AgilOne Combines Marketing Database, Analytics and Execution: Yep, That's a Customer Data Platform

Well, this is embarrassing.

Here I am, all excited about discovering a new category of Customer Data Platform systems, which combine marketing database management, predictive modeling, and decision engines. Then I bump into Omer Artun, CEO of AgilOne , which he founded seven years ago to combine marketing database management, predictive modeling, and decision engines. It makes me feel much less clever.

But I guess I can’t hold that against AgilOne. As Artun tells the story, the company was created to provide marketers with a packaged, cloud-based version of the advanced data management, analytics, and execution capabilities that are usually available only to the largest and richest firms. The key is a set of 400 standard metrics, which AgilOne derives by mapping each client’s unique data into a standard structure. This, combined with advanced machine learning techniques, lets AgilOne build ten standard predictive models (engagement, next product, lifetime value, etc.) and three standard cluster models (products, behaviors, and brands) with minimal effort. The system builds on these to deliver packages of standard alerts, reports, guided analytics, individual customer profiles, and campaign lists. It also makes its data and predictions accessible to external systems such as call centers and Web sites via real time API calls, so those systems can use them to guide their own customer treatments.

This quick summary doesn’t do justice to the cleverness or sophistication of AgilOne’s approach. Clever, because the standardization allows it to quickly and cheaply deliver a full stack of capabilities, starting with database building and ending with advanced analytics, recommendations, and execution. Sophisticated, because it tailors the standard structures to each client’s business, so what it delivers isn’t some simple, cookie-cutter output.

Some of the tailoring is unavoidably manual, such as mapping client data sources to the standard data model. But much is highly automated, such as predictive models, clusters, and recommendations. I was particularly intrigued by the standard alerts, which look for significant changes in key performance indicators such as churn, margin, or average order value.  That sort of alerting is exactly what I've long felt marketers really wanted from their analytics tools.  AgilOne takes this a step further by automatically listing the data attributes with the greatest statistical impact on each item. The company refers to these items as goals to prioritize, which is a bit of a stretch – the most powerful variable isn’t necessarily the one marketers should focus on the most. But, as Damon Runyon said*, that’s the way to bet.

The system also recommends actions related to each alert, such as certain types of marketing campaigns. Again, there’s a bit less here than meets the eye, since the recommendations are drawn from a knowledgebase that’s the same for all clients. But that’s still better than nothing, and clients can customize their copy of the knowledgebase if they want.

The other especially noteworthy strength of AgilOne is data preparation. My original concept of the Customer Data Platform included customer data integration, which involves standardizing and matching customer records from different systems. I’ve pulled back from that because almost none of the vendors actually do such processing. Most assume it will be done elsewhere, or not at all, and only associate records with an exact match on a key such as a customer ID.  AgilOne does the hard stuff: quality checks, outlier detection, name parsing, address standardization, geocoding, phonetic matching, persistent ID management, and more. This is also highly automated and uses the company’s own technology. The lack of these capabilities prevents many companies from building a truly integrated customer database at many companies, so it’s extremely valuable for AgilOne to provide it.

If AgilOne has a weakness, it's at the execution end of the process.  Users can set up campaigns that generate lists on demand or on a regular schedule.  But I didn't see multi-step campaign flows or sophisticated decision management, such as arbitration across multiple eligible offers.  Some of that can probably be managed through advanced filters and custom models, which the system does provide.  However, making it truly accessible to non-technical users requires a specialized interface that the system apparently lacks.

While AgilOne just recently appeared on my personal radar, plenty of other people had already noticed: the company says nearly 100 brands are using the system. Sales efforts have been concentrated among mid-size B2C organizations, typically with at least 200,000 customers and $15 to $20 million in revenue. Pricing is published on the company Web site and is based on the features used and number of active customers. Entry price for the complete set of features starts around $9,000 per month.



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*“The race is not always to the swift nor the battle to the strong, but that's the way to bet.” Runyon himself credited Chicago journalist Hugh Keough.

Mintigo InterestBase Harvests Web and Social Data for Marketing and Sales

Every marketer recognizes that the Web and social media could be rich sources of information about customers and prospects. But harvesting that data has been frustratingly difficult.  Doing it yourself  takes multiple tools to gather different kinds of information, and then patching the result together into personal profiles. Most tools do little more than keyword searches, which only capture a fraction of the potential information and only cover keywords that marketers know in advance are important.

More advanced technology does exist. Semantic engines can extract information such as executive changes and product announcements from press releases and social media profiles. Sentiment analysis can (with limited reliability) detect the attitudes that individuals express. Identity aggregators can link email, social media, and other addresses for the same individual. Predictive models can show how different attributes correlate with targeted behaviors such as purchasing a product.

Few marketers have the skill or resources to pull all these tools together for themselves. Vendors are another matter: there’s inherent scale economy to scanning the Web and social media once and applying the results to many different clients. I recently wrote about Lattice Engines,  which has assembled these pieces to create prospect lists. Infer starts with your own customer data, enhances it with information mined from the Web, and generates predictive scores.

Mintigo has also been mining Web and social data to build prospect lists, starting in 2011. This week it announced a new platform, InterestBase, that gives clients an interface to define target groups, analyze group members’ interests, push prospect lists to marketing automation and CRM systems, and enhance individual lead records.

The foundation of InterestBase is a central repository of 30 million names and 3 million companies (and growing), built by scanning Web sites and social media for job postings, product and technology names, group memberships, accounts followed, hashtags, Javascript calls, and other information. The system uses this data to assign individuals and companies such attributes as job title, company size, technologies used, hiring plans, and interest scores for products and topics. Marketers can use titles and other attributes to define their own target groups, called personas.

Lists containing members of a persona can be assigned to marketing campaigns and sent to external marketing automation or CRM systems for execution. Connectors are currently available for Marketo and Salesforce.com, with an Eloqua connector due soon. A campaign list can include the entire persona universe or a quantity specified by the user. Once the campaign is run, responders are loaded back into Mintigo and the system will identify attributes that distinguish them from non-responders.

Clients can also upload their own lists of customers or campaign respondents.  Mintigo will determine which attributes correlate with group membership, display the most important ones in reports, and use the findings in predictive models that score the entire database on likelihood of purchase or response. Clients can also upload other lists for Mintigo to enhance with its own information. This enhanced data can be used in lead scoring or to help guide salespeople.  External systems like Web sites can also accessed the data in real time via API calls.

The features are interesting, but what really matters about Mintigo is the data: fresh, powerful, and unique information about a large share of the business universe. Richer information lets Mintigo clients identify new prospects they’d otherwise miss, distinguish strong prospects from weak ones, and target messages to each prospect’s interests. The result is substantially more effective marketing and sales operations, finally letting marketers use data the Web has so tantalizingly exposed.

In case you're wondering, I do consider Mintigo a Customer Data Platform: it assembles a persistent customer database, uses predictive models to classify the members, and makes the data available to external systems for marketing execution.  

Pricing for InterestBase is based on the number of names in the client’s prospect pool, based on automated analysis of their actual customers. An average client starts around $3,000 per month.

Salesforce + ExactTarget vs. SAP + hybris: Two Paths to Customer Management

Fresh on the heels of Tuesday's blockbuster ExactTarget / Salesforce.com deal, SAP Wednesday announced acquisition of e-commerce vendor hybris software.  Since Salesforce said that other companies also wanted to buy ExactTarget, it seemed possible that SAP had lost the deal and purchased hybris as a second choice. After listening to the analyst conference call (available at (303) 590-3030 passcode 4623918), I still can't say.

