Twitter and all that - Part II - Customer Support vs Customer Experience Intelligence

Don't know if you've all seen this article from USA Today last week. It had some interesting insights:

http://www.usatoday.com/tech/news/2009-06-25-twitter-businesses-consumers_N.htm

Basically the article profiled some very creative ways businesses are tapping into social media to get closer to their customers. The article recounted some well known stories (ie when Tweeters learned about a power outage during a Stanley Cup Playoffs game, or about how Dell, Comcast, and others are using Twitter to respond to customer complaints or advertise special sales).

I think all these creative uses of Twitter are fascinating, and can create real value for customers (who now have a means of communicating directly to each other and to companies via a medium they prefer and are increasingly flocking to).

From my vantage point looking at Twitter as a source for Customer Experience Intelligence, I still remain convinced that it is only one data point, and perhaps not the best data point, for comprehensive, actionable intelligence, for a number of reasons:

1) The average "tweeter" posts one tweet. Ever. See this study from Harvard, reported by the BBC. http://news.bbc.co.uk/2/hi/technology/8089508.stm While Twitter growth has been explosive, basically a very few people are tweeting a very lot. And a lot of what they have to say is not particularly insightful to companies.

2) Companies ARE getting real value in monitoring tweets, and in reaching out to customers to find out what problems they are having, or what suggestions they have. Most companies can count the number of tweets that require an action in the 10s to 100s per day - not much more than a customer support rep covers in a day, and companies who monitor, are finding that the tweets from users are not bad ways to provide support, or an ear for feedback. But looked at this way - this is really just another channel for customer support. Good, but not necessarily transformational. To the extent a support rep has a conversation with a tweeter - the rep is most likely to want to archive the conversation in a CRM system so it can be analyzed alongside all the other channels of support and feedback that are being captured.

3) Call Centers are still where all the action is, and will remain for some time. From the article:

"For perspective, consider the size of call-center operations for major brands. Comcast says it is unlikely to uproot its operations, which employ 25,000 — most of them in the U.S. — in favor of Twitter. "A majority of our customers prefer to contact us by phone," Eliason says."

"Twitter is for basic troubleshooting," says Zsolt Katona, a marketing professor at the University of California-Berkeley's Haas School of Business. "Be careful not to ignore those who rely on the phone for customer support."

4) Every member of the twitter generation is also quite likely to go to review sites, fill out point of sale surveys (online, of course), and even get tech support via online forums (or even use the phone if they have to) - and the information in these sources is richer (contains detailed insight about the experiences, problems, troubleshooting, and problem resolution). These sources also tend to allow companies to "link in" information about the transaction, the reviewer or poster, etc) permitting far more correlation of the user, the product, the experience that generated the feedback, than a twitter post will ever do.


So what am I really saying? In conclusion - Twitter is a good tool for short insights from a random and small community of fervent users. I use it myself, and find it very useful for scanning trends, seeing what people are saying about products and events, and even for communicating with people who have questions about my company or product (a few months ago I even provided some tech support via Twitter to a person who had a question about Clarabridge's offerings).

But it's not a great source of actionable intelligence from a representative segment of a customer base, and it's not a great source for "text mining" insight that can be cross referenced against specific customers, specific experiences, and specific products. The better sources for rich, qualitative/unstructured high volume customer experience insights are (and will remain for some time) call center verbatims, survey feedback (structured and unstructured feedback), and web forums, web discussion groups, web review sites, and community web sites.

Twitter and all that - Customer PERCEPTION or Customer Experience Intelligence?

There's been a lot of discussion lately about the value that Twitter, Facebook, etc brings to companies looking for customer "insights" or customer "experiences" - the thought process is that if only companies could have a live feed to Twitter or Facebook data that they could keep a finger on the pulse of customer experiences, suggestions, issues, fix problems, and ultimately create a happier, more loyal, more profitable customer base.

While there's value in social media tracking, I'm going to take a contrarian position. I believe that web/social media helps identify customer "perceptions" - but it does a poor job helping companies track real customer "experiences" - and thus the social media content is not a good place to track, measure improvements, and ultimately monitor customer experiences.

