{"id":10301,"date":"2021-04-05T10:00:32","date_gmt":"2021-04-05T10:00:32","guid":{"rendered":"https:\/\/www.revuze.it\/?p=10301"},"modified":"2021-04-05T10:00:32","modified_gmt":"2021-04-05T10:00:32","slug":"roi-of-product-development","status":"publish","type":"post","link":"https:\/\/www.revuze.it\/blog\/roi-of-product-development\/","title":{"rendered":"How Real-Time Voice Of The Consumer Impacts The ROI of Product Development"},"content":{"rendered":"

One of Henry Ford\u2019s most known quotes about products was \u201cAny\u00a0customer\u00a0can have\u00a0a car painted\u00a0any\u00a0color\u00a0that\u00a0he wants, so\u00a0long\u00a0as it is\u00a0black\u201d. This was when Ford was focusing on delivering affordable cars of high quality and had to compromise on colors. The car in question here was the Ford Model T and there were 15 million cars sold. So, Ford Motors made the right decision in focusing on some abilities of the Model T over others (colors).<\/span><\/p>\n

You\u2019d expect that with time, modernization, etc. brands will have hit products like the Model T more often, but the reality is that over <\/span>80%<\/span><\/a> of new consumer products fail, with <\/span>70%<\/span><\/a> of these launches are by established brands and not newcomers.<\/span><\/p>\n

According to <\/span>McKinsey<\/span><\/a>, the way to measure R&D and Product success is primarily in the ratio of new product contribution to revenue. Basically, for every $ spent on R&D, you get back X$ in new product sales that grow your overall revenue.<\/span><\/p>\n

In short, a key measurement of R&D ROI for consumer products is the successful sales of the new products.<\/span><\/p>\n

Voice of the consumer (VOC) challenges<\/b><\/h2>\n

According to Bain and Company, <\/span>80% of brands<\/span><\/a>\u00a0<\/span>believe they deliver superior customer experiences, but only 8% of customers feel the same \u2013 hence the \u201cVOC gap\u201d.<\/span><\/p>\n

Because the number of feedback channels today is growing and the volume of feedback is scaling as well (chat, calls, email, social media, online reviews, Q&A\u2026). According to IDG, over 90% of the world\u2019s data is soon to be made of such customer opinions. So, whatever VOC gap we have so far \u2013 it\u2019s not going to get any better if we keep doing the same in an environment that is becoming more complex.<\/span><\/p>\n

What is common to all existing solutions today that analyze customer opinions is that they heavily rely on human experts in the loop – data scientists, analysts, IT folks that train generic AI machines to search for specific patterns and expressions. Just like any other industry where manual labor is involved – things get slower, expensive, and biased.<\/span><\/p>\n

In addition, this approach of relying on experts to predict what patterns to look for is reactive in nature. The experts can\u2019t predict a new trend or competitor, so they can only add these to the list of patterns once there is enough evidence that this is mandatory.<\/span><\/p>\n

This all leaves us with several key challenges:<\/span><\/p>\n