By: Xavier van Leeuwe, Matthijs van de Peppel, and Matt Lindsay
Director of Marketing and Data, Manager of the Data Intelligence and Customer Relationship Management Team, and President
NRC Media and Mather Economics
A company is embodied by its employees. They are the face, the voice, and the body of the brand. They see the customers, speak to them, and even touch them. Employees determine how a customer feels and whether a potential customer wants to do business with a company.
Besides the crucial role of representing the company and its brand, it’s also true that— despite all of the digital transformations that have taken place — personal sales interactions are still very important for bringing in new customers and negotiating new conditions with existing customers. At NRC, about 70% of the total influx and changes in service are realised by personal sales.
People are not average
A lot of companies look at the performance of employees in terms of groups. There is a customer service group, a call centre sales group, a group of sales representatives from a particular affiliate company, and many other aggregated groups. They all bring in a number of new clients for a certain cost and are evaluated by those numbers.
But if you are interested in optimising sales performance, analysing group averages won’t help you. Indeed, they will mislead you and put you on the wrong track because people differ. They are not average and their performances aren’t either. Some of them are very senior and very good at their jobs while others are still learning, and some are just not made for sales.
At NRC, we made this mistake with group averages. We regularly analysed the performance of our face-to-face sales teams, which sell subscriptions to passers-by in high-traffic shopping locations such as city centres and malls. These teams had a good average ROI, and that’s why we were quite satisfied with their performances — until we got the idea to dig deeper and collected and loaded individual sales data into the data warehouse.
This brought us insights on the numbers for each individual agent. It turned out 80% of the sales were made by 26 people out of the 560 who represented us. The outstanding performance of only 5% of the sales representatives compensated for the miserable results of the other 95%.
Obviously, we were shocked. And we never would have explored this if we had not collected granular data and looked at the individual representatives’ performances.
When we discovered these striking results, we realised we had to go “full monty” in the analysis of the agents. Because we are interested in building long-term relationships, we started to analyse how representatives were performing from a long-term perspective. We looked at the retention of the new customers brought in by the individual agents.
This led to a couple of other profound insights.
The data showed there were sales reps who brought in subscriptions, but more than 50% of those were cancelled in the next week. There were also employees who almost never had cancellations. What was happening to cause such divergent results? What were these sales reps doing right and wrong? And who, exactly, were those people representing our company, day in and day out?
Niels Hoedjes, senior data analyst at NRC, developed “The Shoe” to analyse sales reps. Each bubble represents a sales rep. On the Y-axis is the average number of sold subscriptions per sales shift; on the X-axis is the retention of the acquired customers. The size of the bubble represents the total number of sold subscriptions. Sales reps can be divided in six buckets, ranging from bucket number six, with high sales numbers and high retention, to bucket number one, with low sales and poor retention.
Understanding employee experience
To be able to intervene in the sales process in a relevant way, it’s crucial to understand who these sales reps are, how they experience their work, and what drives them. That’s why we started a project to research the needs and drivers of our internal and external employees involved in sales with service design.
We discovered there were essentially two types of salespeople: those who liked to convince others and those who liked to help new customers find the right product. We analysed their strengths and weaknesses and provided coaching material for each salesperson type, thus increasing their performance and motivation.
We also learned self-confidence was most important for success in selling a lot of subscriptions. The salespeople had to sparkle. However, this self-confidence was more dependent on what happened during the day than on the personality. A bad remark by a single passer-by could drain the energy out of any salesperson. So, we started to coach them on how to overcome these bad moments and not let their performance be ruined for the day.
When approached by sales representatives, customers would often check their mobile phone to compare the online price to the one the salesperson was offering. Our sales reps complained that the online price was better. We had heard this point before, but only when we used a service design procedure did we start to empathise.
Before that exercise, we used to think these were just salespeople trying to find an excuse for poor results. Now we feel their pain when customers check the online shop and turn around, saying they will buy the subscription online. So we changed the acquisition pricing in the online shop so that these customers would see they got a better deal on the spot with that sales representative. Having a better price than the online subscription shop boosts their self-confidence.
Improve sales results
We used our data and research insights to optimise sales with some relatively simple adjustments. Often, salespeople are results-oriented, so they are driven to reach their KPIs. In some cases, KPIs define their rewards. Again, it’s crucial to set the right KPIs to get people running in the right direction.
In our case, we adjusted sales KPIs to stimulate long-term relationships. Originally, that was not the case. There was only one KPI for a sales rep: the number of sold subscriptions. That metric seemed logical but wasn’t; for the salesperson, it didn’t matter if the subscriber was still a subscriber after a year. As a matter of fact, it even helped if the subscriber cancelled, because then there would be a hot lead back on the street.
So what we did was change the KPI from “number of subscriptions sold” to “number of subscription years sold.” Sales rewards became solely dependent on the total length of the sold subscriptions. From then on, one three-year subscription would be worth more than two one-year subscriptions. And when a subscription was cancelled in the first thirty days, there was no reward at all.
Again, this subtle change of the KPI made a big difference for our daily business. The sales managers started to organise sales games to boost the number of subscription years, and the average length of sold subscriptions more than doubled in just one year, from 0.8 to 1.8 subscription years.
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