Why Is B2B Marketing Automation Growing So Slowly?

Let me start by saying that the 50% revenue increase I’m projecting for B2B marketing automation in 2013 is a very healthy one. In actual dollars, the $250 million gain is much larger than the $175 million growth in 2012. So if you’re working in the industry, don’t circulate that resume just yet.

But still, as I noted last week, the growth rate is slowing – and for some vendors seems to have fallen considerably in the second half of 2012. The most notable is Eloqua, which as a public company has to report its results. Its year-on-year revenue was up 42% in first half of 2012 ($45 million vs. $31.7 million) but just 28% in the second half ($50.7 million vs. 39.6 million). That means even the absolute increase was down: $11.2 million vs. $13.3 million. Figures for other vendors are not publicly available but I've seen hints that several have slowed as well.

What really got me thinking about this was prepping for a Webinar I’ll be giving next Wednesday on the future of marketing automation (register here). I had figured to start off with my industry growth figures, but this led naturally to a question about long-term potential, which in turn leads to thoughts of market penetration rates.

We’ve recently been seeing surveys that suggest something close to 50% adoption of marketing automation.  For example, LoopFuse reported 42% of respondents had marketing automation in place, a Forrester study reported 45% use among B2B enterprise marketers, and the Lenskold Group found 70% marketing automation usage.*

If true, these figures would actually be bad news for marketing automation vendors.  They suggest the market is at least half way to saturation, after which growth would slow dramatically.

But most people in the industry are confident the potential is much larger than double the current market. The raw numbers suggest as much: according to data compiler Manta.com, there are nearly 1 million U.S. companies with $5 million or more revenue.** Of these, about 300,00 fall into B2B categories. Raab Associates' VEST report shows about 20,000 B2B marketing automation systems at those companies, yielding about 6% penetration. Not surprisingly, the rate is higher among larger firms.  I’ve excluded business under $5 million revenue from this analysis because that’s a very different market.

What accounts for the discrepancy between actual data and survey results? One answer is that people who answer surveys about marketing automation are disproportionately likely to be users.  So the untapped market is indeed much larger than surveys would suggest.

But here’s another, less comforting explanation. It’s a safe bet that at least half of current marketing automation users are in tech industries – call that 10,000 of the total. The Manta figures show 21,000 companies in the tech categories – computer hardware and software, ecommerce and IT outsourcing, electronics, and information technology. I know you can do this one in your head, but 10,000 clients among 21,000 companies means the tech sector is just under 50% penetrated.  This is pretty much what the surveys are telling us. And, yes, survey respondents do tend to be concentrated among tech companies.

Why is this worrisome?  Well, it suggests is that most B2B marketing automation growth is coming from mid-to-late adopters within the tech industry, not early adopters across a much larger universe.  That’s scary because everyone has expected a huge take-off when marketing automation finally transitions beyond the early stages of market development.  If the transition has already happened in tech and never gets started anywhere else, we'll never see that hyper growth.Quite the opposite: the tech pool will run dry in a year or two and growth will slow to a modest replacement rate.

A more tactical consideration is that  mid-to-late buyers have different purchasing styles (more risk averse, more support oriented, more price sensitive, more brand driven) than early adopters. There’s some evidence that B2B marketing automation vendors are moving their sales and marketing in this direction. This makes it even harder for them to sell to pioneers in other industries, who need the original missionary approach.

If you want another hint that the sky may be falling, how about this: a recent Econsultancy survey found that marketing automation is now lower priority among marketers than a year ago (top three for 11% vs. 15%). Since priority presumably translates to purchase intent, that seems to foreshadow a decline in new sales. I don’t want to make too much of this – the survey was among UK marketers and it also showed a sharp increase marketers who ranked marketing automation among their most exciting digital opportunities. Econsultancy’s explanation for the apparent contradiction was that most marketers already own a marketing automation system, so now they’re turning to exploiting it.  But while that's comforting on some levels, it still suggests lower future sales.

To be honest, I was less concerned about the year-to-year change in percentages than about marketing automation’s low rank in both years – next-to-last in 2012 and ninth of twelve in 2013. An IDC report from 2012 had similar results, ranking marketing automation seventh on a list of nine.

I have my own little theory about the low priority, which boils down to the fact that marketing automation only handles a fraction of marketers’ total activities.  Specifically, it supports email and online events, which a 2011 MarketingSherpa study found account for just 20% of program spending.  Even with direct mail and marketing automation itself, the total reaches only 37%. My theory is that marketing automation has a low priority because it is far from a complete customer management solution – or even a complete customer acquisition system.

There are other signs of early market maturity within my VEST data. One is greater concentration among the industry leaders.  My primary measure is employee counts, which are revealed by more vendors than revenue.  Last year, the top four industry vendors (Eloqua, Marketo, HubSpot, and Infusionsoft) added 49% more employees, compared with just 21% for the rest of the industry.  My revenue estimates show a similar gap although it's less pronounced. Revenue per employee is also about 20% higher for the top four vendors than all others combined; this is a sign of tight margins at smaller vendors, which make it hard for many to survive without outside funding. (That gap has actually shrunk since last year.)  Most tellingly, the past year hasn’t seen the rise of any fast-growing new challengers, in the way that Act-On and Pardot appeared in earlier years. This is yet another of industry stability and, perhaps, nascent consolidation.

So, is B2B marketing automation doomed to be no more than a niche application for tech marketers? Industry optimists would argue no, and point out that if half the clients are in tech, then the other half are not. Point taken. But it would be Panglossian to assume that other industries are simply waiting their turn to adopt marketing automation once the tech industry is saturated. In reality, I constantly discover new (to me) marketing automation products tailored to specific industries such as insurance, real estate, franchises, dentists, local retailers, and so on. I suspect the real reason the B2B marketing automation vendors haven’t had much success entering those territories is that they’re already occupied.

If that’s so, then the industry verticalization I’ve long expected has already happened and general purpose marketing automation vendors will have a much harder time than expected in selling to marketers outside of tech. As I suggested above, they’ll need a much more compelling story than one that leaves them at the bottom of priority lists even for tech marketers. Specifically, they’ll have to expand their scope to incorporate all marketing activities: a 20% solution just won’t be enough.
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* Of course, the minute I posted this blog, I started to see additional figures.  A ClickZ article quoted 25% use by 38 B2B companies within the Fortune 500.  An Aberdeen study reported 30% adoption.  A BuyerZone survey returned 13% usage, although it probably included mostly small businesses. 

** Here's the Manta data if you want to play along at home.

And Our Forecast of B2B Marketing Automation Revenue for 2013 is....

I’ve just finished the latest update of our B2B Marketing Automation Vendor Selection Tool (VEST), available on the raabguide.com Web site. The VEST is our evaluation of industry vendors, aimed primarily at companies choosing a marketing automation system.

The VEST contains a great deal of information that provides interesting (to me, at least) insights into industry trends. But by far the most quoted piece of content is our estimate of industry size, which we put at $525 million for 2012. I’ll get to the 2013 estimate in a bit.

First, I thought I’d explain where the figures come from. We use two methods:

• Vendor revenues. This starts with revenue figures provided by major vendors, supplemented with estimates for the smaller ones. The estimates are based on client counts and estimated revenue per client, and cross-checked with estimates using employee counts and estimated revenue per employee. 

• Sector revenues: We aggregate the vendor-provided client counts by industry segment (micro business, small business, mid-size business, and large business), which we multiply by the estimated average selling price in each segment.

Both methods give similar results.