The SAP and hybris managers unfairly implied during their call that ExactTarget does nothing but email (without mentioning Salesforce.com or ExactTarget by name).  But as Salesforce.com made clear in its own call yesterday, they were most attracted by ExactTarget's multi-channel marketing capabilities.  It's possible SAP wanted ExactTarget for the same reasons and would have described it differently had they been the winning bidder.

In any case, SAP did tell a good story: real-time interactions seamlessly presenting customers with consistent information, dialogues, and purchases across all channels, with a central role for the Web.  This is certainly the long term goal for most marketers, although few are close to delivering it.  As SAP pointed out, it's a customer-centric view of the world, quite different from the operational focus of traditional CRM.  SAP does have some unique assets to support this vision, including back-office systems with sales, inventory, costs, and other data needed to fully inform customer treatments, and the in-memory HANA database to make this data immediately available for real-time interactions.  I haven't done enough research to judge whether SAP can effectively combine these pieces, but they're making the right promises.

I still wouldn't be as dismissive of the Salesforce / ExactTarget combination as the SAP managers.  People integrate CRM with back-office systems all the time.  You can also build great customer experiences with little or no back office integration.  ExactTarget does have some Web personalization features (from its iGoDigital acquisition), although I don't know how well they're integrated with the rest of the system.  Similarly, it has claimed to support real-time interactions in its Interactive Marketing Hub, but I don't know how well that works.  What I do know is that Salesforce and ExactTarget have a reasonable idea of what's needed and the resources to build it.  How well and how quickly they execute remains to be seen -- but you can say the same for SAP.

Incidentally, the common thread for these acquisitions is that both vendors are moving into direct B2C marketing.  It's a big new market for each of them, and makes both much more interesting competitors to IBM, Oracle and Adobe.  Perhaps that's the most important news here.

It would be misleading to give the impression that SAP and Salesforce are equivalent.  The two deals highlight some very fundamental differences:

- SAP is a full enterprise system; Salesforce is about CRM. The SAP managers made the point most clearly when they discussed that their appeal is targeted at the boardroom level: they are selling to companies who want to build their entire infrastructure on SAP's system.  Salesforce is now, finally, adding serious marketing to its CRM system (although there are still some gaps such as media buying), but even so its vision is still limited to customer management, and it is selling at the level of sales, service, and marketing departments -- rarely in the boardroom.  Note that the original concept of CRM already encompassed those departments, so this is less an expansion than a filling of gaps.

- SAP is a suite; Salesforce is a platform.  Indeed, SAP is the ultimate suite: every enterprise function running on a single, tightly integrated system.  I've long argued that the fundamental rule of software marketing is that "suites win", meaning most companies will choose an integrated suite over multiple best-of-breed point solutions.  SAP's success is Exhibit A in my evidence for this, but you could argue it's actually so large that companies might be just as happy with several smaller suites instead (e.g., one for CRM and one for back-office).   This would still let them avoid doing most of the integration work, while not forcing them to commit totally to one vendor's system. 

Salesforce is also an integrated suite, although limited to CRM.  But it has also embraced (and I think invented) the idea of an open platform: a foundation system that can be supplemented by attaching other vendors' products.  This provides easy integration without limiting users to capabilities provided by the suite vendor.  The model has been tremendously successful for Salesforce, particularly at letting it offer advanced functions to its clients without having to pay for developing those functions.  ExactTarget has embraced a similar model, incidentally.

- SAP is largely on-premise software; Salesforce is Software as a Service (SaaS).  It's true that SAP now offers SaaS options, but it was built as on-premise software and its large enterprise clients still mostly run it that way.  hybris also offers both options but runs mostly on-premise (typical for Web content management).  Salesforce of course is the granddaddy of all SaaS companies.

- hybris runs Web sites; ExactTarget is still primarily about email.  The obvious point of this is that Salesforce still needs serious Web site management to provide comprehensive customer treatments.

But the difference goes deeper.  Web sites are inherently real-time systems, while email is inherently batch processing.  This was the essence of SAP's comments today, and while they may understate ExactTarget's abilities, there is a kernel of truth.  Web systems are engineered from the start for high-speed processing, and the e-commerce features of hybris also mean it was engineered from the start to interact with individual customers, not just serve generic Web pages.  Email systems were originally engineered for batch processing, not individual interactions.  Mobile and social messages, which ExactTarget also supports, can also be handled quite well in batch.  I don't know to how far ExactTarget has evolved towards supporting real-time interactions, but its heritage lies elsewhere.

- hybris has 500 customers; ExactTarget has 6,000.  The revenue difference is much less: $100 million for hybris and nearly $400 million for ExactTarget.  What this reflects is that hybris' clients are mostly large enterprises, while ExactTarget has a broad mix of large and small companies.  Each each a good match for the core business of its new owner: SAP also focuses on large enterprises, while Salesforce sells to pretty much everyone. The broad reach of ExactTarget was certainly part of the reason that Salesforce wanted it, but Salesforce already has well over 100,000 clients, so the net increase isn't all that important.

What all this means, I think, is that SAP and Salesforce represent very different approaches to customer management: SAP proposes a single, tightly integrated, highly responsive real-time system where everything is connected and optimized.  Salesforce offers a looser set of connections with less control but more room for variety, change, and innovation.  SAP will sell more to the boardroom while Salesforce will sell to sales and marketing departments.  I frankly expect that both will succeed; it's a big market and each approach will appeal to different customers.  What I really hope is that both will show the market how to do integrated, cross-channel customer management: that way, everybody wins.

Circling back to the original question: I still don't know whether SAP tried to buy ExactTarget.  Based on the what I wrote above, hybris was a better fit.  But the SAP managers spent so much time disparaging email in their call that I thought I smelled sour grapes. Or was it just competitive vitriol?

My Take: Salesforce Acquires ExactTarget, Continues Marketing Automation Industry Consolidation

I've been in meetings all day and just emerged to hear that Salesforce.com purchased ExactTarget.  Having a had a few moments to digest the news (and some lunch), here are some thoughts:

- Good move for Salesforce.  They have been lacking large-scale email capability, which all types of sales and marketing departments require.  So this fills a gap in their core product offerings.  They also get a toe-hold in B2C marketing and in marketing automation (via ExactTarget's Pardot technology).  I'd guess those were bonuses rather than primary drivers of the deal.  Frankly, of the two, entry into B2C marketing seems more important because it's such a large business and Salesforce.com needs to know where it will get its next several billion dollars in revenue.

- Price is reasonable by today's standards.  ExactTarget had $300 million revenue in 2012, so the $2.5 billion price is 8.3x trailing revenue.  Marketo's market cap is $800 million on $58 million 2012 revenue, or nearly 14x trailing revenue.  Oracle paid $800 million for Eloqua, which had around $100 million trailing revenue, another 8x ratio.  (Salesforce's press release projects a net revenue impact of $120-$125 million for 2014.  That includes just six months of revenue, but it's still a much lower annualized rate than the ExactTarget figures.  It seems the difference is largely due to adjustments in deferred and unbilled revenue.)

- Not so terrible for marketing automation in the short term.  Sure, Marketo's stock dropped 8% vs. yesterday's close, on a pretty quiet day in the market (S&P down 0.55%, Oracle down 0.67%).  And, yes, more companies will buy Pardot now that it's part of Salesforce than they would have otherwise.  But I doubt Salesforce will suddenly stop integrating with other marketing automation vendors.  Small, independent marketing automation firms already had a tough time selling against big competitors, so this only makes their lives marginally harder.  The smart ones (and that's most of them) already have a strategy in place to differentiate themselves from the big industry leaders.