Why is that, you might ask?
1) the web is largely anonymous. If a person tweets "I'm sitting in Starbucks, my latte sucks" - you don't really know enough to fix the problem. Where is the customer? What store? Who served it? Is the shopper a frequent customer? How often does he shop there? Is the problem endemic at the store or just a transitory problem? You can't determine ANY of those insights from a 'tweet.'

2) the web doesn't generally contain a 'closed loop.' If a customer complains that he/she is having a problem with a product or service, and they get some insight that helps them fix the problem, is the case "closed?" Who closed it? What was the resolution? You can't tell that from a web forum, by and large.

In short - the web does not contain 'actionable intelligence' - it only contains - 'perceived intelligence' - to get to actionability you need more information from the person, details on the problems, and a closed loop from the resolution process that identifies that issues are tracked to completion.

You can track perceived issues, but you can't really use the insights from web content to identify, fix, and ultimately measure the impact of your changes on your customers.

Far better to track the insights, interactions, and free form conversations, chats, and verbatims from:
- customer calls to a call center
- survey feedback
- online chats
- company moderated (or at least participating) forums where you can reach out to a customer directly and work with them to identify, resolve, and track issues and resolutions.

Tracking your top 10 Dissatisfaction Drivers

A few days ago Bruce Temkin, from Forrester research, dissected a report frequently deployed by Clarabridge customers called the "Negative Influence" report.

As stated in his blog: "The Clarabridge Negative Influence report correlates the negative experiences described in the open ended feedback (based on the specific categories of experiences that are described with accompanying negative sentiment) with a low score, and also weights the ranking of most negative influences based on frequency – how often a specific experience is most often associated with negative assessments."

Rather than reposting his blog - here's a link to the piece. There's some good commentary from readers following the blog that's also worth reading.

http://experiencematters.wordpress.com/2009/04/29/tracking-your-top-10-dissatisfaction-drivers/

My worst customer experience ever

I'll never forget my worst customer experience ever. I was 1 year into my first job after college, working as a management consultant for Ernst & Young. After a nice lunch with some co-workers I stopped in at a local 7-11 convenience store to pick up a pack of Reese's Peanut Butter Cups for dessert.

I got back to my desk, ripped open the package, and upon biting into the first cup noticed that the filling wasn't as creamy as I expected. The consistency of the peanut butter was mealy and granular.

Upon review of the cup, I was HORRIFIED to discover little granular maggots embedded in the half eaten peanut butter cup. Upon further review, I was further horrified to discover that the maggots were actually alive and squirming around in the cup, and that the half eaten portion I'd quickly spit out of my mouth contained yet MORE maggots!!!!

I was, you can imagine, disgusted and livid.

After rinsing my mouth with several cups of water, brushing my mouth 3 or 4 times, and chewing through a pack and a half of gum to freshen my mouth - I proceeded to write a letter to Hershey's (manufacturer of the cups), and to delicately wrap up the half eaten portion of the remaining cup. I packaged up the whole disgusting mess and sent it, FedEx, to the customer support address on the package.

Within 2 weeks I received a sincerely worded response from a nice woman in Customer Relations. She apologized for my experience, assured me that Hershey's has manufacturing, distribution, and retail quality controls and the experience I had is both rare and unacceptable.

I was sent coupons for over $20 of Hershey's products, and a box of Hershey's coffee cups, for my trouble, and was thanked for my business.

I have to admit I was impressed by the relatively rapid response (keep in mind the experience happened in the late 80s, before the advent of internet email and the world wide web) - and in spite of the disgusting nature of my experience, I was willing to give my favorite candy another chance.

I remain a loyal Reese's Peanut Butter Cups eater. But I always break open the cups before I eat them now....

Emotion and Trust and Transparency and Text Mining

Gartner just came out with a listing of the Seven Great Concerns for CEOs in 2009 - and it listed one interesting area that touches on issues of importance to the Clarablog (note highlighted area below).

