Optimove Helps Optimize Customer Retention (And, Yes, It's a Customer Data Platform)

As I wrote last week, it sometimes seems that every system I look at these days is a Customer Data Platform. Of course, this is partly because I’m choosing to look at that type of system, and partly because CDP vendors are reaching out to me. But I do believe another reason is that CDPs are an idea whose time has come: I’ve recently seen at least three CDPs that are just emerging from stealth or beta mode. All were developed because someone else recognized the huge unmet need for getting  better customer data to marketers.

One of the vendors that contacted me was Optimove, a Tel Aviv-based firm that calls itself a “retention automation platform” but definitely fits the CDP criteria. This means that Optimove is a marketer-controlled system that loads data from multiple source systems, puts it in a marketing-friendly format, and makes it available to external marketing execution systems.

Like many CPDs, Optimove also includes a campaign engine that pushes specific marketing actions to the external systems. Optimove’s approach is unusual in basing its campaign interface on a calendar that lays out the campaign schedule for each user-defined customer segment. This makes it easier for marketers to build a comprehensive contact strategy from multiple campaigns.

The campaigns themselves each have their own schedule, allowing them to run once, daily, weekly, or monthly. Users can also limit the number of messages sent to each customer by assigning an exclusion period to each campaign. Other campaigns can be instructed to respect or ignore these exclusion periods, ensuring that high priority messages are delivered in all circumstances. Each campaign triggers a single action, which can be directed to email, banner ads, direct mail, Facebook custom audiences, in-app pop-ups, SMS, app message boards, call centers, or other channels. The connections may be through file transfers or APIs.

Optimove's campaign interface is unusual, but the system is even more unusual in taking performance measurement very seriously. Its standard campaign setup requires users to assign a success measure and to either set aside a control group or set up a multi-way split of alternative treatments. This enables standard reports, including the campaign calendar itself, to show the incremental value provided by each campaign – the critical information needed for long-term optimization. By contrast, most marketing systems make success targets and testing optional if they support them at all. Users can also see a history of all campaign results for a given segment, making it even easier to identify the most productive programs.

The campaign segments themselves, which Optimove calls target groups, are built by accessing data that Optimove has loaded from the client’s data warehouse and operational systems. Optimove has standard data models for different industries, reflecting its current customer base: online gaming (bingo, casinos, poker, sports betting, etc.), foreign exchange trading, and ecommerce. The system assumes the data has already been coded with customer IDs, which something that makes reasonable sense given the focus on retention rather than acquisition. 

Data is typically loaded daily or weekly. After each load, customers are assigned to life stages (typically, new customers, active customers, and churned customers) and to multiple segments based on behaviors and attributes, such as location, product preferences, and spending levels. The system then uses the life stages and segment attributes to assign customers to "microsegments" that cluster analysis has found will behave similarly. It’s important to understand microsegments represent a current customer state that will change over time: that is, each customer belongs to different microsegments at different stages in her life cycle.

Optimove calculates the probability of moving from one microsegment to the next and uses this to predict how a given group of customers will behave in the future.  This is the basis for its lifetime value and churn predictions – key metrics in system reports. This type of forecasting is something else that really should be done by every marketing system, but rarely is. Optimove also provides cohort analysis reports, comparing performance of customers who joined during different time periods. This is yet another important type of information that is not always available.

Optimove does have some limitations. I was surprised there are no standard reports to highlight attributes that separate responders from non-responders within a promotion audience: this is pretty common information that helps marketers to refine their segmentations and better understand what is driving results. Nor does the system current recommend the best action to take with individual or a group. Both features are being worked on for future release.

Optimove was founded in 2009 and currently has about 70 clients, mostly in Europe. It has a few U.S. customers and is looking to expand in this market. Pricing is usually based on the number of customers and begins around $2,500 per month.

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Marketing Automation News from Dreamforce: B2B More Integrated, B2C Stays Separate

I spent the early part of this week at Salesforce.com’s annual Dreamforce conference. Here are my observations.

