Investing in 18-month digital projects just to launch to an audience that doesn’t need your product or service is something many companies—especially manufacturing companies—an experience quite often.
There is a better way that takes the risk out of digital endeavors and allows executive teams to make decisions based on data instead of PowerPoint presentations.
Manufacturers traditionally use a variation of this waterfall approach to bring a new product from conception to completion:
Idea → Due diligence/Research → Presentation to executives → Presentation to board → Budget allocated to project → Resources hired → Project execution (12–18 months) → Project launch → Measure success
The biggest problem with this approach is that it all assumes one thing: people need your product. We’ve all seen many failed launches, and there is a better way to handle this approach for digital products.
The new, capital-efficient approach to digital growth is this:
Idea → Assign small team/agency → Run Validation Tests (1-2 months) → Measure success → Execute (2-3 months) → Optimize and Grow
The end goal for both approaches is the same: build a successful growing product or service that customers need. But how you get there is much different.
With the second approach, you don’t hedge your bet on a single idea. Instead, you diversify ideas and take educated hypotheses on what ideas will turn into full-fledged service offerings and validate them in the market.
The question you should answer is: How do we validate our investment before we invest our capital and resources?
The answer isn’t creating new PowerPoint presentations or creating an executive or governance committee. It’s about proving that a digital product will increase the growth of the company as soon as possible, without the risk of ultimate failure.
With that in mind, I wanted to share two approaches we use to help manufacturers achieve digital growth without taking on risk.
Utilize low-code platforms (and machine learning) to accelerate your product/market validation.
Gartner predicts that low-code application building would gather more than 65% of all app development functions by the year 2024, with about 66% of big companies using a minimum of four low-code platforms.
We leverage systems like MS PowerApps or Webflow that allow you to create powerful custom app prototypes without any coding involved.
There are some key benefits of low-code platforms:
Accelerate time to market: Many of these low-code platforms are ready to go out-of-the-box and have visualization tools that allow you to drag and drop and configure a product instead of coding one from scratch. This ranges from prototyping design and development tools to data platforms powered by AI and machine learning.
Decreased costs: Many low-code platforms are significantly less costly than building a solution from scratch. They also bill monthly and run a cancel anytime type of service. You don’t need an enterprise agreement to use these types of products.
Agility and flexibility: You’re not tied into a single system. There are many tools on the market that serve different needs.
By utilizing these approaches, small teams can build products and test them significantly faster without a huge time commitment or massive cost investments.
In regards to utilizing AI and machine learning, the biggest myth is that setting up models to learn from is a time-consuming process. That is partially true; however, many low-code platforms, specifically data-focused low-code platforms, have AI built-in.
You can upload a raw data set to the platform, and it will perform its analysis on its own. The platform did all of the development work upfront to make your life a little easier. So, if you wanted to make sure that utilizing machine learning can help you validate your ideas, you should start with a low-code platform before investing in creating your own models.
Understand the need for your idea by experimenting on multiple platforms.
The problem with PowerPoint presentations is that the author can form any narrative they want to portray. It doesn’t require it to be true. The story wins, end of discussion.
And as we all know, stories don’t sell products that no one needs.
So, part of your validation is running paid ads on platforms where your prospective customers are active. This does several things:
Test multiple audiences: Instead of declaring that a single audience will love the product, you can run a single ad to multiple segmented audiences to see who responds most favorably. And now you have data that supports your audience hypothesis.
Test multiple value propositions: You may think one value proposition is the clear winner, and the entire team might agree with you, but you’re missing one thing: validation. By utilizing an ad platform, you can quickly see what value proposition is performing the best. This information will inform how you build the product (assuming it is validated).
Drive early engagement: You can get early sign-ups to the product within weeks of the initial idea, even before the development of the project begins. And when the product does officially launch, you have a defined, validated audience to launch to.
Manufacturers don’t have to go all-in on digital to achieve growth. It starts with a lean experimentation approach.
Technology and digital roadmaps lasting 12–18 months are great approaches if the product has proven to create an impact for the organization. This could mean revenue growth, increased productivity, or decreased costs.
The best manufacturing companies are piloting products before going all-in on their investments. By utilizing low-code platforms for prototyping and machine learning as well as digital ad platforms, you can get to market faster and add confidence that you’re building the right product.