Marketing Lessons from Chernobyl

I’ll be speaking about optimization this Wednesday at the Online Marketing Summit conference in Santa Clara, CA. Since I’m very comfortable with the actual topic, most of my prep time has been spent looking for pictures for my slides.

One discovery was the image above, which shows is how I think most people imagine optimization: a team of dead-serious revenue engineers carefully tweaking dials and watching gauges until they find the perfect balance among alternative marketing investments. That the real world isn’t quite so rigorous is a sad truth I’ll cover during the conference.

But this picture isn’t just any power plant. It’s the control room at the Chernobyl nuclear reactor which disastrously exploded in 1986. Look closely, and what do you notice?

Yes, those hats. Apparently the Chernobyl plant was being run by pastry chefs. That explains so much.

My theory is this: the Soviets had a little-known tradition that translates roughly as “switch jobs with your friends day”. The year of the accident, a team of bakers decided to change places with their buddies in the Chernobyl control room. The nuclear engineers spent the day calculating the volume of pie tins and optimizing heat convection in the baking ovens. Meanwhile, the pastry chefs were decorating fuel rods with icing and asking, “What if we replace the reactor coolant with meringue?”

This did not end well.

Well, maybe that didn’t happen. But my imaginary pastry chefs sound a lot like stereotypical marketers: experts in a subjective field where decisions are based on taste, feel, and appearance, and progress comes through intuitive experimentation. Those methods work well in the kitchen, but can’t be safely transferred to a nuclear reactor. Nor do they work for marketing optimization.

Like reactor management, marketing optimization programs need to be based on deep knowledge of the underlying process. They rely on precise tracking mechanisms that support long-term monitoring of detailed results. They need to be run by marketing equivalent of nuclear engineers, not pastry chefs.

This doesn’t mean that data geeks should take over marketing. Chances are, things weren’t going very well in the Chernobyl bakery that day, either. The city needed both bakers and scientists. But having them wasn’t enough: they needed each in the right place. Marketing departments are the same.

Infor Epiphany Marketing and Interaction Advisor: Good Examples of B2C Marketing Automation

Epiphany was one of the high-fliers of an earlier marketing automation boom: launched in 1997 with an initial public offering in 1999, it traded stock for a full suite of marketing and CRM systems before its price collapsed. The remains were scooped up in 2005 by SSA Global, which was itself purchased in 2006 by enterprise software vendor Infor. Through all this, the company’s products continued to sell with little change. The crown jewel turned out to be RightPoint, a pioneering real-time interaction manager now called Interaction Advisor.

Infor has recently renewed its commitment to the Epiphany line, increasing investment in the product and its marketing. Recent improvements include a unified interface for inbound and outbound campaigns, tighter integration among its components, greater scalability, support for more channels, and pre-packaged solutions for specific applications. The vendor has integrated with Orbis Global for marketing resource management and has an AppExchange integration with Salesforce.com. A new user interface is planned for next year.

As I noted in yesterday’s post, B2C marketing systems like Epiphany have actually been more popular acquisition targets than B2B products. Since many readers of this blog are unfamiliar with the B2C products, it’s worth taking a detailed look at Epiphany’s components.

Let’s start with the marketing automation product, Infor Epiphany Marketing. This sits on a marketing database built outside of the system; part of the set-up is mapping that to Epiphany. This is already a contrast to B2B marketing automation, where the database is part of the system and structures are largely limited to contacts, accounts, and marketing interactions.

In addition to the external data, Epiphany does maintain its own database of operational components. These are arranged in a standard model including programs, which can contain multiple campaigns, which in turn can have multiple communications (messages) and cells (contact groups). Communications can be shared by multiple cells, and one cell can use multiple communications. Each communication may contain one or more creatives, which are specific bits of marketing content. Campaigns, communications, and cells can all be assigned to output channels.

The model also contains segments (sets of customers or prospects), events (campaign triggers, which can be based on time, channel, behavior, queries, or feeds from external Web services), and packages (sets of campaigns used for Interaction Advisor). Campaigns can be divided into waves, each with its own schedule. The schedules can have a fixed date, recur at fixed intervals (from minutes to weeks), or be triggered by events.

Events can be captured as they happen, but the system still pushes the responses to a queue to batch the replies.  The queue might be cleared as often as each minute for near-real-time messaging such as a purchase confirmation email. It might wait longer for media such as direct mail, where there are significant economies of scale.  Bear in mind that this limit applies only to outbound campaigns: Interaction Advisor provides true real-time response to inbound interactions.

Users can also create global marketing rules that apply across campaigns.  These help to enforce regulatory constraints, such as age restrictions or opt-out compliance, or company policies such as limits on the number of messages within a time period.

The structure I've just described is substantially more complicated than most B2B marketing automation systems. That complexity adds some cost, but it also lets users can more easily manage shared components and analyze results by communication, channel, program, segment, and other groupings. This is hugely important in managing marketing programs with hundreds or thousands of components, a typical B2C requirement.

Epiphany campaigns are set up by assembling segments in a hierarchical tree, splitting them into cells if desired, and assigning a communication to each cell. Rules and segmentations are built with a powerful query builder that can read any data in the system, including transaction details, and supports relative dates, value ranges, events, ranking (e.g. 100 highest-revenue customers), and negatives (e.g., has not bought a specific product). Again, this is typical of B2C systems, while B2B query builders are sometimes more limited.

Beyond campaign management, Epiphany Marketing provides integrated data mining and predictive models; advanced reporting and visualization, including use of report cells as campaign segments; an executive dashboard; global permissions and security management; and the Orbis Global integration for marketing calendars, workflow, digital asset management, and financials. These are rarely available in B2B marketing automation systems, although exceptions exist.

Epiphany Marketing has its own email engine.  It actually supports two kinds of dynamic content.  One is your everyday dynamic content, where rules within the email determine what’s shown to each recipient. The other, which Infor calls “true” dynamic content, can change the contents after a message is delivered. It does this by calling back to Interaction Advisor for a selection based on current information. Neat trick.

On the other hand, Epiphany Marketing currently lacks an end-user tool to build emails or landing pages. This is one feature found in even the mostly lowly B2B systems. But gap exists because Epiphany and other B2C systems were designed primarily for large organizations where content is created by full-time designers or external agencies. Infor plans to add a content builder next year, and other B2C vendors will probably do the same if they haven't already.

So much for Epiphany Marketing. It’s neither the best nor the worst B2C marketing system.  But it’s a good example of what those products provide and how they differ from B2B marketing automation products.

Interaction Advisor, on the other can at least make a plausible claim to leading its industry. With nearly 200 installations, it may well have more clients than any competitor – systems including Oracle Real Time Decisions, IBM Unica Interact, Pega Next-Best-Action Marketing, SAP Real-Time Offer Management, and a host of others.

These systems all work roughly the same way. They connect with external customer-facing platforms, usually Web sites or call centers, which alert them when a customer or prospect starts an interaction. The systems pull information about the customer from the external system and other sources, apply rules and predictive models to recommend a treatment, and send the recommendation back to the customer-facing system for delivery.

The connections with external systems are made through Application Program Interfaces (APIs) provided by those systems, so the interaction managers handle those pretty similarly – although there are some differences in the types of connections they support.  The more important variations are in their internal decision-making process. Specific considerations include the complexity, scope and difficulty of creating rules; the predictive modeling methods and user requirements; and how the system reconciles conflicting priorities in making its final recommendation.