CEO Issue Three: Loss of Business and Governmental Trust
The institutions that were once counted on to safeguard the economy seem to have failed, and the lack of transparency in the economic system has been exposed. There has been a subsequent loss of trust, as well, amid fears that other unknowns are awaiting. Trust is an intangible element in business but is crucial to transact business. IT can help improve transparency in the way business is done through reputation management, e-discovery and business intelligence. Gartner also expects a strengthening of "data driven" management culture as the risks of moving forward with insufficient data become far less acceptable.

They're right, of course. Everyone wants transparency. People are less tolerant of obfuscation, misleading, mishandling, and mis-marketing from the corporations they do business with.

It's in a company's best interest to "come clean" with the facts, and get messages out to the market. It's also in a company's best interest to LISTEN to customers - hear out their issues, concerns, suggestions, and even EMOTIONS.

Text Analytics, driving business intelligence, identifies, issues, emotions, sentiments, and concerns from customers and cost- and time-effectively helps raise corporate awareness of customers' concerns, perceptions, and needs.

Customer Experience Intelligence is primarily an ROI driver to help improve services and products (see earlier blog posts), but it can also serve an important role in helping companies attain and maintain public trust during times when public faith in the institutions of business and government is weakened.

- Sid

Smart Response - a Smart Way to use Text Analytics to Improve Customer Experience

Companies need to analyze customer experience feedback 3 different ways:

1) "What are the 10 things my most profitable customers are most upset about THIS MONTH?" - Answering this question requires the ability to mine through all customer feedback, segment the data by business-definable dimensions like time and profitability, and establish an easy to view "dashboard" that quickly distills the answer from millions of pieces of text-based information to a concise answer to the question.

2) "What's new with my customers TODAY?" - Answering this question requires near real-time analysis of incoming customer feedback to see what's spiking over normal levels of feedback, so that an operational manager can quickly respond to a adverse event, service problem, or other customer experience issue that's quickly developed, to avoid the issue growing into a full-fledged problem.

3) "What should I do for the customer who just complained to me about a bad experience RIGHT NOW?" Unlike the first two examples - which involve analyzing customers in aggregate or by segments or groupings, this last type of analysis is about smart determination of an individual customer's issue, and smart response to that customer in real-time. If a customer had a bad experience, and took the time to complain - smart response is about using text mining to quickly ascertain, and adjudicate the customer experience with a suggested resolution that can be communicated directly back to the customer.

The first two categories depend on text mining large volumes of customer feedback. The last category depends on the ability to use text analytics in real-time. We announced Clarabridge SmartResponse (tm) http://www.clarabridge.com/default.aspx?tabid=136&PressReleaseID=609 as a response to our client demands for the ability to harvest and analyze feedback in real time as well as respond to individual customer communications.

Our clients have been using our Content Mining Platform to answer the first two types of questions for a while now - they've recognized that it's important to keep tabs on their customers' experiences, needs, and suggestions, and realize that ongoing analysis of trends, problems and suggestions can lead to better decisions, better product offerings, and better experiences - all of which lead to happier, more loyal, more profitable customers:

• Hotels learn what products and amenities customers want, and conversely what they can cut.
• Airlines can track the preferences and needs of most valuable travelers.
• Retailers can evaluate the response to new product lines, and track feedback on product quality, safety, and passion.

Smart Response solves an entirely different type of customer challenge. If your customer is tweeting about a bad experience, you want to respond to them immediately. If they send you an email or fill out a survey to describe a particularly bad flight experience, or stay at a hotel, or treatment at a store, you want to quickly assess the problem, determine the best possible response, and communicate back to the customer with thanks for the feedback, a sincere apology, and if warranted an offer of compensation for their troubles. Smart response is about real-time mining of feedback, real-time assessment of the problem, and real-time determination of possible responses for the customer.

Customers want not just to be heard, but to be addressed. Smart Response truly closes the customer experience loop with the customer.

RE: In2Clouds theory about text analytics and Government Transparency

MM in his blog In2Clouds referenced my recent blog on the business benefits and ROI of Text Analytics in the commercial domain, and suggested that it might be useful to be able to use Clarabridge to mash up government information with constituent feedback, suggestions, complaints about programs, producing better transparency and accountability.

I agree - It would be VERY INTERESTING -- and in fact solutions such as MM is envisioning will become reality in 2009, I'm sure.

