Metrics Matter

I had a very interesting conversation last week with Dyke Hensen, SVP of Product Strategy for PivotLink.  For those of you not familiar with PivotLink, the company is a SaaS BI provider that has been in business for about 10 years (before the term SaaS became popular). Historically, the company has worked with small to mid sized firms, which often had large volumes of data (100s of millions of rows) taken from disparate data sources.  For example, the company has done a lot of work with retail POS systems, ecommerce sites, and the like.  Pivotlink enables companies to integrate information into a large columnar database and create dashboards to help slice and dice the information for decision-making.

Recently, the company announced ReadiMetrix, a SaaS BI service designed to provide, “Best practices-based metrics to accelerate time to insight.”  The company provides metrics in three areas:  Sales, Marketing, and HR.  These are actionable measures that companies can use to measure itself against its objectives.  If some of this sounds vaguely familiar (e.g. LucidEra), you might be asking yourself, “Can this succeed?”  Here are four reasons to think that it might:

  • PivotLink has been around for the past decade.  It has an established base of customers and business model. The company knows what its customers want. It should be able to upsell existing customers and it knows how to sell to new customers.
  • From a technical perspective, ReadiMetrix is not a tab in Salesforce.com like many other business SaaS services.   Rather, the company is partnering with integrators like Boomi to provide the connectors to on premises as well as cloud based applications. So, they are not trying to do the integration themselves (which often trips companies up).  The integration also utilizes a SOA based approach, which enables flexibility.
  • The company is building a community of best practices around metrics to continue to grow what it can provide and to raise awareness around the importance of metrics.
  • SaaS BI has some legs.  Since the economic downturn, companies realize the importance of gaining insight from their data and BI companies of all kinds (on and off premises) have benefited from this.  Additionally, the concept of a metric is not necessarily new (think Balanced Scorecard and other measurement systems), so the chasm has been crossed in that regard.

Of course, a critical key to success will be whether or not companies actually think they need or want these kinds of metrics.  Many companies may believe that they are “all set” when it comes to metrics.  However, I’ve seen firms all too often think that “more is better” when it comes to information, rather than considering a selected number of metrics with drill down capability underneath.  The right metrics require some thought.  I think that the idea of an established set of metrics, developed in conjunction with a best practices community might be appealing for companies that do not have expertise in developing their own.  It will be important for PivotLink to educate the market on “why” these categories of metrics matter and their value.

Social Network Analysis: What is it and why should we care?

When most people think of social networks they think of Facebook and Twitter, but social network analysis has its roots in psychology, sociology, anthropology and math (see Scott, John Social Network Analysis for more details). The phrase has a number of different definitions, depending on the discipline you’re interested in, but for the purposes of this discussion social network analysis can be used to understand the patterns of how individuals interact.  For other definitions, look here.

I had a very interesting conversation with the folks from SAS last week about Social Network Analysis.   SAS has a sophisticated social network analysis solution that draws upon its analytics arsenal to solve some very important problems.  These include discovering banking or insurance fraud rings, identifying tax evasion, social services fraud, and health care fraud (to name a few) These are huge issues.  For example, the 2009 ABA Deposit Account Fraud Survey found that eight out of ten banks reported having check fraud losses in 2008. A new report by the National Insurance Crime Bureau (NICB) shows an increase in claims related to “opportunistic fraud,” possibly due to the economic downturn.   These include worker’s compensation, staged and caused accidents.

Whereas some companies (and there are a number of them in this space) use mostly rules (e.g. If the transaction is out of the country, flag it) to identify potential fraud, SAS utilizes a hybrid approach that can also include:

  • Anomalies; e.g. the number of unsecured loans exceeds the norm
  • Patterns; using predictive models to understand account opening and closing patterns
  • Social link analysis: e.g. to identify transactions to suspicious counterparties

Consider the following fraud ring:

  • Robert Madden shares a phone number with Eric Sully and their accounts have been shut down
  • Robert Madden also shares and address with Chris Clark
  • Chris Clark Shares a phone with Sue Clark and she still has open accounts
  • Sue Clark and Eric Sully also share an address with Joseph Sullins who has open accounts and who is soft matched to Joe Sullins who has many open accounts and has been doing a lot of cash cycling between them.

This is depicted in the ring of fraud that the SAS software found, which is shown above.   The dark accounts indicate accounts that have been closed.  Joe Sullins represents a new burst of accounts that should be investigated.