Interaction Advisor handles all these quite nicely. The vendor divides the process into four steps: dynamic profiling (gathering the data); business rules (selecting options to consider); real-time analytics (self-managing predictive models); and arbitration (selecting the best option based on user objectives).

In practice, the process starts with a call from an external system. This could be triggered by a tag embedded in Web page or call center screen. The call contains information about the current customer and the context. It carries the identity of an Interaction Advisor event, which tells the system what campaigns to apply and how many recommendations to return. The system might need several responses to display multiple ads on a Web page or to give a call center agent some choices.

When the call is received, Interaction Advisor creates a session that will remain open until the interaction is complete. It stores the data it received in memory and then queries other systems, such as the company’s marketing database, customer files, and inventory systems, to assemble whatever other data it needs. This is also stored in memory: there is no persistent customer profile within Interaction Advisor although some information generated during the interaction will be stored permanently.

The system then executes the rules, which first activate the specified marketing campaigns and then determine which offers within those campaigns are available to this customer in this situation. (For example, the system might exclude offers within the campaign for products the customer has already purchased or recently rejected.) There might be additional constraints such as ensuring that offers relate to the contents of the originating Web page or don’t include conflicting products.

Once the eligible offers are identified, the system activates its predictive models. Interaction Advisor supports two types of predictions: Bayesian models that estimate the likelihood of the customer responding to one specific offer, and collaborative filtering that identifies offers most commonly selected together. Both are self-generating and self-tuning, so human model-builders are not needed. The Bayesian models do provide reports on which attributes have the most influence on the likelihood score.  These provide useful business insight and let users check that the models are reasonable.  The system keeps a record of offers made and accepted, which is essential for the Bayesian modeling technique.

The final step is arbitration. This looks at the likelihood scores for the eligible offers, the value (revenue or profit) from each offer, and perhaps other considerations such as inventory levels or sales quotas. Users set up arbitration rules depending on their priorities: they might want to make the offer most likely to be accepted, the offer with the highest value, or the offer with the highest expected value (i.e., likelihood x value). One arbitration scheme can apply across multiple events, even if they involve different campaigns and offers.

Once the offers are chosen, the system passes the customer-facing system a content ID that tells it what to display. Interaction Advisor could also store the content internally and send it instead of an ID.  But most users prefer to let the customer-facing system to manage its own content.

The call-and-response cycle just described applies to a single interaction. Interaction Advisor doesn’t execute multi-step dialogs like a call center script or sequence of Web pages. Users could accomplish that indirectly by creating different events that call different rules, or by creating eligibility rules that take into account previous activities. Since sessions remain active for a user-specified period of time, previous events within the sequence are all immediately available. However, sessions are channel-specific, so a customer simultaneously looking at a Web page and talking to a call center agent would have two independent sessions active. At best, the data in those sessions could be shared by posting it to an underlying database. Posting also makes the interaction history available to in the future.

Products like Interaction Advisor often generate substantially more revenue than manual recommendations.  They are priced accordingly: a single-channel Interaction Advisor installation starts around $150,000 and could run much higher. Fees are usually based on the number of users in a call center or sales agent environment, or the number of recommendations or visitors in an automated environment like a Web site. Most clients install InteractionAdvisor on-premise, although hosted options are available.

Microsoft Buys Marketing Automation Vendor MarketingPilot: Start of Something Big?

Microsoft today announced the acquisition of marketing management system vendor MarketingPilot, which will become part of its Dynamics CRM group. Financial terms were not disclosed.

MarketingPilot is best described as integrated marketing management for mid-tier companies. It has a pretty low profile in the B2B marketing automation world, partly because it serves a mix of B2C and B2B clients but mostly because it started as a marketing operations management system. It only recently added standard B2B marketing automation features including a customer-level marketing database, outbound email, landing pages, lead scoring, Web behavior tracking, reporting, and Salesforce.com integration. Still, with more than 500 clients, including many ad agencies, MarketingPilot is a significant player in the larger marketing software universe. (I profiled them in a February 2011 post.)

The acquisition is significant on several levels. Most obviously, it’s another example of an adjacent vendor finding the marketing automation attractive: following Pardot’s acquisition last week by email vendor ExactTarget, and earlier acquisitions of Leadformix by CallidusCloud (sales effectiveness), Alterian by SDL (Web content), SmartFocus by Emailvision (email), and Demandforce by Intuit (small business accounting). More specifically, it’s a major acquisition by a CRM vendor, helping to fulfill everyone's favorite prophecy that marketing automation and CRM will eventually merge. I still doubt Salesforce.com will get the message any time soon, but maybe they'll listen a little more closely.

But the real significance may be greater. It’s no coincidence that MarketingPilot, like three of the five other deals I just listed, involves a B2C rather than B2B marketing automation product. The B2C products are generally built on a more powerful foundation than B2B systems, in terms of having a more flexible database structure, deeper marketing operations support, and more powerful analytics. B2B systems strengths are concentrated in execution capabilities like email design, landing pages, multi-step campaigns, and social messaging.

The stronger foundations of the B2C systems make them easier to extend throughout the marketing department, which would benefit from tightly integrated collaboration, planning, analytics, and database management. There’s less value to sharing B2B strengths in execution, since each group builds and deploys programs independently. (In other words: acquisition and nurture campaigns are built by separate groups that create their own emails and landing pages, but do want common planning systems, customer data, and analytics.)

This foundation technology matters because it’s pretty clear that the future of marketing systems is to have a shared platform – think Salesforce.com AppExchange, or the similar marts created by Eloqua, Marketo, HubSpot, and indeed Microsoft Dynamics itself – supporting a variety of plug-and-play applications. B2B marketing automation systems are built for lead nurturing and provide a foundation adequate for that purpose. But marketing departments also need acquisition (or, if you prefer, inbound marketing) and customer support (or whatever comes after a lead is handed off to sales). A B2C platform can support those other functions. Even a good B2B platform might not.

I’m not saying that a B2C platform could extend all the way to running CRM. This might be possible but so far it seems that marketing and sales still need separate physical databases for adequate performance. But I can imagine a marketing platform that provides some services to a CRM system, such as predictive modeling, data enhancement, and reporting. Like users throughout marketing,  users in both sales and marketing would benefit from sharing them. So, at least for now, that is the degree of marketing automation / CRM consolidation I expect.

Fulfilling even this somewhat limited vision will take a lot of resources. B2B marketing automation vendors will need to rearchitect their systems on the more sophisticated platform. They’ll also need to significantly enhance their execution layer to take advantage of the platform’s greater power. I discussed some of this in last month’s post on ways to dominate the marketing automation industry: my preferred strategy, of radically easier execution, specifically depends on better analytics to make the systems automatically do more of the work in campaign design, execution, and optimization. That Microsoft of all companies will create a revolutionary advance in simplicity is a bit hard to imagine (snark alert!), but they do have the resources.  Even the potential for that result may encourage other deep-pocketed vendors to try the same thing. That could be the greatest significance of all.

ExactTarget Acquires Pardot: Great Exit for Pardot, Questionable Future for ExactTarget

ExactTarget announced its acquisition today of mid-tier marketing automation vendor Pardot, for just under $100 million ($95.5 million, to be exact). In one way, this was surprising: Pardot had seemed less interested in being purchased than others in the industry. But Pardot was also the only major marketing automation competitor without serious outside funding.  From that perspective it seems predictable that they would need access to more resources.

