SAP KXEN [EOL] Reviews

I’m writing this on my way home from Dreamforce, the Salesforce.com user conference that has become the primary industry gathering for marketing automation vendors. With a reported 90,000 attendees (I didn't count them personally), the show is fragmented into many different experiences. My own experience was mostly talking to marketing technology vendors in the exhibit hall, private meetings, and maybe a party or two. I did attend the main keynote and the “marketing cloud” announcement, but neither contained no major product news and the basic story – that social networks change everything – was true but far from novel.

So what did I learn? On reflection, there were two themes that hadn’t expected when I arrived.

The first was data. I generally think of marketing systems as relying primarily on data from the company’s own marketing, sales, and operational systems. But the exhibit hall was filled with vendors offering information – mostly from Web crawling or social media – to supplement the company’s internal resources. Of course, this isn’t new but it seems that external sources are becoming increasingly important. The main reason is so much valuable public information is now available. A lesser factor may be that there’s less internal information, at least for sales and marketing, because so many prospects engage indirectly and anonymously until deep in the buying process.

But there’s more to data than the data itself. The theme includes easier connectivity to external data, via standard connectors in general and the Salesforce.com AppExchange in particular. A closely related trend is real-time, on-demand access to the external data: say, when a salesperson views a lead record or a lead is first added to marketing automation. This requires immediate matching to find the right person in the supplier’s database, and, sure enough, matching was another popular technology on the show floor. I also saw broader use of Hadoop to handle all this new data: as you probably know, Hadoop effectively handles large volumes of unstructured and semi-structured data, so it’s a key enabling technology for data expansion. A final component is continued growth in the reporting, analytics, and predictive modeling systems that make productive use of the newly-available data.

Some products combine all these attributes, others offer a few, and some just one. Obviously a single integrated solution is easiest for the buyer, but as Scott Brinker recently pointed out in an insightful blog post, platforms like Salesforce.com may actually make it practical for marketers to mix and match individual products without the technical pain traditionally associated with integration. It therefore makes sense to view the data-related systems as a cluster of capabilities that will develop as parts of single ecosystem, collectively raising the utility and importance of external data to marketers.

The second theme, considerably less grand, was lead scoring. I suppose this is really just a subset of the analytics component of the data theme, but I saw enough new lead scoring features from enough different vendors to treat it separately. In particular, predictive modeling vendor KXEN announced a free, cloud-based service to automatically score a new Salesforce.com lead’s likelihood of converting into a contact. (If you’re not familiar with Salesforce.com terminology: contacts are linked to an account, while leads are not. The conversion usually indicates the salesperson has deemed the person a valid prospect and is thus a critical stage in most sales processes.)

The KXEN service requires absolutely no set-up; users just install it from the AppExchange. KXEN then reads the data, builds a predictive model based on past results, and returns the scores on current leads. From a technical standpoint, the modeling is nothing new, and indeed the people I met at the KXEN booth seemed to feel the product was barely worth discussing. But I’ve long felt that an automated, predictive-model-based scoring service was a major business opportunity because it would replace the time-consuming, complicated, and surely suboptimal lead scoring models that most companies now build by hand, usually with little basis in real data. Of course, there are plenty of other predictive modeling systems available for marketers, but I’m excited because I don’t think anyone else has made model-based lead scoring as simple as the KXEN offering. Maybe I need to get out more.

Speaking of which, I met SetLogik at a loud party after several glasses of wine, so I may have been wearing the marketing technology equivalent of beer goggles. But if I understood correctly, it tackles the really hard part of revenue attribution by using advanced matching technologies to connect the right leads and contacts to sales (reflected in closed opportunities in Salesforce.com). Once you’ve done that, determining which marketing touches influenced those people is relatively easy. It’s a unique solution to a huge industry problem. Come to think of it, correct linkages are also critical for building effective lead scoring models, which it turns out that SetLogik also does. (I'll admit it: I Googled them the next day.) So they're part of that theme as well.

