Sales Analytics for the Passenger Vehicles Industry

Anshu Sharma

India is projected to become the third largest passenger vehicle (cars, utility vehicles and vans) market by 2020. As of 2017, India stood fifth, behind China, US, Japan, Germany. India produced and sold a record four million passenger vehicles (PV) in the year ended 31st March 2018. The growth however has been sluggish with a CAGR of 2.7% over the 5-year period ending 2017.

Maruti comfortably leads the market with almost 50% market share and it has Hyundai, M&M, Tata Motors and other players giving it distant company.

The wide gap between the market leader and the followers leaves a lot of scope for these challengers to devise strategies which can help them heat up the competition a little more. While the war is fought at the brand level, it will not be an overstatement to say that a lot of battles are lost at the thousands of dealerships and territories due to sub-optimal sales execution. The subsequent sections will talk about how analytics capability can be established as an important lever in such battles to help the sales org compete more effectively.

Data is the fuel which will be required to setup any analytics capability. In order to setup a sales analytics capability, PV manufacturing companies need to first setup and synchronize their data capturing and integration capabilities. All possible customer touch points need to be identified and mechanisms to capture relevant data needs to be set-up. With the technology available today, there is no limit to the kind of data that can be captured. Hence, the sales org first needs to freeze upon the areas of analytics that it intends to do and then generate data requirement from there on. To aid in this, the subsequent sections provide a high-level view of the different kind of analytics which can be helpful to the sales org in increasing sales. We also talk about the data requirements that will enable these areas.

Treat each dealership territory as a unique micro-market and estimate the market share it captures in its designated territory. In some cases, there can be more than one dealership of the same brand in a defined micro-market, however prudent judgement should be used in defining such micro-markets.

Data Requirement: Sales data for competition dealerships – can be sourced through the field sales force on a monthly basis.

Application: Insights from this analysis will help in drilling down the problem areas and targeted initiatives can be planned for dealerships which are found to be lagging.

Analyse used car exchange and evaluation data to increase effectiveness of the exchange lever. Identify most commonly exchanged brands, vintage of cars exchanged and gaps in expectations in cases of lost deals.

Data Requirement: Used car evaluation data, brand of used car sold, vintage of used car sold, brand purchased in exchange, price offered for used car, gap in expected versus offered price for lost deals.

Application: Identify most common used car brands and vintage to more effectively target customers with exchange offers. Use insights from offer versus expectation gaps to offer more targeted exchange bonus offers for customers.

Segment key accounts basis usage or type of owners, for example taxi fleets, corporates, PSUs, etc. Treat each as a unique micro-market and estimate rough market share and penetration in these segments.

Data Requirement: Key account details, historical purchase data, future purchase estimates

Application: Insights from this analysis can help in improving focus for the salesforce and in rolling-out campaigns targeted towards specific key accounts.

Define various stages in a sales cycle if not already done and build a pipeline mechanism which allocates each customer to a unique stage in the sales pipeline. For example, different sales stages can be: Prospect, Unqualified Lead, Qualified Lead, Demonstration Stage, Test Drive Stage, Finance Applied Stage, Finance Approved, Booking Amount Given, Full Payment Received, Vehicle Delivered, Deal Lost but did not purchase, Deal Lost to competition. Use this pipeline data to analyse movement of customers and drop out points in case of lost deals.

Data Requirement: Customer stage data for each customer in the dealership sales pipeline

Application: Percentage conversion and velocity of movement from each stage to the next can help in designing targeted process interventions. Stage-wise sales conversion insights can provide an estimate of monthly expected sales to management.

Basis the source of lead and other customer interaction parameters, each lead can be allocated a propensity score for conversion into final sale. Each lead can be labelled as High/Medium/Low to indicate its propensity to convert.

Data Requirement: Lead source, push/pull based lead origin, customer profile,

Application: Propensity allocation can be used to provide focus to the salesforce.

All lead sources can be tracked for conversion to identify the relative effectiveness for various lead sources.

Data Requirement: Source-wise lead conversion data

Application: Insights from this analysis can be used to ascertain the effectiveness for various lead sources and campaigns. This can help in optimal allocation of resources across various lead generation sources and mechanisms.

Analysis of performance of individual dealer sales executives (DSE) across various parameters such as leads generated, lead conversion rate, quality of customer data input, and more.

Data Requirement: Performance data such as leads generated, leads converted, individual lead-DSE tagging, trainings attended, years of experience in industry, years of experience at current employer, for each individual DSE.

Application: Overall dealership salesforce health and contributing factors for high performance can be ascertained along with training requirements and other HR related interventions such as reward & recognition, severance, etc.

This analysis just needs to answer two questions, one – why did we lose a deal and two – why did we win a deal.

Data Requirement: Customers’ reasons for buying or not buying the brand.

Application: Design new initiatives or reinforce existing initiatives depending on what is working and what is not basis this analysis.

Companies can derive sales benefits by implementing these analytics and using insights that emerge to design impactful initiatives. It is notable that most of the analytics areas talked about above do not require much advanced skills in analytics or any complex analytical tools. These simple yet meaningful analyses can be performed with a good working knowledge of excel or any other simple analytical tool.

https://www.business-standard.com/article/companies/passenger-vehicle-industry-india-made-four-million-cars-in-fy18-118041600046_1.html

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