Looking over the published materials and listening to the investor relations conference call, it seems that ExactTarget’s motive was gaining access to Pardot’s lead nurturing and scoring capabilities. During the call, company management reported little overlap in clients: ExactTarget sells mostly to B2C enterprise marketers, while Pardot sells to small and mid-size B2B.

ExactTarget also said their relationship with Marketo, announced June 2011 and now ended, had produced little business.  No surprise there: it’s unlikely the ExactTarget sales team understood Marketo or was effectively incented to sell it.  More interesting, they said that trying to sell Marketo had shown that clients really wanted a single, integrated system for B2B and B2C. I wonder about that one: while some companies do need both, they are usually run by separate organizations with little need for tight coordination.

Once the products are integrated, ExactTarget expects their enterprise sales force to sell Pardot to large companies.  But they acknowledged that Pardot needs enterprise-class user rights and security management before that can happen. One advantage of the deal is Pardot will be able to do this with ExactTarget’s existing user rights infrastructure, saving development cost and speeding deployment. Even so, it will probably be some time before an enterprise-ready version of Pardot is available. That’s probably a good thing, since it will take time to train the ExactTarget people on the product and to find an effective price structure.

All told, I see this as a great deal for Pardot’s founders and employees, who get a very nice exit at about 9x 2012 revenue (although COO Adam Blitzer will stay on for now). Pardot clients might benefit from the company gaining additional development resources, but could also suffer as developers adjust the product to serve enterprise clients.  Service might also deteriorate if ExactTarget accelerates sales as much as they seem to expect. Pardot’s pricing (more on that in a minute) probably won’t change much: while ExactTarget could afford to fund competitive price cutting, it’s a public company and wouldn’t want to increase losses still further.

The acquisition's value to ExactTarget is less clear. They do get a strong foothold in fast-growing market and they’re probably correct that additional investment can help Pardot grow even faster. But Pardot’s revenue is just a tiny fraction of ExactTarget’s business: Pardot will take in around $11 million in 2012, vs. $280 million for ExactTarget. Even in 2013, when ExactTarget expects Pardot to yield $15-$20 million, that won’t be much more than 5% of the company total. Pardot's technology is solid but not spectacular, and equivalent products could be purchased for much less. The client base in small to mid-size businesses just doesn’t seem relevant to ExactTarget, and isn’t an obvious direction for it to move.

Back to pricing. The announcement provided a bit of new information about Pardot’s financials: specifically, $7.6 million revenue in 2011, and estimated $3 million revenue in the fourth quarter of 2012.*  Given Pardot’s growth, a $3 million fourth quarter implies $10 to $11 million for the year. This would be just under 50% growth vs. the $7.6 million 2011 figure. That’s actually a bit less than I had expected but seems plausible. It’s also in line with the announced employee count of 115, which is also lower than I had expected. 

As to pricing: figures in our VEST report showed Pardot had perhaps 600 clients in mid-2011 and a bit more than 1,000 in mid-2012.**  That comes to just over $1,000 per month in 2011 and just under $900 per month in 2012. Not a good trend. Revenue per employee is also moving in the wrong direction: the company reported 58 employees in mid-2011 and 95 in mid-2012, yielding a per employee drop from $130,000 to $115,000.  (Alternative explanation: ExactTarget is purposely being conservative in its Q4 estimate, in which case Pardot might be on track for $12 million or more in 2012.  That wouldn't change the thrust of my analysis, although it would erase the apparent decline in revenue per client and revenue per employee.)

Both figures probably reflect accelerated business growth accompanied by aggressive competitive pricing.  In itself, that's not so terrible. But both are also quite low compared with industry peers: Marketo will average about $3,000 per month per client in 2012 and even down-market HubSpot should get $600. Per employee figures are $225,000 for Marketo and $151,000 for HubSpot, which were growing at least as fast as Pardot. We know that Pardot ran a tight ship, so these figures probably don’t reflect high losses. But nor do they suggest much opportunity for profit growth, short of a radical – and highly unlikely – price increase.

This prospect doesn’t necessarily bother ExactTarget, which projected another $10-12 million loss from Pardot and iGoDigital in 2013, reaching break-even in mid-2014. The losses I can certainly see; the profits look less likely. Maybe the difference will come from enterprise sales – but even though margins are surely higher in that space today, competition is heating up and is coming from the same players – Eloqua and Marketo – who battered down mid-market prices in the past three years. If history repeats, ExactTarget may not get the lift it expects.



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* What ExactTarget actually said was they expected $3-$4 million combined incremental revenue for the fourth quarter from Pardot and iGoDigital, an online targeting and personalization company whose acquisition they also announced today.  I don’t have revenue figures for iGoDigital but they had about 35% as many employees (40 vs. 115) and cost 20% as much ($21 million vs. $95 million), so I’d guess they account for at least one-quarter of the revenue total. Incidentally, iGoDigital sounds like a very interesting acquisition in its own right: it may be today’s more important story.


** See my August 12 blog post for these figures and the ones that follow. That post shows Pardot with 400 clients in mid-2011 but a closer look at my files suggests the real figure was probably around 600.

SetLogik Offers B2B Marketers a Real Marketing Database

I’ve now done more detailed research into the SetLogik B2B data management system I mentioned in my Dreamforce post.  If anything, I’m even more impressed.

I originally saw SetLogik as a tool to associate marketing leads with sales opportunities, even when they are not connected directly within Salesforce.com. That’s important in itself, since those missing links are the greatest obstacle to showing the value of B2B marketing efforts through revenue attribution.

But the bigger story, which SetLogik itself recognizes clearly, is that they’re creating a real marketing database. This has been sadly lacking in most B2B marketing automation systems, which supplement the Salesforce.com database with barely-extensible lead profiles and contact histories. In fact, I’ve recently taken to citing the B2B systems' fixed, built-in database as the fundamental difference distinguishing them from B2C systems, which connect to externally-managed databases with any structure.

SetLogik doesn’t replace the database built into the B2B systems.  Rather, it creates a separate database that merges data from marketing automation, Salesforce.com (or, potentially, any other CRM system), and whatever other sources a company has available. The matching capabilities that initially caught my eye are just one part of a larger suite of functions to load, clean, standardize,  merge, and enhance B2B data, ultimately storing it within a database where it can be used with SetLogik tools for segmentation, selection, reporting (including attribution), and predictive model-based lead scoring. Cleansed data and results such as lead scores can be fed back into CRM and marketing automation systems for direct access by their users. SetLogik’s own diagram expresses this separation reasonably well, although I would have suggested they clarify that there’s an independent, persistent database within their cloud.
 

As consumer marketers learned long ago, building a serious marketing database is a big project. The challenge is even greater in B2B, which manages two data levels, companies and contacts, instead of just one level of consumers.  It’s no wonder that B2B marketing automation vendors avoided the issue by piggybacking on the Salesforce.com structure: otherwise, the cost and complexity of building a separate database would have severely limited their growth.

SetLogik’s addresses the problem directly, by creating a nearly-automated system to build the database. The company promises to deliver a completely functional database within 60 days, and to deliver the database plus predictive lead scoring models in 90 days. Compared with the many months or years needed to deploy a traditional marketing database, this is lightning quick.

