Archive
Evolution and Analytics: An Interview with Dr. Gary Fogel
Earlier this year I had the opportunity to sit down with Gary Fogel, CEO of Natural Selection, Inc. to discuss a data analytics approach that his firm specializes in called evolutionary computation.
Evolutionary computation encapsulates the process of natural selection into a computer by following a process which 1) generates a population of solutions 2) scores and ranks these solutions 3) retains and modifies the solutions which perform well and finally 4) iterates this process thousands of times to find the predictive models with the best performance.
I found our chat to be highly informative and thought provoking, particularly with regard to some of the types of applications this approach is best suited for. Excerpts from our talk are featured in the video below.
Some core insights from the talk include:
- Evolutionary computation is valid not only for comparing learning methodologies, but also for optimizing within them. Gary provides insight on how the approach can be used in cluster analysis (2:54), decision tress (3:49), and neural networks (4:17).
- A primary advantage of the approach is that you can look data with large feature sets using a broad set of solutions and search them in ways that people have not necessarily considered before. The methodology can evolve its own answers without relying on pre-programmed knowledge. This flexibility allows researchers to find patterns and relationships in data that might not have been previously realized or inferred (7:32).
- The methodology is highly dynamic allowing for it to engage in continuous learning as environmental conditions change (9:23).
- Evolutionary computation works through a flow of generating solutions, fitness scoring, ranking/selection, variation, termination and then iteration (10:06).
- Approach can provide insight and adapt even when importance of data features and importance of combinations of these features changes over time (12:16).
- NSI has successfully applied evolutionary computation methods for homeland security applications such as risk management for container ship traffic (14:33) and safety of food imports (16:25), life science projects in drug design, cancer diagnostics and personalized medicine (19:20) and in routing and logistics optimization projects for both military and industry (20:32).
Transforming Unstructured Data into Market Intelligence: An interview with WiseWindow
At the end of last year, I sat down with my good friend and former colleague Marshall Toplansky to talk about how he is driving marketing innovation at WiseWindow- a company that he helped found and now runs as President. WiseWindow is a company that is taking the unstructured data found on the Internet and social media and turning that into streams of useful data that marketers can use to track customer attitudes around brand and brand attributes.
You will find excerpts from the interview in the video clip below. We talk about the evolution of sentiment analysis, how WiseWindow is driving innovation in this area(7:45), and the benefits that accrue to companies that have chosen to innovate using his technology(12:30).
My core insights from our discussion included the following:
- Sentiment analysis is rapidly gaining acceptance as an alternative/supplement to traditional methods of measuring consumer attitudes including voice of customer, customer satisfaction, and net promoter score.
- In many ways, sentiment analysis is superior because it can account for a much larger population sample on a broader array of topics than traditional methods and do it continuously. This creates the ability to track the changes in attitudes based on tactics that a company might employ to improve positioning in the marketplace. In short, the fact that sentiment analysis is orders of magnitude faster and cheaper, is leading to new paradigms in terms of how companies can track sentiment nearly real time and then focus resources to respond.
- Related to the point above, the predictive power of sentiment analysis is unleashed when companies are able to find relationships between customer attitudes and business performance.
- WiseWindow is driving innovation in this area by using pattern recognition technology to address the fact that different word combinations reflect different sentiments depending on the context in which a company operates. In sum, it is using web crawling technology in conjunction with natural language processing algorithms to not only track what people are saying about its clients, but also what they are saying about the specific attributes of the products, brands, and competitors.
- While financial services businesses have been early adopters of this technology, successful case studies are emerging in media and entertainment, autos, and election tracking.
For access to the full interview (a little over 60 minutes) I have included a podcast version of the talk. Enjoy!
Transforming Unstructured Data into Market Intelligence: An Interview with WiseWindow
Video from Wharton San Diego Panel Discussion on Analytics
Wanted to finally post the video from a panel discussion I hosted a few months back onto my site. Great discussion and stellar panelists. I will update this post on my key takeaways, as well as create a short form of the talk with highlights, but for now, here is the full-length, unproduced video.
It’s been a while since I have had time to do much on the blog- have been spending the last few months ramping up new clients and also working on a new initiative that I will be launching next year. Stay tuned, there is more interesting and exciting stuff on the way…
Are You “Customer Centric”?
More great stuff from the folks at the Wharton Customer Analytics Initiative (WCAI).
Yesterday, Wharton Professor Peter Fader released a new book entitled Customer Centricity. I had the privilege of reviewing a pre-release copy of the book and was struck by some really interesting and insightful points that Peter highlights in it.