The big news was for geeks. The main theme of the conference was Salesforce1, a new set of technologies that make it vastly easier to deliver and integrate mobile versions of Salesforce-based applications. It is apparently a major technical accomplishment and at least one of my technical friends was hugely impressed. But I can’t say I personally found it all that exciting. Perhaps we’ve reached the point where we expect technology to do pretty much everything, so the line between what's already available and what's new is only visible to experts.  Any way you slice it, focusing on platform technology is much less exciting than last year's vision of "social enterprise".

The bad news was for B2B marketing automation. Conference presentations confirmed that Pardot, the B2B marketing automation system that Salesforce acquired as part of its ExactTarget acquisition, has been separated from the rest of ExactTarget and made part of the Sales cloud. There, Pardot is described only as providing lead scoring and nurture programs, which ignores landing pages, behavior tracking, and other features that B2B marketing automation usually provides (and Pardot includes). In terms of infrastructure, Pardot will eventually work directly from the CRM data objects, rather than maintaining its own synchronized database. (Data outside the CRM structure, such as detailed Web behaviors, will remain separate.)

What this means is that Salesforce sees B2B marketing automation as just an appendage of sales automation.  This is pretty much the same constricted view of marketing automation that Salesforce management has held all along.  The logical consequence is to make lead scoring and nurture campaigns standard features within the Sales offering and discard Pardot as a separate product.  I should stress that no one at Salesforce said this was their plan, but it seems inevitable. If and when that does happen, only the most demanding companies will purchase a separate B2B marketing automation product.

To put a more optimistic spin on the same news: Salesforce will continue to let independent B2B marketing automation apps synch with Sales.  If Salesforce does merge Pardot features into its core Sales product, then marketers who have a more expansive view of B2B marketing automation functions (or who simply want a system of their own) will be forced to buy from someone else.

The interesting news was that B2C marketing automation remains separate. Salesforce’s list of business groups includes the Sales Cloud, Service Cloud, and ExactTarget Marketing Cloud. Did you notice that just one of these has its own brand? As this suggests, and conference presentations confirm, Salesforce has kept B2C marketing distinct from its Sales and Service businesses, most importantly at the data and platform levels. The ExactTarget Marketing Cloud does now include Salesforce’s previously-purchased social marketing components, Radian6 social monitoring and Social.com social advertising. It also includes the iGoDigital predictive personalization technology that came along with the ExactTarget acquisition.

Salesforce did announce some plans to integrate the Marketing cloud with Sales and Service, but they are pretty much arm’s length: Marketing can receive alerts about changes in Sales (and I assume Service) data, even though that data remains separate; Sales and Service can send emails through the ExactTarget engine; Sales and Service can receive content recommendations from the Marketing predictive modeling tool. As near as I can tell, this is the same type of API-level integration available with any third-party system. For what it’s worth, the ExactTarget Marketing Cloud APIs are also part of Salesforce1, but don’t confuse that with sharing the same underlying platform.They don't.

The good news is the B2C marketing vision. It’s not really surprising that Salesforce kept its B2C platform separate, since Salesforce's core technology isn’t engineered for the massive data volumes and analytical processing needed for B2C in general and consumer Web marketing in particular. Happily, this technical necessity is accompanied by what strikes me as a sound vision for customer management.  ExactTarget framed this around three goals: single view of the customer; managing the customer journey; and personalized content across all channels and devices. It described major features for each of these: a unified metadata layer to access (and optionally import) data from all sources; a “customer journey” engine to manage multi-step, branching flows; and predictive modeling to select the best offers and contents across email and Web messages.

This felt like a more coherent approach than Salesforce described for the Sales cloud, where external data and predictive modeling in particular were barely mentioned (or, more precisely, are still being left to App Exchange partners). The ExactTarget cloud still lacks tools to associate customer identities across email, phone, postal, social, and other systems, although there are plenty of partners to provide them. I didn’t get a close look at the details of the ExactTarget functions, which will really determine how well it competes with other customer management platforms. But the general approach makes sense.

News of the revolution may be exaggerated. Salesforce argued during the AppExchange Partner keynote that the AppExchange and Salesforce platform have created a “golden age of enterprise apps” by enabling small software developers to sell to big enterprises. One part of the argument is that the platform itself lets small vendors break through the credibility and scalability barriers that have historically protected large enterprise software vendors. The other is that end-users can purchase and deploy apps without involving the traditional gatekeepers in enterprise IT departments. A corollary to this is that end-users have different priorities than IT buyers – in particular, end users care more about ease of use – so successful software will be different.