I call the system “nearly-automated” because a SetLogik analyst works with each client to set up the data preparation steps, tweaking the standard rules and processes as necessary, and because the predictive models are also built by human analysts. These are advantages, not flaws, since a skilled user adds substantial value to both processes. The system still does most of the work, so the initial data quality set-up takes just a few hours of labor – although the full process typically takes several days because clients need time to make decisions. Similarly, modeling takes about two weeks – again, more wait time than work time.  In fact, the model building is so efficient that the company includes it for free in its Enterprise edition, which starts at $1,400 per month for up to 25,000 records.

None of this would matter if the quality of SetLogik’s results were poor. But, while I haven’t run a test, the company certainly describes the features I'd want. Standard inputs include leads, contacts, campaign members, accounts, and opportunities from Salesforce.com, plus leads and activities from marketing automation. Data preparation includes standardization and verification of addresses in the U.S., Canada, United Kingdom, Australia; phone numbers verification for North America; email format verification (but not sending test emails); table-based transformations and coding for elements like titles and sales territories; and enhancement with client-licensed external data such as D&B listings.

The matching engine uses multiple rule sets, supports both similar and exact matches, and can compare data across several fields (such as mobile vs. home vs office phone number). The system will match at individual and company levels and can link individuals to companies. It will choose the best value for each field and return a consistent best record to all source systems. Predictive modeling can include derived variables, such as number of emails received, as well as raw inputs. The system’s database stores snapshots of old data values so it can track changes and trends. New and changed records run through the system at user-determined intervals that can be frequent as hourly.

The system doesn’t provide an interface for end-users to set their own data processing rules, although one is planned. As SetLogik correctly argues, very few B2B marketers have the interest or skills to do this. In fact, the company’s larger problem is that so few marketers even recognize they need better data cleansing, let alone a separate marketing database. This will likely limit SetLogik's initial clients to the upper tier of sophisticated marketers who do see the problem.  We can hope that the importance of a serious marketing database will eventually become clear to everyone.

SetLogik is a Software-as-a-Service application, available directly from the company or through the Salesforce.com AppExchange. The system has an Eloqua connector today and a Marketo connector in the works. The company promises basic implementation in 60 days, although it is usually much less, and full implementation including predictive modeling in 90 days.

Pricing is based on the system edition and number of records (unique individuals and companies). The Express Edition, starting at $500 per month for 25,000 records, builds the database and feeds the cleansed, enhanced records back to Salesforce.com and marketing automation. Professional Edition, starting at $1,000 per month, adds segmentation, list building, attribution, and other reporting. Enterprise Edition, starting at $1,400 per month, offers all the other features plus predictive lead scores. The price tag gets more serious for large systems – Enterprise costs about $11,000 per month for one million-records – but is still much less than a conventional marketing database. In fact, SetLogik points out that some services built into the price, such as address and phone verification or access to lead profiles within Salesforce.com, would ordinarily cost nearly as much as the entire SetLogik fee if purchased separately.

SetLogik officially released its system in October 2011 and now has several large enterprise clients.

KXEN Packages Automated Predictive Models within Salesforce Apps

As I mentioned in my Marketing Automation Beer Goggles post, KXEN introduced a free lead scoring app  for Salesforce.com users at Dreamforce. KXEN has since given me a closer look at the lead scoring product, underlying technology, and future plans.

First for details on Predictive Lead Scoring itself. As originally reported, it’s free and requires no configuration to set up. The trade-off for this simplicity is users have no control over which variables are included or what the models predict. The variables will include all standard and custom fields on the lead object, which isn’t too bad except that there might be useful data on related objects such as activity details.  At best, marketers could summarize such data and add the summary to the lead object, but that requires human intervention. Predictions are limited to conversions from lead to contact.  This isn’t always what you want, but it does stay within the lead object’s contents.

Some of these limits might be relaxed in future versions of the app.  However, KXEN is wary of making deployment more difficult or letting users make poor decisions such as removing variables they should keep. The system does provide reports showing the contributions made to the scoring formula by different variables and by values within those variables. Recognizing that this is already more than many users will care to review, KXEN plans to add simpler reports over time.

Although the lead scoring app attracted more interest than KXEN expected, it was really developed to illustrate the power of KXEN’s new “Cloud Prediction” model-building engine.  This uses the same automated modeling methods as KXEN’s established on-premise product. What’s new is a REST API that lets external applications send inputs over the Internet, wait while the engine builds a new model, and then receive the completed model formula. Scoring happens within the external application itself – Salesforce.com in this case – allowing the system to update scores as Salesforce data changes without running on KXEN’s own servers. Similarly, the app relies only on data stored within Salesforce.com’s own database, so KXEN doesn’t have to keep a copy.

The limits of the lead scoring app are a design choice: the cloud prediction API allows as much end-user control as KXEN’s on-premise system. But KXEN isn’t planning to expose the full API any time soon.  Instead, it's pursuing the app-based model as a way to expand use of its technology beyond its current base of relatively skilled users.

The next Salesforce.com app KXEN will release – pretty much any day – will tackle prediction of the “next best activity” for a given customer. This is substantially more complicated than the lead scoring app, since it creates a separate predictive model for each activity and then chooses the activity with the highest probability of response in each situation.  This one won’t be free: list price is $50 per user per month.

The next best activity app also requires more user effort to set up, since users must define the activities to model and specify eligibility rules for each activity. The system recommends activities randomly at first, to build some experience with different situations.  After the initial models are built it will still make occasional random selections to keep the models current.  Unlike lead scoring, the next best activity app reads from several Salesforce.com data objects in addition to the lead object.  Eventually, users will get control over which data elements to include.

The next best action app also relies on data stored within Salesforce.com. It won’t store a full history of offers made and rejected, because this would take more data than Salesforce.com can economically hold. That means the system won’t know when agents decide not to make the recommended offer. This can be a problem because the models based on a user-selected subset of cases.  It's a common issue with recommendation systems.

Whether these and similar issues cause serious problems for KXEN's cloud modeling apps remains to be seen.  Some amount of skilled human intervention may be essential to apply modeling effectively.  But it's worth exploring what's really needed: the ever-growing volumes of data and decisions make low-cost, automated predictions increasingly important for marketing success.

 

Three Ways to Dominate the Marketing Automation Industry

I wrote last August that it’s still possible for new B2B marketing automation vendors to challenge the industry leaders. This was based on the observation that several of the smaller vendors have quickly reached the 1,000 client benchmark. But it didn’t answer the more interesting question of what it would take for a new vendor to really bypass the current leaders.

That particular question came up repeatedly during Dreamforce last week. The answer may not matter to marketers who don’t themselves work for a marketing automation vendor. But I think it’s worth pondering anyway, if only as an interesting case study in business strategy.

My own answer is: at stage of the industry, the basic features of marketing automation are pretty much set, so radically different features are not likely to emerge as a major competitive advantage. (That’s not to say new features won’t be important, particularly extensions into areas like social and mobile. But new features won’t be enough because they can be copied too quickly if they're really popular.) Rather, a new industry leader would have to remove the critical bottleneck to industry growth: the shortage of marketers with the skills needed to fully use marketing automation capabilities.

I don’t think I need to spend too much time defending that particular premise: if you want a data point, how about the widely quoted Sirius Decisions figure that 85% of marketers do not believe they are using their marketing automation platform to the fullest. Let's move onto the more important question: how could a vendor change the situation?