Specifically,
Did you realize that some of the greatest brands in business history- Starbucks, Nordstrom and yes, even Apple have a long way to go in terms of optimizing the value of their customers?
Shocking right? But Peter makes a compelling case that some of the most successful (and valuable) companies still have room to grow in terms of deepening their understanding of their existing customers and using this information to create custom offerings that strengthen ties and add to the value of the customer relationships that these companies already have.
Did you know that oversimplification of how you classify customers has significant consequences in terms of the effort/investment that you should be making to make your most important customers happy?
There is a great discussion (and a specific example on the wireless industry) in the book about how an oversimplified view of customer churn can cause companies to overlook and under-invest in some of their most important relationships.
Did you know that analysis of customer value could represent an entirely new way of evaluating investment choices?
In the book, Peter introduces the idea of customer equity (the combined lifetime value of all of its customers) as an increasingly accepted metric for considering the value of certain types of firms. While challenging to execute, Peter points out that this may be an approach that is far more tangible and quantifiable than other measures such as brand equity.
These are but a few of the topics that he compellingly covers in this book. Peter is quick to point out that his ideas on customer centricity are more applicable to certain types of companies, but if you are a part of an entity that has large numbers of customers, sells services on a subscription basis (SaaS providers- are you listening?), has the ability to create custom offers, or is interested in investing in companies that have these characteristics, then this book lots to offer. I would highly recommend it.
For more information on the book and how to get it, click here.
Questions for Panelists at Tonight’s Forum on Data Mining and Predictive Analytics?
This evening, I will be moderating a panel on data mining and predictive analytics. In preparation for this I have doing a bit of research and wanted to share a few observations that will anchor the agenda for the talk.
We are inundated with data. It is driven the rapid changes in how we interact, communicate and function in a world that increasingly connected (both people and things) with the Internet. The amount of data being collected is growing at an explosive rate. In 2010, data collected was equivalent to the cumulative of data generated in the previous 5000 years. And the size of this data store is doubling every 2 years.
Consider the following:
- Facebook has at least 30Petabytes of Data, 30B pieces of content shared/month
- YouTube users upload 35 hours of video every minute
- Twitter delivered 25B tweets in 2010
- Zynga generates15 Terabytes of data every day
None of these companies mentioned above existed 7 years ago.
So we have companies that have far more data than ever before. How does this create a new paradigm? What are the ways in which commerce will be transformed? We’ve had companies exploiting large data sets with tools and techniques that have been around for decades- so what’s different now? Are we just dealing with more noise?
These are the topics that I hope we will gain some insight on in tonight’s discussion.
To get the discussion started tomorrow, I am highlighting three changes that I see.
#1) Data Availability-Exponential growth in data is available to everyone- companies and individuals alike. Whereas massive data sets were once the domain of well capitalized companies, now even startups and individuals can use them to find value.
#2) Tools-Data storage and processing infrastructure and tools are becoming increasingly powerful at fractions of the cost. Open source software, collaboration platforms, cloud infrastructure provide mechanisms for not only collecting and storing the data, but also processing it at scale.
#3) Investment-Markets are rewarding companies on the promise that they will find value in the massive data sets that they create. Have you checked out the valuation of Facebook, Twitter, or Zynga lately? To be sure, these companies are achieving astonishing growth rates, but investors are placing their bets on new ways of monetizing the data streams that these and other companies collect and control.
I would be very interested in getting my readers’ ideas on what to ask our panelists. I will try and incorporate suggestions into the flow of the discussion. For those of you who will not be able to make the session I will be blogging on the key takeaways and insights in the days ahead.
Looking forward to seeing many of you this evening.
Data Mining and Predictive Analytics Panel 9/13
I am pleased to announce that I will be moderating a panel of academics and executives for a discussion on data mining and predictive analytics in September. Data are such a critical ingredient in the future development and defense of profitable business models (on-line or off) and my aim for the talk will be to show how leaders across many industries are adapting and innovating in response to this trend.
Thus far we have confirmed the following speakers:
Dr. Stephen Coggeshall, CTO, ID Analytics
Dr. Elea Feit, Research Director for Wharton’s Customer Analytics Initiative (WCAI)
Scott Gnau, President, Teradata Labs
Dr. Christopher Trepel, Senior Vice President Corporate Affairs and Chief Scientific Officer, Encore Capital Group
I will be posting insights and thoughts from the talk on my blog, but if you are in San Diego on 9/13 and interested in how data are transforming the competitive dynamics of multiple industries, you are most welcome to attend. For event details and registration, click here.
Hope to see you there.
Gaspar Advisors 