Model-product fit

There are three key components of model-product fit, which is how your business model relates to your product: model-product friction, value distribution, and model-roadmap alignment.

Model-product friction

A low-friction product is easy to use to accomplish a product’s JTBD. It’s easy to get started in and keep using. Examples: TikTok, Gmail, Bumble.

A high-friction product has a more complex onboarding and use process. Training or integrations might be involved. Once a user is in the product, they might end up Googling how to do X or bookmarking the product’s help center. Forming a product habit takes longer. Examples: Salesforce, Intercom, Adobe.

Your product’s friction should align with your model’s friction. A low-friction product should have a low-friction business model. A high-friction product should typically have a higher-friction business model.

Here are some examples of products with aligned product and model friction:

When product and model friction don’t align, there’s a risk that product value won’t get realized, the unit economics won’t work, and growth potential will be stymied.

• Low product friction and high model friction: Not competitive. You’ll lose out to competitors who make it easier to pay or offer a more affordable solution. Because of the pricing barriers to entry, you’ll limit the number of users who experience your product’s JTBD—and limit growth.
• High product friction and low model friction: If you have a highly complex product, it probably can’t be learned during a free trial or freemium use. Users wouldn’t get the maximum value from your product. If you were to offer onboarding services to help free trialers get the most out of your product, your CAC would go up and could become unsustainable. Neither option—low product value experience or high CAC—bodes well.
• Low product friction and low model friction: This works, as shown above in the Instagram example. The barriers to entry—and to payment (if applicable)—are low. Free trials, freemium, or just plain free often align with simple products.
• High product friction and high model friction: This works too. On the product side, there might be integrations or training. It might take a while to experience full product value or form a habit. On the model side, you might have a high price point or annual contract.

Because of the importance of aligning product and model friction, successful low-ARPU products tend to be low friction (like social media apps), and successful high-ARPU products tend to be high friction (like enterprise B2B SaaS).

Value distribution

The second element of model-product fit is value distribution.

In our discussion of usage-based pricing earlier, we talked about how a usage-based approach works well for products that are used repeatedly. Customers get value on a recurring basis, so it makes sense to charge them for that value on a recurring basis.

That’s value distribution: the rate at which users experience your product value. Your business model should be aligned with your value distribution.

• Products that are used consistently—and therefore deliver value on an ongoing basis—can be charged for consistently, e.g., through usage-based or subscription pricing.
• Products with more sporadic value, or that have the majority of their value delivered immediately upon purchase, are better suited for an upfront or per-transaction model. That’s the case for physical products and for digital products you only use every now and then, like Airbnb.

Also consider friction here. An infrequently used product with a freemium model might never get any actual conversions. For a frequently used product with a high-friction model, the model would prevent users from developing a habit around product use.

Model-roadmap alignment

The last model-product fit element we want to emphasize here is the importance of paying attention to what your users are paying for.

If you find—from user tracking, interviews, etc.—that the reason people pay for your product is to have access to a particular tool or feature, that knowledge might inform not just your value metric. It might also inform your product roadmap.

Say you have a CRM tool. Through a combination of qualitative and quantitative data, you learn that customers get much more value out of your chatbot than your onboarding tools. Your engineering team might reprioritize your sprint tasks as a result, bringing chatbot optimizations into the next sprint and tabling onboarding tickets.

As ever, it all boils down to what your customers want. When it comes to model-product fit, what that means is what your customers want to pay for.