Most analytical models developed for customer acquisition, retention or growth do not take into account that it is the human that does the marketing, and miss a great opportunity to boost return on investment. Every call center agent and sales representative is different, as is every customer and without a good matchmaking between them; it is not possible to maximize the conversion rates.
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Today, most business intelligence activities in the direct marketing and sales focus on building the ideal list of prospects to sell to, identifying the right channel and offer to use, and in some limited cases, finding the right script to communicate. Although all of these are almost compulsory for effective operations, they leave one very decisive element out: THE HUMAN FACTOR. Ideally, in addition to optimizing all those listed elements, companies should also discover who can sell best to whom and optimize the matchmaking between their sales representatives and call center agents with their prospects.
Due to various reasons, such as demographics, personal history, education and social skills, every salesperson is different from another. Some can better communicate with youth, others with elderly or women, businessmen, expatriates, etc. If half of the sales is about the prospect and the offer to make, the other half is how it is being communicated, which mostly relies on whether the person communicating is equipped with the best skills. Ability to recognize which salesperson is best equipped for which type of customer can lead to substantial improvements in marketing and sales results. If one of the call center agents can relate to and make wonders with the university student customers, why continue randomly assigning middle aged businessmen and retired couples to him or her, when another agent could be performing much better with them?
Matchmaking between the marketing, sales teams and the customers follows a similar approach to most optimization problems, with three main steps:
Preparation of Data: Understanding of the performance of sales personnel with different customers requires historical data on personnel’s performance as well as the profile of prospects each personnel has dealt with. Ideally, this would mean availability of campaign management data (who offered what to whom and when) as well as customer segments information based on various dimensions such as demographics, needs, behavior and value. For companies lacking such data today, even collecting it for the next couple of weeks and months with short-term solutions can provide a usable basis for analysis. Yet, ideally, these companies should revisit their data strategies and start systematically collecting these key information elements.
Identification of Factors: Once data is available, the next step is doing a preliminary analysis to understand what factors (i.e. characteristics of prospects such as age, marital status, income level, needs) affect sales personnel’s performance. Performing simple statistical tests or even charting personnel performance across different prospect properties can reveal the most important ones to focus on. For companies with capable resources, building data mining models to identify the factors and segments most correlated with personnel performance would generate better results. Whichever method is used for analysis, a key success factor is the ability to isolate the effect of offer and in some cases the time of offer. An agent could be performing best with the high income prospects, but this could be due to the fact that the agent has been used for communicating an offer only relevant for these prospects lately. To isolate such cases, preferably, all marketing and sales personnel should be evaluated based on the same conditions (e.g. offer, time of day, script).
Optimization of Allocation: The final step is the actual matchmaking, where based on the factors identified and the data prepared, optimal allocation of prospects to representatives or agents is done. Although this is a matter of allocating the best resource for the selected prospect group, it involves simulation and operations research techniques to come up with the best allocation. As the capacity is also a parameter – after all, the number of resources is limited – an agent does not always get the prospects where he/she would perform the best. It is a matter of maximizing the output from overall sales team, not each individual separately. As an example; consider the following scenario, where three agents have different sales conversion rates for three different segments of customers. Ideally, it would be best if both Agent A and C sells to the Youth prospects and B sells to the Middle Aged. However, if each agent can make 100 calls a day and the lists of prospects to sell to include 100 of each segment, this allocation does not work out.
Let’s consider three alternatives for this case:
A. Without Any Optimization: In this case, the prospects would be allocated to the agents randomly, each getting about 33 from all segments. The overall sales conversion rate in this case would be 9.7%.
B. With Best Performer Approach: Since Agent A is the best performer for Youth, these 100 prospects would be assigned to Agent A. Similarly Agent B would get the Middle Aged and Agent C would get the remaining, the Elderly segment. In this case, the overall sales conversion rate would be 13%, a 34% improvement from the random assignment.
C. With Actual Optimization: The optimization results in allocation of Youth to Agent C, Middle Aged to Agent B and Elderly to Agent A. Even though both the Youth and Elderly segments are served by non-top performers in these segments, the overall sales conversion rate in this case would be 14%, a 45% improvement from the random assignment.
As the example above demonstrates, the return from optimal allocation of prospects to sales resources can create substantial impact on conversion rates, which is worth the effort put in for analysis for most companies.
We recommend that whatever the size of marketing and sales operations a company has, it should initially perform a basic assessment of performance of its frontline staff across different customer segments. In case significant variance exists across these segments, the next step should be pilot testing the concept to see how much of an improvement it would bring and do a full fledge optimization and roll-out afterwards.
Companies should also leverage findings from these analyses in human resources, recruiting agents and representatives who can sell to the underperforming customer segments or training those who might have the potential to do so. After all, the reason that a company can not sell to certain demographics groups might simply be the fact that none of its sales personnel can click with those demographics.
To learn more about making most out of your call center using analytics, please contact firstname.lastname@example.org.