I see a solution that incorporates:

1) Databases containing funding information ($ granted, per program, over time, over geography) coming from Federal Agencies
2) Databases containing spending information ($ spent, per region/state/jurisdiction, over time) coming from the state agencies
3) Performance Management information (job growth, roads created, educational metrics, energy capacity improvements) coming from a variety of public, private, and watchdog sources.
4) Constituency feedback (anecdotes from citizens on program qualitative benefits, outcomes, problems, observed fraud/waste/abuse) coming from a variety of social media sites on federal, state, local and third party sites)

Put all that information together, and you have truly comprehensive view of the life cycle of the government recovery/stimulus effort, and you have a living, breathing, always on, actionable view of the stimulus in action (or not, depending).

These kinds of data fusion/mash up solution visions highlight the real potential of integrating unstructured data containing qualitative feedback with structured data containing dollars and cents and performance metrics, to truly track the what, where, and WHY of complex programs and systems, and use the information to track, analyze, and improve programs.

In short - text analytics are not just for business. Government can and should also get into the game.

Achieving ROI with Customer Experience/Text Analytics Solutions

Welcome to 2009 – we’re in a recession.

Perhaps you are
-a retailer
-a travel/hospitality company
-a consumer goods company
-a high tech products company (hardware or software)
-a financial services company
-a media company

In short, you’re like one of the many companies that have decided to use customer experience intelligence solutions, like the Clarabridge Content Management Platform, to establish a text analytics-based voice of the customer initiative.

Why, you might ask, should you be looking at such an initiative in 2009 – when companies supposed to be cutting costs, getting efficient, and doing whatever they can to stay profitable, solvent, and alive? Is customer experience a “nice to have,” or a “need to have” solution?

Anecdotal insight from existing Clarabridge customers would suggest that it is very much a “need to have” solution. Text analytics, applied to customer experience management, is an important way to weatherproof your company against the economic storm we’re currently experiencing. A few big reasons:

1) ONLY THE EFFICIENT SURVIVE – and Customer Experience Analytics solutions create immediate efficiency. Most businesses collect and process customer feedback (survey content, call center content, email content) manually, and spend too much money, and too much time manually reading, coding, and analyzing content, particularly unstructured, text-based content. One of our customers reduced a staff function from 25 analysts to fewer than 5 after adopting Clarabridge, and increased its ability interpret feedback from a fraction of feedback to 100% of feedback. Another customer cut market research spending year over year by over 25%, more than covering the first 2 years of cost for the solution, and is now getting actionable feedback from customers within 24 hours of capture, down from process that used to take 30 days from capture to analysis.

2) ALERT DRIVEN INSIGHTS CATCH PROBLEMS BEFORE THEY COST YOU MONEY. Text analytics solutions do a great job of tracking experiences quickly, and quantitative metrics can measure the magnitude of a new problem before the problem causes cost, customer satisfaction, and resource impacts to your business. A leading software manufacturer uses alert driven analytics from Clarabridge to catch the “fast-growing” issues associated with new software releases so they can quickly kill bugs and errors BEFORE they affect the entire customer base – averting millions of dollars of support costs.

3) USE A SCALPEL, NOT A CHAINSAW TO CUT COSTS WHILE RETAINING DESIRABLE CUSTOMERS. Many customer-oriented companies engage in a technique called market segmentation to identify and analyze the buying patterns and preferences of discrete segments of their customer base. Before cutting a product or service expense, our clients use Clarabridge to assess how valuable the product or service is perceived to be. Others run trial cost cutting programs and evaluate the impact of the cost cutting on their “desirable” customers – the most loyal, the most profitable, and the highest net worth – to make sure they’re not engaging in an action that will drive away the desirable segment. Hotels are using Clarabridge to assess which amenities can be cut without adversely affecting business travelers. Retailers are assessing which product lines can be cut without impacting frequent customer loyalty.

