Creating Technology-Based Offerings
By Ron Ozminkowski, Ph.D., Founder & President, Analytic Strategies & Consulting, LLC and Senior Vice President, Commercial Analytics, Aon plc.
In earlier posts, we learned about the non-tech factors that largely influence success or failure with technology-based offerings and what it takes to scale those offerings. In this post, we back up a bit to focus on which technology-based offerings to create in the first place.
The obvious place to start is to gain a strong understanding of gaps in goods or services that consumers want but cannot easily get. Once that is understood, the next key question is whether your firm can create something reasonably quickly and affordably, to help close those gaps.
One might think everyone develops offerings this way, so the real challenge is to just do a better job than the competition, perhaps by injecting artificial intelligence into the production process. But as it turns out, the discipline behind offering development varies quite a bit from firm to firm, so adding AI is not the answer. Nor is having enough money to finance initial development. If money and tech were the main attributes of success, there would be fewer layoffs and failures among firms that previously generated hundreds of millions of dollars in investment capital to produce their products or services. Landi (2022) lists several examples.
According to Dr. Nadia Bhuiyan (2011), the path to success is paved with a solid product development strategy, operationalized deliberately and thoughtfully, and guided by metrics related to critical success factors for every stage of the process. She argues that multiple stage gate reviews should be used, to allow careful consideration and reconsideration that guides investment, avoids sunk costs, and uses resources efficiently and effectively throughout the development process.
Importantly, offering tech-based solutions isn’t a one-and-done thing. Building a tech-based solution isn’t the end of the development process. Every successful tech offering has a life cycle requiring ongoing injections of new data, along with maintenance and updates of input models, to keep it relevant and affordable for the market. Skimping on maintenance and updates in tight financial times is tempting, but it can be a path to disaster. As noted in Part 1 of this series, stay focused on maintaining and enhancing customer value at a reasonable price, and your offerings will maximize financial returns throughout their useful lives.
What to Create
Suppose you have a pretty good idea of what the market needs. Maybe you used design thinking to work collaboratively with clients to understand the problems they want to solve, what they see as barriers to solving them, and what a feasible path may be to overcome those challenges. Maybe you’re using an updated data science lifecycle to apply your technology in a careful and strategic way, and maybe you have a good idea of what it will cost to produce your offering.
Despite all this information, many firms still have questions at the micro-level. One of the important lessons about scaling offerings mentioned in Part 2 of this series is to spend the time learning details about which product features appeal to potential clients of different sorts who are located in many different markets you are trying to reach. This information is key to selling across a broad spectrum of clients, some of whom may be in blue oceans (i.e., industries and markets your competitors haven’t reached yet). Kim and Mauborgne (2015) describe how to find blue oceans and tailor your offerings for success there.
A great way to decide which product features to include and where, is to conduct randomized experiments that vary product features by market attributes and locations. Dr. Kieron Dey (2015) describes how to conduct experiments to arrive at the right combination(s) of product features likely to maximize the outcomes desired by customers. His book, entitled Competitive Innovation and Improvement: Statistical Design and Control, uses case studies from the health care and retail industries to describe applications of the science of product feature maximization.
A key advantage of Dr. Dey’s approach is the use of statistical design concepts to compare outcomes over time, and to determine which product features are the best ones to keep. Assuming your new offering has several features that might produce value, and that many but not all these features would be useful everywhere, Dr. Dey suggests testing randomly generated subsets of features that randomly selected providers should apply (e.g., stores or other venues such as digital, telephone-based, etc.). To maximize scaling, these combinations of features and providers also can be randomly assigned across test markets of various sorts in different locations.
The randomization scheme Dr. Dey describes assures that each product feature and combination is tested against its absence, enhancing your ability to make causal inferences about which ones are best. His approach works even if there are many (e.g., up to 20) features to test. Moreover, this can be done with relatively small sample sizes, typically in just a few weeks or months, depending on the product and the outcome of interest. Results from this process will show how much each feature, and each combination of features, moves the needle in desirable or undesirable directions, for the outcomes of interest to you and your potential clients. See his book for details.
Using a Rigorous Product Development Method to Make Your Offering
As mentioned earlier, a rigorous product development method should be used to guide its development process. Several product development methods have been developed since the 1980s. Dr. Nadia Bhuiyan (2011) reviewed the literature describing these methods, synthesizing them to produce “[a] framework for successful new product development,” which she published in the Journal of Industrial Engineering and Management. In that paper, she provides several metrics and critical success factors that can be used at every stage of the product development process to maximize its value.
