Product expansion – selling new products to your core market, is almost always easier than market expansion – selling products into new markets. Makes sense. Existing customers already know your company. But how well does your company know its customers? Product expansion may be easier but it’s still hard. Without a deep understanding of your customer’s business, a new product introduction, even to your core, is risky to say the least.

Smart tech, such as AI and IoT, improves upon traditional methods of collecting customer data. Through a virtual link, smart tech allows companies to quantify how customers use their products and what they use them for. This is the primary data upon which existing products are innovated and new products are invented. It is the data that guides new physical products, new information products and, as we’ll discuss here, new fintech products.

A few years ago, I keynoted at a big insurance conference. In my presentation I demonstrated the power of smart tech data by comparing the driving skills of my two children and their insurance rates. My son, a teen at the time, drove like his grandmother – slow and careful. My daughter, not so much. She drove too fast and was constantly distracted by her phone. Who do you think had the higher priced insurance premium? My son of course. Not because he was the worst driver, because he wasn’t, but because his cohort of young male drivers are involved in more auto accidents than young female drivers.

Presenting how individualized driver data could remedy this disconnect hit a nerve. Not just for auto insurance but for all forms of insurance. Questions after my presentation and my discussions with folks during the rest of the day confirmed that they were freaking out. Freaking because they felt competitively disadvantaged by not having access to the same primary customer data fast-growing upstarts such as Lemonade, Beam and Vitality had. Individualized customer data produced by smart tech is disrupting the insurance industry. So, if your company makes, operates or services physical products why not do a bit of disrupting yourself?

Speaking of disruptors, consider Tesla. Tesla vehicles are smart vehicles that employ AI and IoT. Like other smart products, they are software-defined and data-driven, and through a virtual link, Tesla knows how their customers use their vehicles and what they use their vehicles for. So, who knows their customer’s driving behaviors better… an old-school insurance company or Tesla?

The use of proprietary and primary data launched a brand-new business: Tesla insurance. Storming the market with a low-risk product expansion strategy, Tesla’s new fintech product will always be profitable and can be purchased directly by the driver with their Tesla app, disintermediating the traditional channel and increasing the policy’s margins. The high fidelity of their customer data enables Tesla to underwrite the car insurance policy for each driver differently. Bad drivers like my NASCAR-driving daughter, are either not offered insurance or offered it at a higher premium. Good drivers like my granny-driving son, pay a lower, more competitive price. Makes sense. It’s a win-win for all involved and it’s a product expansion strategy many companies can copy if they make their product, service or environment smart. And the same customer data can also be used to launch other fintech products like financing, as well as information products and of course other physical products too.

In the future I’ll discuss how this same proprietary data can be used to create data-driven business models previously impossible to deploy.

When architected correctly, smart products yield proprietary primary customer data that can be used to drive lower-risk product expansion. Like Tesla, your company can also offer insurance and other high-margin, competitively priced products that precisely fit each customer’s needs.

HOW IT’S DONE

The first step in creating an insurance product for your tech or traditional physical product is to deploy usability and utility sims to understand how your customers use your product and what they use it for.  These simulations are run to produce or predict the risk variables to be fed into the insurance underwriting model which is used to output a risk level.  For example, all things being equal, a customer that uses the product a lot would pay a higher premium than a customer that only uses it occasionally.  The underlying data is collected by sensors and fed to the sims’ models over the internet of things.

While software products don’t require this type of insurance, the same steps of using usability and utility sims can be used, with sensors being replaced with internal “listener” data constructs.  This can spawn information products that augment the software product’s value.  For example, inventory control software could be improved by connecting to an inventory count service to eliminate manual entry if this was something determined by the sims as being something important.

Contact us if you’d like to understand more or to schedule a free 30-minute consultation if you have a company to improve.