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Deborah Mills-scofield's book recommendations, liked quotes, book clubs, book trivia, book lists (read shelf)
Monday
Dec152014

The Biggest Joy of the Season? Giving.

A huge thank you to all my clients who have made an impact not just on their customers and employees, but on their communities...who have matched 10% (and usually a lot more) to change and improve lives.  I am humbled and blessed to work with you and make such a difference.
By Jess Esch

Friday
Dec052014

Big Data In Your Shampoo? 

Did you wash your hair this morning? Did you know big data probably played a role in the viscosity and aroma? Maybe! This guest post by Amir Golan, VP of Business Development at Signals, shows how important it is to look for the small signals and patterns in big data that are easily lost. 

 

Interminable Growth Pains

Once upon a time, before the era of big data analytics, corporations had similarly routine business growth issues and threats: i.e.: after years of being the market leader in a specific product category, they quickly begin to lose market share, they wanted to introduce their product into a new market. In the case of the former, they typically would want to know why and how could they innovate their current product to regain their position as number one. Back then, they would run focus groups to see what customers liked/didn't like about the product and would check competitors that began to do well. Then, based on the insights drawn from this sample, the company would decide the reason for the recent losses and propose changing a feature to address that specific "pain." While it may have mitigated the clients' losses some of the time, the innovation was inevitably reactive, unscientific, and not robust nor holistic.

The Contemporary Picture and the Role of Big Data

Fast forward to today. While companies' stories start similarly, their approaches to research are completely different. Recently, a large consumer packaged goods company decided they wanted to enter the haircare world and they needed help defining the product opportunity. Through "listening" of social media, they were able to identify a need for a new type of hair product because consumers online (on Twitter, Facebook, forums) were complaining about having to mix hair wax with oil to get the texture they desire. By capturing these discussions, structuring them, and analyzing them, they then determined the size and depth of the “signal” and figured out that the demand for it was strong. By applying similar internet-scraping of competitor websites, articles, patent filings, job postings, and more, they were able to get a picture of what their competition was offering and what they were developing. By cross-analyzing the two, they determined the unmet need-- a real opportunity-- because they found a gap in the market; people wanted a hair product with a specific texture and no other companies sold or were planning to sell it.

They then took it one step further and asked, "How would you find the technology and material to meet this need?" Again, they scraped data from the open web on other industries' IP filings and academic publications providing the key feature they were looking for: a certain texture. After discovering a new material that achieves the same texture in a foot cream, they were able to shorten their time to market by finding and partnering with the leading researcher in developing this ingredient and launching their first hair product successfully. By constantly monitoring all of these data sources, the company was also alerted to new threats entering the product category, so that they could adjust proactively.

It's this sort of product intelligence, capitalizing on the infinite amounts of big data available on the open web paired with the right solutions and tools, that is enabling companies to innovate and launch better more successful products. But what is it exactly?

Product Intelligence: What Is It?

While historically easier said than done, the work this company did to increase certainty and decrease risk in new product development is increasingly feasible. Enter Product Intelligence: a new hybrid intelligence emerging from the smoke and mirrors of the Big Data and innovation jargon, that proves to be a little more practical and actionable. It provides 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 it’s literally make or break.

The secret is in connecting the analyses and insights derived from Big Data to real NPD and innovation decisions. Product Intelligence makes the stars align, ensuring that the relevant signals (i.e.: the desired hair product texture) from the right types of data (i.e.: millions of conversations on hair products) are connected together to bring the best insights to the right decision maker at the topical moment in the NPD decision process.  

So, how would you prepare for a stage gate meeting that includes a "Go/No-Go" decision on continuing to develop a specific product? Either as a member of the product team or as the "gate keeper," you might make this decision based on a gut feeling, prior experience, a partial understanding of the ecosystem, OR, increasingly, based on Product Intelligence.

Decision-makers are reveling over this research approach and solution that systematically provides a stream of evidence-backed insights that support the gate meeting's key questions, therefore reducing uncertainty and risk and optimizing the chance of developing successful products.

How Does This Change the New Product Development Process?

Let’s take it one layer deeper, and try to understand why this brings something novel and different to current approaches to research for new product development, both internal and outsourced 

Technology + Methodology

Some big data folks say “it’s the algorithm” and they are only partially right. Just as crucial is the methodology- asking the right questions from the outset that are relevant to the gate decision. That is, the decision drives the data to be collected and the questions to be asked. Then, Product Intelligence connects those questions to the right analytical models and identifies the most relevant data sources to populate the models. Instead of boiling the "big data ocean," Product Intelligence can identify the exact parameters of the needs, wants, technologies, requirements, and IP supporting a new product and optimizing its success.