If you’ll pardon a bit of whining, I’ll point out that the estimates are getting harder to build because several vendors no longer provide much information. Some are now part of public companies, which don’t break out revenue or clients for a particular product. Others are still privately held but have stopped releasing precise figures for competitive reasons or to help manage expectations. I can only say this makes me doubly appreciate the vendors who do provide firm data.

Anyway, back to the estimates. Information dribbling in over the past few months has shown a downward trend: Eloqua reported its income for the 2013 would be up 34%, compared with 39% growth the year before;  figures released when ExactTarget bought Pardot suggested Pardot’s 2012 revenue would grow no more than 50%, compared with more than 100% in 2011; Neolane just yesterday reported 40% growth in 2012 vs. 47% the year before.  Only Infusionsoft kept up its 50% rate in both years. Of the other major vendors, Marketo and HubSpot have both been quiet recently.

All told, then, I was expecting the industry growth rate to fall compared with last year’s estimated 62%, which itself was probably a bit high.  Let's say the 2012 base was really $500 million, which gives a 54% growth over 2011.

When I finally assembled the new VEST data, things did indeed look a bit slower.  I won’t go into the gruesome details of my adjustments for incomplete information. But the result was that sector estimates showed a six month growth of 22% in total clients and 26% in revenue run rate. The revenue figure is more meaningful because it adjusts for the size of clients, which the raw client count does not. Doubling that to a twelve-month rate would yield expectation of 52% revenue increase.  That seemed about right, and better than I had feared.

My estimates for the major firms also came up with 52% revenue growth for 2013.  I'll be tad conservative and go with 50%. How scientific can you get?

Bottom line, then: Raab Associates official estimate of B2B marketing automation vendor revenues for 2013 is 50% growth from $500 million in 2012, which yields $750 million.

(Note: this is an updated version of the original post.  Some details have changed slightly but the forecast remains the same.)

The Marketing Funnel is Dead: Here's What Will Replace It

Okay, I freely admit that headlines like “the marketing funnel is dead” are a cheap trick to attract attention.
But I swear I came by this one honestly. Too tired to do any serious work on a recent plane flight, I scanned a random white paper that argued the traditional idea of a funnel didn’t capture the need to treat customers individually as they move towards a purchase. So far my head was nodding in agreement plus maybe a little drowsiness. But then came the punch line: instead of a funnel, marketers should think of managing each customer’s progress as a process, which is best represented – wait for it – as an escalator.

Now I was fully awake, and not in a good way. How is an escalator any less linear than a funnel? Have I missed some crazy new form of multi-path escalator networks? Maybe so: I don’t get out much, and who knows what these kids today are up to? But assuming that’s the not case – and I do after all read Twitter – the escalator analogy is no better than a funnel at illustrating today’s situation.

Nor, to be a bit more serious, is the concept of managing buyers as a process. It’s true that a process can have branches (although not the escalator-like linear process this paper described). But a process is still something the marketer controls. Whereas, the dominant fact of marketing today is precisely that marketers don’t have control: the buyer does. It’s the buyer who decides at every step what she’ll do next.

The picture that comes to my own mind is a tornado: a totally uncontrollable, unpredictable force that leaps across the landscape setting down wherever it wants. By this analogy, the best a marketer can do is to build storm-proof structures that will function successfully no matter what buyer does. I don’t think that’s quite the right image – after all, buyers are not destructive – but it does convey the frightening powerlessness of marketers in today’s world.

The better analogy is probably a maze. Marketers can build an environment that defines the options available to buyers, even though the buyers still make their own decisions about the path they take. The maze also shows how buyers can follow different paths and still end up at the goal, can go in circles indefinitely, and can exit without reaching the goal. It also implies correctly that the marketer’s skill determines quality and effectiveness of the buyer experience, just as the maze designer’s skill determines how much fun it is for visitors. If you really want to push the analogy, you can argue that we’re talking here about a corn maze – because ultimately customers can break out of the predefined paths if they want to.

I guess we’re all lucky that my flight didn’t last much longer, since I was beginning to think about how corn needs water (funding?) and if there’s a drought the ears of corn will have small kernels (customer value?). A more useful insight is that mazes have different regions, so the maze analogy replaces the notion of sequential lead stages with a more nuanced view of non-linear buyer states that can be the same distance to the goal yet differ in other significant ways. Buyers can also jump from state to state without necessarily moving through regions that are adjacent – like a tornado touching several spots within a maze, come to think of it. But enough with the metaphors.

Let’s just stick with the main point: the marketing funnel is really and sincerely dead. The purchase process is no longer linear and, even if it were, marketers couldn’t control how buyers move through it. The image of maze may not be perfect but it does show that buyers can follow many routes, that they’ll make their own choices, and that marketers still play an important role by defining the buyers’ environment. At least it’s a start.

Of one thing I’m certain: the buying process is not an escalator.

MMA Modernizes Marketing Mix Models

I’ve been spending a lot of time recently looking at marketing measurement systems. This means that you, Dear Reader, will be spending a lot of time reading about them. A good place to start is Marketing Management Analytics, known to its friends as MMA.

MMA was founded in 1989 and is one of the pioneers in marketing mix modeling. Mix models remain the heart of the company’s business. But while traditional mix models look at direct correlations between advertising and sales, MMA’s current approach takes a more layered view. This includes what the company calls “multistage” attribution, which looks at intermediate touchpoints between an advertisement and the final purchase, and “customer cascade analysis”, which measures the long-term impact of advertisements on brand equity. The company has also beefed up its consulting services to help make its findings more actionable.

MMA’s foray into attribution is intriguing, since it puts the company into some degree of competition with attribution specialists like VisualIQ, Adometry, and ClearSaleing. But MMA works with aggregate data such as total spend and impressions, with a major emphasis on mass media like television. Those other vendors work primarily with data about individual buyers, which comes largely from digital and direct media. MMA's clients are traditional mass media advertisers, in consumer packaged goods, automotive, financial services, retail, pharmaceuticals, and communications, and it is working for CMOs who are allocating budgets across channels. The other vendors' clients are concentrated in ecommerce and they are answering more tactical questions about spending within the digital channels. What they all share is the goal of measuring the incremental impact of expenditures in specific media.

MMA recently released the latest version of its flagship software, Avista.  The system is still focused on traditional marketing mix models, although it can incorporate the "multistage" approach of measuring the impact of one channel on another.  The new release, Avista 8, was designed to make it easier for marketers and media planners work directly with the system, rather than relying on technical experts. The main interface displays curves that represent the relationship between spending on each tactic and final sales. Marketers use sliders to adjust the spending levels and the system then estimates the sales that would result.

Avista can also run optimization routines to automatically find the most effective spending mix. Users can limit how much spending on any one tactic can increase or decrease, can create groups of tactics that draw from a shared budget, and can choose the target of the optimization (maximum profit with a given budget, minimum spend to reach a target revenue level, etc.). Outputs can show details by brand, product, region, sales channel and time period. Users can save scenarios and compare them to each other. Once they’ve chosen a scenario, Avista can convert it to a high-level media plan for buyers to execute.

The system also has a forecasting feature that runs the same models but also lets users change assumptions about factors other than marketing spend, such as weather, competitive behavior, and distribution channels. Results can be displayed on reports, which in turn can be assembled into custom dashboards.

MMA also offers its clients a data access tool called MarketView, which lets them view and lightly analyze the data assembled as model inputs. This is a popular service by itself, because model inputs often include data the marketers have never seen before. Giving them early access helps to speed the modeling process by letting them verify the quality of the data.

Infusionsoft Bags $54 Million for Small Business Marketing Automation, Spends a Bunch on GroSocial

Infusionsoft today announced it has acquired social lead generation vendor GroSocial. This comes two weeks after Infusionsoft raised $54 million in new funding.