Of course, this is exactly what the AppExchange partners wanted to hear and exactly the strategy behind Salesforce’s platform approach in the first place. But that doesn’t necessarily make it untrue: and, if correct, it would indeed be a revolution in the enterprise software industry.

But some revolutions are bigger than others.  Even in an app-based world, individual users won't be making personal decisions about how to run core business processes.  Rather, systems will be chosen at the department level because companies can more or less safely assume that whatever the department chooses will integrate smoothly with the corporate backbone. That's certainly a change but bear in mind that departmental buyers will have the same preference as corporate IT groups for working with the smallest possible number of vendors. This means there will still be the familiar tendency for individual vendors to add more functions over time. So industry dynamics may change less than you’d expect.

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ReachLocal Provides Turn-Key Lead Management for Small Business

There are about 3 million companies with revenue between $1 million and $5 million in the U.S., according to Manta. This is an enticingly huge market for marketing automation vendors, and one that seems largely untapped. The largest marketing automation vendor in the segment, Infusionsoft, has under 20,000 clients. This is barely scratching the surface.

But this perspective is misleading. Many small businesses do their marketing through CRM, email, and search advertising. Search marketing is particularly important as online searches replace newspapers and telephone directories. Companies that provide small businesses with online directories and ratings, search engine optimization, Web sites, and paid search marketing all have client bases that dwarf the small business marketing automation industry.

Those other vendors could easily see marketing automation as a natural line extension, since it would help their clients make better use of the traffic those vendors generate. Last month ReachLocal – a $450 million public company that purchases online ads for more than 23,000 local businesses -- moved in exactly this direction.

ReachLocal’s new service, called ReachEdge, provides clients with a custom Web site, contact database, automated email streams to leads and customers, and automated alerts to company staff.  All the Web and advertising design is done for the client. There’s no automated lead scoring or branching campaign flows: when a new lead enters the system via a Web form or phone call, the user receives an alert, reviews whatever information was provided on the form or voice mail message, and manually classifies the lead as active, long term, new customer, or existing customer. Each category kicks off its own stream of messages (to the leads) and alerts (to company users), which can be spaced over time. Messages are sent by email; alerts can be sent by text, email, or a mobile app. Users can enter notes, add tags, and record revenue on contact records, providing a very light CRM option, or they can manually export the contact list to an external CRM system. Revenue can be used in campaign Return on Investment reports.

And that’s it, features-wise. If you’re used to looking at all-in-one small business marketing automation systems like Infusionsoft, Ontraport, or Venntive, the list may seem laughably primitive. But it’s a safe bet that many ReachLocal advertising clients have no interest in anything more complicated. The stumbling block facing all of marketing automation – that it takes more training, skills, and effort than most potential users can invest – is higher for very small businesses than anyone else. ReachLocal has reduced its clients' preparation to a minimum, and then left it up to them to pursue each new lead individually.

When a vendor does this much of the work, the key questions are less about the system than quality of the marketing.  ReachLocal said that each Web site is custom designed, based on interviews with each client by U.S.-based industry specialists. I looked at a samples for three different plumbers (here, here, and here) and found they were indeed different and detailed enough to be effective. I’ll assume that advertising and email are similar. ReachLocal’s service includes one hour of customization per month and a completely new Web site every two years. The price is $299 per month, which is comparable to low-end marketing automation systems although higher than simple auto-responders.

Let me be clear: ReachEdge doesn’t provide the process automation or even email segmentation of a conventional marketing automation system, let alone serious CRM, ecommerce, or external integration. So small businesses that want to market aggressively will probably find it insufficient. But small businesses that just want to generate a stream of new leads while they focus their energies elsewhere may well find ReachEdge an appealing alternative.

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Marketing Automation User Satisfaction: Clearly, There's Room for Improvement (and maybe a little vodka)

Last week’s post on marketing automation and its discontents prompted several questions about whether the level of dissatisfaction is any higher with marketing automation than other systems. To some extent, this is asking whether the glass is half empty or half full; and, as the illustration suggests, the answer matters less than the fact that there’s room for improvement. But I do have some data to share on the question of relative dissatisfaction.