4) SAVE BIG BUCKS WITH DATA-DRIVEN, INSIGHTFUL BIG DECISIONS. Over time, companies inevitably have a moment when they have to make a BIG decision. Perhaps it’s to roll out an expensive new product. Or kill a high support cost product. Or tear down a property that seems unfixable in some way. Before making the decision that can impact the company by millions of dollars, our customers use Clarabridge to validate the decision. One customer considered whether to tear down or modernize an old property due to customer complaints about noise levels and the age of the building. Using Clarabridge they were able to determine quickly that customer feedback from the property was not statistically more or less negative than feedback from newer properties, and the company subsequently decided NOT to tear down the property, averting millions of dollars of unnecessary expense.

In short – return on investments (ROI) can be clearly tied to operational, analytical and strategic use of Customer Experience Analytics, and solutions from companies like Clarabridge don’t just generate cost savings in the short term, they allow better decisions to preserve loyalty in the long term – preserving customer relationships, loyalty, and corporate profitability

Text Analytics / Customer Experience Intelligence 2009 - Predictions

I was recently asked what I "see as the 3 most important text-analytics technology, solution, or market challenges in 2009?"

Good question. 2008 has been an expansionary year for the text analytics space along many dimensions - expanding appreciation of the business value of text analytics, expanding deployments across corporate enterprises, and a general expansion and evolution of the market segment away from a 'tech' sell to more of a solution sell.

2008 also seemed to validate that text analytics, and specifically text analytics applied to voice of the customer analytics, (tapping into and transforming customer feedback and experience content into rich actionable analytics) are largely "recession-proof" if sold and deployed effectively.

My company, Clarabridge, is having a great year - we continue to grow, achieve a fantastic level of deployment success, and continue to add value to customers in many ways:

- we help customers do more analysis, with less cost
- we let our customers spot problems more quickly than before (within a day or two, vs within a week or month), so
- our customers can take action on customer feedback and opinion quickly and decisively (i.e. if they spot a service or product issue they can address it and fix it quickly and decisively)

As a result of the time and cost efficiencies inherent in text analytics-based customer experience solutions, we enable decisions and customer experience improvements far more cost effectively than manual reading, coding, analysis and decision making would allow.

Partly due to the successes of our customers, and willingness of our customers to share their success stories of cost savings and decision making efficiencies with other customers, partners, and prospects, we've continued to grow our customer base -- even in a slowing economy this year demand for our customer experience/text analytics solutions remained strong and grew solidly.

2008 proved that customer experience-based text analytics solutions are needed in both good times and bad - in good times firms want to maintain their reputation, fix issues quickly, and outcompete their rivals, and in bad times firms want to do all they can to be responsive to their customers to maintain their loyalty and profitability.

I think 2009 will largely see a further evolution of the space as we've seen in 2008, a bit more 'mainstreaming' of the solution, and the development of interesting partnerships and technology integrations between vendors and solution providers.

Specifically, then, my 3 predictions:

1) Text Analytics solutions will expand from a functional/departmental initiative to an enterprise imperative. In 2006-2008 - text analytics was deployed to enable the analysis of marketing, call center, and survey/market research departments, and the results of the text analysis were used primarily within a single department.

In 2008 we saw at Clarabridge a few of our more progressive customers looking to evolve and expand from departmental approaches to an approach that supports multiple organizations. We see a few prospects (who will likely become customers in 2009) who actually want, on DAY ONE of deployment to have a solution that can that meets the needs of many groups and departments in an enterprise.

As a result of having a cross-functional, cross organizational goal, I expect in 2009 that for a class of "enterprise buyer" selling cycles will become more complex, the solutions will need to show more a priori business value and relevance to many stakeholders, and because the solutions are not just deployed in one area, IT requirements and architecture criteria will become more important as organizations will want to know more how the solution is going to fit with "enterprise" standards.

As a result of this trend - in 2009 selling and deploying will become more complex, more constituencies will become involved in the decision, and at the same time articulating and demonstrating business value will be even more important to more business stakeholders.

2) Text Analytics will evolve from being an isolated to an integrated solution. Towards the end of 2008 we started seeing more interest from companies and partners looking to seamlessly integrate the results of text analytics back into operational systems. -- i.e. they wanted to process customer verbatims and merge the categorized, scored results back into call center applications, or moderated web forums.