The SAS solution accepts input from many sources (including text, where it can use text mining to extract information from, say a claim).  The strength of the solution is in its ability to take data from many sources and in the depth of its analytical capability.

Why is this important?

Many companies set up Investigation Units to investigate potential fraud.  However, often times there are large numbers of false positives (i.e. investigations that show up as potential fraud but aren’t) which cost the company a lot of to investigate.  Just think about how many times you’ve been called by your credit card company when you’ve made a big purchase or traveled out of the country and forgot to call them and you understand the dollars wasted on false positives.    This cost, of course, pales in comparison to the billions of dollars lost each year to fraud.    Social network analysis, especially using more sophisticated analytics, can be used to find previously undetected fraud rings.

Of course, social network analysis has other use cases as well as fraud detection.   SAS uses Social Network Analysis as part of its Fraud Framework, but it is expanding its vision to include customer churn and viral marketing  (i.e. to understand how customers are related to each other).   Other use cases include terrorism and crime prevention, company organizational analysis, as well as various kinds of marketing applications such as finding key opinion leaders.

Social network analysis for marketing is an area I expect to see more action in the near term, although people will need to be educated about social networks, the difference between social network analysis and social media analysis (as well as where they overlap) and the value of the use cases.  There seems to be some confusion in the market, but that is the subject of another blog.

The Importance of multi-language support in advanced search and text analytics

I had an interesting briefing with the Basis Technology team the other week.  They updated me on the latest release of their technology called Rosette 7.   In case you’re not familiar with Basis Technology it is the multilingual engine that is embedded in some of the biggest Internet search engines out there – including Google, Bing, and Yahoo.  Enterprises and the government also utilize it.  But, the company is not just about keyword search.  Its technology also enables the extraction of entities (about 18 different kinds) such as organizations, names, and places.  What does this mean?  It means that the software can discover these kinds of entities across massive amounts of data and perform context sensitive discovery in many different languages.

An Example

Here’s a simple example.  Say you’re in the Canadian consulate and you want to understand what is being said about Canada across the world.   You type “Canada” into your search engine and get back a listing of documents.  How do you make sense of this?  Using Basis Technology entity extraction (an enhancement to search and a basic component of text analytics), you could actually perform faceted (i.e. guided) navigation across multiple languages.  This is illustrated in the figure below.  Here, the user typed “Canada” into the search engine and got back 89 documents.  In the main pane in the browser, you can see that an arrow in a number of different languages highlights the word Canada, so you know that it is included in these documents.  On the left hand side of the screen is the guided navigation pane.  For example, you can see that there are 15 documents that contain a reference to Obama and another 6 that contain a reference to Barack Obama.  This is not necessarily a co-occurrence in a sentence, just in the document.  So, any of these articles would contain a reference to Obama and Canada.  This would help you determine what Obama might have said about Canada. Or, what the connection is between Canada and the BBC (under organization).  This idea is not necessarily new, but the strong multilingual capabilities make it compelling for global organizations.

If you have eagle eyes, you will notice that the search on Canada returned 89 documents, but the entity “Canada” only returned 61 documents.  This illustrates what entity extraction is all about.  When the search for Canada was run on the Rosette Name Indexer tab (see upper right hand corner of the screen shot) the query searched for Canada against all automatically extracted “Canada” entities that existed in all of the documents.  This includes all persons, locations, and organizations that have similar names. This included entities like “Canada Post” and “Canada Life” which are organizations, not the country itself. Therefore the 28 other documents with a Canada variant are organizations or other entities.

Use Cases

There are obviously a number of different use cases where the ability to extract entities across languages can be important.  Here are three:

  • Watch lists.  With the ability to extract entities, such as people, in multiple languages, this kind of technology is good for government or financial watch lists.  Basis can resolve matches and translate names in 9 different languages. This includes resolving multiple spelling variations of foreign names. It also enables organizations to match names of people, places, and organizations against entries in a multilingual database.
  • Legal discovery.  Basis technology can identify  55 different languages.    Companies would use this technology, for example, to identify multiple languages within a document and then route them appropriately.  Additionally, Basis can extract entities in 15 different languages (and search in 21) so the technology could be used to process many documents and extract the entities associated with them to find the right set of documents needed in legal discovery.
  • Brand image, competitive intelligence.   The technology can be used to extract company names across multiple languages.  The software can also be used against disparate data sources, such as internal document management systems as well as external sources such as the Internet.  This means that it could cull the Internet to extract company name (and variations on the name) in multiple languages.  I would expect this technology to be used by “listening posts” and other “Voice of the Customer” services in the near future.

