In an increasingly connected world, the automotive industry is embracing opportunities from the Connected Vehicle and Connected Dealer to reach the Connected Customer. At the recent Automotive Analytics Executive Forum, we heard terrific success stories and far-reaching experiments aimed at facilitating the best customer experiences whether buying a vehicle or getting it serviced.
The connected vehicle will drive new business models and innovation
The connected vehicle and smart mobility are enabling new business models for the automotive industry. Having done analytics for decades, we at SAS have a long history helping our customers leverage analytics as the key differentiator for growth. For the connected vehicle, that means making your customer interactions more intelligent, and accelerating the pace of insights for personalized experiences, product development and improvements to quality, while enabling new business models.
Global megatrends can radically change the mobility industry
McKinsey’s Andreas Beiter covered key highlights from their study on the Automotive Revolution – Perspectives Towards 2030. We learned how four disruptive automotive technology-driven trends (connectivity, diverse mobility, autonomous driving, and electrification) will expand revenue pools for Auto OEM’s by 30% (up to $1.5 Trillion).
Despite a shift towards mobility, vehicle unit sales will continue to grow (albeit at a lower rate of 2% per annum). Consumer mobility is driving more “sharing”, and will make city type (i.e. density) and customer demographics (i.e. income) key predictors to future consumer segmentation. With so much opportunity, it will be an increasingly competitive market with new entrants and “co-opetition” becoming more common in the future.
Mapping insights and improved interactions throughout the customer journey
Suzanne Feeney from Hyundai Motor Company and Duan Peng from Hyundai Capital America provided keen insights during a panel discussion on the importance of understanding customer mindset and behaviors to sell more vehicles and services. When balancing growth with retention, analytics helps companies understand attitudes, needs and preferences to drive better customer segmentation and multi-channel customer experiences.
In particular, dealer programs and data science can combine to improve marketing, sales and product development efforts. We also learned how machine learning can be applied to some of the industry’s most difficult challenges, such as incentives and optimized pricing strategy and 1:1 customer offers. Finally, we learned about how analytics success will enable sustained management support from executives – and that results (ROI) drive adoption.
Model governance aims to promote better and stronger models
Organizations that are mature at applying analytics within their business face the challenge of effectively compiling an inventory and managing hundreds of models. Richard Sha and Howard Forrest from Toyota Financial Services outlined how cataloging enterprise wide models can both speed time to value in future model development, while helping to understand the interactions between models. We also learned how governance can properly enable you to operationalize your models in the best way throughout the business.
Agility is the key to win fast
Cloudera’s Dave Shuman shared the keys to winning with big data, with agility being paramount to success. Several of their clients are repeatedly iterating by following a process of “getting the data, exploring and analyzing to drive experiments, then deploying those successful ones into production.” Iteration combined with a “fluid (agile) roadmap” is enabling leading companies to win (and yes sometimes fail) fast.
What analytics maturity looks like now
Jill Dyche lent her thoughts from a Best Practices standpoint on new insights with respect to analytics maturity. Several maturity “indicators” were discussed including the presence of a “data team”, an “agile” story, clarity around {the role of Analytics in} digital business, a move towards a services model {of enabling the business}, clarity on “user categories” within the business and lastly the importance of packaging analytics well for the enterprise.
With respect to “packaging”, we’ve seen great examples from companies that invest in analytics as a program and have an enterprise role (function) designed to support analytics – wherein they have proven to be very good at embedding analytics into their strategic initiatives roadmap (i.e. supporting customer growth, quality, customer satisfaction, etc.). While that may seem like common sense, it’s not always common to have that clarity. Do you?
Your input on future events is appreciated
As we continue to bring together industry thought leaders at executive forums and client events, we’re looking for your input on key topics, format - perhaps you’d like to present yourself. Comment here with your ideas or connect with me on LinkedIn.