This new requirement - not just for text analytics, but for operational integration of text insights and scores into downstream applications and solutions, provides an interesting opportunity for text analytics vendors to consider - whether to be a standalone application, or to be a more tightly integrated with partner products and offerings. I think the progressive text analytics companies will be wise to look for strategic partners with whom to integrate their solutions.

3) Text Analytics solutions in the customer experience space will quickly get bigger and faster. 2008 taught Clarabridge that to succeed, we needed to support ever bigger data sets, with ever faster processing requirements, and as a result we quickly learned to create our solution for scale and growth. Companies with small volumes of text data are keeping their history for perspective and trending. Companies with large customer bases and diverse lines of business are creating huge data sets - with 10s to 100s of millions of customer interactions being processed through the Clarabridge text analytics engines. Data volumes will continue to grow in 2009 and usage requirements will also go up as more users seek to explore, analyze and act on the insights in customer experience solutions.

There's no reason to expect scalability requirements won't continue to grow in 2009 and beyond. Successful vendors will be those who can see beyond today's data and user volumes and design for an order or two more magnitude in their offerings.

As long as vendors grow with the market and deliver value and efficiency to customers, they will succeed as this market evolves. Clarabridge will be one of those successful vendors.

- Sid Banerjee
CEO, Clarabridge

Insight by Design: Another pitch for text analytics gone awry

Insight by Design: Another pitch for text analytics gone awry

New Study: "Exploring the Link Between Customer Care and Brand Reputation in the Age of Social Media "

Just came across press releases on this study, including this one:

http://sncr.org/?p=110

http://www.brandweek.com/bw/news/recent_display.jsp?vnu_content_id=1003793178

A few salient quotes from a Brandweek article on the study:

"Forget focus groups. Consumers are giving it straight to brands, and each other, via online social media in big numbers, according to a recent study by the Society for New Communications Research, Palo Alto, Calif."

...The study found that 74% of respondents choose companies or brands based on customer service experiences shared by other Web users on the Internet. Eighty-one percent of those polled said they believe blogs, online rating systems and discussion forums give consumers “a greater voice” in customer service. However, only 33% of respondents felt that companies take customers’ opinions seriously."

...Of those industries judged to be doing the best job in using social media to respond to customer service issues, technology, retail and travel companies took top honors. Dell and Amazon were noted most often as those companies doing the best job handling customer care problems via social media.

Utilities, healthcare and insurance firms fared the worst. "

Well - it's good to see that others are seeing what we're seeing at Clarabridge. In no particular order - my thoughts:

1) desirable, savvy, computer-literate, affluent customers value the customer experience, and don't want to waste time with companies that waste their time with sub-par experiences

2) those customers are not shy about making their opinions known on, and offline.

3) When they get upset - the words, the emotions, and the passions are captured in all kinds of places. In the notes in a customer support interaction. In the passionate responses of a customer survey (increasingly if not exclusively on-line surveys nowadays). And of course (as this study notes) in the forums, and groups online where like minded consumers congregate.

If you're one of those companies in travel, hospitality retail, and technology, you're probably interested to learn more. Not surprisingly, when you look at the customer rosters for firms like Clarabridge - they are full of those kinds of companies who want to learn more about customer feedback, and use the feedback to improve their customers experiences. Customers like Gaylord Hotels, Marriott, Gap Stores, United Airlines, Intuit, H&R Block, etc. They're using text mining platforms like Clarabridge's CMP to crawl web content, or text mine survey and call center verbatim content, and they're tapping into customer feedback, learning how they fare, fixing problems, benchmarking against their competitors. In short - they're GETTING IT.

As a consumer of health care, utility, and insurance firms, I'm not surprised that they're not. I'm also not surprised because I mostly don't see many of those companies using customer experience analytics solutions very much.

It's not enough to recognize that customers are posting their rants, passions, and feedback on the Internet and in surveys and in their conversations with companies. Companies need to:

- Connect to, and Collect the content from wherever it is.
- Mine and Refine the content, to translate text (in all its clumsy, wordy verbosity) into ideas, concepts, experiences, and sentiments that can be quantified,
- Analyze and Discover themes, issues, problems, opportunities, and passions of customers about the products and services they consume.