While this technology is not a text analytics analysis platform, it does provide an important piece of core functionality needed in a global economy.  Look for more announcements from the company in 2010 around enhanced search in additional languages.

My Take on the SAS Analyst Conference

I just got back from the SAS analyst event that was held in Steamboat Springs, Colorado.   It was a great meeting.  Here are some of the themes I heard over the few days I was there:

SAS is a unique place to work.

Consider the following:  SAS revenue per employee is somewhat lower than the software industry average because everyone is on the payroll.  That’s right.  Everyone from the grounds keepers to the health clinic professionals to those involved in advertising are on the SAS payroll.   The company treats its employees very well, providing fitness facilities and on site day care (also on the payroll). You don’t even have to buy your own coffee or soda! The company has found that these kinds of perks have a positive impact.  SAS announced no layoffs in 2009 and this further increased morale and productivity.  The company actually saw increased profits in 2009.   Executives from SAS also made the point that even thought they might have their own advertising, etc. they do not want to be insular.  The company knows it needs new blood and new ideas.  On that note, check out the next two themes:

Innovation is very important to SAS.

Here are some examples:

  • Dr. Goodnight gave his presentation using the latest version of the SAS BI dashboard, which looked pretty slick.
  • SAS has recently introduced some very innovative products and the trend will continue. One example is its social network analysis product that has been doing very well in the market.  The product analyzes social networks and can, for example, uncover groups of people working together to commit fraud.  This product was able to find $32M in welfare fraud in several weeks.
  • SAS continues to enhance its UI, which it has been beat up about in the past. We also got pre-briefed on some new product announcements that I can’t talk about yet, but other analysts did tweet about them at the conference.   There were a lot of tweats at this conference and they were analyzed in real time.

The partnership with Accenture is a meaningful one.

SAS execs stated that although they may not have that many partnerships, they try to make the ones they have very real.  While, on the surface, the recent announcement regarding the Accenture SAS Analytics Group might seem like a me too after IBM BAO, it is actually different.  Accenture’s goal is transform the front office, like ERP/CRM was transformed.  It wants to, “Take the what and turn it into so what and now what?” It views analytics not simply as a technology, but a new competitive management science that enables agility.  It obviously won’t market it that way as the company takes a business focus.  Look for the Accenture SAS Analytics Group to put out services such as Churn management as a service, Risk and fraud detection as a service.  They will operationalize this as part of a business process.

The Cloud!

SAS has a number of SaaS offerings in the market and will, no doubt, introduce more.  What I found refreshing was that SAS takes issues around SaaS very seriously.  You’d expect a data company to be concerned about their customers’ data and they are. 

Best line of the conference

SAS is putting a lot of effort into making its products easier to use and that is a good thing.  There are ways to get analysis to those people who aren’t that analytical.  In a discussion about the skill level required for people to use advanced analytics, however, one customer commented, “Just because you can turn on a stove doesn’t mean you know how to cook.”  More on this in another post.

Five Predictions for Advanced Analytics in 2010

With 2010 now upon us, I wanted to take the opportunity to talk about five advanced analytics technology trends that will take flight this year.  Some of these are up in the clouds, some down to earth.