- Tougher for marketing automation in the long term.  I've long argued that CRM and marketing automation should be part of the same system.  Like a broken clock, the time has come when I'm right.  Marketing automation sits between email and CRM, in the sense that it uses both heavily.  So Salesforce has effectively surrounded the marketing automation vendors with its purchase, even ignoring Pardot.  This means that Salesforce will be in the room with a solution when email and CRM users discuss expanding into marketing automation.  In many cases, clients will extend their Salesforce deployment without considering anyone else..

- Salesforce isn't done, or at least shouldn't be.  Email and CRM are two big customer-facing systems: you get absolutely no prize for knowing that your Web site is the third.  (Ok, social is in there someplace too, but it's still more smoke than fire.)  A truly complete customer-facing solution would encompass Web content management as well.  This is another idea I've long pushed, and its time will come too.  Indeed, I see many Web content management vendors already adding marketing automation-type features.  Salesforce itself might not move into this space quite yet, but it seems inevitable that they'll do it eventually.

- Adobe, where art thou?  Since I'm exercising all my favorite hobby horses, we might as well let this one out of the stable.  (Actually, someone else mentioned it to me earlier today, so at least I'm not alone in my obsessions.)  Of course, Adobe already has a strong presence in Web site management and it keeps making noises about having a "marketing cloud".  Um, excuse me guys, but you really need email and marketing automation for that.  Silverpop -- already a large Adobe partner -- is the obvious acquisition candidate to fill that gap.  Sadly, Adobe has shown no signs of moving in this direction -- but time moves on, whether or not my broken clock is ticking.  (I don't know what that last phrase means, either, but sooner or later Adobe will buy something.)

Addendum: I've now had time to listen to the analyst conference call from this morning (available at 800-585-8367 passcode 89103168).  It doesn't change my analysis above, but clarifies that Salesforce's main goal was finding a single system that would support sophisticated cross channel marketing campaigns, with particular stress on heavy automation and new devices such as mobile.  They do seem more interested in B2C than I would have thought.  

Another comment made twice was that it was a competitive acquisition.  As others have pointed out, this means there's at least one other big company looking to buy a similar integrated marketing system.  There aren't many of those available -- traditional B2B marketing automation vendors are too narrow to fit the bill.  I'll mention Silverpop again as an option, and maybe Responsys and other high-end email products.  B2C marketing automation vendors including Neolane, ClickSquared, and perhaps RedPoint could be candidates but may be too small to be of interest.

Neolane Interaction Tightly Integrates Real-Time and Outbound Marketing Campaigns

As I mentioned last week, there haven’t been many new B2C marketing automation products in recent years. But this doesn’t mean the industry has been stagnant. New developments have come from established vendors who are steadily expanding their products.

Neolane has been one of these, growing from its roots in email to encompass other outbound and inbound channels, and more recently with a slew of social and mobile marketing features. One of its offerings, originally launched in 2009, is its real-time interaction manager, Neolane Interaction.

Interaction performs the same basic functions as other interaction managers: it receives a request from an external touchpoint that is engaged with a customer, selects the best treatment, and returns the recommendation to the touchpoint.  However, it differs in several key details:

- it supports both batch (outbound) and real time interactions. This is unusual, because efficient processing usually takes different data structures and methods for batch vs. real time. But Neolane cites one customer delivering 10,000 Web recommendations per minute and another sending ten million customized emails per week, so it has apparently found a way to handle both. Supported channels include email, Web, social, mobile, call center, point of sale, and SMS.

- it uses the same offer library for real-time and outbound campaigns. This means that offers can be used interchangeably in both types of campaigns – something that considerably simplifies program design and analysis. Each offer can include separate content for the different channel formats. Users specify which offers are available in which channels, to ensure an offer isn't recommended where it shouldn't be.

- it draws data from the Neolane marketing database as part of its offer selection rules. This is different from most interaction managers, which query external systems rather than maintaining their own persistent customer database. (Neolane could also do external queries.)  Again, this approach raises a technical eyebrow, since traditional marketing databases are not structured for real-time interactions. But Neolane seems to pull it off.  I suppose it helps that Neolane can attach to any data structure, so clients who want real-time interactions presumably build a real-time-friendly database.

- it supports both anonymous and identified customer interactions. Most real-time interaction managers make customer-specific recommendations, although Web recommendation engines are often designed for anonymous visitors. It makes sense for Neolane to support both, again serving the greater goal of providing one marketing system to meet as many needs as possible.

These items all grow out of the fundamental fact that Interaction is a module within the larger Neolane system, rather than a separate product. Other features of the system are more typical of dedicated interaction managers:

- eligibility rules for each offer. There are also eligibility rules for offer categories, which save effort by applying the same rule to multiple offers.

- offer arbitration (i.e., choosing which of several eligible offers to return). Offers can be ranked using fixed weights assigned to each offer; by calculating weights with a formula that draws on customer data; or with an unusual “autolearn” function that adjusts the weights to ensure that the offer will be seen. The system does not incorporate any of predictive modeling, although model scores built elsewhere could be used in weighting formulas.

- each recommendation is independent of a larger dialogue flow. At best, marketers wishing to deliver a sequence of treatments could create eligibility rules that check for previous treatments. This limitation is typical of real-time interaction managers.

- a simulation function that estimates how often each offer would be presented to an audience with specified characteristics. This helps marketers see the results of different eligibility rules and offer weights, which can be difficult to estimate in advance. It’s found in some but not all interaction managers.

The downside of Neolane’s approach is that customers who only want an interaction manager must still purchase the full Neolane system. This isn’t necessarily cost-prohibitive: the company says its average deal for the base system plus Interaction is around $350,000. Actual fees depend on the number of interactions processed. The system is available for on-premise, hosted, or “hybrid” deployment (on-premise installation with Neolane-hosted email delivery). Neolane says about 60% of its clients choose an on-premise option.

To end on a positive note: Neolane sold 22 new Interaction installations in 2012, up from 17 the year before. This makes it one of the company’s fastest-growing products.

Customer Data Platforms: My New Whitepaper Explains the Excitement

You may have noticed that I've been uncharacteristically aggressive in promoting the Customer Data Platform concept.  Sorry, but I just can't help it: a new system category is even more rare than a new B2C marketing automation system (which, as yesterday's post pointed out, is much rarer than a new beetle).  More important, I think the category itself is a very important development that could really help marketers solve some big problems.  So it's worth several shoves to get the ball rolling.

Along those lines, I am embarrassingly excited to report my formal whitepaper explaining the CDP in depth has just been published.  It was sponsored by ReachForce but they had no influence on the actual content. (In fact, they probably would have preferred something a bit more on-message for their own marketing, so let me thank them for their indulgence.)  You can download it here.  Comments are welcome!

Selligent Brings a New B2C Marketing Automation Option to the U.S.

I’m writing this post on my old DOS-based WordPerfect software, to get in the proper mood for discussing business-to-consumer marketing automation.* The late 1990’s were really the last time we saw major innovation in the B2C market, when vendors like Unica and Aprimo released their then-innovative systems to create lists for direct mail and, somewhat later, email campaigns. Since then, the number of independent B2C marketing automation vendors has actually dwindled** as major and not-so-major products were purchased by larger companies to bundle into integrated suites or just use internally. Even the leading survivors, including Neolane (founded 2001) and RedPoint (founded 2006) have their roots in traditional outbound campaigns, although they now support Web, social, and real-time interactions to varying degrees.

Still, I’m excited as an entomologist with a new beetle*** to see another vendor in the space. Selligent  isn't brand new – the company was founded in 2000 and its marketing automation system dates back about six years – but the company says it was built from the ground up to manage real time interactions. A deep dive left me seriously impressed.

