an “aha” moment occurred when it became apparent their agents were having problems handling customer objections. CallMiner provided evidence indicating that agents were not always utilizing established rebuttals for comments such as “I am not sure about the price,” or “I need to talk with my spouse.”

The Open English QMF score was then updated to emphasize attempting at least two of the suggested rebuttals. Conversion rates improved as QMF score monitoring and training contributed to agent success.

Efficiency and effectiveness were addressed as well by utilizing CallMiner’s ability to identify silence parameters. There is always an acceptable level of silence, but excess silence could point toward areas that need improvement, such as hanging on too long with answering machines. A silence threshold was established within CallMiner to spotlight calls that were likely unproductive. Open English attributes 1,000 hours per month gained by avoiding unnecessary silence with savings of $5,000 per month. Agents were also able to make more calls as a result.

Perhaps most impactful was CallMiner’s ability to help Open English link its sales conversion process to marketing efforts. Open English created an automated score within CallMiner that tracked Urchin Traffic Monitor (UTM) parameters from websites, search, social media, and other marketing campaigns. This enabled correlation between marketing and conversion success. A CallMiner application programming interface made it easy for Open English to create on-demand access to this detail for marketing. As a result, marketing has become more agile in managing its campaigns.

Open English has worked closely with CallMiner to the benefit of both organizations. CallMiner’s Spanish module has addressed Open English’s almost 100 percent Spanish-speaking customer base. Open English has contributed toward improvement of the Spanish module by adding to its dictionary based on dialect components gathered from a range of Spanish speaking geographies.

Future initiatives include using CallMiner to correlate interactions with conversions that occur over a period of time. Details from this metadata could be used to modify follow-up calls or ignore those that rarely result in a sale.

CallMiner has provided Open English with insight into its prospect base that was previously unattainable. Additional benefits have helped span what many consider as a frequently seen chasm between marketing and sales. As a result, Open English is able to put business intelligence to work to increase revenue and reduce costs for its entire organization. 

THE RESULTS

After implementing CallMiner Eureka 9.4, Open English has seen:

• increased conversion rates by using script adherence examples to modify agent behavior;

• improved call center efficiency by examining the “percentage of silence,” eliminating 50 to 100 wasted labor hours and equating to approximately $5,000 a month; and

• linked marketing efforts to sales conversion to improve agility.


[Editor's Note: The original print version of this story included an incorrect number of wasted labor hours saved by the CallMiner solution. The online article has been updated with the correct information. We regret the error.]

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