How Data is Redefining Consumer Hardware Business Models

Good morning. Adding a sequel to something successful is a risk. But if you recently watched Dune 2, you know it is possible.

Last week, we learned how Eight Sleep is turning one of the most traditional businesses - the mattress industry- into a smart technology and predictive health business.

I pledged part 2, which needs to dive into this topic more—the topic of turning traditional businesses into data-driven money-making machines. A trend traditional businesses like the mattress industry have SLEPT ON… yeah, you saw that one coming, didn’t you?

I got even more inspired by a personal emergency in my house last weekend. I tried to update the firmware of my Wi-Fi router. I didn’t expect this to be a high-stakes task, but during the process, my router did his last reboot and never returned back to life. After many attempts to factory reset, I decided it was time to upgrade. Could I have done more? Maybe. But it’s been four years since I got this one, and I was sure to find some cool new features like Wi-Fi 6 worth a trip to Best Buy.

Strolling through the aisles, one thing became clear. There are almost no devices left that do not offer some connected, personalized, data-driven version of their product.

We all know how products like Peloton, Fitbit, the Apple Watch, and others changed the fitness industry by introducing products that understand their users needs, habits and health better.

Let’s take a look at how other industries are changing. And spoiler - we’ll find one common theme that evolves across all of them.

5-minute-read

One Theme to Rule Them All

Kitchen Appliances

Kitchen appliances like ovens and microwaves have been relatively undisrupted for decades. The traditional approach was to sell a device and let your customers cook. The only sensor we had for years was the heat sensor that indicates a hot cooktop.

Companies like June Oven and Tovala are embedding a lot more sensors, embedded software and cloud services to make sense of sensor data to understand customer preferences and habits and turn meal prep into a personalized experience.

Although June Oven doesn’t seem to operate anymore since Weber acquired it, it set a good example.

Tovala is combining meal kits with its hardware.

Personal Care

Electric toothbrushes like Oral-B’s Genius X use AI now to track your brushing patterns and offer personalized advice. It’s basically more frequent than the six-month dentist visit, where you hear you need to floss more.

And there are also new skincare products from L’Oréal that create personalized skincare advice.

Eyewear

Traditionally focussed on vision correction and fashion, eyewear is merging with tech in different ways. Warby Parker uses digital tools to track how you interact with glasses (e.g. how long you wear them, how lighting conditions change). And Bose Frames is bringing together frames with audio technology. Ray Ban bringing Meta Smart Glasses integrated with Meta AI. Is it getting out of hand? You tell me.

The Traditional Hardware Model vs Data-Driven Hardware

The examples above show how traditional standalone products are adding software and analyzing data to create ongoing value—not just through the hardware but through personalization, user insights, and predictive analytics features.

Focus shift traditional vs data-driven

By integrating usage data tracking, user preferences and other valuable insights from data, these traditional businesses are unlocking new business models.

They love it because software business models are higher-margin than what they are used to. Selling a software subscription with any of their products massively increases their gross margin.

Unlocking New Business Models Through Data

Above, we have seen examples of how data is transforming business models. For customers, these added features should mean a better customer experience, more targeted use, and more value from the product. The goal is also to increase customer satisfaction and retention or upsell products like L’Oréal is doing with the Skin Genie app.

However, the best, most impactful features don’t come for free.

Imagine how excited a mattress company would get if you told them that there was a way to not just sell your mattress and get a one time payment for it but also get an ongoing payment from a customer as long as they sleep on the bed you sold them.

New opportunities to monetize via:

• Subscriptions

• Predictive maintenance care services

• Targeted offers - add-ons and upsells based on user behavior

But it’s delicate. It doesn't work across the board, and some companies fall into the greed trap.

The Wrong Reason to Monetize

There have been attempts to add monetization to features that have always been available in a product. A classic one was BMW's plan to charge $18/month for heated seats. Customers went berserk, and understandably so. It’s not a differentiator, and it has been included in certain packages before as a one-time payment. How is an ongoing payment going to make the heated seats better? More heat? Balance the heat based on butt cheek temperature differences?

The Right Reason to Monetize

The example above is not a feature that would evolve in its nature or get better. Static features that don’t change over time suggest a one-time payment, especially if a feature has previously not been behind a paywall or competitors offer it as a “base” feature.

There are a few characteristics that make a feature justifiable:

• Evolving nature - continuous value, new content, and improvements.

• Access to exclusive or evolving services - continuously updated content like fitness programs or extra cloud storage and cloud services.

• Ongoing and improving personalization - AI models trained and consistently improved from user data can offer a highly personalized experience which gets better over time and requires continuous development.

• Continuous support and maintenance - based on monitoring and predictive maintenance models or even remote diagnostics.

• Ecosystem integration—We have previously discussed Apple’s ecosystem as a prime example. Apple’s cloud connects all its devices, and a subscription makes it seamless to add new ones.

Although it seems very tempting to start new companies with the premise of doing a lot of cool data modeling, hiring the smartest minds, and building very valuable data models, this is also an extremely risky business.

Challenges for Consumer Hardware Startups

TL;DR - they have a small chance of survival. We don’t need to argue - below is the data. Although the first chart is from 2017, it was an era in which a lot of consumer hardware gained traction.

Although the chart above is from 2017, the chart below shows a massive downturn in funding rounds in consumer electronics.

These types of companies are not securing big paychecks anymore.

Investors seem very shy about funding new hardware startups. This is a result of recent disappointments. If disappointment is not a massive understatement for companies like Magic Leap, which busted $3.5 billion in equity funding, or Essential, Andy Rubin’s (the creator of Android) phone company, which collected $300 million in funding but then sold its IP to a company called Nothing. So, Essential investors ended up with nearly “Nothing.”

Investors don’t want to end up with nothing…

They are well aware of a common trend in consumer hardware. Rapid progress has made it hard to keep a device in the market for long. Just look at the TV market.

Take TVs, for example. What I used to pay for a TV with 1080p a few years ago—I now pay the same for a TV with 4K OLED 85” displays, a fancy operating system, no bezel out of-your-mind HDR, Dolby Vision displays…, and all for the same price.

The challenge is to keep the advantage. When your product gains traction in the market, other manufacturers will start copying and adding it to their product line. You see your margins shrink because you need to start competing in a losing price battle to the bottom.

And the solution to this challenge leads back to the services we discussed in this post.

Conclusion: The Future of Your Life is Data-Driven by Design

So, what’s the takeaway? Should one never attempt to start a company in the consumer hardware space? Not necessarily. Success requires thinking beyond the first product sale. To support the business financially, they need services and features that help them continuously stay relevant and in a premium price segment.

The startups I see thrive in this field have a plan from day one to build an ecosystem around their product. They often have strategies on their roadmap, including:

• Data-driven insights for both themselves and the consumer

• Complementary services that enhance the product’s value

• Hyper-personalized experiences based on individual customer data

However, it is difficult to upsell these services immediately. Startups can’t afford to bleed money for too long. Fast iterations with a customer focus should quickly reveal features worth charging extra for.

Established companies with large consumer bases and established brands can afford longer-term experiments with data-driven personalization. They have the financial stability to sustain data modeling and build top-line products with premium features based on what sticks.

Interestingly, companies might find that customers are willing to pay more than expected. Take Eight Sleep, for example. They raised prices for their AI-powered sleep cover from $2,000 to $2,400, and customers didn’t blink. They wanted the best product, and the higher price helped ensure the company's survival. It is a clear testament that AI-powered features add value customers can appreciate if done right.

Have a great rest of the week,