The first thing you need to know about Selligent is that while it’s new to the U.S. market, it is well established in Europe, where it serves 400 brands in eleven countries. It’s particularly strong among retailers and publishers. This experience translates into the types of refinements that can only be based on client demands. To pick a couple more or less at random, these include a matching engine that selects among hundreds of news articles to find those most relevant to individual subscribers (a sort of dynamic-content-on-steroids that’s important to publishers) and tools to manage product give-aways and rewards for viral sharing (important for retailers). They also include standard B2C features that are still lacking in most B2B products, such as precise control over user access to specific pieces of content, data, and system functions; planning hierarchy to schedule and budget for multiple marketing programs in separate organizations; and rules to limit the number of marketing messages each customer receives.

The basic architecture of Selligent is typical for B2C marketing automation systems. That is, it attaches a marketer-friendly interface for data access and analytics to an externally-built customer database. Brave users can add new fields and even entire data tables, and can import external data directly into the system. But the data must be matched on a fixed identifier, such as account number or email address, or combination of fixed identifiers. Data standardization, identity resolution, and change history (such as new vs. old mailing address) are largely handled elsewhere.

Selligent also uses the familiar flow chart interface to design its campaigns. Beyond the superficial graphics – where Selligent is no better than average – it takes a connoisseur to spot the subtle differences among these implementations. Selligent does hit a number of fine notes, including A/B tests, option to reunite branches after a split, mix of data segmentation with marketing outputs in the same flow, ability to enter and leave flows at multiple points, option to direct customers to other flows, separate schedules for individual objects within a flow, and automated warnings of incomplete designs.  Did I mention hints of almond?

But the big differences are not visible. For example, you see that a campaign flow can include a survey.  What you can't see is that surveys go beyond progressive profiling (replacing questions as they’re answered) to include branches based on customer profile and in-session answers, ability to re-ask questions after a specified period, asking questions in different languages but storing answers in one language, using the same question on multiple surveys and storing all answers in a single location, and providing statistics on completion rates, average time to complete, and which pages have the most validation issues and drop-outs. This is definitely above average for survey features in marketing automation tools, and more than competitive with many dedicated survey systems.

Selligent flows also support business processes that include external steps, such as manual review of an order. To do this, the system assign a “state” to a customer and then reacts differently within the flow based on that state. A separate “FrontOffice” module provides an interface for call center and other agents, and can apply rules to route customers to specific centers, teams, and agents. The system supports Microsoft’s computer-telephone integration (CTI) features, but doesn’t integrate with other phone systems. Basic campaign flows can send data to external systems via file exports or by calling an Application Program Interface (API).

As these examples may suggest, one key theme for Selligent flows is tight integration across all channels. Perhaps the best example of this – and something I’ve looked for in many other systems and never found – is that its flows can control movement from one Web page to another. That may not sound unique, but if you look closely at other marketing automation flows you’ll see they typically end with serving up a Web form and then start independently with events triggered by completion of a form. All well and good, but it means you need to build a separate flow for each step in a multi-stage interaction. Even the real-time interaction management systems treat each recommendation independently, so the only way to create a multi-part dialog is to build separate campaigns or to build one campaign with complex qualification rules that test for completion of each dialog step before moving to the next. Both approaches involve much painstaking labor and room for error. Selligent, on the other hand, lets users connect one page to another on the flow diagram; the system then automatically embeds URL links to make the connection. I’ve had three clients ask me for this in the past year alone.

Back in the realm of the normal, Selligent also offers a graphical email/Web page designer that supports the usual design features, personalization, dynamic content.  More impressive, it has integrated multi-variate testing and lets users convert an email to a Web page, or vice versa, by pushing a single button. A single email or Web page can be shared across multiple campaigns with  changes to the master object automatically deployed everywhere it’s used. Selligent doesn’t automatically store previous versions of contents as they’re edited and doesn’t provide formal check out/check in to avoid conflicting edits. But users can save and restore backup copies.  The system also keeps an audit trail of who made changes and issues a warning when someone opens a document that someone else is already editing.

Reporting in Selligent is based on information gathered from each object in a campaign flow. The specific data depends on the nature of the object.  Users can also insert objects that capture specific information such as the number of people who have passed through. There are no standard campaign reports; instead, users build their own reports by assembling the object-level information. I found this a bit odd, but Selligent said each client wants something different and prefers to create their own. The vendor has recently added a Business Intelligence module, using Quiterian (recently purchased by Actuate and renamed BIRT Analytics) which provides ad hoc analytics and visualization. Data is exported from Selligent to the Quiterian database, but this happens automatically and selections made in Quiterian are automatically copied back to Selligent as segments for marketing campaigns.

Selligent does have its weaknesses. It doesn’t easily support some advanced queries and splits, such as finding the top 100 customers per store or selecting the highest-spending person per household. It has no built-in predictive modeling or integration with third-party modeling systems, although it can easily import externally-created model scores. Somewhat surprisingly, it lacks geographic radius selections (although Quiterian has these) and doesn’t adjust sending dates or hours for the local holidays or time zones. Apparently it hasn’t needed these in Europe, where clients run separate campaigns for individual countries, countries don’t span time zones, and local units, such as provinces, are small enough to make distance-based selections unnecessary. This will surely change as it adapts to the U.S. market.

Pricing for Selligent is based on modules used and number of unique customers. There is no separate charge based on message volume or number of users. The system can be purchased as a vendor-run service, deployed on-premise, or deployed locally with Selligent executing the emails. Price for the base system starts around $6,000 per month for 250,000 contacts.  The vendor says its cost is often equivalent to what high-volume clients are paying for email alone.  As in Europe, Selligent expects to sell primary through marketing agencies and service providers in the U.S. market. 


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* Not really. I tried, but WP.exe won’t run on my current computer. Too many bits or something.

** See my list of mid-tier B2C systems.  Seven of the twelve listed are now owned by someone else.

*** Probably more excited. New beetles are pretty common.  Nearly 250 have been discovered this year alone in New Guinea  and Central/South America.

Silverpop Announces Universal Behaviors to Provide Better Cross Channel Customer Experience

At their annual Amplify conference last week, Silverpop unveiled the culmination of a two year project that conveniently matches the Customer Data Platform (CDP) concept I’ve been describing for the past month. While the timing is just coincidental, Silverpop’s Universal Behaviors provide more evidence that a new breed of system is emerging.

Silverpop’s new features load customer behaviors from all sources into a central database, match identities to create a unified customer view, and make the resulting information available for real-time, automated interactions across all channels. The central database and cross-channel treatments are two of the three capabilities I’ve defined for a Customer Data Platform. Silverpop falls short on the third CDP function, which is integrated predictive modeling.  But it has partners who fill that gap.

Many CDPs have been quietly maturing for several years.  Silverpop's two-year gestation cycle is a good example.  I can't say precisely why so many are emerging more or less simultaneously, but suspect a combination of business conditions and ever-more-urgent marketer needs.  The long-term drivers are clear: more marketing channels make customer attention harder to attract, spread behavior across different media, and require coordinated contacts across channels. As a result, marketers need a unified customer database, unified campaigns, and way to deliver messages across whatever channels customers use now or in the future. This is what they get from a CDP.

It’s less surprising to see another CDP system than to see it coming from Silverpop.  After all, Silverpop’s twin heritages in B2C email and B2B marketing automation both use simple data models: flat lists for email and basic lead/contact/account tables for B2B marketing automation. Both types of systems traditionally merge customer data using only email address.  Neither build a company's primary marketing database or shares data with external systems. So its quite unexpected to see Silverpop ingest data from any source, cross-reference any set of individual identifiers, offer access to the data, and send messages for delivery by other systems.