As I mentioned earlier, data and lead scoring were themes that emerged for me during the conference. I did have some other themes in mind when I started, which are also worth sharing. I’ll do that another day.

Finally, it’s worth noting that the conference itself was tremendously well run. It sometimes felt that one-third of those 90,000 people were Salesforce.com employees hired to stand around and answer questions. Where they found so many cheerful people outside of the Midwest I’ll never know. Congratulations and thanks to the Salesforce.com team that made it happen.

Written by Nigel Magson & Andy Hindson

We were delighted at being given the opportunity to review KXEN.  In analyst terms it’s a bit like being given the chance to drive a Ferrari, so not one we were going to turn down. Andy has been using some of KXEN modules for a number of years, whilst the rest of the team are used to analytic tools such as SAS/ SPSS/FastStats or Smartmodeller.  Read on for our overview of one of their key products, InfiniteInsight, and forgive us if we sound too much like Jeremy Clarkson.

KXEN (originally for Knowledge Extraction Engines) is a market leading predictive analytics and data mining software business, they have sales offices around the world and are headquartered in San Francisco with Research and Development based in Paris.  They are recognised by the key players including; Forrester and Gartner.

The main markets they service are Financial Services, Telecoms and Retail where customer and data volumes are high, and consequently the opportunity to add value through predictive analytics is greatest. These are also the organisations which tend to have large analytical teams and similarly sized budgets to support them and the tools they require. Consequently, KXEN’s product development direction has been in support of these key markets including the development of the (Modelling) Factory-the deployment and configuration of automated models that run in real-time on organisations systems infrastructure. They readily admit they’re not the tool for small datasets.

KXEN positions its tools as key enablers for maximum productivity by data-mining specialists and business analysts alike.  More recently they have developed InfiniteInsight Genius which they claim puts data mining capability into the hands of Marketers. It achieves this by simplifying the modelling process through the use of a GUI that guides the Marketer through the modelling process.

KXEN also provides an API for their predictive analytical engine which has been widely adopted by Marketing Services Providers (MSPs) including; Alterian, Experian and Neolane, and the Database and Business Intelligence community including; Oracle, Teradata and Sybase.

InfiniteInsight Product Set

InfiniteInsight is the name that describes and prefixes all the KXEN family of modelling products.  The version available (version 6.0.0) for this review had the following modules;

• Explorer


• Social


• Modeler


• Toolkit

The KXEN modules that will not be reviewed here are;

• Scorer


• Factory

InfiniteInsight Scorer module is self-explanatory and enables the KXEN User to apply the model(s) they have built in a number of ways including; scoring directly onto the database or enterprise class, which is the integration of the score back onto an Organisations operational systems e.g. Call Centre or Web Site.

KXEN has invested in this area and supports all the major databases (Teradata, Sybase, Netezza, SQL Server, IBM DB2, etc) and the main statistical modelling packages (SAS and SPSS).

InfiniteInsight Factory is primarily aimed at the high end of the predictive analytics market and enables the full automation or industrialisation of modelling processes which are configured and deployed to run 24/7.

InfiniteInsight Review

KXEN customers deploy InfiniteInsight for a variety of predictive analytics tasks including optimization of the customer lifecycle; acquisition, cross-sell and up-sell campaigns and customer retention.  Additionally, in Financial Services it can play a key role in reducing risk and fraud, and within Telecoms the focus is churn management, social network analysis and cross-sell of further services.

Look and Feel

We felt that the InfiniteInsight GUI feels dated and would benefit from an overhaul with due consideration given towards incorporating a sleeker modelling workflow.  Navigation isn’t always straightforward as the menu naming isn’t obvious. This said, like most software once you know what you’re doing you forget about this.  On the plus side InfiniteInsight does provide a rich set of features and options that can be tailored and tweaked to address the nuances presented by different data scenarios.

Using InfiniteInsight

Start InfiniteInsight and you’re presented with the Modelling Assistant pictured above. 