The first insights come from G2 Crowd, a research firm that ranks software based on user ratings and social data. I have my doubts about comparing software this way* but users certainly know whether or not they're happy.  The folks at G2 were kind enough to reformat some of their data for me.**


According to the G2 figures, marketing automation users are in fact more enthusiastic about their choices than almost anyone else. CRM in particular has a vastly worse rating, but even email, Web analytics, and Web content management show more detractors and fewer promoters. I’m not sure how to interpret this – is the average marketing automation system really easier and better than those other types of software?  Or is something else going on: maybe satisfaction is lowest in the most mature categories, like human resources, enterprise resource management, and accounting, because experienced users are the most demanding?

A second set of insights comes from Ascend2 and Research Partners, which asked its panel which inbound marketing tactics they considered most effective and most difficult to execute. Here we see a very different story: marketing automation and lead nurturing (listed separately) are clear outliers in a bad way: among the less effective tactics and the hardest to execute. In fact, they are the only two tactics where the difficulty score was significantly higher than the effectiveness score (i.e., above the diagonal line in the chart below).***

The Ascend2 study also found that 18% of respondents used marketing automation extensively, while 43% made limited use of it, and 39% didn’t use at all. This is similar to the BtoB study I cited last week, which found that just 26% of marketing automation users had fully adopted their system.  I believe those effectiveness vs. difficulty ratings hint at the reason for those results: most marketers don’t fully deploy marketing automation because they find it too much work compared with the benefit they’d gain. In other words, the hurdle to marketing automation adoption is not laziness, but a rational evaluation of the return from investments in marketing automation vs. other activities.

That rational judgment could still be wrong.  After all, marketers who haven’t fully deployed marketing automation don’t know how effective it really is. Ascend2 addressed this by asking marketers to rate their performance and comparing answers of the 12% self-rated “very successful” with the 20% who rated themselves “not successful”.

Those answers contain some positive news: of the very successful group, 45% were extensive users of marketing automation, compared with just 9% of the not successful.

But even the very successful marketers gave marketing automation only the fifth-highest effectiveness rating, which doesn’t differ much from the sixth-highest rating in the not successful group.

Similarly, the very successful marketers rated marketing automation as sixth most difficult (actually, tied for fifth) while the not successful marketers ranked it as fourth-hardest. In other words, marketing automation is indeed a bit easier than it seems before you start, but even the most experienced and most successful marketing automation users consider it pretty darn hard and just modestly effective.

So what we have here is a mixed message: marketing automation does correlate with success and its users might even be relatively satisfied, but it's still a lot of work for limited results.  You read that as good news or bad, but, either way, it shows the need for more work before marketing automation can reach its full potential.


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* My basic objection is that users have different needs, so a system that satisfies one user may not be good for another.

** G2’s explanation: “The data for this chart comes from the over 7,400 enterprise software surveys users have completed on G2 Crowd as of Friday 10/18/13. For every product review we ask "How likely is it that you would recommend this product to a friend or colleague?" on a 0-10 scale. We segment reviewers that rate a product 9-10 as Promoters, 7-8 as Passives, and 0-6 as Detractors. The product segmentation data is aggregated to determine Net Promoter Score at a category level.”

***It's barely possible that the answers would be different if the Ascend2 study had asked about marketing in general rather than "inbound marketing purposes".  But I doubt it.

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idio Does Sophisticated Content Recommendation

Systems in our new Guide to Customer Data Platforms range from B2B data enhancement to campaign managers to audience platforms. This may lead you to wonder whether there’s anything we actually left out.  In fact, there was: although the final choices were admittedly a bit subjective, I tried to ensure the report only included systems that met specific critieria including a persistent database, customer-level data, marketer control, and marketing-related outputs to external systems. In most cases, I could judge whether a system fit before doing a lot of detailed research. But a few systems were so close to the border that I only made the final call after I had evaluated them in depth.

idio was one of those. The company positions itself as a tool to deliver “personalized and relevant multi-channel communications”, which sure sounds like a CDP.  Indeed, it meets almost all the critieria listed above, including the most important one of building and maintaining a persistent customer database. But I ultimately excluded idio because it is tightly focused on identifying the content that customers are most likely to select, a function I felt was too narrow for a proper CDP. The folks at idio didn’t necessarily agree with this judgment, and pointed to planned developments that could indeed change the verdict (more about that later).  But, for now, let’s not worry about CDPs and take idio on its own terms.