So how do Universal Behaviors work?  Each Behavior is first defined in Silverpop with a fixed set of attributes. Source systems then capture Behaviors and post them via an API to Silverpop.  They are stored in MongoDB, a “NoSQL” database that supports high input volumes and multiple record structures.  This is another departure for Silverpop, which uses the Oracle database in its core systems.

Behaviors include whatever customer identifiers the source system can provide: email address, cookie ID, phone number, account number, etc. Silverpop uses matches from external systems to link all identifiers associated with an individual: for example, a Web transaction might include cookie ID and email address, while an email could contain email address and account number.  Silverpop could later take a Behavior with any one of those identifiers and associate it with the same individual.  But there are limits to Silverpop's customer integration powers: it doesn’t do “fuzzy” matching to merge similar identifiers or import third-party reference databases that contain such links.  I'm beginning to see those capabilities are specialties that are not necessarily core features of a CDP because they're best purchased from third party vendors.

The initial release of Universal Behaviors, set for July, will support predefined Behaviors from ArgyleSocial social listening, Webtrends Web site behaviors, Digby location-based marketing, Invodo video, and several as-yet unannounced vendors, as well as Silverpop’s own location-based and SMS offerings. It will later add more partners, provide a system development kit (SDK) for mobile apps, and eventually allow any company to build its own connectors.

Once Universal Behaviors are loaded into Silverpop, they become available within the system for queries, program triggers, rules within programs, dynamic content, personalization, scoring, and analysis – pretty much anything that could be done with standard Silverpop data.  Program outputs such as messages and lists can be pushed in real time to external systems to manage interactions.

Silverpop has also created native integrations with Adobe and Episerver Web content management systems.  These let those systems submit a visitor ID to Silverpop and receive Silverpop data to use in dynamic content and personalization. Connectors for other CMSs will be added as clients request them. Clients could also write their own integrations using a published Silverpop API or use tags to display Silverpop-generated content on any Web page. Currently, CMSs can access selected customer attributes but not the Universal Behavior database itself.  Silverpop plans to provide full data access in the future.

The mobile app SDK will go even further, allowing apps to execute Silverpop functions such as adding a customer to a program or sending an email.  This is in addition to the standard features of submitting Universal Behaviors, reading Silverpop data, and rendering Silverpop-generated content.

The critical point in all this is that Silverpop will integrate other customer-facing systems instead of only executing interactions itself.  The integration includes sending data to Silverpop, reading data within Silverpop, and receiving Silverpop marketing treatments.  In other words, the role of Silverpop shifts from delivering customer treatments to helping other systems find the best treatments to deliver. Of course, Silverpop still retains its original execution capabilities for email and some other channels.  But it’s perfectly conceivable that a client could hook the new Silverpop features to someone else's email delivery system (not that anyone at Silverpop mentioned the possibility).

This separation between a central data-and-decision platform and multiple independent execution systems is the core concept underlying the Customer Data Platform. I’m increasingly convinced it is the only way that marketers will be able to keep up with ever-expanding channels and customer expectations.  By developing a structure that fits the CDP model, Silverpop has responded to a pressing client need and established itself in an important new category.

HubSpot Releases Social Inbox and Reveals So Much More

I spent yesterday afternoon at HubSpot’s “Open House” in Cambridge, MA, during which they briefed the community on their business progress, introduced their new Social Inbox, described their  approach to marketing and sales alignment, explained their “culture code”, and answered questions.

The most concrete news, Social Inbox, extends existing HubSpot features by more fully integrating social media monitoring and response with the HubSpot interface. The Social Inbox presents a list of Twiter posts by user-specified individuals or containing specified key words. Users can drill into each post to see a complete profile of the poster. The big deal in HubSpot’s eyes is the profiles include all information the HubSpot database about each person, and are even color-coded with the sales lead stage. The data includes Web and email behavior captured directly in HubSpot, data imported from Salesforce.com, and whatever else the system has available. Users can respond directly, forward a post to someone else, or add the poster to a HubSpot campaign. The system can automatically alert users to new Tweets as they happen or on a regular schedule.

HubSpot said they couldn’t find any other product that combines this type of social monitoring with access to such deep profiles. I can’t immediately think of one either, although it might exist. Either way, uniqueness is less important than the value provided, which is considerable.

What’s ultimately more interesting, however, is that Social Inbox is aimed at managing one-on-one interactions between users and individual contacts.  This sort of contact management is quite different from HubSpot’s traditional focus on attracting inbound traffic or even from conventional marketing automation.

The new features came up again later in the day, when the audience asked several pointed questions about whether HubSpot would eventually add a full CRM capability. This caused by far the most discomfort of any topic addressed by a management team which provides itself on transparency. Answers ranged from a coy “we think about a lot of things” to a fairly definitive stream of conscious listing of the arguments against adding CRM.  The currently dominant line of thought seems to be that HubSpot already provides adequate features for clients who want light contact management, while adding full CRM features would only lead to a losing battle with Salesforce.com. Unstated but hovering in the background was the fact that Salesforce.com is an investor in HubSpot and might some day consider buying them to expand its own marketing scope. CRM would make HubSpot less attractive to Salesforce, since it would create a set of redundant features that need to be supported or removed.

But the most fundamental reason that HubSpot management seems genuinely disinclined to add CRM is that they see HubSpot’s mission as transforming marketing. There’s a distinctly messianic gleam in CEO Brian Halligan’s eyes when he says this and the vision is no doubt shared widely across the company. In fact, it’s arguably more surprising that HubSpot has overcome its marketing focus to introduce the contact management features already in place. My take is that customer needs – another HubSpot mantra – have driven the system in this direction despite management reluctance. The system has a will of its own.

Admittedly, I’ve been arguing this for a long time: the need for integrated customer treatments will eventually lead marketing automation, CRM, and Web content management to become a single system, or at least to share a common customer database. HubSpot’s current vision of highly personalized data-driven marketing is consistent with this. The current vision is also quite different from the original HubSpot vision of attracting traffic through huge volumes of great (but not personalized) content. But the new vision is a logical extension of the original: once you’ve attracted people and start to learn their preferences, the more you’re able to make targeted content recommendations. And, the more content you have available, the more you need those recommendation to point people at the right materials.

This brings HubSpot right back to contact management, because the same data used to recommend marketing content can, and should, be used to recommend treatments during personal interactions. It’s possible to simply push recommendations to an external CRM platform, but setting a connection for each point of contact quickly becomes a lot of work. The temptation to eliminate that work by building an integrated CRM system is hard to resist.  As I say, the system has a will of its own.

Incidentally, there is another way to look at this. The traditional view sees marketing as making automated contacts, while sales and service use human agents, supported by CRM, for individual interactions. This is why CRM seems foreign to a marketing system. But the automated-vs-human division is no longer so clear cut. Social media marketing is mostly done by humans through one-on-one messages, while many sales and service interactions are automated. In this view, HubSpot needs contact management features even if it rigorously restricts itself to serving marketers alone.

The problem with this approach is that it denies sales and service the benefit of HubSpot’s data and customer understanding – a terrible waste of corporate resources. So this view also pushes HubSpot towards a unified marketing and CRM system, or at least a database and recommendation engine that’s accessible by both HubSpot and a separate CRM. I swear I didn’t mean to end up here, but this does lead to the Customer Data Platform I’ve been discussing over the past few weeks. I don’t think HubSpot management wants to move in that direction, or even that they necessarily should.  But these things have a will of their own.

