The most convenient thing about data intelligence is that the same resources gathered in one part of enterprise can also be used by another.
Data on sales patterns can be applied to supply chain optimization or marketing efforts, and nearly everything informs intelligent customer profiles. To get the most out of their data, companies need to maximize its usage across departments.
Consider auto manufacturer Nissan. They’ve created an intuitive, futuristic experience for drivers while lowering their operational costs.
How? By implementing a layered approach to data science that spreads data utilization across the operational structure from sales to manufacturing and maintenance.
Pulling Sources Together
Nissan is emerging as a leader in turning data into actionable business insight. They use a large percentage of their available data, which comes from sources like:
- Regional sales data (sorted by vehicle model, color, and type)
- Website activity
- Consumer interactions with online “vehicle design” features
- Marketing campaigns
- Social media
- Dealer feedback
- Warranty information
- Vehicle status reports from GPS/system monitoring functions
- Driving data
To avoid privacy issues and protect drivers, Nissan anonymizes most vehicle-generated data. For example, instead of noting “this specific vehicle had a computer fault” they track the percentage of vehicles which throw the same fault.
Putting Data to Work
Data is at the heart of Nissan’s growth strategy. Asako Hoshino, Senior Vice President of their Japan Marketing and Sales Division, put it best at a speech at a 2016 Ad Week conference:
“You can’t just be bold, because your success rate will not increase. You have to couple boldness with science. It has to be grounded in science, and it has to be a data set that will underline and support the big decisions you make.”
Nissan uses their data to increase sales in carefully targeted ways. They run the usual sales tracking by region and vehicle, but they also seek out additional details.
Potential customers looking for a test drive fill out an online request form that gives Nissan location-specific data about popular colors, models, and features. This feeds into a tailored inventory for the region and guides dealership placement. It also helps to create highly targeted advertising.
Advertising is another area where Nissan excels. They use advanced visualization tools to make real-time performance metrics on their marketing campaigns accessible to senior leadership.
The data builds a dynamic profile of customers, suggesting which incentives might work best in certain markets and which tend to .
Like much of Nissan’s data structure, marketing data has wider applications. It’s used to create research and design initiatives that deliver features customers actually want.
Some features matter more to consumers than others, but there’s room to show off new technology while still keeping the features that drive sales. Data highlights these opportunities for technological distinction.
Technology is a big pull for today’s drivers, especially when it saves them time and money. Nissan pushes data-centric “connected car” features like predictive maintenance, advanced navigation software, remote monitoring of features, and over-the-air updates that take a lot of the guesswork out of vehicle ownership.
Increasing sales is only half the benefit of data science. Nissan has reduced their operational costs as well. Predictive maintenance- using data to service equipment before it breaks down- keeps their manufacturing process working smoothly.
That’s essential in a market where cars need to be more customized but still built to high standards on a short timeline.
Drivers have busy lives as well, which is why Nissan has a customer-facing application of their predictive maintenance data. They track aggregated vehicle data to detect potential flaws and plan repairs before they become expensive recalls (or worse, cause accidents).
When a vehicle does come in to a dealership for repairs, technicians can use the onboard data to quickly and easily verify warranty claims. This saves the driver time while lowering investigation costs and preventing unwarranted repairs.
In 2011 Nissan set a goal to achieve 10% market share in North America. Nissan North America reached 10.2% market share in February of 2017.
They relied heavily on data science for guidance, specifically in providing targeted inventory and marketing to smaller regions while giving local leaders the right analytics tools to plan their own sales campaigns.
What Nissan Does Right (And What Others Can Learn)
Breaking down data silos
Data silos had been a major hindrance to Nissan’s data science efforts. In late 2016 to early 2017 the company began to address this by employing Apache Hadoop to create a “data lake”. The data lake holds 500TB of data, all potentially accessible for analytics.
Using data in multiple ways
Data is usable by key leaders throughout the company and can be referenced wherever needed. This leads to data-driven decision making at every level. It has the side effect of lowering the individual “cost” of data since it’s reused multiple times.
Encouraging internal adoption throughout the business
Data can be transformative – but only if it’s used. Nissan North America invited key data users from a variety of business areas to an educational internal event on data. They held workshops on their data platform and visualization tools, encouraged networking between IT and end users, and provided resources for further training.
As a result, active users of the analytics platform went from 250 to 1500 by end of its first year. IT saw fewer data requests, most of which were asking IT to add verified sources instead of looking up information.
Creating a layered approach to data science looks intimidating, but it can be as simple as uniting reporting streams in a single place. Concepta’s developers can design a dashboard solution tailored to your company’s unique needs, presenting real-time streaming data through dynamic visualizations. Set up a free consultation to find out more!