Gajendra Jangid, CMO & co-founder of Cars24, about Marketing Mix Modeling
Gajendra Jangid is the Co-founder and CMO of CARS24 and is in-charge of developing the strategy for advertising and branding, as well as customer outreach. He spearheads the initiatives to accelerate business growth by building the brand, taking new innovations to the market and enhancing customer experience. Prior to CARS24, he took care of operations for Schlumberger, the world's leading provider of technology for reservoir characterization, drilling, production, and processing to the oil and gas industry.
What are the core advantages that MMM has brought to your business in terms of marketing efficiencies?
Our approach, towards Marketing Mix Modeling, is to generate key insights in terms of the marketing channels we deploy and determine each channel’s contribution to the overall business by closely analyzing historical channel performance and channel spend data.
Our marketing channels involve offline media such as television, radio and OOH as well as online channels such as affiliates, OTT, search, display and programmatic to name a few.
The exercise, overall, has helped us identify which markets are most responsive to individual, or a collection, of marketing channels. For example, we were able to identify that Pune and Hyderabad as markets where our prospective audience could be targeted aggressively via Facebook. Similarly, insights such as increased investment in Google Search Non-brand in markets such as Mumbai proved effective.
“The exercise, overall, has helped us identify which markets are most responsive to individual, or a collection, of marketing channels.”
How has MMM helped improve company revenues?
Cross media effects, wherein several marketing channels have been deployed in succession, basis a cumulative overlap could be accurately measured via Marketing Mix Modeling. This, in turn, has helped us determine the media that performs best in individual markets, thereby allowing us to optimize our overall marketing spend.
Optimizing our spend, and reducing spillage, has helped enhance revenues by diverting budgets to channels, as well as regions, more efficiently. Similarly, spreading budgets in accordance with this approach has enabled to achieve an incremental increase in our business KPIs, increases clearly measurable by improvements in our top, middle and bottom funnel.
Furthermore, the generation of responsive via this modeling exercise has also been able to help us determine the head room available to deploy marketing investment across media channels, region-wise, not only to achieve incremental growth but also to ensure that such growth is achieved without deploying a medium that is either decaying or plateauing.
What is your biggest success case with MMM and can you share some details on that in terms of improved efficiencies in your business?
Marketing Mix Modeling has simplified the budget planning process for us, to a great extent.
Undertaking this exercise has enabled us to move from the initial ‘hit and trial’ approach to a more methodical and statistical approach, thereby optimizing the spends allocated to each medium across every region and identify areas where transactional ROI was less.
One such success case was the initiation of a television deep dive study wherein BARC data, provided at granular levels measured the effectiveness of television media planning across genres, dayparts and ad duration. This, in turn, helped us identify an optimal television channel mix, outline the best performing genres in accordance with our target audience and reduce wasteful expenditures often incurred while planning television on the basis of sampled insights.