Provenir Adds Social Listening to Customer Decisions: Another Customer Data Platform

I’m still collecting examples to illustrate my new category of Customer Data Platform (CDP) systems. The latest is Provenir, a company founded in 1992 that has long sold a system to make credit risk and fraud decisions in real time. Over the past year, the company has added “social listening” capabilities and begun offering itself to marketing agencies as a customer interaction manager. It has met with good success and is now offering its “social listening platform” more broadly. *


It’s a slight stretch to call Provenir a CDP, because it doesn’t manage a permanent customer database.  Rather, like most interaction managers, it calls data from external sources during each decision.  But Provenir does have some customer matching capabilities and stores at least some information internally. Moreover, it completely meets the other three CDP criteria: predictive modeling, real-time decisions/recommendations executed through external systems, and a non-technical user interface. It’s also sold as the “glue” connecting data sources, modeling, and execution systems, which is exactly the role played by a CDP.  So, what the heck…welcome to the club!

Provenir is organized around process flows, which cover a particular task such as reacting to a Web site visit. Users define each process by building a flow chart, or, as the cool kids call them today, a graph.** These, um, graphs***, can contain branches, loops, and other advanced structures.  The nodes can also contain other graphs that define a subprocess in more detail. Nodes can perform a wide range of operations including data gathering, calculations, updates, decisions, and messages to external systems. Although setting these up is inevitably rigorous, Provenir makes it as painless as possible by providing help such as letting users draw lines to map fields from one system to another; building rules through score cards, tables and decision trees; and warning if a flow is incomplete.

Provenir relies on external systems to assemble, integrate, and store customer data.  Users can build matching processes with system graphs, although the vendor recommends connecting to other products to load reference data or do advanced "fuzzy" matching.  Provenir can monitor source systems for selected events and issue queries to assemble data as needed. The social listening features can monitor Twitter for keywords and Tweets by specified individuals.  These can trigger process flows that can retweet a message, send a direct Twitter message to the poster, or respond through another channel. The system can also monitor and post messages on Facebook. Other channels will be added over time.

Predictive modeling in Provenir is also done in external systems. The system can import PMML code or call models in SAS, R, or even Excel. Data mapping functions can automatically extract the list of required variables from PMML, do basic transformations and calculations when loading model inputs, and manage parameters, constants, and local variables.

Decisioning is Provenir’s greatest strength. The process flow…I mean graph…is inherently very flexible, and the ability to define rules as tables, trees, score cards, and other formats adds even more power. Users can set up champion/challenger tests as splits within a process flow; results are stored in a database for analysis and reporting. Users can also build simulated data sets, containing specified distributions of particular variables, and use these to forecast results of their flow designs. Such simulation is one mark of a mature decision system.

Provenir has some built-in messaging capabilities, but most decisions are executed externally.  The system has been connected with email, Web content management, call centers, campaign management, text messaging, and other execution platforms.

Pricing for Provenir’s social listening product is based on the size of the customer database. Starting price can be as a low as several thousand dollars per month. The system is usually sold on a Software-as-a-Service (SaaS) basis, but on-premise licenses are also available.


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* For extra credit, compare and contrast Provenir’s primary Web site  with the site for their listening division.

** Defined in Wikipedia as “mathematical structures used to model pairwise relations between objects”.

*** Would it be even cooler to call them grafs or, better still, grafz?

I've Discovered a New Class of System: the Customer Data Platform. Causata Is An Example.

It has taken me a while to connect the dots, but I’m now pretty sure I see a new type of software emerging. These systems that gather customer data from multiple sources, combine information related to the same individuals, perform predictive analytics on the resulting database, and use the results to guide marketing treatments across multiple channels. This differs quite radically from standard marketing automation systems, which use databases built elsewhere, rarely include integrated predictive modeling, and are focused primarily on moving customers through multi-step campaigns. In fact, the new systems complement rather than compete with marketing automation, which they treat as just one of several execution platforms. The new systems can also feed sales, customer service, online advertising, point of sale, and any other customer-facing systems.

Given how much vendors and analysts love to create new categories, I’m genuinely perplexed that no one has yet named this one. I’ll step in myself, and hereby christen the concept as “Customer Data Platform”.  Aside from having a relatively available three letter abbreviation (see Acronym Finder for other uses of CDP), the merits of this name include:

- “Customer” shows the scope extends to all customer-related functions, not just marketing;
- “Data” shows the primary focus is on data, not execution; and
- “Platform” shows it does more than data management while supporting other systems

But, you may ask, is this really new? Certainly systems for Customer Data Integration (CDI) have been around for decades: these include specialized products like Harte-Hanks Trillium and SAS DataFlux, CDI features within general data management suites like Informatica and Pentaho, and integration within cloud-based business intelligence products like GoodData and Birst. Many of those products have limited capabilities for working with newer data sources like Web sites and social networks, but the real distinction between them and CDPs is that the older systems are mainly designed to assemble data.  Some also provide analytics, but they don't extend to real-time decisions based on predictive models.

Similarly, there have long been specialized systems for real-time interaction management (such as Infor Interaction Advisor and Oracle Real Time Decisions) and for predictive modeling (SAS, IBM SPSS, KXEN). Some interaction managers do create predictive models, and the really big vendors (IBM, SAS, Oracle) have all three key components (CDI, real-time decisions, and predictive models) somewhere in their stables. But systems that closely couple just those features with the goal of feeding data as well as recommendations to execution systems? Those are something new.

By now, you’re probably wondering if I’ll ever get around to actually naming the vendors I have in mind. I’ve recently written about some of them, including Reachforce/SetLogik and Lattice Engines.  I also include RedPoint in the mix, because it has all the key capabilities (database development, predictive models, and real time decisions) even though it also offers conventional campaign management. Others I haven’t yet written about include Mintigo and Gainsight. Of course, each has a different mix of features and its own market position.  Indeed, several have specifically told me they do not compete with the others. Fair enough, but I still see enough similarity to group them together.

All this is a very long-winded introduction to Causata, yet another member of this new class. By now, you can probably guess Causata’s main functions: assemble customer data from multiple sources, consolidate it by customer, place it in an analytics-friendly format, run predictive models against it, and respond in real time to recommendation requests from other systems including Web sites, email, banner ads, and call centers. And you’d be right.

But that’s not the end of the story. With any product, it’s the details that matter. Causata is particularly strong in the data management department, accepting both batch and real-time data feeds and storing data as different types of events (email sent, Web site visit, call center interaction, etc.), each having predefined attributes. The system also has a particularly sophisticated “identity association” service, which looks for simultaneous events involving different identifiers as a way to link them, and can chain identifiers that were linked at different times. When I spoke with Causata about two months ago, the association rules were pretty much the same for all clients, but they promised users would get more control in the future. Users could already choose which types of associations to use in specific queries.

Causata stores the assembled data in HBase, a Hadoop-based database management system that is particularly well suited to large data volumes, many different data types, and ad hoc queries. In addition to the raw data, the system can store derived values such as aggregations (e.g., number of Web page view in past 24 hours) and model scores. Users can run SQL queries to extract data for analysis and predictive modeling in third-party software including QlikView, Tableau, SAS, and R. Prebuilt QlikView reports show the predictive power of different variables for user-specified events. The lack of native analysis and modeling tools creates some friction for users, but also lets them stick with familiar products. So the pros and cons probably cancel each other out.

The system’s decision tools are straightforward. For each situation, users define a “decision engine” that can select among multiple options, such as campaigns, products, or marketing content. These options can have qualification rules. To make a decision, the system can test the options in sequence and pick the first one for which a customer is qualified, or pick the option with the highest predictive model score. Users can also specify a percentage of customers to receive a random option, to gather data for future decisions. An engine can return multiple decisions for situations that require more than one option, such as a Web page with several offers. Causata has some machine learning algorithms to help with the decision process. It plans to expand these to automatically select the best option in a given situation.