GroSocial is an interesting acquisition: about it three years old, it has about 20 employees and more than 25,000 customers.  Starting price is $30 per month after a 30 day free trial.  The system makes it easy for small business to generate leads through social marketing campaigns and track results.  It will continue to operate independently. The deal makes sense and will help Infusionsoft expand its social media capabilities, which have been limited. (TechCrunch reported rumors that the company paid $25-$30 million for GroSocial alone, but that seems high to me.)

Still, the $54 million investment is the more interesting story.  The sheer amount is impressive; previous funding for Infusionsoft totaled just $17 million. According to CEO Clate Mask, most of the money will be used for acquisitions, product development, and accelerated customer acquisition. He said that the new funds will let Infusionsoft grow at about the same 53% pace as last year, compared with the 45% or so they had planned to grow otherwise.

The investment can be read as validation of Infusionsoft’s strategy of offering unified marketing automation, CRM, and ecommerce exclusively for very small businesses. But that isn’t necessary: the strategy is already validated by Infusionsoft's continued growth, with 2012 revenues of $39 million and plans to triple its employee base to 1,000 in the next three years.

I think it's more useful to learn from Infusionsoft’s experiments with deployment models. The company has alternated between charging an implementation fee and not charging for it – and determined that a paid fee, and the more extensive hand-holding this permits, is more effective at building a long-term business. Implementation services go beyond training to actually setting up initial marketing programs. Naturally, Infusionsoft still strives to make its system as easy as possible to use, but its experience shows that new clients still need extensive help.

This conclusion may strike you as obvious. But there is, at least implicitly, a continued debate within the marketing automation industry between vendors who believe that they can make systems smart enough for new users to run without help, and those who believe human support remains essential. The “smart systems” group aims to build sophisticated technology that can automatically gather information, identify the best response, create the appropriate programs, and present them to marketer for approval. The “human support” group believes this level of automation isn’t practical or desirable, and instead focuses on building service organizations to train marketers and, when necessary, do the work for them. Both groups are rejecting the belief that marketing automation systems can be made simple enough for marketers to do the work themselves with either automated or human help.

That third theory – let’s call it the “ease of use” school – has been the dominant approach of the B2B marketing automation industry for the past few years. I’m tempted to say it has failed, and to cite the well-known statistics showing how few marketers use their systems fully.* But “failure” seems a harsh term for an industry growing at 50% per year. Still, there’s a shared sense among industry vendors that there’s a critical shortage of marketers able to use marketing automation tools effectively and that this is limiting industry growth. I increasingly see companies following the other two theories – smarter systems or greater human support – as a way to overcome this. Infusionsoft’s approach is part of this trend.

I myself have always been partial to the "smart systems" approach.  But that may be just because I like technology.  It's certainly true that more companies are following the human services strategy and reporting good success.  Of course, the services strategy is easier to execute: you just hire some sort people, who are admittedly rare but still easier to find the magical marketing robots.  This makes the strategy more appropriate for small marketing automation vendors who can't afford huge technology investments.  Bigger companies are attracted to the technology-based approach because it lets them limit their services staff, which makes them more attractive to investors.  The big companies can also hedge their bets by building up partner networks to provide services.

So the jury is still out on which approach will prevail -- but I do think that "ease of use" by itself is no longer in contention.


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*Actually, I have trouble laying my hands on the actual statistics. Only study I can find is from Loopfuse in 2011, which found just under 30% of marketing automation users do lead scoring. But I’m pretty sure there are others.

IBM Interact Adds Interactions to Enterprise Marketing Management

My continuing tour of real time interaction managers landed with the good folks at IBM two weeks ago, where I caught up with what’s now IBM Interact. The product was originally launched more than a decade ago by Unica as Affinium Interact.*

The concept of Interact has stayed quite consistent over the years, although the underlying technology has been overhauled several times. The general trend of the changes has been closer integration with other components of the IBM/Unica marketing suite. For example, the original Interact had its own flow chart interface, but the system now uses the same segmentation interface as IBM Campaign. The two modules can also share segment definitions, offers, and interaction history. There’s also some integration with other IBM marketing products, notably the Product Recommendation component inherited from IBM’s CoreMetrics acquisition.

Interact's concept is the same as other interaction managers: touchpoints send it data about a current interaction; the system uses rules, models and data to select one or more offers; and the offers are sent back to the touchpoint for delivery. The differences among these systems are matters of nuance: Interact stores its own permanent customer profiles, while some other systems must re-load data from external systems during each interaction.  Interact assigns fixed scores to offers within each segment definitions, while other systems use scoring formulas shared across segments (although Interact can do that too).  Interact can create self-training predictive models, not all competitors have this option.


A couple of other features seem more or less unique. Interact determines whether customers are eligible for an offer using either qualification rules or Campaign-generated “white lists” and “black lists”; other systems use rules alone. Interact can also assign offers at global, segment, or individual levels, while other systems don’t provide all those choices.

It’s unlikely that any of these differences make Interact significantly more powerful or easier to use than competitors. In practice, the system’s major appeal will be its close integration with Campaign and other IBM products. It is now part of the IBM’s Enterprise Marketing Management (EMM) group, which includes both Unica and Coremetrics, both acquired in 2010. This group supports IBM’s larger strategy of selling systems that use huge quantities of data to run all aspects of large organizations. The company has identified marketing organizations as a major potential market within this strategy and is spending aggressively to both develop that market and take advantage of it.

You might think that Interact plays a central role in IBM’s marketing ecosystem: after all, real-time interactions are the epitome of data-driven marketing. But just a tiny fraction of IBM’s 2,500 EMM customers use Interact (actual figures are confidential) and most deployments seem to be focused on specific -- dare I say tactical? -- applications in one or two channels. The company’s EMM focus seems to be more on analytics and outbound marketing: for example, its most recent EMM acquisitions were Tealeaf Technology (Web experience analysis)  and DemandTec (merchandising analysis) . But it does report increasing interest in Interact among its clients, and high hopes for future growth.



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*A year’s free AARP membership to everyone who remembers the Affinium brand and can sing the jingle.**

** Okay, just kidding.  There never was an Affinium jingle, so far as I know.

Top Five Metrics for Revenue Generation Marketers

Marketing measurement is a perennially popular topic. I myself have just completed a white paper on Top Five Metrics for Revenue Generation Marketers, sponsored by LeadMD, and touched on it in a separate Gleanster study, Revenue Performance Management - The Evolution of Marketing Automation. With both of these on my mind, I also paid new attention to Eloqua’s list of five key revenue performance indicators (listed in the ‘Take a tour’ graphic on this page). The obvious question was whether these three sources agreed about what’s important.

The answer is: not exactly. The following table compares the top metrics from each paper, with analogous items on the same row:

The only item that’s clearly shared across all three lists is the number of leads generated, and even that takes a bit of squinting to include Eloqua’s measure of “reach”, which is really the number of leads currently at different stages. You could also argue that close rate and conversion rates are pretty much the same thing, and therefore also present on all three lists. (Again, a bit of squinting is required). Three of the other items appear just twice (return on investment, revenue, and time to close). The remaining three occur just once.

What accounts for the inconsistencies? I’d say mostly it’s the nature of the lists. The Gleanster list is from a survey of what marketers actually do: it’s no accident that nearly all items are quite easy to calculate. (Return on investment is a glaring exception, and I very much doubt that 73% of marketers actually calculate it today. So let’s just assume that figure is aspirational.)