  • Text Analytics:  Analyzing unstructured text will continue to be a hot area for companies. Vendors in this space have weathered the economic crisis well and the technology is positioned to do even better once a recovery begins.  Social media analysis really took off in 2009 and a number of text analytics vendors, such as Attensity and Clarabridge, have already partnered with online providers to offer this service. Those that haven’t will do so this year.  Additionally, numerous “listening post” services, dealing with brand image and voice of the customer have also sprung up. However, while voice of the customer has been a hot area and will continue to be, I think other application areas such as competitive intelligence will also gain momentum.  There is a lot of data out on the Internet that can be used to gain insight about markets, trends, and competitors.
  • Predictive Analytics Model Building:  In 2009, there was a lot of buzz about predictive analytics.  For example, IBM bought SPSS and other vendors, such as SAS and Megaputer, also beefed up offerings.  A newish development that will continue to gain steam is predictive analytics in the cloud.  For example, vendors Aha! software and Clario are providing predictive capabilities to users in a cloud-based model.  While different in approach they both speak to the trend that predictive analytics will be hot in 2010.
  • Operationalizing Predictive Analytics:  While not every company can or may want to build a predictive model, there are certainly a lot of uses for operationalizing predictive models as part of a business process.  Forward looking companies are already using this as part of the call center process, in fraud analysis, and churn analysis, to name a few use cases.  The momentum will continue to build making advanced analytics more pervasive.
  • Advanced Analytics in the Cloud:  speaking of putting predictive models in the cloud, business analytics in general will continue to move to the cloud for mid market companies and others that deem it valuable.  Companies such as QlikTech introduced a cloud-based service in 2009.  There are also a number of pure play SaaS vendors out there, like GoodData and others that provide cloud-based services in this space.  Expect to hear more about this in 2010.
  • Analyzing complex data streams.  A number of forward-looking companies with large amounts of real-time data (such as RFID or financial data) are already investing in analyzing these data streams.   Some are using the on-demand capacity of cloud based model to do this.  Expect this trend to continue in 2010.

Operationalizing Predictive Analytics

There has been a lot of excitement in the market recently around business analytics in general and specifically around predictive analytics. The promise of moving away from the typical rear view mirror approach to a predictive, anticipatory approach is a very compelling value proposition. 

But, just how can this be done?  Predictive models are complex.  So, how can companies use them to their best advantage?  A number of ideas have emerged to make this happen including 1) making the models easier to build in the first place and 2) operationalizing models that have been built so users across the organization can utilize the output of these models in various ways.  I have written several blogs on the topic.

Given the market momentum around predictive analytics, I was interested to speak to members of the Aha! Team about their spin on this subject, which they term “Business Embedded Analytics.” For those of you not familiar with Aha! the company was formed in 2006 to provide a services platform (i.e. SaaS platform called Axel ) to embed analytics within a business.  The company currently has customers in healthcare, telecommunications, and travel and transportation.  The idea behind the platform is to allow business analysts to utilize advanced business analytics in their day to day jobs by implementing a range of deterministic and stochastic predictive models and then tracking, trending, forecasting and monitoring business outcomes based on the output of the model.

An example

Here’s an example.  Say, you work at an insurance company and you are concerned about customers not renewing their policies.  Your company might have a lot of data about both past and present customers including demographic data, the type of policy they have, how long they’ve had it, and so on.  This kind of data can be used to create a predictive model of customers who are likely to drop their policy based on the characteristics of customers who have already done so.  The Aha! platform allows a company to collect the data necessary to run the model, implement the model, get the results from the model and continue to update it and track it as more data becomes available.   This, by itself, is not a new idea.  What is interesting about the Axel Services Platform is that the output from the model is displayed as a series of dynamic Key Performance Indicators (KPIs) models that the business analyst has created.  These KPIs are really important metrics, such as current membership, policy terminations, % disenrolled, and so on.   The idea is that once the model is chugging away, and getting more data, it can produce these indicators on an ongoing basis and analysts can use this information to actively understand and act on what is happening to their customer base.  The platform enables analysts to visualize these KPIs, trend them, forecast on them, and change the value of one of the KPIs in order to see the impact that might have on the overall business.   Here is a screen shot of the system:

In this instance, these are actual not forecasted values of the KPIs (although this could represent a modeled goal).  For example, the KPI on the lower right hand corner of the screen is called Internal Agent Member Retention.  This is actually a drill down of information from the Distribution Channel Performance.  The KPI might represent the number of policies renewed on a particular reference date, year to date, etc. If it was a modeled KPI, it might represent the target value for that particular KPI (i.e. in order to make a goal of selling 500,000 policies in a particular time period, an internal agent must sell, say 450 of them).  This goal might change based on seasonality, risk, time periods, and so on.

Aha! provides tools for collaboration among analysts and a dashboard, so that this information can be shared with members across the organization or across companies. Aha! Provides a series a predictive models, but also enables companies to pull in the models from outside sources such as SAS or SPSS. The service is currently targeted for enterprise class companies.

So what?

What does this mean?  Simply this:  that the model, once created, is not static.  Rather, its results are part of the business analyst’s day to day job.  In this way, companies can develop a strategy (for example around acquisition or retention), create a model to address it, and then continually monitor and analyze and act on what is happening to its customer base. 

