Archive for December, 2008
Imagine if your customer call center let you know nine months to a year later that your company was experiencing wide-spread problems with a product. That is the phenomenon that happens with market research, you receive customer opinions months after the opinions have actually been gathered (and even longer after they are formed), decreasing its relevance.
A market research project timeline frequently can run months before you even get to the point of data collection. The amount of time expended to get to that point involves countless meetings to determine what you’re even going to research. Then after the data collection, it will take several months to actually obtain actionable customer insight. Then companies must decide to how to incorporate the insight obtained – if they do at all. And really, what is the point of doing research at all if you can’t act on it?
Rather than gathering data through tiresome and expensive consumer focus groups or surveys, companies should realize that the information is out there for companies to grab now. Whether via online communities, blogs or forums, customers are already giving thousands of verbatim feedback comments about products and services – comments that could have a significant effect on your brand and profitability if you can act on them, and potential negative consequences if you don’t.
As a recent Wall Street Journal article , “The Secrets of Marketing in a Web 2.0 World,” referenced “…as a way to obtain consumer feedback and ideas for product development, the online community is much faster and cheaper than the traditional focus groups and surveys used in the past.”
As with all comments, whether received online or offline, companies struggle to analyze the feedback effectively. As we’ve stated previously, hand coding isn’t an effective way to arrive at smart business decisions. Social media content analytics like The Customer Insight Portal allow companies to obtain actionable customer insight in minutes, not months.
In a tightened economy, users frequently will hear the adage ”Do more with less.” For market researchers, this isn’t as hard as it may seem if they can capitalize on the feedback that is already out there.Read Full Post | Make a Comment ( 2 so far )
Many of the market researchers and custom experience managers we have met during the course of 2008, view customer verbatims, the open-ended comments received by companies through surveys, contact centers and many other channels, as a potential goldmine of customer insight. But verbatims are also seen as a waste of time by many because of the manual, unscientific nature of manual coding, which often destroys their relevance when clear business decisions need to be made.
Here is a sampling of the absurd:
“We take 100 comments every 6 months and have them manually coded. That’s it.”
“I spend 50% of my time just reading verbatims.”
These come from well-placed managers at market-leading corporations.
Manual coding is slow, biased (one coder won’t do it the same as the next, I don’t care what you say), and certainly is expensive. Manual coding also relies on categories. Some categorization methods don’t allow comments to be coded into more than one category. When’s the last time a customer said something in a sentence or two and didn’t cover a lot of ideas? Never. Also categorized comments don’t do anything in terms of identifying relationships between ideas and meaningful trends.
Slow and expensive market research initiatives aren’t an answer that works for many companies. Plus, there are many more organic sources of vertabims arising, especially online and from Web 2.0 sites, meaning companies need to look at all the verbatims to gather the information needed to understand the behaviors and needs of their customers.
To be a customer insight driven organization, a business can’t ignore 80 percent of the information your customers provide you. By ignoring the resources at their fingertips, companies are missing a huge opportunity to identify emergent customer issues, and to test and measure new methods for making customers happy and loyal.
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Satmetrix will use Leximancer’s powerful Web-based content analysis platform, The Customer Insight Portal, enabling Satmetrix to add customer analytics and unstructured content mining to their already highly popular Net Promoter platform.
Net Promoter, paired with Leximancer’s breakthrough platform, will allow hundreds of Global 200 companies to outperform their competition in customer loyalty, retention and growth.
For more information on the partnership, click here.Read Full Post | Make a Comment ( 1 so far )
Building on recent momentum, Leximancer recently added a strategic OEM Partner in Polecat Ltd. The London-based Polecat will utilize Leximancer’s breakthrough analytics platform through Leximancer’s web service interface in conjunction with MeaningMine, Polecat’s own brand and strategic marketing platform.
The combined services will provide customer service, brand management and customer intelligence professionals with key insights derived from unstructured external and internal data sources.
Click here for more on the partnership.Read Full Post | Make a Comment ( 3 so far )
Last Wednesday there was an article in the Wall Street Journal (subscription required) that’s worth revisiting here (Marketers Reach Out to Loyal Customers, Emily Steel, Nov. 26, section B2), because its key message is one that we’ve been talking with customers and partners about a great deal over the past couple months. Namely, in these turbulent economic times it is more important than ever to invest in a company’s existing customers. As Ms. Steel noted, “acquiring a new customer costs about five to seven times as much as maintaining a profitable relationship with an existing customer.” And her main point was that “with the critical holiday sales season at hand, there’s a new character joining Santa and his elves on the advertising circuit: the analytics geek.” Yes, we’re analytics geeks here at Leximancer. And proud of it!
We pay attention to numbers. Of all the information that’s available for companies to use in gaining competitive advantage a mere 20% is addressable by today’s major business intelligence vendors (Oracle, IBM, SAP etc). The other 80% is pure unstructured chaos, which as a result is largely ignored or only sampled in bits and pieces. While just the numbers suggest a problem here, the fact is that direct customer comments—through contact centers, surveys, and self-service support sites for example—are in that 80%. How can solid business and advertising spend decisions be made without careful consideration of customer input? As noted by the venerable Jack Welch, “an organization’s ability to learn, and translate that learning into action rapidly, is the ultimate competitive advantage.”
