Forecasting the adoption of a new product may seem difficult – and it certainly can be.
However, it is not impossible.
With the right ingredients and the right approach, businesses can make reliable predictions around product adoption.
Below, we will cover 10 tips that can help businesses:
- Forecast the adoption of a new product
- Design products that are better suited to their target user base
- Create products that perform well and generate tangible profits
Because much of a product’s success relies on its ability to sync with its users, we will learn several ways that users can inform predictions related to product adoption.
10 Tips to Help with Forecasting the Adoption of a New Product
Here are 10 tips that can help businesses better understand and predict how users will adopt a product and how that product will perform in the marketplace.
1. User testing
User testing, or usability testing, is an important stage in the product development process.
Test users will:
- Try prototypes and MVPs
- Offer insights into a product’s design and functionality
- Provide feedback
This information can be used for a few purposes, including enhancing product design and informing forecasts related to product adoption.
2. Feedback and data from early adopters
Once a product or MVP has been released to an audience of early adopters, product creators will have more data to inform their predictions.
Early adopters are those audience members who like to test new technology and ideas before their peers.
Feedback and data from these users can inform product developers about:
- How well the product meets its users’ needs
- Key design attributes, such as usability and utility
- How the product will be used and adopted by a larger audience, further down the line
As with the other tips covered here, it is best not to use a single tip to fuel forecasts.
Instead, product developers should use a variety of sources, resources, and lenses to inform their predictions.
3. Software usage data
Software usage is another important element that can offer insights and information into current product adoption.
This data can help product creators:
- Learn how users currently adopt the product
- Identify sticking points and growth opportunities
- Improve product adoption efforts
- Predict how future users will adopt the product
Obviously, software analytics can only be implemented in working prototypes or MVPs – not on products that haven’t left the drawing board.
4. Historical trends
Historical trends can be used as a reference to help product creators understand past behavior and activity.
There are a number of areas that can be examined, including:
- Marketplace trends
- Adoption trends
- Historical trends for a specific target audience
- Trends and data from a business’s existing user base
To repeat: no single resource should be used for forecasting, including historical trends.
5. Current trends
One way to predict the needs of a user base in the future is to examine today’s trends.
Combined with historical data and the other information sources covered here, current trends can help point towards future trends, needs, and demands.
And when product creators have a better idea of what tomorrow’s marketplace will look like, they will better be able to make predictions around product adoption.
6. Others’ future forecasts
Many research firms, such as Deloitte or Gartner, regularly analyze and make predictions about the future of industries, economies, and markets.
Naturally, not all research analysts are created equal.
The quality of these future predictions can vary greatly from one party to the next.
However, when used in conjunction with the other ideas covered here, others’ future forecasts can help inform one’s own.
7. User psychology and profiling
Understanding users is essential to designing great products and services.
A deep understanding of users can help businesses:
- Create products that users actually need and want
- Design digital adoption strategies that deliver better results
- Forecast how users would adopt a product or service
Data sharing and collaboration among multiple business units can benefit product creators immensely, helping them create unified views of their customers.
Those perspectives, in turn, can further enhance predictions about how users will interact with new products.
8. Assessing product-market fit
Product-market fit assessments can help businesses learn:
- What the market wants
- Whether the product is a right fit
- And how that product will be adopted by an audience
After all, product-market fit plays an enormous role in the success or failure of any product.
A product may be stellar and groundbreaking … but if it doesn’t fit the market, it won’t be adopted.
9. Predictive modeling
Today, AI-enabled techniques such as predictive modeling can be used in a wide number of applications.
When fed the right information, predictive modeling techniques can generate its own forecasts, further augmenting one’s own predictions.
To predict product adoption, businesses can analyze much of the data covered so far, such as:
- User data, such as psychometrics, demographics, and so forth
- Marketplace data
- Software usage statistics
To name just a few.
To reiterate: predictive modeling should augment, not replace, one’s own predictions. The technology is still in its early stages, and is by no means perfect.
10. The right combination of information and forecasting techniques
As mentioned, no single technique, resource, or approach should be the basis for forecasts.
Users themselves are certainly the best resource for predicting future behavior – past activity, psychological information, feedback, and so on.
However, to truly make effective predictions, it pays to work with as many lenses as possible.
Combining the right information with the right approaches will help product creators make accurate forecasts around the adoption of new products.
Also … and just as importantly … it will help them develop products that are more usable, useful, and relevant.