Decision engines are called by external systems through a Web services API that can respond in under 50 milliseconds. This is fast enough to manage Web banner ads – something not all interaction managers can achieve. Model scores and other data are updated in real time during an interaction.

Causata can be deployed on-premise by a client or as a cloud-based service. The vendor says a typical implementation starts with three or four data sources and is deployed in about 30 days – very fast for this type of system. In February, Causata introduced prebuilt applications for cross-sell, acquisition, and return programs in financial services, communications, and digital media. These will further speed deployment.

Pricing is based on the number of data sources and touchpoints, with additional charges based on data storage. Cost begins around $150,000 per year.

Lattice Engines Automates All Steps in Prospect Discovery

There’s nothing new about using public information to identify business opportunities: it’s why lawyers chase ambulances and bankers phone lottery winners. But the Internet has exponentially grown the amount of data available and made it easily accessible. What’s needed to fully exploit this resource is technology that automates the end-to-end process of assembling the information, identifying opportunities, and delivering the results to sales and marketing systems.

Lattice Engines was founded in 2006 to fill this gap. The system scans public databases, company Web pages, and selected social networks to find significant events such as title changes, product launches, job openings, new locations, and investments. It supplements this with data from the clients' own systems including customer profiles, Web site visits, and purchases. It then looks at past data to find patterns which predict selected outcomes, such as making a first purchase, buying an additional product, or renewing. It uses these patterns to identify the best current prospects for each outcome, and makes the lists available to marketing systems or sales people. The sales people also see explanations of why each person was chosen, what they should be offered, and recommended talking points.

Each of these steps takes significant technology. Lattice Engines currently monitors Web sites of five to 10 million U.S. businesses, checking daily for changes.  The system’s semantic engine reads structured texts such as management biographies and press releases, extracting entities and relationships but not trying to understand more subtle meanings such as sentiment. Clients specify blogs to follow, which receive similar treatment. The company also monitors Twitter, Facebook company pages, Quora, and LinkedIn profiles of people within each sales person’s network. Additional data comes from standard sources such as business directories and from special databases requested by clients. Information from all these sources is loaded into a single database available to all Lattice Engine clients.

Lattice Engines also imports data from the clients own systems, although of course this isn’t shared with anyone else. Again, there’s some clever technology needed to recognize individuals and companies across multiple sources. Lattice Engines doesn’t try to link personal and business identities for individuals.

All this information is placed in a timeline so that modeling systems can look at events before and after the target activities. The models themselves are built automatically, once users specify the target activity, product, and time horizon. Users can then build a list of customers or prospects, have the model score it, and send high-ranking names to marketing or sales for further contact. Results can be exported to a marketing automation system or appear within the sales person’s CRM interface. Lattice Engines is directly integrated with cloud-based CRM from Salesforce.com, Microsoft Dynamics, and Oracle, and via file transfer with SAP CRM. Users can export lists to Excel and Marketo, with connectors for Eloqua and other marketing automation systems on the way.

The net result of this is a single system that performs all the tasks needed to exploit the wide range of information available about customers and prospects.  Marketers could theoretically use separate systems for each step in the process, and integrate the results for themselves.  But few really have the skills to do this.  And, in most cases, it would be more expensive than purchasing a single system like Lattice Engines.  It's particularly helpful that Lattice Engines supports both prospecting and customer management -- further reducing the need for multiple products, and further encouraging cooperation between marketing and sales departments. 

Pricing for Lattice Engines starts at $75,000 per year and grows based on the number of data sources and sales users. Client data volume doesn't affect the cost, since Lattice Engines’ own databases are vastly larger than any client data. The company has close to 50 deployments, nearly all at large B2B marketers including Dell, HP, Microsoft, ADP, and Staples.

Adometry Combines Attribution with Optimization

So…my last two posts on attribution systems (MMA and VisualIQ ) were among the least popular ever, right down there with Marketing Lessons from Chernobyl (which, let’s face it, was in pretty poor taste). But vox populi isn’t always vox Dei, eh? I think it’s an important topic, so here we go again.

The lucky recipient of that less-than-stirring introduction is Adometry, which in no way deserves any disrespect. From humble beginnings in click fraud prevention, they have grown in recent years to be one of the leaders in algorithmic response attribution. Their latest expansion moves them beyond digital channels to offline media including direct mail, television, and print. They have also moved from attributing past results to using predictive models to optimize current and future campaigns. Impressive.

The core of Adometry’s attribution methodology is to compile the sequence of marketing messages seen by each individual, and then compare results of individuals whose sequence differs by only one message. Any difference in results is then attributed to that message. This is conceptually simple, but requires clever treatments to handle low volumes for specific sequences and to isolate the impact of attributes such as placement, time slot, creative, and list segment. Adometry also lets users model against multiple events in the customer life cycle, such as sign-ups, first purchase, and repeat purchase. It calls these all conversions, which I personally found a bit confusing but suppose would quickly get used to.

The system also classifies each conversion as attributable, multi-touch, and multi-channel, depending on whether it was linked to at least one message (attributable), to multiple messages (multi-touch) and to messages in multiple channels (multi-channel). For each category, it shows the conversion count and revenue: so, for example, you see the number and revenue for multi-touch repeat purchases. That’s a lot of information to digest, but does give a great deal of insight into the effect of different promotions and channels on different parts of the business. This encourages marketers to look beyond any single measure, such as cost per order, that tells only a small part of the business story.

The system’s optimization process begins with the attribution analysis, but then adds auto-generated predictive models to estimate the impact of future ad plans, including interactions across channels. Users can enter scenarios with budgets for multiple channels and campaigns, and then apply other constraints such as limits on the change in spending per channel. They also define output measures for the system to optimize against: like other optimization systems, Adometry can only optimize against a single measure, but this can be a composite of several items. For each scenario, the system will determine the optimal budget allocation and show the expected results across each output measure. Users can modify the recommended plan and have the system re-forecast the results. The final plan can be output to a spreadsheet for further editing. Adometry can also be connected directly to ad buying platforms, including systems for real time bidding on individual impressions.   The company says optimization typically yields a 20% to 40% improvement in ad-to-sales ratios.

The database of marketing messages per individual can be used for other types of analysis. These include reach and frequency reports, which show the number of individuals reached in total, reached in each channel, and reached exclusively for each channel. The reports count impressions as well as individuals; show how many people were reached in each combination of channels; show the number of people with each number of impressions (one, two, three, etc.); and show the current member count in each funnel stage.

Adometry’s data comes primarily from tags embedded in advertisements, emails, and other online messages, which drop cookies to identify who sees which message. The system can also draw data from Web server logs or third party tags. Adometry can further enrich its database by appending external information about individuals, using both online and offline sources. This lets it profile the audiences associated with different events, channels, campaigns, and other attributes. Optimization models can use data that can’t be tied to specific individuals, such as weather, economic conditions,  and mass media like television and print. The system can also verify which ads were actually seen by individuals, providing more precise inputs to the attribution calculations.

Pricing for Adometry is based on the number of channels and volume of data. It starts around $100,000 per year for the smallest clients with enough volume to use the system effectively (about 30 to 50 million impressions per month). Currently, more than 50 companies use Adometry’s attribution services.

ReachForce Buys SetLogik: One-Stop-Shopping for B2B Marketing Data Plus Database

B2B marketing data vendor ReachForce today announced its purchase  of SetLogik, which provides technology to build cloud-based marketing databases and do predictive modeling against them. (See my post from last October for more on SetLogik.)