It's all about Customer Experience Intelligence. It's all about what Clarabridge does. It's not a fad. It's a trend.

---Sid.

Text Mining and Customer Experience - the blog I intend to write...

Since this is my first entry, I'll use this first posting to organize my thoughts and think about what I want to write over the next few days and weeks.

A bit of background:

Who am I - I'm a technology executive with more than a few years working in and around the business intelligence, data warehousing, and more recently the text mining and customer experience management and analysis space. A few years ago I started a text analytics company that's really taken off in 2007. I decided to start the blog to have a place to document my thoughts on the business we're in, the creative ways people are starting to analyze the terabytes and petabytes of unstructured customer-originated data that are increasingly available to business decision makers, and to speculate on issues that are at the intersection of business, information technology, customer experience, marketing, maybe politics, and the pace of change in all of the above...

Over the next few days and weeks ideas I want to write down include:

1) Why is there a market for text analytics, and specifically the business application subset that my company's involved in, which we call Customer Experience Management and Analysis. How did it evolve to be a viable space for a company like the one I'm working for?

2) What the heck should we call the space in 2008? Should the company's space be oriented around a technology (such as text mining, text analytics)? Or should we define our business so that it's more aligned to a specific class of business problems that it solves (i.e. customer experience management and analysis)? A good label for a company should support a nice acronym - should the acronym factor into the naming of the space?

3) What are the benefits our customers get from our solutions? Specifically,
- why do they buy our stuff
- what are the initial benefits they get when they deploy our solutions?
- what's the long term benefit of using our solutions? How do you define ROI up front, and more importantly with sustained use over time? Are our solutions "transformative?" How?

4) When you are building a solution that is grounded in technology components that are packaged and developed into a business solution, (as our company's offerings are), how much do you build, vs, license, vs partner with others in the surrounding ecosystem that supports your business area? What parts of your solution are critical, and differentiating, vs what parts are best accessed via partners? Why did we make some of the technology development decisions that we made when we built our platform? How do we expect our technology to evolve over time?

5) A brief history of Text Mining applied to customer market research -- How did the space evolve? What preceded Customer Experience Management and Analysis? What was the ancient history of the space? I have a view that text mining is a logical evolution of technology and analysis that dates back to the era of mass production, leading through the eras of mass distribution, mass retailing, and now mass consumption, and that as every era has developed over the past century, the market has required "instrumentation" - and that text analytics is the latest instrumentation that is now being applied to business (more on this as I write this post).

6) as an adjunct to posting topic #1, - is the market for text analytics and customer experience management and alalysis "wide open" or are there barriers to new entrants that are making it harder to just enter into the space if you aren't already in the thick of it? What are the critical business and technology factors that any potential entrant needs to consider before diving into this space?

7) Case Studies, Case Studies, Case Studies. If customers won't go on record (if they do you can read about it on the corporate web site), what are the interesting stories that have emerged from our deployments over the past couple of years? Changing the names and perhaps industries to protect the unauthorized, what real insights have customers gleaned through mining through customer experience data? How have they reduced the time, and cost intensity of predecessor approaches to seeking out, examining, and acting on customer experience insights? What "quick wins" have they found? Why are our customers continuing to use our solutions year after year?

8) Case Studies/Lessons learned. What SHOULDN'T Text analytics be used for? What have we learned through trials, tribulations, and failed efforts, and what did we need to do to turn lemons into lemonade? How can you avoid making mistakes by learning from our experiences?

9) What is the Customer Experience Maturity Model, and where do you sit on it? Why is it important to move up the curve from customer experience "novice" to custome experience "guru?"

10) Political Experience Management and Analysis - what can we learn from the presidential campaigns about mining customer sentiment, experience, and using it to sharpen your "customer experiences" and relationships?

11) Our Partner Ecosystem - what is it now? What do I want it to be? How can you be a partner of our company if you're a prospect, consultant, marketing services company, systems integrator, data provider, or just an interested party?

If I can get all these topics written, I should have a resonable blog. If anybody has additional ideas for posts, feel free to comment.

Happy 2008. Challenge me....