It seems to me there are three ways to approach this:

- make the systems radically easier to use. This is by far my preferred solution. It may seem an unobtainable goal: after all, ease of use has been a top priority of marketing automation vendors for years, and you’d think that by now all those smart people would have made things about as easy as they can be. But I think the right basis of comparison is Google AdWords, which made entry-level search engine marketing so incredibly simple that pretty much anyone can do it with no training at all.

As with AdWords, a radically simpler marketing automation system would just ask users to make a handful of basic decisions about content and target audience, and would build everything else automatically. Again like AdWords, the system would automatically optimize the programs based on results. This implies a degree of automation well beyond today’s marketing automation products, although increasingly common features like dynamic content and integrated predictive modeling offer a hint at how it could happen.

You could argue that marketers don’t want to delegate so much responsibility to a system, but many seem to have delegated to AdWords quite happily. Of course, AdWords also lets more sophisticated marketers make more decisions for themselves, and I’d expect any marketing automation system to provide that option as well. And, again as with search engine marketing, I’d expect the most sophisticated marketers to adopt more specialized systems than AdWords itself—but those will marketers will remain a minority.


- make it radically easier for marketers to use existing functions. This is not about making the functions themselves simpler: per my earlier comment, I’ll accept that all those smart folks have done about as much as possible in that direction. But I think more can be done to help marketers learn to use those functions more quickly and with less work.

What I have in mind specifically is “just in time” approaches that make it very easy for marketers to learn how to do a new task once they've started it, rather than taking separate training classes or looking up detailed instructions. This means context-sensitive help functions that can guess what you’re trying to do and offer advice when you seem to be having trouble. It also means lots of little instructional snippets instead of monolithic tutorials that have to be consumed all at once. This is standard stuff in the software industry, although some companies do it much better than others.

I think a marketing automation vendor who really focused on this would have a major advantage among new users, who are exactly the key audience.  If you want a specific benchmark for this approach, it’s that people can perform tasks with zero advance training.

- provide services so marketers don’t need to use the systems themselves. Quite a number of vendors have taken the services-based approach. In a way, it’s an admission of defeat: no, we really can’t make the systems simple enough for mere mortals. But I'd be happy to trade pride for success.

 The trick to this approach is to keep the service cost low enough that you can actually make money.  That comes down to things like prepackaged templates for creative materials and campaign flows, highly automated processes so the service staff can work efficiently, and standardized methodologies so inexperienced (ok, that's a euphemism for low cost) individuals can be easily trained to provide adequate service. Again, these are pretty standard things but I don’t think any vendor has really designed their system and business model around them.

Note that a system designed for efficient use by highly trained service people would look quite different from one designed for easy use and learning by lightly trained end-users. So this approach would really imply fundamental change in how vendors build their products.

As I said earlier, my preferred option is the first one, making systems radically simpler. But I’m guessing the more practical one is the middle choice of providing more effective help using systems similar to today’s. It’s possible that middle option isn’t viable: maybe vendors can’t provide enough additional help to make a difference. But I don’t think I’ve seen any vendor really focus on that option – and won’t concede I’m wrong until I have.

Marketing Automation Beer Goggles: What I Think I Learned at Dreamforce

I’m writing this on my way home from Dreamforce, the Salesforce.com user conference that has become the primary industry gathering for marketing automation vendors. With a reported 90,000 attendees (I didn't count them personally), the show is fragmented into many different experiences. My own experience was mostly talking to marketing technology vendors in the exhibit hall, private meetings, and maybe a party or two. I did attend the main keynote and the “marketing cloud” announcement, but neither contained  major product news and the basic story – that social networks change everything – was true but far from novel.

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

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

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

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

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

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

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

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

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

ClickDimensions Grows Quickly by Offering B2B Marketing Automation as a Microsoft Dynamics CRM Add-On

When I first wrote about ClickDimensions in a February, 2011 post, the concept was intriguing – a marketing automation add-on to Microsoft Dynamics CRM – but the product itself had been available for less than six months and claimed barely 50 clients. Since then, the company has grown its customer base more than ten-fold (it won’t release specific figures), won the Dynamics Marketplace Solution Excellence Partner of the Year award, signed up more than 250 channel partners around the world, and attracted outside funding. Sounds like the idea has legs.

The product has matured as well. The most important addition is a flow builder that supports branching campaigns. This is a bit limited – each node can only have yes/no branches – but it includes a reasonable set of actions including send an email, wait, notify user, add or remove from list, and run CRM workflow. It can also check for whether a contact has opened an email or clicked on a link. This is comparable to standard marketing automation products.

Other enhancements include an expanded survey builder that can skip questions or pages based on previous answers; a/b testing (two splits only) within emails; subscription management; and improved builders for email, landing pages, and forms. The system already provided dynamic email content, although users have to write the selection rules in a scripting language – something many marketers will find intimidating. Web behavior tracking, lead scoring, and social discovery (searching for and importing public data on LinkedIn) are also available.

None of this would make ClickDimensions stand out from other marketing automation systems if it weren’t for its fundamentally different architecture. ClickDimensions works directly from the Dynamics CRM data files, rather than creating a parallel, synchronized database like most marketing automation products. Additional tables needed by ClickDimensions are also custom objects within the Dynamics system. The result is direct connection between the two systems. ClickDimensions functions are also accessed within the Dynamics interface.

ClickDimensions isn’t the only vendor to take this approach. CoreMotives (purchased last March by Silverpop) has a similar architecture within the Microsoft Dynamics world and Predictive Response (which I haven’t looked at in detail) is a similar add-on to Salesforce.com. Still, as the shortness of this list suggests, the dominant approach to marketing automation remains separate, synchronized data files.

This could well change: as marketing automation becomes more widely understood, it will be purchased by less sophisticated companies. These buyers are already customers of CRM resellers who can easily offer ClickDimensions and similar CRM add-on products. That gives the add-on vendors efficient access to a huge market. The CRM vendors themselves would have the same advantage should they choose to add marketing automation  features. 

In practice, most buyers neither know nor care about the architectural differences between the two approaches. So long as the add-on architecture will work – and there’s no reason to doubt it does for all but the very largest implementations – success may well be determined by who reaches the most buyers first. As ClickDimensions’ fast growth already suggests, its reseller-based approach could be a decisive advantage as the marketing automation industry enters its next stage.  Only time will tell.

Moving On: Lessons from the B2B Marketing Trenches

 
I’ve just ended my six month tour as VP Optimization at LeftBrain DGA, and am now returning full time to my usual consulting, writing, and general shenanigans. It was fun to work again as a hands-on marketer. Here are some insights based on the experience.

- lots of content. We all know that content is king, but sometimes forget the king has a voracious appetite. A serious demand generation program might move contacts through half dozen stages with several levels within each stage and several messages within each level. This could easily come to forty or fifty messages, each offering a different downloadable asset.  The numbers go even higher when you start to create separate streams for different personas. Building these materials is major undertaking, first to understand what’s appropriate and then to create it. But deploying the initial content is just the start: you then have to monitor performance, test alternatives, and periodically refresh the whole stream. Finding efficient ways to do this is critical to keeping costs and schedules within reason. (Note that I’m talking here about email programs to nurture known contacts, not acquisition programs to attract new names. That takes another massive content collection.)

