David Shankman
Failure. The most common experience companies have with new product introductions, unfortunately, is failure. According to a recent report, up to 86% of new product innovations fail. And failure is common across all industries, despite rigorous testing methodologies. So why is it that so many innovations are doomed to fail? According to Dr. R. Sukumar, President and CEO of Optimal Strategix Group, “The problem lies in the process. Many companies continue to use the same innovation process that has been used for decades. But companies now have access to better and more sophisticated insights and analytical methodologies that can successfully guide and transform ideas generated through brainstorming into concrete offers with a much higher chance of success.”
These newer methodologies leverage deeper insights and advanced quantitative techniques and sophisticated software to understand and quantify what customers value when making a purchase decision. Having this level of understanding early in the process increases the organisation’s innovation efficiency and reduces cost by allowing for sharper focus on the right ideas. Companies can also use these same insights to more successfully commercialise new products.
Overall, deeper customer insight leads to superior innovation, superior commercialisation and minimised risk of failure. The question remains, how can firms take advantage of this contemporary approach?
Time for an Innovative Change
For decades, the same innovation process relying on exhaustive concept evaluation methods has been used. However, the high rate of innovation failure signals that change is needed.
Where does the problem lie? While concept testing methods (e.g., BASES) play a valuable role in evaluating a concept’s potential, quantitative measurement of key customer purchase drivers has been virtually absent from the early stages of the process. This lack of quantitative measurement fails to provide marketers with the detailed information necessary to create products specifically tailored to the customer’s wants and needs.
Traditional approaches have not delivered a consistently high level of successful introductions, in spite of requiring significant investments of time and money. Customer needs must be more accurately defined and quantified to avoid developing ideas that do not match the customer’s “ideal offer”.
Fixing this problem requires a quantitative power tool such as adaptive conjoint analysis, a method of garnering and quantifying a rich understanding of the customer. When this is combined with demographic, behavioural, perceptual, and attitudinal data, this approach offers a true solution to the dilemma. Adaptive conjoint methods take sophisticated data and make it simple to interpret. Leveraging a greater understanding of real-world customer values, Adaptive Conjoint methodologies can even be used in place of traditional concept testing. Implementing an adaptive conjoint approach, creates a more efficient, focused, and productive innovation process.
Speaking on the benefits of adaptive conjoint, Dr. Sukumar notes, “The adaptive conjoint method is an invaluable component to innovation success. In using this approach, various options can be explored to understand which customer outcomes, benefits, or emotions are most motivating for customers. The approach can also uncover benefits and consumer outcomes that do not currently exist and provide an understanding of the impact their introduction would have on customer purchase decisions.” Additionally, consumer decisions are now more complicated and emphasise a larger number of benefits or outcomes. For example, in a recent review of consumer decision making, it was noted that nearly 12 benefits are required to explain what matters to consumers in how they might make a choice for a product. Only six benefits overlapped across consumers and even that was only among a group of 30% of the sample, indicating the heterogeneity and the diversity in how consumers make choices.
Adaptive Conjoint in Action
We’ve tested the benefits of applying Adaptive Conjoint to the innovation process in a number of organisations. A major small equipment manufacturer had generated well over 100 innovation ideas, but was struggling to refine them into products that would strongly appeal to its customers and qualify for its long-term new product development pipeline. In order to provide a clear and rigorous way to prioritise these ideas an advanced adaptive conjoint methodology was used.
The approach worked in the following way:
- A list of category-relevant attributes, potential product benefits and outcomes for consumers, features, and emotional contexts were generated.
- Customers grouped the attributes into three categories of importance (e.g., most important, important, less important).
- Respondents then ranked the attributes in each of the categories from the most important to least important, thereby resulting in an overall attribute rank order.
- The attribute importance questions were broken down into a sequence of constant-sum paired comparison questions. The paired comparisons were chosen adaptively for each respondent in order to maximise the information elicited from each paired comparison question.
- Levels of importance for the features were then estimated from the constant-sum paired comparisons.
As a result of this approach, the equipment manufacturing company was able to distill the ideas into 10 solid projects in their pipeline, arranged by market and the products that supported each market. These projects closely reflected the key purchase drivers of the customer targets, increasing the likelihood of in-market success.
The resulting product introductions were successful, with sales coming in higher than company expectations. In addition, the data has been leveraged to improve the company’s positioning and external messaging and communication. As evidenced, the output from adaptive conjoint methods can be easily leveraged to focus the innovation process on higher potential areas.
Conclusion
Many failed attempts at innovation stem from a lack of understanding of what matters most to the customer. The fact is, even the best processes cannot generate success from the wrong ideas.
An increased focus on using adaptive conjoint methods to quantify true customer drivers reduces both the cost and time components of innovation and improves the likelihood of generating successful products and services. Adaptive conjoint methods are the research power tools crucial to building an organisation’s house of innovation.
Given their sophistication, adaptive conjoint methodologies help to improve the innovation process by ensuring the following:
- Each respondent is exposed to all attributes
- Survey duration and respondent fatigue are reduced
- Valid results are achieved even with small samples (e.g., people with certain diseases, expensive target customer groups)
- Ability to handle a significant number of attribute options (more than 50)
- Improved predictive accuracy compared to other methods (e.g., Constant Sum, MAXDIFF)
David Shankman is Principal of Innovation and Strategy Consulting, Optimal Strategix Group