The following table, taken from her paper and reproduced here with her permission, lists the stages of new product development that have been used in many industries. It also lists their critical success factors, evaluation metrics, and the tools or techniques where these metrics are typically applied.
| Stage | Critical Success Factor | Metrics | Tools and Technique |
| New Product Strategy | |||
| Idea Generation | |||
| Screening and Business Case | |||
| Development | |||
| Testing |
A few key insights from her paper also are worth mentioning.
First, new product development is a collaborative process that should involve staff in every part of your business (e.g., strategy, analytics, development, implementation, marketing, sales, finance, etc.). Getting insights from across the business will clarify the vision for your offering, build support for it, and address challenges and competing priorities that may hamper development. The new-product strategy should thus be clear, well communicated and demonstrably well understood.
Next, empirical evidence obtained from the marketplace should illustrate the likely value of the new products being considered. This is a critical element of successful scaling that was mentioned in Part 2 as well. The voices of many possible consumers should influence whether an offering development process is undertaken. Potential customers should react to alpha- and beta-versions and working prototypes, so they have a good idea of how the offering will function.
Several types of testing should be used throughout the process to ensure the product will do what it is intended to do and not break any other operational or production processes. As mentioned earlier, user testing for satisfaction and value should occur in every market you hope to reach, getting input from customers from every socioeconomic group you hope to serve. This is important because preferences vary by age, gender expression, race, income, educational levels, geography, literacy, and many other factors. This means that what works well for some people in some places may not work well elsewhere. Testing and experimenting can reveal the nuances you need to know about to refine your offering where required to have broad-based success with it.
Following a rigorous new product development process also will help you reliably forecast the return on investment in your tech-based offering. As Dr. Bhuiyan suggests, incorporating ‘yes, proceed’ or ‘no, stop’ stage gate discussions at every major step of the development process will help you learn whether to continue development or stop. This way, sunk costs can be avoided, key assumptions about the market can be reevaluated, or other challenges can be worked out prior to production.
Limitations and Final Comments
A short post such as this cannot address every facet of offering development. Readers are encouraged to investigate the references and links I provided to learn important details.
It is easy to convey a sense of what should happen in the offering development process. What really happens is always situation specific. You may find easy or difficult sailing depending upon healthy or dysfunctional relationships between stakeholders inside and outside your firm. There are always competing priorities and often a desire for speed that will add pressure to skip important steps in assessing the market, understanding the competition, or product design and testing. The notion of sunk costs is often particularly difficult to deal with. There is a strong human tendency to keep going once a substantial investment has been made, even if failure is likely. A better approach is to learn from failures as they occur throughout the process so they can be avoided for the next offering development process.
Finally, you will probably have to prioritize your offering investments. Following the steps above can help you do that systematically. Additional advice on prioritization can be found here. While those comments focus mainly on which data science models to move forward with, the concepts described can also be applied more broadly. Let me know how it goes; I’d be happy to help.
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Acknowledgments
Thank you to Dr. Nadia Bhuiyan for permission to reproduce Table 1. Thanks as well to Robert Elfinger for copyediting. Any remaining errors of interpretation or presentation are mine.
References
N. Bhuiyan, A Framework for Successful New Product Development (2011), Journal of Industrial Engineering and Management 4(4):746-770.
K. Dey, Competitive Innovation and Improvement: Statistical Design and Control (2015), Boca Raton, FL: CRC Press.
W. Chan Kim and R. Mauborgne, Blue Ocean Strategy: How to Create Uncontested Market Space and Make the Competition Irrelevant (2015), Boston, MA: Harvard Business Review Press.
H. Landi, Primary Care Startup Forward Cuts 5% of Workforce Amid ‘Extremely Tough’ Market Conditions, Fierce Health Finance, July 11, 2022, on
https://www.fiercehealthcare.com/health-tech/primary-care-startup-forward-cut-5-workforce-amid-extremely-tough-market-conditions?utm_source=email&utm_medium=email&utm_campaign+HC-NL-FierceHealthFinance&oly_enc+id=3892A5333067C9W
J.A. List, The Voltage Effect: How to Make Good Ideas Great and Great Ideas Scale (2022), New York, NY: Currency, an imprint of Random House.