When most analytics vendors talk about operationalizing predictive analytics, they generally mean putting a model in a process (say for a call center) that can be used by call center agents to tell them what they should be offering customers.  Call center agents can provide information back into the model, but I haven’t seen a solution where the model represents the business process in quite this way and continuously monitors the process.   This can be a tremendous help in the acquisition and retention efforts of a company. I see these kinds of models and process being very useful in industries that have a lot of small customers who aren’t that “sticky” meaning they have the potential to churn.  In this case, it is not enough to run a model once; it really needs to be part of the business process. In fact, the outcome analytics of the business user is the necessary feed back to calibrate and tune the predictive model (i.e. you might build a model, but it isn’t really the right model).  As offers, promotions, etc. are provided to these customers, the results can understood in a dynamic way, in a sense to get out ahead of your customer base 

Text Analytics Meets Publishing

I’ve been writing about text analytics for a number of years, now. Many of my blogs have included survey findings and vendor offerings in the space.  I’ve also provided a number of use cases for text analytics; many of which have revolved around voice of the customer, market intelligence, e-discovery, and fraud.  While these are all extremely valuable, there are a number of other very beneficial use cases for the technology and I believe it is important to put them out there, too.

Last week, I spoke with Daniel Mayer, a product-marketing manager, at TEMIS about the publishing landscape and how text analytics can be used in both the editorial and the new product development parts of the publishing business.  It’s an interesting and significant use of the technology.

First a little background.  I don’t believe that it comes as a surprise to anyone that publishing, as we used to know it has changed dramatically.  Mainstream newspapers and magazines have given way to desktop publishing and the Internet as economics have changed the game.  Chris Anderson wrote about this back in 2004, in Wired, in an article he called “The Long Tail” (it has since become a book).  Some of the results include:

  • Increased Competition.  There are more entrants, more content and more choice on the Internet and much of it is free.
  • Mass market vs. narrow market.  Additionally, whereas the successful newspapers and magazines of the past targeted a general audience, the Internet economically enables more narrow appeal publications.  
  • Social, Real time.  Social network sites, like twitter, are fast becoming an important source of real time news. 

All of this has caused mainstream publishers to rethink their strategies in order to survive.  In particular, publishers realize that content needs to be richer, interactive, timely, and relevant.

Consider the following example.  A plane crashes over a large river, close to an airport.  The editor in charge of the story wants to write about the crash itself, and also wants to include historical information about the cause of plane crashes (e.g. time of year, time of day, equipment malfunction, pilot error, etc based on other plane crashes for the past 40 years) to enrich the story.  Traditionally, publishers have annotated documents with key words and dates.   Typically, this was a manual process and not all documents were thoroughly tagged.  Past annotations might not meet current expectations. Even if the documents were tagged, they might have been tagged only at a high level (e.g. plane crash), so that the editor is overwhelmed with information.   This means that it might be very difficult her to find similar stories, much less analyze what happened in other relevant crashes.  

Using text analytics, all historical documents could be culled for relevant entities, concepts, and relationships to create a much more enriched annotation scheme.  Information about the plane crash such as location, type of planes involved, dates, times, and causes could be extracted from the text.  This information would be stored as enriched metadata about the articles and used when needed.  The Luxid Platform offered by TEMIS would also suggest topics close to the given topic.  What does this do? 

  • It improves the productivity of the editor.  The editor has a complete set of information that he or she can easily navigate.  Additionally, if text analytics can extract relationships such as cause this can be analyzed and used to enrich a story.
  • It provides new opportunities for publishers.  For example, Luxid would enable the publisher to provide the consumer with links to similar articles or set up alerts when new, similar content is created, as well as tools to better navigate data or analyze it (this might be used by fee based subscription services).  It also enables publishers to create targeted microsites and topical pages, which might be of interest to consumers.

Under many current schemes, advertisers pay online publishers.  Enhancing navigation means more visits, more page views, and a more focused audience, which can lead to more advertising revenue for the publisher.  Publishers, in some cases, are trying to go even further, by transforming readers into sales leads and receiving a commission from sales. There are other models that publishers are exploring, as well.  Additionally, text analytics could enable publishers to re-package content, on the fly (called content repurposing), which might lead to additional revenue opportunities such as selling content to brand sponsors that might resell it.  The possibilities are numerous.

I am interested in other compelling use cases for the technology.

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