A recognized European automotive trading company faced a typical challenge – how to get more cars sold to B2B customers with the limited size and capacity of their sales force team?
Each active customer was supposed to be contacted at least once a month. The best customers once a week. The better the customer, the more frequent contact was required.
With thousands of customers it was unrealistic for the ordinary sales reps to assess when to call whom. They needed a system to tell them, because in practice they made tens of calls daily, but more or less by intuition.
We believed that we could create a guidance system that would assess individual customers, sales reps and calls in order to bring-up a list of customers to be contacted in the near future preferably.
A customer scoring model leveraging recency, frequency and monetary value of their past purchases has been implemented and engaged. This way we could sort all customers by their importance, respective of their propensity to make another purchase in the near future.
As long as the Call Duration Records were available, we could determine when each customer had been last contacted and suggest the next call date based on the required contact frequency – of course within business hours.
To increase the relevancy of offerings during the call we also trained a predictive model on the past purchases, which returns cars that the particular customer is most likely to order. Sales reps then stopped offering just “something”, they rather started to let the customer choose from a few, very relevant, options.
When the management saw it, they let us implement that algorithm also in the company’s online e-commerce portal.
Stock-aging and optimization
Many of the cars traded are being sold before getting produced. The rest of them get sold after being produced – which means that they get physically delivered and stocked.
For the company it is desired to shorten the sales cycle as much as possible. Not only because of cash flow, but also due to car value getting diminished over time.
The priorities of stocked cars to get sold were blended into the products offered more actively. That helped to smarten the sales process and mitigate the stock aging problem.
Combining data and turning them into action
The described models and processes rely on data from multiple sources.
– ERP system (customers, products, orders, stock)
– PBX system (outgoing and incoming phone calls)
– metadata and model parameters
A data warehouse has been set up, which ensures data processing and cleansing on a daily basis. Interactive reports were created, which let the users see their list of customers to be contacted, as well as recommended cars to be offered.
Sales force team engagement
The sales team has been making phone calls to the current and prospective customers. They had to be trained and instructed in using a simple yet efficient dashboard. The dashboard lists their daily tasks and gives all team members a comparison how are they standing against each other.
Several team members used to be slightly reluctant to use a new piece of technology, but they saw good work results of their colleagues, everybody adopted the new way of working in the end.
Issues we faced
Perhaps the biggest issue was data quality. The list of customers and their contact phone numbers within the ERP system was very unclean. Duplicated customers, different phone number formats and matching them to the call duration records from the PBX system was challenging.
Despite the uneasy situation with incomplete and inaccurate data we were able to deliver the model and execute on it. Our data-cleansing procedures made a fair part of questionable records useful again.
When the sales force team previously used to work with customers as per their best judgement, they achieved average revenue per sales call about €13.
Then our Behavioral Prioritization. Product Recommendation and Stock Optimization models got introduced. Salespeople started to work based on scored customers and recommended times to contact them.
Despite a high inaccuracy of the contact list and considering only every second or third recommendation – since then the average revenue per sales call changed to approx. €21.60.
Your biggest loss is your unearned revenue. How much does every day of unused data potential cost your company?
As you can see, it is not rocket-science. You can create and apply a similar system in your company (probably in several months) on your own. Make sure that your data architect creates a modular, transparent and well-documented system able to include and blend multiple behavioral models. Also be ready to spend most time on data preparation, quality and workflow automation.
If you don’t feel like creating and maintaining such a system on your own, you can also hire an external data integrator.
Or contact Insightee today – to get your data crunched quickly, easily and with a full potential used from scratch.
Unlike other BI vendors (usually offering data integration and some fancy analytics) – Insightee is different. The “prescriptive” Insightee actions will directly turn your ERP data into extra revenue.