The full description on idio's home page reads “idio understands your customer’s interests and intent through the content they consume and uses this to deliver personalized and relevant multi-channel communications” and that pretty much says it all. What idio does is ingest content – typically from a publisher such as ESPN, Virgin Media, Guardian Media, or eConsultancy (all clients) – but also from brands with large content stores such as Diageo, Unilever, and C Spire (also all clients). It uses advanced natural language processing to extract entities and concepts from this content, classifying it with the vendor’s own 23 million item taxonomy.

The system then monitors the content selected by its clients’ customers in emails, Web pages, mobile platforms, and some social platforms and builds an interest profile for each customer.  This in turn lets the system recommend which existing content the customer is most likely to select next. The recommendations are typically fed back to execution systems, such as email generators or Web content managers, which insert links to the recommended content into Web pages, emails, or newsletters.  Reports show selection rates by content, segment, or campaign, and can also show the most common topics published and the most commonly selected. Pricing is based on recommendation volume and starts around $60,000 per year for ten million recommendations.

Describing idio’s basic functions makes it sound similar to other recommendation systems, which doesn’t really do it justice. What sets idio apart are the details and technology.

• Content can include ads, offers and products as well as conventional articles.
• The natural language system classifies content without users tagging each item, a huge labor savings where massive volumes are involved, and can handle most European languages.
• idio's largest client ingests more than 1,000 items per day and stores more than one million items, a scale far beyond the reach of systems designed to choose among a couple hundred offers or products.
• Interest profiles take into account the recency of each selection and give different weights to different types of selections – e.g., more weight to sharing something than just reading it.
• Users can apply rules that limit the set of contents available in a particular situation.
• The system returns recommendations in under 50 milliseconds, which is fast enough to support online advertising selection.
• It stores customer data in a schema-less system that can make any type of input available for segmentation and reporting, although not to help with recommendations.
• It can build a master list of identifiers for each individual, allowing systems to submit any identifier and access a unified customer profile.
• It can return a content abstract, full text, images, or HTML, or simply a pointer to content stored elsewhere.
• It captures responses directly as the content is presented.

Most of these capabilities are exceptional and the combination is almost surely unique. The ultimate goal is to increase engagement by offering content people want, and idio reports it has doubled or even quadrupled selection rates vs. previous choices. All this explains why a small company whose product launched in 2011 has already landed so many large enterprises among its dozen or so clients.

Impressive as it is, I don’t see idio as a CDP because it is primarily limited to interest profiles and  content recommendations. What might yet change my mind is idio’s plan to go beyond recommending content based on likelihood of response, to recommending content based on its impact on reaching future goals such as making a purchase. The vendor promises such goal-driven recommendations in about six months.

Idio is also working on predicting future interests, based on behavior patterns of previous customers.  For example, someone buying a home might start by researching schools, then switch to real estate listings, then to mortgages, then moving companies, and so on. Those predictions could be useful in their own right and also feed predictions of future value, which could support conventional lead scoring applications. Once those features become available, idio may well be of interest to buyers well beyond its current customer base and would probably be flexible enough to serve as as Customer Data Platform.

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Customer Data Platform Guide Reviews Tools to Build Marketing Databases

Raab Associates’ new Guide to Customer Data Platforms is now available (click here to buy).

You may not find that news to be fall-off-your-chair exciting. In fact, you’re more likely to wonder whether the world needs yet another report on anything at all. Fair enough. So before telling you what’s in the CDP Guide, I'll tell you why it exists.

Simply put, marketers need better databases. If you’re a working marketer, you almost surely know this from personal experience. But someone who only read industry news and vendor promotions might think all anyone had to do was to plug in the latest cool application and it would immediately be filled with fresh clean data like water from a tap. Dirty big data is our industry’s dirty little secret.


