The Kodak example
Take Kodak as an example. In 1998, the world’s largest photographic brand employed 170,000 employees, had a market cap of $28bn and sold 85% of all photographic paper worldwide. But in just three short years it declined to the point of bankruptcy.
While the company changed course to invent the digital camera, it totally failed to grasp the impact of the digital wave. Moore’s Law - which says all exponential technologies fall short of expectation for a long time before they become superior and then mainstream in only a few years - played out for Kodak. The company did little to prepare for later disruption.
While it sounds counter intuitive, this will happen as innovations happen with artificial intelligence, health, self-driving and electric cars, education, 3D printing, agriculture and even jobs. This is the reality of the 4th Industrial Revolution: a world where everything is connected, everyone is “plugged in” and software and operating platforms disrupt most traditional industries.
To offer an example, let’s take a look at a data analysis investigation we did on a client recently – let’s call her Tracy.
Our investigation stems from our business capability to collect, process and interpret data. We receive over 35m data messages daily, with the number growing by approximately half a million a month. We then shape these messages into meaningful bits of information to assist our customers and partners to improve and streamline their businesses.
Tracy is a trendy and sophisticated millennial equipped with the latest technology and gadgets. She’s online and connected 24/7, 365 days a year. Not only is she tech savvy, she’s also generally immune to most traditional marketing and sales pitches. Two critical things about her: she expects exceptional service and values personalisation.
We decided to take a look at how Tracy’s needs and habits have changed over the last few years and how we go about building future value for her. By analysing the massive quantity of data and information we had on her, from where she lives, works and plays to how often she drives and how much time she’s spending in her car, we were able to draw out certain patterns and predict future occurrences. At the end of the day, we’re looking for the wants and needs that haven’t even been noticed yet.
What we know about Tracy is that her address has changed three times in the last six years, from Joburg to Cape Town and back to Joburg again. She has gone from being a student to being employed, self-employed and back to being employed again.
In the last six years she has only visited a traditional dealership twice, and the second visit was not to her primary original equipment manufacturer (OEM). We know she services her vehicle at an independent workshop and we also know from the time she stopped at the other OEM she was likely dropping a family member off to collect their car from a service. Her primary grocery store has also changed from Pick n Pay to Woolworths – a sign of more disposable income perhaps, or just that it’s more conveniently located?
As we monitor these trends we can see that her mobility habits have changed. Mileage travelled is down 37% from 2009 to 2015 and the duration of an average trip is also down by 15%. Weekend and night time driving are significantly less – perhaps because of services like Uber?
What we can deduct from these snippets of info is that the cost of Tracy’s mobility is increasing. While Tracy still has a car, she uses it less, making the cost per kilometre to run her car higher. So she may consider downsizing to a more affordable and cost-effective option for her changing needs.
Explored further, is Tracey a market of one, or with more investigation can we begin to support a theory that the longer someone owns a car, the less mobile they actually become? What implications does this hold for the insurance industry and for how insurance is bought in the near future?
Ultimately, insurers might want to offer new services for people like Tracy. For instance, Tracy might decide that the most cost-effective option is to rather rent a car when she needs it. Perhaps then she could be offered reduced car rental rates for good driver behaviour. Also, as she is constantly connected,
Tracy might benefit from receiving real-time alerts on upcoming hazards during her car journey, such as potholes or traffic at a standstill just over the hill. And she might want to receive logistics services alerts to let her know the delivery time for her parcel or when the plumber will arrive based on the real-time data from their vehicle tracker.
Data transformed into insight helps us to address consumer needs effectively, and without this insight the data is basically a collection of facts that have not been interpreted to deliver meaning.
For the insurance industry and many others, what happened to Kodak is highly likely to happen to them in the next 10 years. Just who will see it coming and adapt their offerings timeously remains to be seen.
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