- content isn’t everything. It’s an old saw among direct marketers that the list determines most of your response rate and the offer controls for most of the rest.  Actual creative execution (copy, graphics, format, etc.) accounts for maybe 10% of the result. We proved this repeatedly with tests that used the different content at the same stage in the campaign flow: basically, results were similar even with content originally designed for different purposes. Conversely, the same piece of content had hugely different results at different places in the flow. What this meant in both cases was that response was primarily driven by the people at each stage, not by the specifics of the materials presented.

- simplicity helps. That results are primarily driven by audience doesn’t mean that content doesn’t matter. We did a fascinating (to me, at least) analysis of 100 emails, logging specific features such as number of words and readability scores and then comparing these against open, click-through, and form submit rates. A clear pattern emerged: simpler emails (shorter, fewer graphics, easier to read) performed better. In fact, the pattern was so clear that there's a danger of over-reaction: at some point, a message can be too short to be effective (think of the mayor in The Simpsons, who just repeats “Vote for Me”). So the real trick is to find an optimal length, and even then to recognize that some messages truly need to be longer than others.

- simplicity isn’t everything, either. We did a lot of testing – it was my favorite part of my job – but the content tests were often inconclusive: sometimes shorter won, sometimes longer won, most often the difference was too small to matter. Given that we were starting with competently-created materials, that’s not too surprising. On the other hand, we consistently found that forms with fewer questions yielded better results, typically by a ratio of 3:1. This is one example of a non-content item with major impact; another was contact frequency (more is better, but, as with simplicity, only up to a point). There were other aspects of program structure that I would have tested had time and resources permitted; the goal was to focus on variables with the potential for a substantial impact on over-all results. This generally meant moving beyond individual content tests to items with larger and more global impact.

- test themes, not details. Don’t misinterpret that last sentence: I’m not against content tests. What I'm against is tests that only teach one small, random lesson, such as whether subject line A is better than subject line B. The way to build more powerful tests is to build them around a hypothesis and then try several simultaneous changes that support or refute that hypothesis. (I’ve shamelessly stolen this insight from Marketing Experiments, whose methodology I hugely admire and highly recommend.) So, if you think simplicity is an issue, create one test with shorter subject line and less copy and fewer graphics and a simpler call to action, and run that against your control. This is exactly the opposite of conventional testing advice of changing just one thing at a time.  That approach made sense back in the days of direct mail when you were running a handful of versions per year, but isn’t an option in the content-intensive environment of modern online marketing. And even if you had the resources to run a gazillion separate tests, you’d still need to see larger patterns to guide your future content creation.

- multivariate tests work. As if the infinite number of potential tests were not enough of a challenge, most B2B marketers also have relatively small program quantities to work with. We multiplied our test volume by applying multivariate test designs, which let us use the same contacts in several different test cells simultaneously. This probably needs a post of its own, but here's a quick example: Let’s say you need 10,000 names per test cell and have 20,000 names total. Traditionally, you could just run one test comparing two choices. But with a multivariate design, you’d create four cells of 5,000 each. Cells 1 and 2 would get the first version of the first test, while cells 3 and 4 would get the second version. But – and here’s the magic – cells 1 and 3 would also get the first version of the second test, while cells 2 and 4 would get the second version of the second test. Thus, each test gets the required 10,000 names, but you can still see the impact of each test separately. (Here’s a random article that seems to do a good job of explaining this more fully.). We generally limited ourselves to two or three tests at a time. More complicated structures are possible but I was always concerned about keeping execution relatively simple since we were doing all our splitting manually.

- metrics matter. As it happens, most of the programs we executed rely heavily on form submissions to move people to the next stage. This meant that form fills were the key success metric, not opens or click-throughs. Although these generally correlate with each other, the relationship is weaker than you might expect.  Some exceptions were due to obvious factors such as differences in form length, but the reasons for others were unknown.  (I often suspected but could never prove reporting or data capture issues.)  Of course, most email marketers are used to looking at open and click rates, so it took some gentle reminding to keep everyone focused on the form fill statistics. The good news is we prevented some pretty serious mistakes by using the right measure.  Note that form fills are especially important in acquisition programs responders are lost altogether if don't complete a form that let you add them to your database.

- test results need selling. As you’ve probably guessed by now, I spent much of time lovingly crafting our tests and analyzing the results. But others were not so engaged: more than once, I was asked what we found in a test whose results I had published weeks before. This wasn’t a complete surprise, since other people had many other items on their mind. But we did eventually conclude that simply publishing the results was not enough, and started to go through the results in person during weekly and monthly status meetings. We also found that reviewing individual results was not enough; when we found larger patterns worth reporting, we had to present them explicitly as well. Again, there’s no surprise in this, but it does bear directly on expectations that managers will find important data if reporting systems simply make it available. Most will not: the systems have to go beyond reporting to highlight what’s new, what it means, why it matters, and what to do next. Although some parts of that analysis can be automated, most of it still relies on skilled human effort.

- reports need context. Reporting was another of my responsibilities, and we made great strides in delivering clearer and more actionable data to our clients. One of the things I already knew but was reminded really matters was the importance of putting data in context. It wasn’t enough just to show cumulative quantities or conversion statistics; we needed to compare this data with previous results, targets, and other programs to give a sense of what it meant. To take one example, we reported the winner of a series of email package tests, without realizing until late in the analysis that the response rate for the test as a whole was much lower than previous results. This was a more important issue that the tests themselves. We had other instances where entire waves were missing from reports; we only uncovered this because someone noticed they were missing – whereas, a proper comparison against plan would have highlighted it automatically. Again, such comparisons are widely acknowledged as a best practice: my point here is they have immediate practical value, so they shouldn't just be relegated to the list of “nice but not necessary” things that no one ever quite gets around to doing.

- survival is more important than conversion. That phrase has a vaguely religious ring to it, and I suppose it’s also true in a theological sense. But right now I’m talking about reporting of survival rates (how many people who enter a nurture program actually end up as customers) vs. conversion rates (how many people move from one program stage to the next). Marketers tend to focus on conversion rates, and of course it’s true that the survival rate is mathematically the product of the individual conversion rates. But we repeatedly saw changes in program structure or even individual treatments that caused large swings in a single conversion rate, which was often balanced by opposite changes in the following stage. Looking at conversion rates in isolation, it was hard to see those patterns.  This was an even bigger problem when each rates was calculated cumulatively, so the impact of a specific change was masked by being merged into a larger average. More important, even when there was an obviously related change in two successive rates, the net combined impact wasn’t self-evident. This is where survival rates come in, since they directly report the cumulative result of all preceding stages. Of course, conversion rates and survival rates are both useful: I'm arguing you need to report them both, not just conversion rates alone.

- throughput matters. Survival and conversion rates show the shape of the funnel, but not the dimension of time. We did report how long it took contacts to move through our programs – in fact, a sophisticated and detailed approach was in place before I arrived – but the information was largely ignored. That was a pity, because it contained some important insights about contact behaviors, opportunities for improvement, and results of particular tests. A greater focus on comparing expected vs. actual results would have helped, since calculating the expectations would have probably required a closer focus on how long it took leads to move through the funnel.