The other two lists are prescriptive: that is, they show what an expert feels should be done, not what marketers actually do. Look closely, and you'll see that the lists are quite similar.  Four of the five measures are shared.  Even the two non-matching items are related: my fifth item is cost and Eloqua's is return, which is a combination of cost and revenue. 

The apparent difference between the lists is that mine looks more simplistic. It starts with a three-part formula for calculating revenue: (number of leads) x (close rate) x (revenue per closed lead). A fourth factor, cost, combines with revenue to create return on investment. The fifth factor, time, is needed to forecast revenue by period.

Eloqua’s list breaks those same factors down by stages. That is, instead of a single close rate is has a set of conversion rates from one stage to the next. It similarly breaks number of leads into reach (number of leads at each stage), revenue into value (expected revenue from leads at each stage), and time into velocity (number of days spent at each stage). This makes total sense, and if you read my paper, you’ll see that I recommend breaking the measures into stages in almost exactly the same way.* The reason is that reporting on stages gives much greater insight into what’s working well or poorly, and thus helps marketers to see where they should make changes. Providing this sort of actionable information is probably the most important purpose for any measurement system.

In short, Eloqua and I pretty much agree on what marketers should measure. Now if the marketers themselves would join the consensus.

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* The paper also gives plenty of sage advice on how to actually build a system based on these measures.

RedPoint Offers Broad, Deep B2C Marketing Automation

On days when I have nothing else to be cranky about, I sometimes fuss at how business-to-business software vendors hijacked the term “marketing automation” despite its long and relatively honorable history describing systems for consumer marketing. Oracle’s recent agreement to purchase Eloqua reinflamed that wound, since much of the commentary ignored Oracle’s extensive existing suite of consumer marketing systems.

More productively, the deal also shifted discussion from components within a marketing suite to where marketing systems fit within the larger world of unified customer management. This perspective has always been part of consumer marketing, where the classic description of Customer Relationship Management (CRM) was “marketing, sales and service”. That formula fell out of favor when the most prominent CRM system became B2B-oriented Salesforce.com, whose very name reflects its origins in B2B sales automation.

One reason that B2B perspectives have dominated the recent discussion of marketing automation is that the major B2C marketing automation products have receded from view after being absorbed by larger corporate platforms: Unica is part of IBM, Aprimo is owned by Teradata, Portrait Software is owned by Pitney Bowes, Epiphany is owned by Infor (which last month acquired marketing resource management leader Orbis Global, a bit of news I'd missed), Alterian is owned by SDL, and SAS is owned by, well, SAS. This diminishes most products’ independent profiles (Aprimo is the exception), although they are still selling nicely. Even some of the less prominent B2C products have been acquired recently: Conversen by Experian and Entiera by FICO, although neither of these are enterprise software vendors.

The net result of all this harvesting by big buyers has been to clear the ground for the next crop of B2C marketing automation systems. These fill a demand for powerful but moderately-priced options by both marketing services providers and mid-sized companies. Those buyers often find that systems from big enterprise software vendors become too expensive or require too many ancillary components from their corporate parents.

One vendor taking advantage of this opening is RedPoint. Founded in 2006 by veteran CRM consultants and technologists from Accenture, the firm has created an exceptionally extensive marketing automation product including not just campaigns and content management, but also database maintenance, which isn't usually part of marketing automation.  Although IBM, SAS, and Pitney Bowes provide a similar scope, RedPoint is unique in having built all those components itself and keeping them tightly integrated.

The data features are especially impressive. RedPoint offers a rich set of standard data management features including loads from files, databases, and semi-structured formats like XML and EDI; transformations including regular expressions, file splits, and table joins; batch and on-demand process flows.  There's also a novel option to use Web service calls for on-demand data appending, a particularly noteworthy concept.  Beyond those, the system provides specialized functions to manage customer data including name/address parsing, standardization, and matching. Users can apply the system's built-in rules for these or modify the rules to meet their own preferences. All data functions are managed with a sophisticated workflow engine that allows fast development of new marketing databases -- often in a matter of weeks, not months.  This removes (or at least shrinks) the single greatest obstacle facing many new marketing systems.

The system was also designed to scale.  It can work with standard files and SQL databases, with native connectors for SQL Server, Netezza, Oracle, and Teradata. Support for Hadoop, Hbase, PIG, Hive, Cassandra, GreenPlum and other “big data” technologies is due early this year. Clustering is available at all application service layers and the system supports true multi-tenancy (multiple instances running on the same installation).

Campaign management features are equally deep although less unusual.  A drag-and-drop flow builder allows complex, rule-based branches and can react to behaviors during the campaign. Users can define audiences with batch selections, scheduled processes, and “subscription groups” of anonymous individuals (such as Web site visitors) who match specified behavior profiles. Splits can be based on logical conditions, random selections, or auto-generated cells with all possible combinations of specified variables. The auto-generation feature, which can easily produce segmentations with hundreds or thousands of cells, is a hallmark of sophisticated B2C marketing automation systems. It is used for segmentations such as RFM (recency, frequency, monetary value) cells, age/gender/cluster cells, branch or dealer assignments, and product splits.

RedPoint also provides self-training predictive models.  These are currently used within the data management functions and matching algorithms. They will be soon applied to select offers for Web personalization.

Content management includes an editor to create outputs for email, Web, SMS, Twitter, Facebook, FourSquare, LinkedIn and other formats. Users can build shared templates that are later modified for individual projects. Objects can contain Web forms and dynamic content blocks driven by selection rules or mapped to audience segments. The system can generate Web tags to capture user behaviors and can react to those behaviors in real time. It manages approval workflows, version tracking, and precise control over which users can access different objects and functions.

Pricing for RedPoint is based on a combination of deployment services, software licenses, and hosting fees. A minimum system starts around $60,000 per year. There are more than 100 installations, about half sold directly and half through partners such as marketing service providers. The system can be hosted by RedPoint, by a service partner, or by the client.

Oracle Buys Eloqua: Winners and Losers for B2B Marketing Automation

Oracle announced today that it has agreed to purchase B2B marketing automation leader Eloqua for $23.50 per share, which comes to $871 million. This was a bit of a surprise, given that Eloqua just went public in August. The stock had been hovering around $17.50 recently, so $23.50 is a 34% premium: reasonable but not exciting. It suggests that neither Oracle nor Eloqua management felt the company was substantially undervalued.

The deal makes obvious sense, in that it gives Oracle a much stronger position in the fast-growing B2B marketing automation industry*.   Oracle does have an existing B2B marketing automation product, based on the technology it acquired from Market2Lead in 2010.  Market2Lead was very good system, but it lacked the huge market presence that Oracle gains from Eloqua. Oracle may also be gaining a more sophisticated “cloud native” platform, since other Oracle products grew largely from on-premise roots.**

So that’s all fine, but what industry observers really want to know is how Salesforce.com will react. Before addressing that, let’s acknowledge that it’s an "inside the Beltway" concern.  Working marketers care more about how this affects the products and services they’ll get as current or potential Eloqua customers.

The jury on that is very much still out.  Eloqua’s press release promises that Oracle will “significantly increase engineering investments in Eloqua products” and “make Eloqua the centerpiece of its Oracle Marketing Cloud”.  But that’s what they all say, eh? It seems more likely that Oracle will slow down Eloqua enhancements as it evaluates the product’s direction and decides how to best integrate existing Oracle technologies. Indeed, the company says as much in its FAQ on the deal: “Oracle plans to integrate several of its key technology assets, such as Big Data and Business Intelligence, to deliver enhanced value to Eloqua’s products.” That may be the best for Eloqua’s customers in the long run, but the changes will take time to deliver and necessarily distract from near-term product enhancements.

