Dear Dr. Jay,
We’ve been testing new concepts for years. The magic score to move forward in the new product development process is a 40% top 2 box score to purchase intent on a 5 point scale. How do I know if 40% is still a good benchmark? Are there any other measures that might be useful in predicting success?
I have some good news—you may have a big data mining challenge. Situations like yours are why I always ask our clients two questions: (1) what do you already know about this problem, and (2) what information do you have in-house that might shed some light on a solution? You say you’ve been testing concepts for years. Do you have a database of concepts already set up? If not, can you easily get access to your concept scores?
Look back on all of the concepts you have ever tested, and try to understand what makes for a successful idea. In addition to all the traditional concept test measures like purchase intent, believability, and uniqueness, you can also append marketing spend, distribution measures, and perhaps even social media trend data. You might even want to include economic condition information like the rate of inflation, the prime rate of interest, and the average DOW stock index. While many of these appended variables might be outside of your control, they may serve to help you understand what might happen if you launch a new product under various market conditions.
Take heart Norm, you are most definitely not alone. In fact, I recently attended a presentation on Big Data hosted by the Association of Management Consulting Firms. There, Steve Sashihara, CEO of Princeton Consultants, suggested there are four key stages for integrating big data into practice. The first stage is to monitor the market. At CMB, we typically rely on dashboards to show what is happening. The second stage is to analyze the data. Are you improving, getting worse, or just holding your own? However, only going this far with the data doesn’t really provide any insight into what to do. To take it to the next level, you need enter the third stage: building predictive models that forecast what might happen if you make changes to any of the factors that impact the results. The true value to your organization is really in the fourth stage of the process—recommending action. The tools that build models have become increasingly powerful in the past few years. The computing power now permits you to model millions of combinations to determine the optimal outcomes from all possible executions.
In my experience, there are usually many attributes that can be improved to optimize your key performance measure. In modeling, you’re looking for the attributes with the largest impact and the cost associated with implementing those changes to your offer. It’s possible that the second best improvement plan might only cost a small percentage of the best option. If you’re in the business of providing cellular device coverage, why build more towers if fixing your customer service would improve your retention almost as much?
Dr. Jay Weiner is CMB’s senior methodologist and VP of Advanced Analytics. Jay earned his Ph.D. in Marketing/Research from the University of Texas at Arlington and regularly publishes and presents on topics, including conjoint, choice, and pricing.