KXEN has recently beefed up the Explorer module that enables the Analyst to create the analysis or modelling dataset. This includes some perplexing function names (probably a legacy from the French translation?) such as; data manipulation which is functionality to merge datasets together and Perform an Event Log Aggregation which is actually the process of aggregating child data to the parent table e.g. summing transaction values for customers into a new table.  This function does possess some powerful functions for creating date based aggregations across potential time periods that may be of interest for modelling; years, quarters, months and days. Again this is fine once you’ve got used to it.

It should be noted that data manipulation creates SQL code that has to be executed on the source database, so creation of the analysis dataset is external to KXEN InfiniteInsight, although the actual modelling process is internal.

There are some useful features available within Explorer for defining multiple modelling variables to build concurrently through the use of wildcards but I suspect that many Analysts will opt for preparing their modelling dataset using different tools.

The Social component naming is slightly misleading as it has nothing to do with social media such as LinkedIn, Facebook or Twitter.  It provides functionality to create links and map relationships within transactional data and display these networks of influence.  This is very powerful and it’s primarily aimed at the Telecoms market where the nature and volume of their data supports its use, but we could see that it would have applications to areas, such as social media if the supporting data was available.

The Toolkit allows the User to review and visualise existing datasets (Open the Data Viewer). Transfer a data source to another location or format (Perform a Data Transfer) or export a list of distinct values (List Distinct Values in Data Set).  The final option is to generate statistics on the variables in the data set (Get Descriptive Statistics for a Data Set). Again, apart from perhaps the Descriptive Statistics option I suspect that the Analyst will be using different tools for the basic data processing tasks available.

Modeler is the key module for defining and building the different types of modelling scenarios and the focus of the rest of the review.  The first task is to define the dataset you wish to build a model upon.  If a modelling dataset has been previously defined then this can be selected or a new dataset can be chosen, or Explorer can be used to define the data.  Each variable in the data set is defined as to its type; nominal, ordinal or continuous, and this type dictates how the variable will be treated and encoded during modelling.

Building Models

Modelling dataset defined, the Analyst selects which type of model they want to create; Classification/(Ridge) Regression, Clustering, Times Series or Association Rules (Next Best Offer).  I’ll use the regression model to illustrate how Modeler works; a target variable is selected along with a set of explanatory variables.

At the heart of Modeler are a set of algorithms which have harnessed Structured Risk Minimisation (SRM).  SRM delivers efficiencies to the modelling process regards the Analysts time as they do not need to worry about;

• Number of explanatory variables they present to the model


• Concerns about multicollinearity


• Making any assumptions about explanatory variables distribution


• Concerns regards missing variables

As a consequence the modelling process is faster and more efficient, as all the above are time consuming activities if checked and validated from first principles.

The question is therefore, how does InfiniteInsight^TM Modeler achieve this?  The answer is that much of the modelling grunt work is automated.  In a traditional approach to modelling, time spent preparing data would account for 40-60% of the time, however, this is drastically reduced as Modeler automatically encodes data as it is loaded. For instance, continuous variables are assigned to 20 “bins”, each containing 5% of the data, this is configurable but is generally left as is.

The modelling dataset is also automatically split based upon the cutting strategy into Estimation, Validation and Test.  Various cutting strategies are available and the help provides guidance as to which may be most appropriate for your particular modelling scenario, there is also the option to configure your own specific cutting strategy.

Estimation generates the different models, Validation will select best model among those generated, incorporating selection of only those explanatory variables which make a significant contribution and Test will verify the performance of the selected model on unseen data.  This is the “hold-out” data and enables the calculation of the models robustness.

A model diagnostics report is created which is easy to interpret with experience, the two key measures being Ki (Model Quality) and Kr (Model Robustness).  Additional information is on hand, including contributions by explanatory variables so it is simple to see which variable is contributing most to the model and, of course, the obligatory gains curve.

We didn’t pursue the analysts track test to see whether we could build a better model in a conventional tool.  The point is really irrelevant.  KXEN will build great models given the right data.  It will do this faster than a conventional analyst ever could.  It can update & redeploy faster, and it can do it over multiple models.  Case closed. If you only ever build a couple of models and can spend plenty of time over them, don’t bother with KXEN.

