The Advantages of Product Intelligence For Your Company

While the PR on Big Data suggests that it is the newest and holiest grail for strategists, marketers, product developers, and business decision-makers of all stripes, a new hybrid intelligence is emerging from the smoke and mirrors — one that may be prove to be a little more practical and actionable: Product Intelligence.

What makes Product Intelligence interesting to us as a field of focus is that it is a superb application for Big Data providing highly targeted, real time intelligence that serves up insights INSIDE of the new product development process at the exact moment when conclusive, authoritative insight is most needed.  When its literally make or break.

What Is It?

What differentiates product intelligence from other research?  It provides real-time, data-driven insights for new product development decisions and innovation initiatives based on the large multiples the scale of big data.

What features will attract consumers to my product?  How do customers perceive it relative to competing products?  In which geographic markets will it be the most successful?  Product intelligence can tell you this.

Imagine thisyoure developing a personal hair care product and youre looking for a particular niche, lets say hair color in China, which could be called a mature market.  You can listen to 25 people or perhaps 500 or 5000 in focus groups or online panels.  Or you can listen to 500,000.  Thats the unique advantage and why big data got the name Big.

How Product Intelligence Differs From What Came Before

How is product intel different than other forms quantitative research?

Most of the quant research being done today utilizes structured data.

But the field of data science has advanced and now allows us to analyze vast quantities of unstructured data and customize the questions and build the type of decision support that can be tailored to NPD decisions.  So if youre looking to understand the hair color market in China for Women 50+, who are first time users, its now possible to go deep, and to extract granular insights.

Why is it important to anyone launching new products or services?

Most of us know that launching new products is expensive and risky.  The statistics paint a grim picture of high failure rates:

• 66% of new products fail within the first 2 years (Booz & Co.)
• 80% of new products stay on store shelves for less than 12-months (HBR)
• 96% of innovations fail to return their cost of capital (Deloitte)

Product intelligence, harvested from vast and new information sources and technologies can improve these grim stats, de-risk the process, and can be organized to fill in the insight gaps within the process.  How?

• Data-driven insights customized for each NPD stage gate
• Greater amount of information can inform the decision making process
• Technologies can speed the time to insight
• Based on most comprehensive and relevant data-sets (vs. specialists, static reports, focus groups, consultants)
• All of this can speed the decision processes, make them more efficient, and increase the success rates of new products

A Case Study

New technologies allow you to structure, process and analyze these different types of information to uncover unmet needs and answer new questions.

As an example, what if through social media you identified an unmet need for a new type of hair product. Consumers complain about having to mix hair wax with oil to get the texture they desire.  You can identify the size and depth of the signal and figure out whether demand is strong or weak.

If you determined it was a real opportunity, how would you find the material to meet this need?  Cross-pollination analysis to identify new applicable ingredients from adjacent industries.  Through IP filings and analysis of academic publications, you identify a new material that delivered this same effect in foot creams.  Then, after identifying this ingredient, you could identify the leading researcher in developing this ingredient, and a small chemicals company that specializes in this.

The results?  You start with an unmet need you confirm it and find a material that meets that customer demand and a company that produces this material and an expert in it.  Now you have actionable intelligence for analyzing and developing a new product.

The Secret Sauce: Structured Meets Unstructured Data

All of this is possible because of Big Data, big unstructured data.  It has forever changed the way Decision Support works.  We can now combine structured and unstructured data to drive new insights.  Think of the difference between structured and unstructured data this way:

Structured: everything in a spreadsheet market data, IP databases, etc.

Unstructured: the internet of things social media sites, emails, video, etc

We can also update and monitor those insights in real time. This only works because we can now apply a stringent transformation of multiple big data sources into concise intelligence and findings.  Some people say its the algorithm and they are only partly right. Its the algorithm, but more crucial is asking the right intelligence questions from the outset to identify the most relevant data sources and having the right models to apply to both types of data sets.  The ability to utilize unstructured data and combine it with structured data is, at this moment, truly magical. We can pinpoint the needle in the haystack. We can narrow in on the exact parameters of needs, wants, technologies, requirements and IP to support meeting those needs.

What Marketers Need To Know

You should demand the kind of product intelligence and actionable decision-support weve described as you identify market white spaces, define product opportunities, improve market launches, monitor market developments after launch, etc.

You must first understand the concrete value that Product Intelligence can deliver, rather than getting lost in the buzz of the Big Data trend like the latest fan craze. Familiarity with the process and outputs will enable you to help your strategy & marketing teams ask smarter questions to define customer demands and shape new product ideasand then filter this info down into supporting product & R&D teams to act on these developments.