The problem isn’t new but it is getting worse. As customers interact across more channels, marketers need to not just meet them in every new location but recognize them and carry on a continuous conversation from one touchpoint to the next. Marketers can also become more effective by enriching that conversation with information from external sources such as Web pages, social media, and commercial databases. Both the carrot of better results and the stick of customer expectations are ever-more-urgently driving marketers towards building better databases.

The good news is that plenty of smart vendors have also recognized this need and are trying to help marketers on their journey. I call their systems Customer Data Platforms and define them as “a marketer-controlled system that supports external marketing execution based on persistent, cross-channel customer data.”

If there’s one absolutely critical point in that definition, it’s that CDPs put marketers in charge of building their own database. Taking control is the only way that marketers will ever get the databases they desperately need. It’s why CDPs are so important.

But too few marketers know who the CDP vendors are, what they do, and how they differ. The Guide to Customer Data Platforms is designed to provide this information. If the CDP vendors are tour guides on the path to better data, the CDP Guide is the reviews you read to decide which one you’ll hire. As far as we know, no other study serves this purpose.

Given its goal, the heart of the Guide is the vendor profiles: three to five pages on each vendor, describing capabilities for data management, predictive modeling, marketing campaigns, and message delivery, plus background on the vendor’s technology, clients, company history, and pricing. You’ll want to read those closely when you’re selecting a vendor. But first you’ll have to decide whether a Customer Data Platform is something to consider. Here is some information to help make that judgment.

- CDPs are something new. CDPs are systems that help marketers build and update customer databases, and make those databases available to support marketing programs. That may not sound very new, but most B2B marketing automation products today build very limited databases while most B2C marketing automation products rely entirely on an external data warehouse. The systems that do build databases are designed to be used by IT departments, not marketers. And many CDPs provide predictive modeling or best-treatment recommendations that go well beyond the storage functions of a basic data warehouse.

- You still can’t do this at home. CDPs may be tools for marketers, but that doesn’t mean that marketers build the databases themselves. Rather, CDP vendors provide services that build the database with varying degrees of marketer involvement. The difference is that the marketers work directly with the CDP vendors, instead of relying on IT staff that often has other priorities and an imperfect understanding of marketing needs. This makes it much easier and quicker for marketers to get the database they need.

- CDPs are an outgrowth of existing system types. Most CDP systems were created for a purpose that happened to require the same database-building capabilities as a CDP. These purposes fall into three groups which I discussed in last week’s post, so I won’t repeat them here. They’re work understanding because vendors in each group have a different set of skills, one of which will probably come closest to your needs.

- Convergence is coming. Even though the CDP vendors started with different applications, their shared abilities for identity matching, database management, analytics, and integration will allow them to support more of the same functions over time. As marketers understand the value of their databases more clearly, CDP vendors will be able to focus on selling their data platform features rather than applications the platform supports. Of course, once the platforms themselves are common, vendors will climb the value chain by offering better predictive analytics and cross-channel treatment optimization.

- Details count. CDP features may eventually converge, but for now the systems differ in many small ways that make a big difference. To take one example, nearly every CDP creates predictive models. But some can only predict response to specific promotions, based on who has responded before. Others can do the much more sophisticated analysis needed to predict which offer will best advance a long-term goal such as becoming a new customer. And even among those that model against long-term goals, some can actually estimate the incremental impact of a specific offer and others can just see most common correlations. We found similarly subtle differences in how data is collected (via the vendor’s own Web tags or by importing from other systems), the range of data sources (just marketing automation and CRM or those plus many others), natural language processing to extract useful information from text sources such as Web pages, how much history is kept and how it’s used, program execution, and end-user control. The CDP Guide clarifies these distinctions, but it’s still up to marketers to evaluate which differences will matter in their own business.

The CDP Guide itself contains quite a bit of other useful information, including a formal definition of CDPs, detailed explanations of what to look for in a CDP, and a history of marketing databases starting with the Sumerians (don’t worry, I skipped the boring parts). Again, the goal is to provide one package with everything you need to get started along the path of buying a CDP system.  From there, it's up to you.