Conclusion

Picking up our car analogy.  As one of the supercars of the analytics world of course we want one, despite and because of its occasional quirks.  KXEN software is an excellent addition to the customer insight team’s toolbox where it would comfortably sit alongside traditional analytical software to explore features discovered in InfiniteInsight and tools to build and engineer modelling datasets.   As an overall component of your CRM architecture, it will increase the speed and efficiency of the Modelling / Analysis team enabling them to quickly understand the feasibility of whether a particular scenario can be modelled.

KXEN commercials place it at the high end of analytical engines, but if you are considering this, a bit like a supercar, price won’t be your only interest.  By recognising the commercial benefits of delivering powerful models fast, from speed and reliability of updating, through to ease of deployment, a convincing business case could be constructed that would counter the clearly higher price tag of such a solution. The purchase decision for InfiniteInsight won’t be made on the GUI or its data preparation functionality.  What it will be judged on is its ability to build quality models that deliver efficiencies and demonstrable ROI.  Modeler delivers this through the algorithms at the heart of the product which have harnessed Structured Risk Minimisation (SRM).  With KXEN software installed and operating, the business might find itself in a position to decide whether it wants more productivity from the existing modelling team or achieve the same but with a smaller team.  Hopefully for the analysts out there the former! 

KXEN’s product positioning, claims that it is aimed at marketers but our experience is that marketers are not the users of such tools. We see this as  still very much the domain of the marketing analyst or statistician who will be tasked with the modelling work as marketers do not generally have the data wherewithal or statistical background that is needed to execute the full modelling lifecycle. The software, however good, still requires the modelling scenario to be properly framed, after which an appropriate modelling universe needs to be defined along with the target variable and potential explanatory variables.  All these actions require ‘hands-on’ data work to engineer a modelling dataset that is ‘fit for purpose’.

Of course, data is the key ingredient for any modelling work, and the quality of that data is paramount to the success of model, as is the breadth and volume of data available within the organisation in an accessible form.  If these criteria are met then KXEN InfiniteInsight will undoubtedly provide the quickest answers and deliver quality models.

Addendum - Structured Risk Minimization

The primary challenge for statisticians has been to build highly accurate models that are also reliable. This is particularly challenging with the advent of Big Data where there are high volumes of potential variables to use. Traditional statistics generally only produce an accurate model with a few variables, so an expert is needed to reduce the number of variables before building a model. The more variables there are, the more difficult it can be to build a reliable model. Only the expertise of the statistician or competent analyst guarantees the reliability of the model.

SRM was a breakthrough in mathematics and statistics made by the Russian mathematicians Vladimir Vapnik and Alexey Chervonenkis, which for the first time makes it possible to automatically build reliable and accurate models. In contrast to traditional statistical models, SRM models become more accurate and are still reliable as the number of variables is increased. Model Accuracy and Reliability are determined by the data, not by the expert. Certainly worth a further read if you are interested in how the engine works.

I last got an update from KXEN when they launched
InfiniteInsight Genius. Since then they have been rolling out cloud-based analytic applications built around their core modeling engine. In particular they have launched a new product, KXEN’s Predictive Offers, their second cloud-based predictive analytic application.

KXEN has historically been focused on B2C companies, especially large ones, using predictive analytics to improve the customer lifecycle – telecoms, financial services, retail and e-business. They have delivered both their core platform and a series of predictive applications to over 500 customers worldwide. KXEN’s automated modeling technology supports data preparation, model building, deployment and scheduled refresh.

KXEN has begun building applications in the cloud and on-premise using this platform. The on-premise applications are generally embedded in other companies’ complete solutions, with the third party selling a solution that embeds KXEN’s engine. The cloud apps are part of widening KXEN’s footprint to smaller companies and are being sold directly by KXEN through app stores like Salesforce.com’s AppExchange). KXEN’s cloud apps are designed to be configured and deployed by a cloud app admin (e.g. a salesforce.com administrator) or a marketer and consumed by the end user (e.g. call center agent, sales rep, etc.) without any data science know-how or analytical training.

