Analytics for Innovation: Why You Need to Read the External Signals
Editors Note: This presentation was given the Innovation Enterprise Product Innovation Summit.
How can you use big data to increase your chances of success at the fuzzy front-end through big data analytics? At the recent IE Product Innovation Summit in Boston, we shared our firm’s learnings to date and showed how it can work in a case study on Keurig.* (*Keurig is not a Signals’ client and all research is Signals’ own for demonstration purposes.)
There is tremendous potential for strategic insights to be derived from external big data – yet most data science remains in the proverbial lab and is not made actionable for decision makers. Our goal was to harness the power of external big data to produce high-level business insights that are immediately actionable for business leaders.
Why the need? While many in our audience understand these issues, they bear repeating because they haven’t changed. The old ways we make decisions around product research aren’t working, as shown in the image above.
The market is moving faster than ever: consumer preferences are more dynamic, the rate of global innovation and technological development is incessant, and our current information methods can’t keep up with it. New players are threatening the status quo: Apple makes watches, apparel companies are building wearable sensors, and technology companies are building automobiles. Colliding worlds means that traditional approaches to establishing market leadership and maintaining competitive advantage just aren’t working.
Analytics is a new approach to identifying consumer preferences and selecting product features when designing new products that can increase the chances of success at the launch.
We looked at one brand who faced a particularly tough year in 2014 – Keurig. They faced challenges in the increasingly competitive market of home-brewing machines and had several major misfires in designing and launching their Keurig 2.0 machines. They paid for it with lost consumers’ market share.
What does all this mean to you in your product development efforts?
We would encourage you to disregard everything you know about big data! Disregard anything you know about how you make decisions today on innovation and new product opportunities. Disregard everything you know about your current product research.
I am going to ask you to think about how decisions will be made 5-10 years from today.
What does 5-10 years out look like?
In making these critical but recurring decisions around what products to develop, what features to include, and how to select the right technologies, information to inform these decisions are typically at our disposal as information inputs, and need to be integrated into the decision-making process.
5-10 years out we will be using analyses from External Big Data, ERP, and CRM. This will become the table stakes.
The new scale of information creation has generated a new environment. We need a working definition of Big Data. Here’s ours:
The quantitative definition of big data is very challenging. It’s basically a moving target as data storage capacity and computing power are constantly improving.
– Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications.
– It also refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.
There is a ton of hype, but the reality is that huge amounts of data are being generated that cannot be ignored.
This is all significant – but more important is that you know that there is value in big data. That is why 97% of execs at Fortune 500 companies plan to adopt a big data strategy if they haven’t already.
We always ask: in order to build a product to optimize its success, what are the things we would need to know? In the image below are the kinds of things we would need to know to make the right decisions in the product development process.
Here is how data analytics could have helped Keurig have a very different year in 2014. Below is Keurig’s stock price in 2014 and the amount of consumer discussions occurring about Keurig online.
Unsurprisingly, there is a direct correlation between major events and activities, stock performance, and volume of consumer discussions.
There is a lot of “noise” being made by stock and financial analysts, and press surrounding the recalls.
What are the early signs that could have told Keurig about their risk?
Intelligence’s role is about helping find those insights earlier – as early as the first signals appear.
What are the early signs that could have told Keurig about their risk?
Intelligence’s role is about finding those insights earlier – as early as the first signals appear.
Before EACH of these headlines appeared, there were numerous external signals that could have been read by the Keurig leadership. Quantifiable signals such as social chatter, consumer preferences and concerns, and larger social patterns and industry trends.
We applied our analysis of those external signals to a series of recommendations below.
What was the product that Keurig should have built? The following recommendations are based upon our analysis of the most prized features about Keurig’s brand – in addition to insights based on the home brewing industry as a whole that come from signals that are accessible through external data.
Industry Growth & Trends
– Offer natural and organic flavors (analysis [that is not presented here] indicates this is an area where SodaStream has been very successful)
Messaging & Marketing:
– Sleek Design
– Analytics we looked at supported that
– Reusable cups
Quality Assurance & Customer Service
– Not covered in the analytics here, but Keurig’s brand also suffered majorly from a safety recall – Messaging and brand efforts should include emphasizing customer service and customer response
What are the takeaways?
Keurig got there eventually in terms of choosing the right features to incorporate into their product – so what is the “so what”? Keurig got to the right product requirements eventually that hit on the major points – but now they are waiting on this in delay mode.
Intelligence from external signals, up front, could have helped them avoid catastrophe in the first place, and more quickly bring them to the next opportunity that would address what consumers wanted.
image credits: signalsgroup.com
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Kobi Gershoni is Chief Research Officer and Co-founder of Signals Group, where he oversees Signals’ research methodologies and analytics team. Kobi has conducted research for multi-national corporations and venture capital funds, overseen hundreds of Signals client engagements. Kobi served in an IDF intelligence unit as a senior analyst and information officer. Follow him @SignalsGroup
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