The impact on customer service is likely to be even more negative.  Eloqua’s culture is very focused on customer success, and it has been a clear leader in areas like marketer training. Oracle is less customer-focused and generally less nimble (I'm being polite here).  It will be Oracle’s culture that dominates the combined organization.

Small businesses in particular can expect little love from an Oracle-ized Eloqua.  The company had already been pulling away from that market and now will almost surely give it even less attention. One very specific reason is that B2B marketing automation vendors have always touted client counts as a competitive success metric, which encouraged them to sell to a lot of small clients to inflate that number. Oracle doesn’t report client counts, so that motivation will be gone.

What if you're an enterprise marketer?  In that case, this might well be a good thing.  If you look back at my earlier post this week on the industry future, I argued that the major marketing automation systems will become platforms that support a range of independently developed applications, similar to the Apple and Android app stores or the Salesforce.com AppExchange. As part of the Oracle “Customer Experience Cloud”, Eloqua itself will plug into a larger platform: so it’s pretty much the same model but on a larger scale.

The advantage is that this platform (shown in Oracle’s diagram as the “Customer Experience Foundation”) is unequivocally designed to span all customer-facing activities in the company.  A marketing automation platform can't do this because it bumps up against the competing platform of CRM. A platform that truly includes all customer-facing activities can be more powerful than one limited to marketing automation. This applies especially to the data structures, which are limited for different reasons in both marketing automation and cloud-based CRM systems. (The reasons: marketing automation databases are constrained by the need to synchronize the CRM data structures; CRM databases are limited by the challenges of delivering adequate performance at reasonable cost for operational processing.)


Of course, a platform that serves all customer life stages also by definition contains all information about each customer.  This is another Good Thing, since it provides a truly complete customer view and thus enables the best possible coordination of customer treatments across systems and throughout the relationship.

I’m not saying Oracle is guaranteed to fulfill this potential (see my earlier comments under "nimbleness, lack of"). But at least it’s possible. And, just maybe, Oracle managers will see the value of Eloqua’s “appcloud” marketplace and expand rather than kill it. Wouldn’t that be nice?

Okay, now we can talk about Salesforce.com.  There’s a case to be made that this whole purchase is just a way for Oracle’s Larry Ellison to annoy Salesforce’s Marc Benioff: after all, Eloqua isn’t costing that much more than the Hawaiian island that Ellison bought himself not long ago and it might give Ellison greater pleasure.  It’s certainly worth a chuckle at Oracle headquarters that Eloqua was recently selected as Salesforce’s own marketing automation tool.

More significantly, the Eloqua purchase poses an awkward dilemma for Salesforce, which wouldn’t let Market2Lead continue to integrate with Salesforce after Oracle bought it. Taking the same line with an Oracle-owned Elqoua isn’t quite as easy, and in fact is probably impossible. So now Salesforce finds itself forced to give Oracle access to prime customers, which cannot be a pleasant prospect. We’ll see how they handle it.

The Eloqua purchase certainly exposes the downside of relying on AppExchange partners to provide significant functionality needed by Salesforce clients.  Yes, Salesforce.com gets to leverage those partners’ efforts, saving its own funds for other, more strategic investments. But if a big partner like Eloqua goes away, there’s some danger it could take Salesforce clients with it. This doesn’t matter when there are plenty of alternative partners to provide Salesforce clients with similar capabilities, which has been the case with marketing automation.  The calculus changes when a few large vendors start to dominate the marketing automation space – especially among enterprise clients, who have special needs that only a few vendors can meet. More concretely, Salesforce now has to think long and hard about Marketo’s future. The expectation has always been that Marketo would remain independent, eventually as a public company. But what if they get bought by potentially serious competitor like SAP or IBM, either before or after a public offering? Salesforce might well decide to buy them itself just as a defensive measure.

As I say, this is really just inside gossip that's not terribly relevant to most working marketers. But who doesn’t like a good soap opera? Stay tuned…

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* Raab Associates estimates the industry grew about 50% this year, to $525 million.  I haven’t come up with a considered estimate for next year, but suspect the rate will fall a bit on a percentage basis, even though absolute dollar growth will be about the same or higher.

** In fact, Oracle has two B2B marketing automation products, the other being Oracle Fusion Marketing.

Future of Marketing Automation: Grow or Die

‘Tis the season for industry predictions. I’ve already fielded a couple of requests for my thoughts, which usually requires some pondering before I reply. But this time I was able to answer right away because I’ve just finished a white paper on the future of B2B marketing automation, sponsored by Leadformix and available here for free download.

The question answered by the paper is “What will marketing automation vendors do next?” This is different from the perhaps-more-important question of “What will marketers do next?” I don’t claim any particularly deep insights into the latter: they'll continue to adapt to new media and buying habits, I guess. Like everyone else, I’m seeing greater use of social, mobile, and video; more cross-channel campaigns; closer cooperation between marketing and sales; and expanded use of analytics. If I had to predict one thing that isn’t utterly obvious, it's that B2B marketers will be more involved with managing customer relationships after the initial sale. The reason is that post-sales interactions are increasingly automated and marketers have the best tools to manage automated interactions effectively. The task is really the same as a sophisticated lead nurturing campaign: to monitor customer behavior and respond appropriately.

While my vision of future marketing may be rather broad, I think I see the future of marketing automation in clearer detail. This is what’s covered in the white paper. To summarize the argument:

• marketing automation vendors must grow or die. Today’s B2B marketing automation systems are used primarily for lead nurturing. This means they don’t help other marketers who do lead acquisition and marketing administration (planning, budgeting, project management, etc.). They also have limited interactions with sales and service departments, who own the post-sales customer relationship. Marketing automation vendors who want to expand their business need to service these other groups or risk some other system becoming the central platform for marketing management. If that happens, the other systems will slowly encroach on marketing automation functionality and eventually replace it.

• marketing automation can expand in either direction along the customer path: backward to acquisition or forward to sales and service.  Most expansion to date has been towards sales, in the form of add-ons that give salespeople access to information about behavior of their leads. But CRM systems are deeply embedded in sales departments, so they block growth in that direction.  I therefore expect marketing automation vendors to instead shift toward features for acquisition marketers. These would include not just “inbound marketing” through social media and search engine optimization, but also purchasing media such as online and offline advertising.

• marketing automation can also be divided into layers of delivery systems, campaign management, and platform functions.  Delivery systems manage touchpoints such as Web sites, email, and social media publishing.  Campaign management is the rules and models to select names for promotions.  Platform functions are supporting technologies such as the marketing database, planning and budgeting, content management, analytics, and security. Current marketing automation systems do all three to the degree needed for lead nurture campaigns.  Extending to other users will require more powerful platform functions in particular. Whoever controls the platform can best expand throughout the marketing department.

• marketing automation vendors will increasingly fall into two groups: a handful of big platform vendors and larger number of small specialists. The platform vendors will offer a broad range of functions internally and further extend their range by exposing their platforms to third party developers through “app markets”. The specialists will have narrower scope but be very good at serving companies with specific needs such as low cost, marketing services, or  industry editions (for sports, investments, franchises, etc.)  Both types of companies can succeed although the platform vendors will tend to dominate over time.

• new competition will come from outside the industry, especially from delivery systems. This seems counter-intuitive: delivery systems are by definition channel-specific and function-generic (a term I just invented to mean they serve all functions within marketing, sales and service). This means they are in the strategically weak position of selling commodity products without strong ties to any particular set of users. But the delivery vendors recognize this weakness and can afford to overcome it by investing in campaign engines and platform features. This is exactly what's happening when email vendor ExactTarget purchases Pardot or Web content management vendor SiteCore adds email campaigns and a database. It (almost) goes without saying that CRM vendors are also potential competitors: they already have the platform features; what they mostly lack are campaign engines.