- acceleration is hard. A greater focus on timing would have also forced a harder look at the fundamental premise of many B2B campaigns, which is that they can speed movement of prospects through the sales funnel. The more I think about this, the more doubts I have: B2B purchases move according to their own internal rhythms, driven by things like budget cycles, contract expirations, and management changes. Nurture programs can educate potential buyers and build a favorable attitude towards the seller, thereby increasing the likelihood of making a sale once the buyer is ready. They can also track, through lead scoring, when a buyer seems ready to act and is thus ripe for contact by sales. That’s all good and valuable and should more than justify the nurture program’s existence. But expectations of acceleration are dangerous because they may not be met, and could unfairly make a successful program look like a failure.

- drip needs attention.  Like that leaky faucet you never quite get around to fixing, drip programs often don't get the attention they deserve.  In practice, the vast majority of people who enter a nurture program will not move quickly to the purchase stage; most will stall somewhere along the way. This is where the drip program must work hard to keep them engaged. Again, every marketer knows this, but it’s easy to focus attention on the fascinating and complicated stage progressions (remember all that content?) and relegate the drip campaigns to a simple newsletter. Big mistake. Put as much effort into segmenting your drip communications and encouraging response as you put into stage conversions. If you want a practical reason for this, look at your mail quantities: chances are, you’re actually sending more drip emails than all your active stages combined.

- proving value is the ultimate challenge. It’s relatively easy to track contacts as they move through the marketing funnel, but it’s much harder to connect them to actual revenue in the sales or accounting systems. I whined about this at length in June, so I won’t repeat the discussion. Suffice it to say that some sort of revenue measurement, however imperfect, is necessary for your testing, reporting, and program execution to be complete.

Whew, it’s good to have all that out of my system. As I said at the beginning, I did enjoy my little visit to the marketing trenches. Now, it’s goodbye to that world and hello to what’s next.

HubSpot's Latest Marketing Software Sends the Right Message

Poor targeting

HubSpot yesterday launched a “completely re-envisioned and rebuilt” version of its marketing system at its Inbound 2012 user conference. The main thrust of the new release was dynamic customization of emails, landing pages, and forms based on each contact’s profile and behaviors. This is a major expansion beyond HubSpot’s original focus on “inbound marketing” to attract leads.

Specific changes include:

- a new contact database that is much more flexible than the original HubSpot database, allowing access to all types of email and landing page interactions within HubSpot and to social media activities imported to the system. The new database is built with HBase, which accesses Hadoop files. More on that later.

- “smart lists”, which are rule-based definitions of contact groups whose membership is updated automatically as contact data changes. Apologies if that’s a bit jargony; it just means the lists are always current.

- “smart calls to action” which are dynamic content blocks driven by the smart lists. That is, users define which contents go to members of different lists. The blocks are stored in a library and the same block can appear within multiple emails, HubSpot landing pages, or external Web pages.  In practical terms, this means things like: users who have already downloaded one piece of content can automatically be offered something else.

- “smart forms” (do you sense a pattern?) which don't repeat questions a client has already answered. This isn’t quite true progressive profiling, which would replace questions that are answered with ones that are not.  But it removes a major annoyance.

- workflows (hah! Bet you expected “smart flows”) that are triggered by smart list-style rules and can include multiple steps with multiple actions assigned to each step. Available actions include changing contact data, sending a record to CRM, updating a lead score, setting a lifecycle stage, and changing the call to action.

- social media tracking that captures responses to system-generated social media messages within the contact database.  The responses are associated with specific individuals, so they can be used in smart lists and workflow rules.

- iPhone apps to view some reports and individual contact data

This is all good stuff, although far from revolutionary. Dynamic email content, for example, is available in 14 of the 22 systems in our VEST report. HubSpot recognizes that these are not new features but argues they’ve made them easier to use than competitors. I’m not so sure – the rule builder underlying the smart lists and workflows looks pretty much like every other rule builder, and the workflows themselves are also similar to the sequential flows in other systems.

This isn’t a criticism of HubSpot, but just a recognition that these are inherently complicated features which plenty of smart people have already tried to simplify. Radically better approaches may yet be found – I had an interesting chat about some possibilities with HubSpot co-founder Dharmesh Shah – but so far, the state of the art is what it is.

The new release also includes improved email and landing page designers, A/B testing for landing pages (not available in the entry-level version of the system, alas), enhancements to the app and service marketplaces, and expanded training services. The company said the coming year will bring enhancements to existing components including the blogging and search engine optimization applications.

What’s really important about all these changes is not whether they’re unique, but how well HubSpot has pulled them together and how it teaches its clients and resellers to use them. This is where HubSpot has always been strongest, and the vision it set out this week – of highly relevant marketing messages for each individual – is indeed advanced. (It’s also one I agree with – see this post on why the marketing funnel is dead.) If HubSpot can get marketers to focus on that sort of targeting, which is quite different from traditional campaign-oriented promotions, they can indeed have a revolutionary impact on their clients and the marketing industry.

And what about HBase? Although HubSpot didn’t talk about it in its marketing materials, switching from a conventional relational database to the Hadoop-based system is almost certainly the most radical feature of the new release. So far as I know, HubSpot is the only marketing automation system using HBase.

I discussed this a bit with HubSpot Chief Product Officer David Cancel, who joined the company when it acquired Performable, which was itself built on HBase. Cancel said HBase takes more resources than a conventional database engine but provides direct access to all details of each contact’s behavior history. One immediate benefit is that HubSpot now allows custom fields – up to 1,000, in fact – which it didn’t previously.  Ad hoc reports against the HBase data isn't available yet but is due before the end of 2012.

Longer term, I suspect HBase will make it easier to add custom objects and to deal with unstructured and semi-structured data such as Web logs and text comments. This could make HubSpot fundamentally more flexible than most B2B marketing automation systems, whose data structures are tightly linked to CRM data structures. As I mentioned last week, the main exceptions to that rule today are the high-end marketing automation products, which were built for consumer marketing applications and assume a custom data structure. Having that flexibility in product for small-to-mid-size businesses could open up some possibilities that truly do make HubSpot unique.

(Wondering about the alligator man picture?  Well, one of the sessions at the HubSpot conference said that having pictures in your blog posts increases readership, so I thought I'd give it a try.  If you want me to justify that particular image: she's getting a message she doesn't want.)

Raab Report: Act-On, Eloqua, Pardot, and Marketo Vie to Lead in Mid-Size B2B Marketing Automation Segment

Today I’ll present the third and (mercifully?) final installment in my series of posts on leaders in the different B2B marketing automation sectors, as determined by the ratings in our VEST report. I’ve saved the best for last, in the sense that the small to mid-size sector is the heart of the industry and its most complicated arena.

We define small to mid-size business as companies with $5 million to $500 million revenue. This covers a broad range of marketing users with widely varied needs. Most require the full set of marketing automation functions but apply these in simple ways. They have one to fifteen marketing automation users. This sector generates nearly 60% of 2012 revenue ($200 million) from 33% of the installations (9,400 as of mid-2012). The VEST report provides separate client counts for small business ($5 million to $20 million revenue) and mid-size business ($20 million to $500 million). These account for 16% and 41% of revenue and 16% and 17% of installations, respectively. Although small businesses generally buy lower-priced systems, they have largely the same requirements as mid-size companies.

The leaders quadrant in this sector is quite crowded, with Act-On, Eloqua, Pardot, and Marketo all jostling for position. Silverpop, Neolane, and Genius are all lurking nearby. In case you haven’t caught on to my color coding, blue type indicates that Eloqua and Neolane are leaders in the large company segment, while red type shows the others have their strongest position in this sector.

