In my interactions with the automotive dealerships, I have often observed that they don’t use ‘customer segmentation’ methods while reaching out to their customer database. In some cases, they may use segmentation methods that are effort intensive and primitive (Using MS Excel) at best. This often leads to either huge inefficiencies or potential revenue leakages or both and thus negatively impact the bottom-line.
In a recent experience, this and its impact was starkly visible:
Dealerships periodically organize the car care promotions/events in partnership with their automotive manufacturers. The objective is to get the maximum customers to their workshop to participate in these events. This improves customer loyalty as well as increases the revenue for the service (aftersales) function of the dealership.
To increase customer participation in such events, the Dealership must inform and engage with the customer segment that has the best propensity to participate. For example, there might be no benefit in reaching out to those customers who have consistently missed their last 7-8 periodic maintenance (PMS) schedules at any authorized workshop. Such customers are unlikely to participate and thus all costs and effort in reaching out to them will be a waste.
The issue is that right customer segments are not easily available in various DMS. Hence in many cases the dealerships end up giving their entire customer lists to their call center staff !! In more enlightened cases, the MIS team “tries” to extract meaningful information from Tons of data related to past customer segments and behaviour (often for a period of more than 5 years from the DMS) using Excel and pivots across different files. Often the results are sub-optimal. The Call Center staff are then expected to contact customers from these lists, share campaign related information such as discounts and offers and schedule the customers to visit the workshop for either Free Service or Paid Maintenance. The net result is inflated effort, costs and leakages (since all potential customers are not even contacted)
It was clear that the usage of sub-optimal lists in these dealerships was not because they did not want to do the right customer segment; it was simply that the teams were not empowered with the right tools handle and process large data volumes, and to extract and execute the right customer segmentation strategy.
They say that the proof of the pudding is in the eating. AUTOSherpa Systems did an experiment at 3 dealerships which empowered the teams at these companies with the right tools. Here’s the STEP-BY-STEP of what we did for these clients with ‘Segmentation Analytics’ and AUTOSherpa CRM.
- We used ‘AUTOSherpa Bi’ to design a forecast of customers based on the factors such as mileage & tenure. For this, we considered data of last 5 years, where available.
- On ‘Bi’ app, we offered the segmentation and filters for this forecasted data. Segmentations such as vehicle ageing, fuel type, customers who have signed-up for extended warranty, those who renewed insurance, probability of them answering the call of call center staff, pin codes, RTO Codes etc.
- For ‘Lost Customers’, we offered the flexibility to dealership staff to create their own logic (e.g. a Volkswagen dealer defines the lost customer as the one who has not turned to his workshop for over 15 months from the last ‘Service due date’. Whereas a Hyundai dealer may categorize the ‘Lost customer as the one who has not showed up for 12 months since ‘Service due date’)
- For this segmented database, dealers created customized calling lists on ‘AUTOSherpa CRM’ for different campaigns. This allows the dealership to trigger calls on the mobiles of their call center staff.
Project Output –
- For the dealership’s contact center, this exercise led to increase in customer Contact % by 23%. From the baseline performance, we saw a jump in service bookings by 30%. Imagine what it means for a dealership’s Service Revenue in absolute terms?
- Prior to this experiment, dealership was sending fortnightly SMS to their entire data database of 8000+ customer. With effective segmentation, they now focus on a niche dataset of 2497 customers. This also helped them reduce their cost of communication e.g. SMS, email campaigns etc.
In conclusion, using the Right Customer Segmentation Strategy and engagement tools, a dealership can significantly improve its bottom-lines. However, in order to develop and execute the right strategy, the auto-dealerships will need to go beyond archaic systems such as MS Excel and empower their teams with the right tools.