These trends have specific implications for marketers who are selecting a marketing automation system.  You'll need to download the white paper to find out what they are.

Sitecore Migrates from Web Content Management to Cross-Channel Customer Engagement

It’s more than three years since my original post about Sitecore’s plans to transform itself from a Web content management system to a platform for cross-channel customer experience management. It seems to be working out – since that time, revenue has grown 40% per year and the number of clients has nearly doubled from 1,600 to 3,000. The company has attracted additional funding, added support for producing dynamic print content, launched an “App Center” for pre-integrated third party products, built a cloud deployment on the Microsoft Azure platform, and extended to new channels via partnerships covering community management (Telligent), online video publishing (Brightcove), and social marketing campaigns (Komfo).

In other words, Sitecore has been steadily executing on the strategy they described in 2009. If there has been a change, it’s recognition that the company can’t build everything itself: hence the partnerships and app center, which use other developers’ products to extend Sitecore to other channels. Sitecore says this makes sense because giving customers a consistent experience across all channels requires only central data and central content management. Letting external systems deliver the actual interactions causes no particular harm.

This vision should sound familiar: it’s the idea behind the real time decision management systems I’ve been reviewing recently. The difference is architecture: the decision managers place decision logic at the center and see customer data and content as peripheral.

More specifically, the decision managers assemble a consolidated customer profile by pulling data on demand from external systems rather than storing it internally. This is actually a pretty minor difference, since in practice most companies will both maintain a central customer database with key profile information and make direct connections to other systems for details such as transactions. The Sitecore model is pretty much the same: it creates its own customer database and supports real time connections to other systems via Web services or database queries.

The approach to content is a more significant distinction.  Most decision managers assume content is best stored with the touchpoint systems, while Sitecore wants to store most content itself. I chalk this up to their heritage as a content management vendor. Both approaches have their merits: central storage makes coordination easier but requires continued extension of system features to handle new formats; touchpoint storage makes it harder to know what content is actually available and appropriate. So far, Sitecore has been making the investments to manage new formats centrally, at least to the extent of making the contents visible to functions like message selection, access control, and approval workflows. It doesn’t necessarily extend to actually creating or modifying the contents themselves. Maybe that’s a good compromise.

The other important difference is decision rules. These are obviously the main focus of decision management products. Sitecore doesn’t talk about them much, although does deliver them in the form of campaigns flows and dynamic content rules. The campaigns can manage activities across multiple channels – such as sending an email response to a Web visit – although they are not as sophisticated as the multi-branch, looping logic, advanced decision arbitration, and integrated predictive modeling available in the best decision management systems.


On the other hand, Sitecore makes very powerful use of its close control over content.  Each item can be assigned scores on several attributes, such as how much it relates to technology, product, or industry topics.  The system then tracks the content consumed by each individual and compares their behavior to “profile cards” of personas such as frequent site visitors with high interest in business. Each person is assigned to the profile card they match most closely; people can be switched to a different card if their behaviors change. This nearest-fit approach is more flexible than the rigid inclusion criteria of traditional segments or lists. Cards are used in decision rules to select contents and treatments for each person.

Although I just compared Sitecore with decision management systems, its more immediate competitors are marketing automation vendors.  Like Sitecore, they aim to be a company’s core marketing platform. Sitecore’s campaign flow, email and decisioning features are roughly comparable to the same features in mid-tier marketing automation products, while its Web site management and content creation are generally stronger. Marketing automation systems still probably have advantages in analytics and other areas, although it’s hard to generalize. Sitecore does offer the key B2B marketing automation capability to synchronize with CRM products including Salesforce.com and Microsoft Dynamics CRM.

One clear difference is that most marketing automation systems today are software-as-a-service products, while Sitecore is sold as licensed software, running either on-premise software or on the Microsoft Azure cloud. Pricing starts around $125,000 for an enterprise deployment.  Smaller companies would pay less, but Sitecore will never be a system you can get for $1,000 per month.

MindMatrix Adds Sales Support to Marketing Automation

One easily predictable trend in B2B marketing automation is that vendors will tailor their systems to specific industries. This is happening to some extent, but not as quickly as I had expected. The reason may be that B2B marketing automation products have a narrower scope than B2C systems, meaning there’s less advantage in creating vertical editions. For example, the data model of B2B systems is largely fixed, so industry-specific data models (a major component of vertical systems) are largely irrelevant. 


But while I see just a few general systems trying to become vertical specialists, I do keep finding specialist products trying to serve additional markets.   I wrote in February about one set of these vendors:  Balihoo and others that specialize in helping central marketing organizations work with channel partners such as dealers and franchisees. In May I wrote about Demandforce, which specializes in local service businesses such as dentists and auto repair shops and had just been purchased by Intuit.  I'm sure plenty of other specialists exist as well.

MindMatrix  is one of them. Founded 14 years ago, the company has built its business serving the real estate industry, where individual agents and local agencies work in conjunction with large national franchises. The company has nearly 250 clients and about 34,000 end-users, making it larger than most B2B marketing automation vendors. MindMatrix already has 30% of its business outside the real estate market and has recently begun to promote itself as a general purpose marketing automation system – or, more precisely, as the “next generation” of such systems.

The company’s justification for this claim is that it adds sales-marketing alignment to standard marketing automation features. Concretely, this refers to centrally-created marketing materials that are automatically personalized for individual sales people; desktop and smartphone alerts for Web activity by sales targets; and creation of personal Web sites, landing pages, and social media accounts. As the table accompanying my Balihoo post indicates, these are pretty much standard features for channel partner systems. But MindMatrix is correct in saying that they’re not part of mainstream B2B marketing automation.

Mindmatrix does a good job with these features. Content personalization is especially sophisticated, supporting dynamic content (i.e., conditional logic) within templates; drawing personalization variables from user, partner, contact, and other tables; and providing precise control over which content attributes can be edited by a salesperson or other end-user.  Personalized output formats include not just email, but also Web pages, Powerpoint, and online or pritned PDFs. The content can be sent from the smartphone app as well as the desktop. Emails can be sent through Microsoft Outlook and tracked in the MindMatrix contact history.

The system also provides a full set of standard marketing automation features. These include landing pages and forms for lead capture; email and postal mail; lead scoring on attributes and behaviors; branching multi-step campaigns; and bi-directional synchronization with Salesforce.com, Microsoft Dynamics, Outlook, and SugarCRM. Integrations with ACT! And Saleslogix are under development.

Lead scoring rules can consider response to system-generated documents, such as proposals and presentations, arguably allowing more accurate scoring than other systems. Another unusual feature, due for release in January 2013, can add personalized Web messages, polls and chat requests as pop-ups within an external Web page.

The system’s campaign flow builder is reasonably powerful, with support for test splits, filters on based on time and on complex behaviors such as number of Web page visits, updates of contact data, and sending contacts to a different campaign. The interface lays out steps in the flow like a deck of cards, making it unusually straightforward. Flows can be shared with sales users, with some or all features locked down to prevent unauthorized changes.

Does all this really make MindMatrix the next generation of B2B marketing automation? I don’t quite think so: although sales integration is indeed important, I believe the next generation will be focused on serving other groups within the marketing department, including acquisition (advertising and social media) and administration (budgets, planning, content creation, workflow, etc.). Integration with sales will be important but there’s only so far marketing automation systems can go before they compete with CRM – a contest that marketing automation will inevitably lose, since CRM will remain the primary system for sales.