Marketers need an automatic, systematic and scalable system that can enable unstructured textual information to become a real enterprise asset—good for uncovering new customer insights and unknown customer insights. While Ms. Steel’s article was more focused on targeted advertising spend that leverage quantitative analytics, the companies that quickly address customer comments in a systematic manner are the ones that are taking it to the next level. And while the holiday season brings opportunity to this sort of progressive marketer, our economic conditions demand renewed customer orientation regardless of the season—to both ensure revenue performance as well as reducing the cost of customer acquisition.Read Full Post | Make a Comment ( 2 so far )
Perhaps you have noticed that there really are no successful text analytics systems, which are in general use on people’s desktops. It is fair to ask why this is the case.
It isn’t that people don’t have the need to absorb larger bunches of text. In fact, I might take a guess that the basic approach taken by the makers and vendors that have preceded us isn’t appropriate to what people want to get from text data.
Alternatively, Leximancer is built to analyze big, medium or small; English or Greek or Malay; medical, CPG or high-tech; long or short bodies of unstructured text from just about any source. The idea of what we’re accomplishing is new, and so is our way of making customers and partners successful.
The purpose of this posting is to examine what text offers most people, then compare this with what previous attempts at text analyzing software have tried to do, and failed, as well as arm you with questions to consider when evaluating options.
Text tells the story.
Text tells us the story. A good story lays out the ideas and characters with their attributes. We read the text to set the scene – to explain the situation that we have dropped in on. It is like the first episode of a TV series. After that, we read on to see how the characters and ideas interact. There are changing relationships.
A survey or report or set of online product reviews are no different. We need to see what issues, products or services are front-of-mind for the authors or responders, what attributes they assign to these issues and products, and how they see the relationships. We then move on to start answering questions and fixing problems. This is how we apply the knowledge gained.
In concrete terms:
1. We discover the concepts of the situation from the text.
2. We discover the explanations, or insights, from the text.
3. We can then act on these insights to alter the system.
Step 1 is important and neglected. You cannot understand the situation without understanding the background ideas.
You cannot understand an IT textbook using the concepts from political science. You would struggle to paint a seascape with a palette suitable for a childs cartoon. Unfortunately, this problem is insidious and leads to mistakes that we fail to notice. Why? Because if we naively analyze some data with a set of ideas that we know well, and we fondly expect will apply to the data, we may never see that we are missing a quite different perspective.
Most text analyzing systems will not automatically extract a clear set of the concepts and actors that characterize the text. Systems that come with predefined sets of categories, dictionaries and entity lists are a menace. You cannot risk interpreting your data filtered through an understanding created by someone who is not familiar with your data and your situation, even if the answer looks simple and neat. This leads to
Question 1: Does the system’s set of categories, entities, and concepts reflect a real understanding of my data and my situation?
Some systems use predefined categories that are manually tuned by the vendor during pre-sales. The vendor’s consultants will sift through your data and construct extensive lists of terms, pattern matchers and possibily rules. The analysis will then look okay at that time, but things change. New issues will arise in your business, and the terms and entities will change over time. This leads to Question 2:
Question 2: How much time and effort did the vendor invest in tuning the category dictionaries, rules, and entity lists before go-live? When your data inevitably changes, can you afford to feasibly repeat this process to maintain the fidelity of your analysis?
If the analytics system does not use predefined categories, it may use document or word clustering. Many such systems do not produce clear or validated concepts. Remember that for easy and regular use, the discovered patterns of meaning need to be stable and clear. Don’t be fooled by people who say that this sort of system works because it looks attractive and even compelling. There are ways to check whether discovered term clusters are real measures of meaning, or whether they are wasting your time. Here are some questions for vendors who offer term or document clustering or other concept map solutions:
Question 3: If the product uses document clustering: how does the system scale with vast numbers of documents? If a document contains several different ideas, can it be in two topics at once? If I cut up the same documents into different chunks, would the pattern of clusters be similar? Text content isn’t always organized in predictable ways, so this is an important set of questions.
Question 4: If I take two different documents either by different authors or in different languages, would the discovered patterns of meaning look similar between the two? Multinationals – think about this if you want a consistent, true view of your customer comments.
Step 2 is almost totally ignored. Text information can tell you a story so you can improve business performance—with customers, with marketing. What else would you really want to do with it?
Quantitative, categorical, and numerical data mining is really good for establishing metrics and testing to see if pre-defined metrics change. Great. Do this. It is really good for predicting whether a pre-selected situation is matched, such as customer churn probability.
But don’t forget that analyzing text comments from customers or competitor product reviews on the other hand excels at telling you what is happening. Because text is human communication – that is what it is for. So why waste this extremely valuable and rich source of intelligence?
Think of it this way. If your metrics show your sales are rising, everyone feels great. If your metrics show you your results are falling off a cliff, how do you work out how to fix the system? This is the feedback you need for controlling a system. Your text data will tell you how to turn things around faster and more accurately than almost any other source of management information.
Unfortunately, this is where most text analytics systems fail or don’t even bother. Here are some other questions:
Question 5: Does the system suggest chains of meaning which are well supported by the data, and which I can understand and explain to a manager? In other words, is it an explanatory model?
Question 6: Can I test hypotheses (educated guesses) based on the perspective of the customer?
Question 7: How does a simple list of terms tell me much about the reasons for what is happening, without having to do a whole lot of guessing or having to read large amounts of text after all?
Step 3: Set your bar high and expect an automatic, systematic and scalable system that can enable unstructured textual information to become a real enterprise asset—good for uncovering new customer insights, new product ideas, and business process improvements that were previously unachievable. And now act on what you find!
I hope this helps. People are still doing a whole lot of writing and talking trying to tell you things. I think we need to listen more carefully, understand what they are saying and then act thoughtfully.
By Andrew E. SmithRead Full Post | Make a Comment ( 5 so far )