The Big Data challenge for auto firms
Car makers in India are using analytics to drive business, but they are yet to optimize its use for relevant information
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Mumbai: When the sales of XUV500, one of Mahindra and Mahindra Ltd’s best-selling sport utility vehicles, started dropping after a strong run for a-year-and-a-half, the company turned to social media and traditional feedback channels to work on a turnaround strategy.
The sales and marketing team at India’s largest sports utility vehicle maker began monitoring Facebook posts, YouTube videos and tweets that involved the XUV500, and used the feedback and analysis to eventually launch a variant of the vehicle that was stripped of certain features to make it more affordable. The launch of the cheaper variant helped the company boost sales. Mahindra officials declined to give details.
The company started deploying analytics three years back, but over time, it has began using “more efficient tools that return more relevant data”, said Vivek Nayar, chief marketing officer at Mahindra and Mahindra.
Kumar attributes the growing complexity in collating and analysing data to the increasing amount of user-generated data that has been expanding because of multiple social media sites. Besides, the data generated internally by companies, too, is growing by leaps and bounds as auto firms add models and variants, and expand to more countries.
The challenge, said Kumar, lies in collating relevant data from all sources.
A case in point is that of car market leader, Maruti Suzuki India Ltd, which has been expanding its footprint in rural India. The company plans to sell cars in 200,000 villages this year against 94,000 last year.
Mayank Pareek, chief executive officer, sales and marketing at the firm, said Big Data analytics has been an important tool for the company. While direct mailers lead to a mere 1% conversion in sales, the use of analytics helps increase the number by 8-9%, he said.
Companies in mature markets have moved beyond using Big Data for merely descriptive (reporting who is saying what) or predictive purposes (forecasting trends) and are using analytics for prescriptive purposes (finding solutions), according to Capgemini’s Kumar.
“There is more to it than the traditional usage,” he said.
For instance, Big Data analytics can be used for production planning to managing warranty issues for a vehicle. The use of analytics to find solutions (prescriptive) has also been gaining traction. It is likely to get expedited when the goods and services tax (GST) comes into effect. This becomes critical with auto firms setting up plants in multiple locations. “It’s only a matter of time when the complete value chain will deploy analytics for cost optimization,” said Kumar.
Sudipta K. Sen, regional director, Southeast Asia and vice-chairman at SAS Institute (India) Pvt. Ltd, said auto makers in India are taking advantage of predictive analytics to forecast sales of specific car models and predict their demand in a particular region, to effectively synchronize manufacturing, streamline the dealer channel and communicate targeted offers to potential buyers. “In addition, they are also able to predict emerging issues, helping them in being proactively prepared for warranty and service requests,” Sen said.
According to him, analytics is characterized by four Vs: Volume—that is extremely huge; Velocity—that increases at a great speed; Variety—a combination of structured, unstructured and semi-structured data; and Veracity—the authenticity of data cannot be ascertained. However, the analysis of Big Data is only useful for a company if it leads to the fifth “V”, or Value.
Big Data analytics identifies trends and sentiments, and behavioural analysis, but does not provide the structured report. Therefore, the company has to be sure of the analytics it is looking for, Sethi said. “Big Data is a means to an end and not an end in itself—it’s all about deriving business value,” said Sethi.
“At Hero MotoCorp, Big Data is not about ‘if’ and ‘when’. In fact, it should include ‘what all’—the scope of work—and ‘why’—the value from analysis,” he said.
Mahindra’s Nayar, too, underscored the criticality of relevance. “It’s about relevance as it all comes at a price. It’s irrelevant trying to sell a vehicle to someone who has bought a new vehicle six months back,” he said, adding that one needs a “sharp shooter” approach.
Mahindra has a base of 2 million customers across its personal and commercial vehicles. The company closely tracks these customers, all through the ownership period, and they serve as a ready reckoner when it introduces a new model. The company has been using profiling as a tool to target the audience, based on their activities on social media. Some of these initiatives have helped the firm re-market a model which was earlier rejected because of certain factors, said Nayar.
The firm uses Google search analytics for its digital presence and Facebook to match its own data with that of the social networking site. This, Nayar said, will help the firm sharpen its sales and marketing.
Sethi said the return on investment in Big Data cannot be measured in days or months or years. “In fact, some of the decisions are made within few hours of receiving the analysis and one can actually see the results in no time.”
Recently, the company pulled up one of its dealers, immediately after one of the prospective buyers expressed displeasure regarding the service quality on Facebook.
Industry experts believe that despite the use of Big Data, auto firms in India have just scratched the surface compared to their counterparts in mature markets. “We still have to see the real application of Big Data here,” said Vikash Jain, principal at global consulting firm, Boston Consulting Group India.
Globally, connected cars, which are at the pilot stage in a few places and have become a reality elsewhere, are fast gaining ground, said Jain. They are competing with computers and are set to revolutionize the way one uses cars. By transmitting data to car makers in real time, such cars throw open immense possibilities in preventive maintenance, insurance, marketing, etc. according to Jain.
This is in stark contrast to India where most service engineers at the workshops don’t even have the history of car servicing and rely on the manual prescribed by the company, he added. “Big Data as phenomena is still nascent in terms of what companies have begun to do,” said Jain.
This is the fourth in a seven-part series.
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