There’s an obvious peanut butter-meets-jelly type of logic to this match. Reachforce’s core business is assembling data on marketing prospects, which it then sells for as many uses as possible: appending to Web leads, enhancing existing databases, and buying as lists. The SetLogik acquisition takes this a step further by letting them build databases to hold their data, thereby expanding the market beyond people with a database already in place. Conversely, having a readily-available data source encourages marketers to build their own database. SetLogik’s predictive modeling features make it even easier for marketers to get a return on their investment once the database is in place. Everybody wins!

The two products will be combined in what ReachForce calls the “Connected Marketing Data Hub”. The name is frightfully generic, but the key points are:

• cloud-based system, making it easy to deploy
• comprehensive customer view including data from marketing automation, CRM, transaction systems, and ReachForce’s own sources
• continuously updated and cleansed
• connectors available for Salesforce.com, Eloqua, and Marketo  

In other words, the ReachForce solution supplements rather than replaces your marketing automation or CRM database. As I wrote in my earlier SetLogik review, one particularly attractive result is the ability to match sales revenues with marketing leads, always a challenge in measuring the value of marketing programs.

ReachForce has just begun to offer the combined system, which is currently deployed at one pilot client. Pricing is based on data volume, whether the client wants a one-time append or continuous cleaning, and on the data sources included. Minimum is $625 per month for continuous cleaning on 50,000 records.

Marketo Files for IPO: Will High Growth Outweigh High Losses?

Marketo made good today on its promise to file for an initial public offering (IPO). Congratulations to them for reaching this step. It’s a major accomplishment.

The S-1 registration statement gives considerable new information about Marketo’s business. Revenue for 2012 is reported at $58.4 million, an impressive 80% growth rate vs. 2011 although not quite the doubling that the company had forecast earlier.

More significant, the company continues to have huge losses – it lost $34.4 million in 2012, or 59% of revenue. By comparison, Eloqua lost just 7% of revenue in the year before its IPO, and even Salesforce.com, the benchmark for all Software as a Service (Saas) start-ups, lost just 20% of revenue in its final year as a private company.

 A loss that big is pretty scary. Part is due to heavy spending on sales and marketing – 65% of revenue – but that’s not the whole story: Salesforce.com had also spent 65% on marketing before its IPO (although Eloqua spent just 40%).

The difference is that cost of revenue (costs of delivering service to clients, including subscription, support, professional services, and other) was 42% for Marketo, vs. 20% for Salesforce.com and 32% for Eloqua. That figure hasn’t changed in recent years, suggesting economies of scale have yet to appear. A high cost of revenue makes it hard for a company to become profitable even as it grows, since much of the new revenue is spent on the new customers. SaaS economics aren’t supposed to work that way.

Marketo’s other operating costs (research and development and general and administrative) are also high – 52% of revenue, compared with 35% for Salesforce.com and 33% for Eloqua. That percentage has also been pretty much stable for the past three years – again suggesting that expected scale economies haven’t appeared yet.

Another way to look at it is this: Marketo would earn just 6% profit even if its sales and marketing costs were zero. So its losses aren’t simply due to high investment in new customers.  The comparable figures for Eloqua and Salesforce were 39% and 45%, respecitvely.

The S-1 also reports the company had 339 employees as of December 2012. Of course, the average for the year was much lower but, ignoring that, this still yields a perfectly respectable $172,000 revenue per employee. But it also means expenses are $273,000 per employee – much higher than the $200,000 rule of thumb. I know everyone at Marketo works incredibly hard, but something is clearly out of line in their cost structure.

Perhaps stock investors will look only at Marketo’s growth rate. There is certainly an argument that the company will eventually become profitable as it spreads its fixed costs over more revenue.   On the other hand, as I argued recently in DemandGen Report,  it may not be possible for any large marketing automation firm to thrive as an independent.  If that's correct, then Marketo's growth will never happen and the investors' only hope will be a buy-out by a larger firm.  Let’s hope the stock market sees hope somewhere in all this: otherwise, Marketo stock will be much harder to sell than its software.

InfusionCon 2013: InfusionSoft Keeps Its Focus on Helping Entrepreneurs

I spent part of last week at Infusionsoft’s annual conference, InfusionCon, drinking the Kool-Aid and soaking up the Arizona sun.


Pleasant as the 80 degree temperatures were to a refugee from the still-wintry Northeast, the real warmth at the conference came from 2,300 attendees bubbling with enthusiasm for their entrepreneurial adventures and how Infusionsoft supports them. Keynote speaker Jay Baer captured the mood perfectly when he went “all Oprah” on the crowd by promising them each a free Camaro. (Either he was joking or I registered incorrectly.) The group was indeed drenched in Oprah-style self-empowerment.

As you’ve probably guessed, this isn’t my native habitat. Even though Raab Associates itself is a small business and runs in part on an Infusionsoft-like system (OfficeAutoPilot), I’m a professional manager by training and most of my clients are mid-size and big businesses. What really matters, though, is that Infusionsoft itself remains committed to its small business customers, despite growing to nearly 400 people and $40 million revenue. This consistency is no accident: Infusionsoft managers are quite vocal on their very conscious efforts to build a culture that is committed to helping entrepreneurs and is itself entrepreneurial. It’s a tall order, but there’s some serious missionary zeal at every level, so they might just succeed.

In any event, I did manage to spend most of the conference in my own comfort zone of analyzing Infusionsoft’s business. A long conversation with Chief Marketing Officer Gregg Head provided some interesting tidbits, including:

- the company’s customers fall into three main groups, each roughly one third of the total.  hese are: Internet-enabled business coaches and experts, who are selling books, videos and other products in addition to their personal time; local service providers, such as dentists, home services, and fitness centers; and businesses selling to other small businesses.

- most clients want either to increase sales or free up the owner's time. The latter goal – taking back your life from an all-consuming business – seemed to resonate more than anything with the attendees. Reducing costs is a lower priority.

- Measuring return on investment isn’t much of an issue. Small businesses can see changes in revenue or free time immediately.  Detailed analysis isn't needed.

- Some companies are too small even for Infusionsoft. A client must have a stable revenue base to expand, or be successful enough that the owner is looking for some free time. The average Infusionsoft client has been in business for five years, which means that nearly all were in business for at least several years before purchasing the system.

- Facebook is by far the most important online channel for Infusionsoft customers, in many cases replacing Web sites as the primary online presence. Search engine marketing and blogs are much less important. The primary sources of new customers are still offline: referrals, partners, events, and direct mail. (Incidentally, trendsters, direct mail in general and post cards in particular are hot. But that might be old news. I did receive a message about personalized pizzas today, but am pretty sure it was an April Fools joke.)

And what of Infusionsoft itself? The company did announce its next release at InfusionCon, although by its own admission the changes were incremental enhancements in usability rather than major expansions in function. The main items were more efficient scheduling of personal tasks, a simple way to prepare quotes, and branding templates that automatically deploy style changes across all types of content. Campaigns can also now easily include GroSocial Facebook campaigns (GroSocial being a social marketing firm acquired by Infusionsoft in January.) Modest as these changes are, the company says its users wanted them more than new acquisition channels.

Infusionsoft also announced several non-technical initiatives, again with the goal of making users more productive. These included a set of prebuilt campaigns. including actual content; on-demand training videos integrated with the product, and accelerated expansion of sales and service partner networks. The onboarding process has also been revamped to deliver results in 30 days rather than 60, the main change being that Infusionsoft staff now does more of the actual setup for new clients and spends less time on a conceptual success map.

All these changes confirm what was already obvious: that Infusionsoft’s entrepreneurial customers are a separate breed from the professional marketers who use traditional marketing automation systems. The functional differences between the two sets of systems may be hard to spot, but there’s no mistaking the difference in the services and attitudes that surround them.