Although MindMatrix has been sold primarily as system to coordinate central marketing with channel partners, it is also used with internal sales groups. The company is actively targeting small companies, with a starting price of $499 per month for the marketing features and another $25 per sales person per month. This is competitive with conventional marketing automation products for small-to-mid-size businesses.  It's a pretty good deal considering the additional sales alignment features that MindMatrix provides.

[x+1] Origin Digital Marketing Hub Offers Cross-Channel Decision Management

My recent posts on real time decision systems have all described products from vendors of batch-oriented, outbound campaign management systems. Expansion to real time decisions helps those vendors cement their strategic position as a complete solution for marketing departments. But technically the two sets of systems have little in common: outbound systems create lists for direct mail and email, while real time systems generate recommendations for Web sites and call centers. Knowing this, you might suspect there are other real time decision management vendors with roots in Web marketing. You would be correct.

[x+1] Origin Digital Marketing Hub is one example. [x+1] originally began as Poindexter Systems, which offered real-time Web ad optimization based in predictive models and anonymous user profiles. This was an early form of what now called a Data Management Platform (DMP), which one articulate blogger defined as “a very smart, very fast cookie warehouse with analytical firepower to crunch, de-duplicate, and integrate your data with any technology platform you desire.”

You could also see DMPs as a type of marketing database because they have the key characteristic of being organized around individual prospects and customers. It’s true that DMPs identify individuals with cookies, not a conventional name and address. But both types of systems can still perform the basic marketing database functions of sending messages to individuals and tracking their responses.

That original DMP is still the foundation of the [x+1] suite. But the company has also extended into Web ad buying (Origin Media DSP), Web site recommendations (Origin Site), attribution (Origin Analytics), and cross channel marketing (Origin Digital Marketing Hub). Supporting multiple channels and potentially storing names and addresses puts [x+1] Hub into direct competition with other real-time decision management products.

I’ll assess the Hub against my real time decision management framework in a minute. But first let's look at [x+1]’s features that are not found in a typical real time decision manager. These include:

- Web tag management: the system provides Javascript code to tag Web pages and advertisements, drop cookies on visitors’ computers, and then use those cookies to track visitor behaviors. The system also supports server-to-server connections that capture user behavior without relying on cookies. Most real time decision systems rely on external systems to capture this data.

-Web audience management: [x+1] Hub can integrate Web audience data from external compilers such as BlueKai and eXelate, enabling marketers to use that information for decisions and targeting. In theory, any decision manager could access the APIs of those providers, but [x+1] is designed specifically to integrate their data and manage the associated charges. [x+1] can also help sell the client’s own data to external syndicators.

- Web media buying: [x+1] can manage real-time bids and other Web advertising purchases. Users set up campaigns with budgets, cost targets, date ranges and other parameters for the system to execute automatically. The system can also track media purchases made outside of [x+1]. Reports provide detailed information on reach, frequency, pacing, inventory, and other advertising-specific metrics.

- attribution: the system tracks visitors through user-defined funnel stages, as defined by visits to specified Web pages or media exposures.  It then uses regression analysis to estimate the influence of each promotion and promotion attributes, such as ad size and format, on stage movement . This is much more sophisticated than the first touch, last touch, or fractional attribution methods available in standard marketing systems.


These features make clear that [x+1] Hub isn’t directly comparable to conventional real time decision systems. But [x+1] does offer itself for real time decision applications, and the whole point of decision management is to centralize decisions within a single system. This means that [x+1] Hub is inevitably competing with the other products to be the one thing that rules them all.

So, how does [x+1] Hub stack up against my decision management criteria?

- connecting to external systems. Like other real time decision managers, [x+1] Hub can connect to external systems via Web services and batch file imports.  It can also capture Web traffic via the Javascript tags and server-to-server connections. However, displaying the returned messages on a Web site requires code created outside of the system.  [x+1] Hub has existing integrations into call center, search, mobile, SMS, social, and email products.

Visitor profiles are stored permanently within the system and can contain whatever attributes the user chooses. The base set includes visitor behaviors, http header attributes (browser, operating system, location derived from IP address, etc.), information imported from external data vendors, and a history of messages presented to each individual. The system can link cookies from [x+1], the client, and third party vendors once these are identified as the same person. Partners including LiveRamp, i-Behavior and Datalogix can link online and offline identities.

Web behaviors and imported data can trigger actions including as assigning a visitor to a segment, adjusting a counter, exporting data, and sending a message through an external system. The results of these actions are stored in the [x+1] database where they can be inputs to other decision rules.

- making decisions based on rules and predictive models. Decision rules in [x+1] Hub are organized into two layers: the system first tests a visitor against one or more “targeted experience” definitions until it finds a match; then, it tests the visitor against a sequence of “targeting rules” associated with the winning experience. Each rule returns a specified offer or creative treatment. Offers and creatives can also have their own eligibility rules, which apply across all campaigns.

Rules can include if/then logic or predictive models. If the models are used, [x+1] can generate scores for multiple responses and pick the best option based on response probability, expected value, or other formulas. This lets the [x+1] select the best option for each individual even though the system always selects the first rule the visitor matches. There are also default choices in case the visitor fails to meet any other rule.

The models are set up by [x+1] technicians. Scoring formulas can incorporate external data, such as inventory levels or sales goals, so long as these are accessible to [x+1] via data import or API connections. Users can also specify the percentage of responses that will receive each option, allowing the system to deliver a fixed mix of results even if the models would favor some choices less or more often.

The system can return multiple offers in response to a single request. Users can block these from containing duplicate offers. Users can also set up “creative groups” of incompatible offers and have the system return only one offer from each group.

- integration with campaign and content systems. [x+1] Hub is not part of a suite with its own outbound campaign manager, although it can be integrated with other vendors’ campaign management products. Similarly, the system also doesn’t store or render content but can connect with third party content management systems. [x+1] does maintain a registry of content IDs that are sent back to execution systems, which look up and render the related messages.

- deployment model. The entire [x+1] suite is sold as a subscription. This can include the software only or software plus supplemental services. On-premise deployment is technically possible but no client has yet selected it. Pricing is based on system functions and volumes. It starts around $12,500 per month but can be lower if the client is also buying media through [x+1].

All told, [x+1] Hub seems functionally competitive with stand-alone decision managers. Still, the system’s main appeal will be to marketers who want the DMP, media buying and attribution features. Those marketers should find that [x+1] Hub lets them coordinate real-time customer treatments across all channels without purchasing a separate decision management system.

Pitney Bowes Interaction Optimizer and Dialogue Offer Unified Inbound/Outbound Marketing Campaigns

In his classic Harvard Business Review article Marketing Myopia, Theodore Levitt argued that railroad companies could have survived the rise of the automobile had they considered their business to be providing transportation, not running trains. Someone at Pitney Bowes clearly got the message.  The postal equipment giant has aggressively moved to become a provider of “customer communication technologies”, making 83 acquisitions costing $2.5 billion since 2000.  Purchases have included Group 1 Software (2004), MapInfo (2007) and Portrait Software (2010), which are now part of a customer analytics and interaction group within the company’s software division.

Portrait itself brought an agglomeration of previous acquisitions, having expanded its original customer relationship management system by purchasing Quadstone analytics in 2005 and Million Handshakes marketing automation in 2008. Their descendants are now modules within integrated Portrait suite, including Portrait Explorer (visualization), Miner (predictive modeling), Uplift (model-based treatment selection), Foundation (data access and integration), Dialogue (multi-step outbound campaigns), and Interaction Optimizer (real-time decisions).