The variety of users within this segment is reflected by the differences among the leaders. Act-On, Pardot, and Genius specialize in smaller companies than Marketo or Silverpop, which in turn serve generally smaller clients than Eloqua or Neolane.  Act-On’s position on top of the product fit range is a bit misleading: when you look at the components of that score (see below; this comparison chart is another VEST feature), the vendors are all very close.  In fact, the only category where Act-On scores higher than everyone else is pricing.

This isn’t at all to say that the products are equivalent.  Rather, it means they each have different strengths and weaknesses that balance each other out when measured with generic scoring weights. For actual buyers with clear priorities, the difference among these vendors’ scores will almost always be much larger.


As with the other sector charts, the vendors in the upper left are also worth considering: they have strong product fit but relatively low market position.  SalesFusion appears here as it did in the micro- and large-business charts: what can I say, they have rich features at a good price. (And, no, they’re not my client.) eTrigue is the other noteworthy contender; it and LeadFormix are both close to the leader quadrant based on their vendor fit.

If there’s any one lesson from all these charts, it’s that picking the “leading” vendor is no guarantee of making a good choice. Our three sets of weights yield different sets of leaders, and even those vendors have different strengths and weaknesses. I’ve said it a million times but I’ll say it again: there’s no substitute for understanding your own needs and finding out which vendors match them best.

Raab Report: Neolane, Aprimo, and Eloqua Rate Highest for Large Company B2B Marketing Automation

Tuesday’s post looked at the micro-business sector leaders according to our VEST report and gave a bit of background on how the ratings are created.  Today let's take a look at the same diagram for large businesses, which we define as companies with $500 million revenue or more.

These companies have large marketing departments that may manage hundreds of campaigns for different products in different locations. Our scoring reflects their need for special features for automated content selection, project management, complex lead scores, and tight control over the rights granted to individual users. This group had about 1,400 clients in mid-2012, generating an estimated $85 million in revenue. This is 5% of industry installations and 25% of industry revenue. Many of these were small departmental implementations; there are probably fewer than 500 true enterprise-wide deployments. The non-specialist vendors such as IBM Unica and SAS, are not included in these figures but also have significant revenue in this segment.

As before, vendors closer to the top have the most appropriate features for this segment, and those further to the right have the most similar customer base and company resources. The chart shows Neolane and Aprimo (owned by Teradata) as the clear leaders, with Eloqua also very strong.  Marketo and Oracle (specifically, Oracle CRM On Demand Marketing) are considerably further back in the leader quadrant.

It’s important to recognize that Neolane and Aprimo are fundamentally different from the others. Both are general purpose marketing automation systems that serve large numbers of B2C as well as B2B clients. The clearest technical distinction is the marketing database: Neolane and Aprimo are designed to connect with custom-built, external marketing databases, whereas B2B marketing automation products like Eloqua, Marketo, and Oracle are based on an integrated database using a CRM data model (usually Salesforce.com, although Oracle is tied to Oracle's own CRM). This doesn’t mean that every client actually connects them to CRM system. But it does mean that the standard data models match the CRM data models and, in many cases, that abilities to expand the data model with custom tables are limited.  One reason that Neolane and Aprimo rank so high in this sector is, precisely, that large businesses often want more database flexibility than the CRM-based approach allows.

As with Tuesday’s chart, the other important place to look on the chart is the upper left, which captures companies that have suitable features for this segment but are too small to rate as leaders. SalesFusion (which also ranked highly in the micro business segment; a good trick) and TreeHouse Interactive stand out in that region. So does MarketingPilot, a newcomer to the VEST that is more like Neolane and Aprimo in serving a mix of B2C and B2C clients. See my 2011 MarketingPilot review for more details, bearing in mind that they’ve added capabilities since then.

Raab Report: OfficeAutoPilot, Infusionsoft and HubSpot Rate Highest in Marketing Automation for Very Small Business

One of the most important features of our VEST report on B2B marketing automation systems is that it divides marketing automation users into distinct segments, each having a different set of needs. This matters because the systems all do roughly the same things, making it hard for inexperienced buyers to tell them apart. Many vendors – especially those who target the middle sector – also try to serve all types of companies, adding to the confusion. Where the vendors differ is in the details of how they implement the common features, applying approaches that are generally best suited to one type of marketing organization.

The VEST segmentation is based on company size, as measured by revenue. I'm painfully aware that this isn’t the ideal way to group users, since companies of the same size can still vary greatly in their needs and marketing sophistication. But revenue is objectively measureable and most marketing automation vendors can provide reasonably accurate client counts by revenue group. So we use it as a proxy for the other client differences.

The primary way we report on the different segments is by applying different weights to the same feature in our vendor scoring for each segment. This lets us rank vendors based on how their features and company strengths match against each sector’s needs. A key part of the approach is to penalize vendors with negative weights for features that are too advanced for a particular customer group. So far as I know, no other analysts do this in their scoring.  It avoids a common problem with scoring systems, that systems with the most features always win.

The chart above shows our ratings for the micro business sector, defined as companies with under $5 million in revenue. These are very small companies, typically run personally by an owner. They rarely have a full-time professional marketer on staff. Primary marketing interests are group emails, landing pages, and simple lead nurturing through email auto-responders. Before marketing automation, they typically use an email system (which also provides landing pages and simple nurture campaigns) or sales automation product for their marketing. They often do not integrate marketing automation with a separate sales automation system, either because they don’t use one at all or because they rely on CRM features within marketing automation itself. As of July 2012, marketing automation vendors reported more than 17,000 micro-business installations, just over 60% of the industry total. But, because prices are lower than other segments, the segment generates only an estimated 18% of industry revenue, or $65 million for full-year 2012.

Companies in this sector have very limited marketing and technical resources. As a result, their overriding needs are ease of use and a broad range of features within a single product. What they don’t need are very complex campaigns, extensive planning and budgeting, and custom database  designs. Our scoring reflects those priorities.

As the chart shows, the leaders in this segment are OfficeAutoPilot, Infusionsoft, and HubSpot. The first two are micro-business specialists; in particular, they have built-in CRM and order processing. HubSpot isn’t quite as highly tailored to this segment, which is why it is a little further from the top than the other two. (The vertical dimension is product fit, which basically means features.) But HubSpot has a very large number of clients in this segment, so it is still quite far to the right. (The horizontal dimension is vendor fit, a combination of customer count, segment concentration, and vendor resources.) Act-On and Marketo also have strong positions in this sector, even though their features – especially in Marketo’s case – are not necessarily the best fit. Again, bear in mind that revenue is a very crude segmentation, so many Act-On and Marketo clients in this group probably have requirements closer to those I’ve assigned to the middle tier.

The other important set of vendors are those at the upper left of the chart: companies with a strong feature fit even though they are smaller than the leaders. SalesFUSION and MakesBridge stand out especially in this group for micro-business users. Oracle’s presence is, frankly, pretty odd: it’s due to a low per seat price and the vendor’s position that it has a built-in CRM module. In fact, nine of the 22 vendors say they provide a CRM option, which may be technically correct but in most cases probably isn’t realistic. This is even more proof – as if it were needed – that buyers need to explore the products in detail before making a purchase.