The market opportunity for these kinds of applications exists because cloud or SaaS CRM vendors have generally weaker analytics than their on-premise competitors. Meanwhile more companies, and smaller companies, are focusing on using analytics to deliver better customer relationships. Without an analytic story these SaaS offerings are going to be at a serious disadvantage relative to their on-premise competitors when competing for larger deals like B2C call center deployments where analytics really matter. KXEN plans to enable these SaaS CRM applications with powerful predictive analytics.

The basic infrastructure involves KXEN’s engine running in the EC2 cloud. Data is assembled natively in the SaaS CRM application (initially just Salesforce.com but in practice any SaaS CRM solutions could be integrated) to create the historical dataset needed to build the model. The dataset is then sent to the KXEN cloud which asynchronously builds the predictive model (typically in an hour or two for even salesforce.com’s largest customers) using KXEN’s automated model building platform, and is then pushed back as native code (e.g. Salesforce.com’s Apex) that executes inside the SaaS CRM. This means that the CRM application can score a customer (or an offer, or a lead) in real-time without accessing the KXEN server during a transaction or interaction.

KXEN’s cloud applications are, for the moment, very focused on Salesforce.com and tie to the new Salesforce.com mantra about being a “customer company.” Becoming a customer company means moving from a transactional, inside-out, company-centric approach to CRM (legacy) to one that is more customer-centric, outside-in, and relationship oriented. This requires ending the use of generalized messaging that dominates companies’ interaction with their customers and moving to one focused on personalized, targeted messaging. Customer companies need to adopt a next best activity mindset (something I wrote a white paper about recently) and this drives a need for analytic decision-making in customer treatment.

To adopt a real-time approach to next best activity companies have historically had to build a complex system that required a lot of IT maintenance. KXEN’s cloud apps are designed to let Salesforce.com admins or a marketer install the app, configure the application (set up their offers and any rules associated with them for instance like inventory, eligibility, marketing priority, etc.) and then allow the KXEN predictive engine to do the rest. It will learn what works and build increasingly accurate predictive models. These applications are thus designed to work without the need for an analytic data scientist, leveraging the automation at the core of the KXEN platform.

Today KXEN offers Predictive Lead Scoring (for lead targeting), Predictive Offers (for next best activity). Soon it plans to add Predictive Retention (to make retention offers integrated with Predictive Offers) and Predictive Case Routing (for resolving service issues).

The new Predictive Offers application is integrated into the salesforce.com UI and displays the selected offer alongside the main forms for an end user. It presents the offer recommendation in either banner mode or console mode (a Service Cloud feature for high-volume call center agents)and a script to be displayed for the selected offer. The user can see both why a particular offer is being presented (or not presented) for the current user and the overall model driving each offer’s score. This scoring is done in real-time and natively inside Salesforce.com, so that changes in a customer’s information during a call for instance result in an immediate update of the predictive scores and thus the best offer. These offers can also be delivered into self-service or other environments connected to Salesforce.com.

Offers are defined in an administrative UI built for the salesforce.com admin or a marketer that allows the definition of the offer, association of creative and script, and the definition of rules. No analytical knowledge is required, making the applications easily consumable by the business. With a new marketing offer there is no data so the application will automatically try the offer randomly (in a “learning” mode subject to a defined maximum percentage for learning transactions). This enables it to gather data about what works and what does not. Once enough data is gathered to build a model the offer moves from the control group to the “predicted” offers. In a similar way the administrator can assign a certain number of transactions to a random selection from the predicted offers for “improvement mode”, allowing them to be tried when they would not normally come up as the best offer. This prevents pigeonholing or local maxima. Both of these are clear best practices in predictive analytics and it’s great to see them automated in the application.

KXEN is one of the vendors in our Decision Management Systems Platform Technologies Report, and you can get more information on their products by visiting kxen.com.