Concept testing in new product innovation is worth the cost. In B2B there are normally fewer customers and concept testing is easier and less expensive. But if you are in B2C with millions of potential customers for your new product, concept testing costs do work out.
[Note: This post was originally published on May 30, 2012. Thanks to reader interest we have done minor updates to the post on March 9, 2021.]
For prices starting at under 100,000 $ from leading concept testing agencies, you can get to know what your potential customers think about your new product concept. For consumer food products, let’s say you get one bit of feedback: some folks are wondering about the high sodium level. You can come up with a low-sodium variant and grab large sections of the older population that’s trying to watch its sodium and salt intake for blood pressure. Think of the millions of new customers that the product would have missed without a timely concept test !
So how does a concept test work? If you have developed a concept, i.e. a fairly well articulated idea of the product,preferably a prototype that customers can see, you ask the following question:
- How likely are you to buy this product ?
The key point is that you ask a sample of your target market. The target market could be defined by age,income,education,location or behavior but the closer you can get to asking your target market the better is your handle about what might actually happen when you get to launching your product.
The answers to the “How likely are you likely to buy… ” need to be classified into a five point Likert scale like
- Very likey to buy….. score 5
- Likely to buy…… score 4
- May or may not buy…..score 3
- Unlikely to buy ….score 2
- Very unlikely to buy… score 1
When you add up the scores of the first two items, you get an overall idea of how well the idea is going to fly with your target market. At this stage customers responding are not actually spending money at the retail store so the percentage folks who politely say Very likely buy/likely buy might actually turn out to be less than half eventually . Thus in the sample say 70% fall into the very likely/likely category – after product launch the numbers might be under 35% who actually buy. Here past data is helpful for caliberating customer responses in similar categories. For example, earlier BASES ( Booz Allen Sales Estimates System) now a part of the Nielsen group, has been gathering data since 1986. BASES is able to quickly compare with past purchase intention data to give a good sense of how the purchase intention estimates might work out in consumer product categories.
More importantly, the innovator can get some important feedback and change product characteristics early in the development process to increase chances of later market success.