Robust + Comprehensive Insights

Another major differentiator is that decision makers can rely on and feel confident with the evidence- the robustness and comprehensiveness of the evidence and insights extracted from big data increases certainty in innovation decisions. In our hair product story, for example, the product team needed a good understanding of their target consumers in order to validate their hair product needs before they progressed to the next phase. You can use traditional research methods and interview 25 people or perhaps create an expensive program and bring 500 people to focus groups. Or you can do what they did and "listen" to 1,000,000 different voices from forums to key opinion lenders and potential consumers and connect the dots between them. The layering of these unstructured voices with other structured data sets (i.e.: polls) creates a holistic and robust view of consumer segments and their needs, both met and unmet.

Reduce Investments of Time + Money

Our CPG product team was also able to dramatically reduce the investment of time and money on irrelevant concepts early on in the process. Instead of deciding to rebrand or develop a different hair product with the same texture, they understood that the texture was the reason for the loss of market share and were able to quickly and easily tweak their existing product to meet the need, provide value to the customer, and regain market share.

Real-Time Ecosystem Monitoring + Topical Decision-Making

Another unique feature of Product Intelligence is the ability to constantly monitor and update these insights in real time, which allows corporations to keep up with their rapidly-evolving ecosystems, know about threats (and opportunities) before it's too late, and strategically and proactively plan to avoid or capitalize on them.

 

Amir Golan is the VP of Business Development at Signals Intelligence Group Ltd. He manages strategic accounts and oversees Signals’s partnership program. Prior to joining Signals, Amir worked at different strategic consulting firms and worked in a variety of intelligence frameworks. Amir served as a member of the Board of Directors and of the Finance committee of the Tel Aviv Stock Exchange-listed ISSTA-Lines LTD (ISTA.P) Amir earned his MBA and BA in Political Science and Middle Eastern studies from Ben Gurion University in the Negev. Follow Amir at @golan_amir.

Wednesday
Dec032014

52 Ways to Build Trust

Many thanks to Barbara Kimmel of Trust Across AmericaTM for letting me contribute to Trust Inc.: 52 Weeks of Activities and Inspiriations for Building Worldplace Trust (Vol 3.).  My mantra, Experiment-Learn-Apply-Iterate, is a way to start building trust in one's own capabilities and one's team (pg 29).  Get the book, try out these various ways and you'll be surprised at how it works! (And if you want, buy Vol 1 & 2 as well (ok, i'm in Volume 1 too)).

Sunday
Nov302014

Death by Data

Data isn’t important in decision-making. What? Shocking! Then why aren’t we shocked when someone says that all decisions must be totally data driven? Perhaps it depends what we mean by data, which is usually something quantitative. 

We need to get out into the world and gather data by watching, observing, listening, asking – qualitative data. We don’t live in a binary world – it’s not either-or, it’s and-both.  We need quantitative and qualitative data. We need to consider both equally valid forms of data.  After all, as the sociologist William Bruce Cameron said (guess Einstein didn’t *),

Not everything that can be counted counts. Not everything that counts can be counted.”

Quantitative data needs to be part of the equation, part, not all.  More and more I see companies defining “data” as purely quantitative, dismissing or minimizing, at their peril, the importance of the qualitative.  Quantitative data can tell us a lot.  It an also tell us little.  Quantitative data has limitations – as does everything. These limitations are because the data usually is…

  • About existing “stuff”. It tells us about our current features, functions, customers and markets.  It tells us what customers are [stuck] using now, not what they really want.  It doesn’t tell us what our “stuff” could become or what new customers, markets and applications are out there;
  • Based in the present or the past.  We don’t have much ‘future’ data: what will, could, should or might be and what we could do to make that happen;
  • A glimpse in time.  It can be a year, five years, ten years, but it’s always piece of the bigger picture;At the Edge (Pemaquid Point, ME)
  • About the what, where, why and maybe even how, but rarely the why. Data usually doesn’t tell us much about fringe factors or trends that impact it.  It’s hard to have data show us the subtle societal, cultural, behavioral “whys” of influence;
  • Used to make things more efficient instead of more effective. Yes, efficiency (or optimization to be more eloquent) still rules for most of business today.  Data helps us figure out to eliminate unnecessary steps, improve productivity, reduce costs, etc.  Data doesn’t necessarily tell us why things need to be improved in the first place or new, different ways of doing, period.

As I like to tell my engineering students, most of today’s wicked problems aren’t optimization problems; they are system and design problems.  Think of the remote controls on your den table! Optimization issues are a symptom, not a root cause.  Data doesn’t necessarily tell us how to make the problem go away because it doesn’t tell us why the problem is there in the first place.  We have to actually get out of the office and look at how the problem is being addressed, not addressed, or not well enough by human beings.  We need to see how things are organized, structured, laid out, used, not used and under what conditions, circumstances and contexts. 

Data can tell us a whole lot about how our sites and stores and companies are working or not working, but data can’t necessarily tell us the whys – why it is or isn’t working, or working well enough. Without getting out and observing reality first-hand with all our five senses, we risk optimizing our organization into extinction. 

* http://quoteinvestigator.com/2010/05/26/everything-counts-einstein/

Monday
Nov242014

A Very Blessed Thanksgiving