Dialogue and Interaction Optimizer are closely linked, sharing a user interface for campaign definition and both using Foundation to connect with external systems. The interface, called HQ, lets marketers define a hierarchy of campaigns linked to multiple marketing activities, which in turn contain multiple channels and offers. Offers are linked to products, which have customer-level eligibility criteria.

Marketing activities have budgets and response forecasts, which can be set for the activity as a whole or for each channel / message combination (called a treatment). An activity can be assigned an activity type, priority, and scoring rule, which are used to prioritize recommendations during inbound interactions. Activities can also be associated with tasks assigned to the user or others.

HQ provides dashboards showing a campaign calendar, personal and delegated tasks, and results by campaign, offer, and channel. The dashboard can be extended to include external data.

IO connects with touchpoints and other data sources through Foundation, which can accept via Web service calls or SQL queries. Foundation integrates the information it gathers and passes it to IO through a Web services interface. The system usually refreshes the data with each new request, but can be configured to retain data in memory during a multi-step interaction. IO is also integrated with GX Software BlueConic to track and segment Web site visitors. BlueConic-generated events can trigger IO messages and BlueConic-captured behaviors can be loaded to the IO database.

Recommendations in IO are based on marketing activities. Each recommendation has audience and message definitions. The audience can be defined by any combination of static lists, dynamic selections, and scoring rules. Messages belong to a single channel and provide content in a channel-specific format. The content may be an actual message or a pointer interpreted by the touchpoint. IO provides a HTML generator to create messages.  These can be personalized with data from the customer record. Messages can be linked to offers, although this is optional.

When IO receives a recommendation request, it checks against the audience and offer definitions of all active recommendations to identify those that are available to the current customer in the current situation. It sorts the options based on activity type, priority, and scoring results, which can be applied in whatever sequence the user defined during campaign setup. More advanced prioritization could be built into the scoring rules but requires a modeling specialist. After the recommendation is selected, it is sent back to the touchpoint for delivery.

Scoring models can be created and automatically updated within IO or imported from external systems. The self-updating models are less accurate than batch built models but make sense where conditions change quickly or very large numbers of models are needed. External models can be created in Portrait’s own modeling tools or with third party software. Scores are calculated within IO using current data.

IO recommendations are generally called by an external touchpoint but can also be embedded within a Dialogue campaign flow, used to generate outbound campaigns. Dialogue provides a drag-and-drop flow builder with a broad range of capabilities to manage data, direct data flows, send messages, and access social media. Campaigns can execute as batch processes or events triggered by database stored procedures. Other Pitney Bowes product offer additional features for database management, data quality, and message creation.

Both IO and Dialogue are available as on-premise software or hosted by Pitney Bowes. Pricing of IO is based on the database size and number of channels supported. It starts around $75,000 for a 100,000 row database for one channel for a perpetual on-premise license. The system has fewer than 50 installations.

A Framework for Real Time Decision Management: How SAS RTDM Fits In

I’ve had a couple of consulting projects recently that involve real-time decision systems (a.k.a. real time interaction managers), which are used to select the best treatment during a Web visit, telephone call, or other interaction.  This type of software has been around for two decades or more and repeatedly proven its value, but still has relatively few implementations.

There are many possible reasons for the slow adoption.  Maybe marketers don’t realize how much  improvement they get from driving recommendation with predictive models rather than simple rules.  Perhaps the decision capabilities built into delivery systems are already adequate.  The delivery systems are controlled by Web and call center managers who are not incented to generate revenue and may not be interested in a shared decision engine to coordinate customer treatments. Maybe each of these plays a role.

Still, the interest among my own clients has been enough to spur a fresh look at the vendors in this field.  To gather this information systematically, I need a framework that lists standard features and options within those features.  This makes it easy to isolate critical differences among the products.

For real-time decision managers, the framework includes:

• connecting to external systems.  This includes the touchpoints (customer-facing execution systems), such as Web sites and call centers, and other systems with relevant data, such as order processing and marketing databases. Connections to touchpoints are typically through Web Services calls; connections to other sources are usually made through API calls and SQL queries. The connections are set up during system implementation and then used in real time to look up information about a specific individual during an interaction. One important difference among real time decision systems is whether they look up information each time they are asked for a decision, or whether they look it up once at the start of an interaction and then retain it in a session until the interaction is complete.  The session reducing workload and helps to run multi-step dialogues. Another difference is whether the system maintains its own permanent database of individual profiles and contact history or must query external systems for all data.

• making decisions based on rules and predictive models. Rules are always available; systems differ greatly in how hard they are to build and maintain. Predictive models are optional.  They be built outside of the system, built within the system in periodic (batch) processes, or built and updated automatically. Systems also differ considerably in how they choose among competing treatments, a process called “arbitration”. The ranking may be as simple as picking the offer most likely to be accepted, or it may involve complex user-specified considerations such as offer value, sales targets, and business priorities. Some systems let users apply weights to multiple factors.

• integration with campaign and content systems. Early decision systems were not connected to outbound campaign managers or to content stores. But today they are often part of a larger marketing suite that includes an outbound campaign manager. The decision system may share campaign flows, offer definitions, customer data, analytics, and other features with the campaign manager. This simplifies training and facilitates integrated, cross-channel customer treatments. But even the unified systems typically run the outbound campaigns and real-time decisions on separate engines, each optimized for its particular type of processing. Regarding content: the decision systems traditionally returned a content ID that the execution system converted to actual content internally. When the decision manager is part of a marketing suite that includes a content repository, it can return the content itself.

• deployment model. Most real-time decision managers are deployed the old-fashioned way, as on-premise software. This gives clients the greatest control over security and performance. Some are cloud-based or vendor hosted (not precisely the same thing, but close enough), which simplifies deployment. Several vendors offer both options.

With that framework in mind, let’s take a look at another product in this group: SAS Real Time Decision Manager (RTDM).

RTDM meets all the framework requirements: it receives a Web Services request from an external system for a decision, runs the request through rules and models, and returns one or more choices. The results are usually displayed in a slot on a Web page or call center screen, although they could also be presented in an email, mobile device, or other channel.

RTDM leans toward the simpler end of most framework options. Each request loads fresh data from the touchpoint and other source systems, even within a multi-step interaction. At best, users can create continuity by storing a session token at the end of one interaction and retrieving it at the start of the next interaction. The systems returns tags, IDs or URLs but not actual content.

Decisions are based primarily on rules. These can incorporate predictive models, but the models themselves are built outside of the system, using SAS or other products, and do not self-adjust based on results. The system can select among multiple results by sorting on one or more user-specified variables, although any more complex arbitration requires custom coding in the SAS language.  Such  formulas could be registered in the system and reused across campaigns. Users can define a group of treatments, called a “campaign set”, that share a single set of eligibility rules. Individual treatments can also have their own eligibility rules that are applied whenever the treatment is used.

RTDM is tightly integrated with SAS’s campaign management system, SAS Marketing Automation. It shares the same campaign flow interface, treatment library, and database of contacts and responses. Predictive models built with SAS tools are also available to both.  Both use other SAS platform components including data structures, reporting tools, and other general functions. RTDM can be installed on-premise or hosted by SAS.

RTDM has been around in some form since 2008, although integration with the Marketing Automation treatment library is more recent. The system has sold more than 50 licenses, although fewer than half have been deployed. SAS says most deployments have been single-channel, single-purpose projects. Deployment has come slower where RTDM is part of a larger multi-channel deployment involving other SAS marketing products.  The other components must be put in place before the client is ready for RTDM.

Pricing of the system is based on the number of decisions processed or call center seats.  Cost starts around $150,000.