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How Raab Associates Converted to ZohoCRM In One Weekend: a B2B CRM Success Story

Raab Associates is really two businesses: the technology consulting practice run by Yours Truly, and a marketing agency specializing in children’s books run by my beautiful and brilliant wife Susan. We keep them largely separate, but I am inevitably involved in her technology decisions. So when her ancient Goldmine CRM system finally crashed last week, we both scrambled to pick a replacement.

From my usual lofty perch in enterprise software world, Susan's requirements seem stick-figure simple: accounts, contacts, opportunities, lists, and mass emails. So our first thought was to find a system that offered those plus some cool new things like social media profiling. But a quick scan of the market showed that none of the neat new systems also offered the basic functions with with enough refinement and flexibility to meet Susan's needs.

This pushed us back to the more standard CRM options.  To my dismay, we found ourselves ruling out one after another for various. I even briefly suggested we reconsider Goldmine, an thought that was quickly rejected.  Eventually we took an unhopeful look at ZohoCRM, which I know as a popular small business system but had never considered particularly advanced. Happily, the system has a very thorough online user manual, so I was able to check it out in detail.

Even more happily, the answers all came back positive as I imagined working through Susan’s basic business processes in Zoho. Build contact lists, check. Mass emails, check. Opportunities linked to campaigns, check. Pull-down status list and callback date on opportunities, check. Custom filters across all field types, check. End-user report writer, check. Multi-field search, check. A bunch of other details that I no longer recall, check check check. Reasonable cost, double check: we would have grudgingly paid a couple hundred dollars a month for a solution, but Zoho’s mid-tier Professional edition costs all of $20 per month with no limits on database size (Susan has about 14,000 contact records – well above the minimum for many small business systems). We may even splurge for $35 per month enterprise edition, which provides some advanced automation features but is probably overkill for most small businesses.  Just call me Diamond Jim.

At this point, we were ready to sign up for the free trial account, which was a simple process and didn’t ask for a credit card. Let me point out that I purposely hadn’t signed up sooner because I didn’t want to waste time exploring a system that I wasn’t pretty confident would meet my needs. Diving in too soon is a classic mistake among software buyers – and, in this instance at least, I actually followed my own advice.  (While I'm patting myself on the back, I'll also point out that we evaluated the software against our actual business process, not an arbitrary feature checklist.  That's another best practice that too few buyers follow.)

We now pulled a small set of test records from Goldmine to test the import function. The online manual guided me through the exact steps necessary, complete with a handy checklist of preparatory tasks.  When I went to load the file itself, I got the first of many delightful surprises: Zoho took a guess at mapping the input fields, based on their names, and got about half right. That’s a pretty sophisticated function and a big time-saver. It’s the sort of refinement you don’t see in a new system because it’s not essential to get the product into market, but gets added after enough users request it and the developers have some breathing room. Zoho has actually been around since 1996 (although CRM came later), so they’ve had time to add a lot of those little helpers.

In any event, the test import worked perfectly the first time out, which was a great feeling of accomplishment. Susan and I played with the system a bit more now that we had some real data in it, and found all sorts of nice little options, like being able to rename objects (she calls an opportunity a “pending record”), rearrange the fields on each screen, change the order of sections, and move fields from one section to another.  Again, none of these is cutting edge, but they’re not always available and make a big difference in making the system more usable.  The interface itself was also highly intuitive – lots of nice dragging to move the fields around, for example. There were plenty of other unexpected goodies that I would have otherwise needed to configure or live without, like automatically listing the associated contacts when you view an account record, and listing the associated opportunities – I mean, pending records – when you look at a contact. And, oh yes, you can control which fields are displayed on those related records.

At this point we were feeling pretty good about actually pulling off the conversion, so I spent all day Sunday manually cleansing those 14,000 contact records to ensure the critical data was populated. Even Zoho couldn’t help with that one. I finished around midnight and had a moment of panic when I saw that Zoho would only import 5,000 records at a time.  But it turned out to accept all three batches without waiting for the first batch to finish, so I was able to submit them and get some sleep.

I woke up bright and early (well, actually, late and cranky), feeling pleased that Susan could start using the system without missing a business day.  Alas, we found that somehow there were twice as many account records as expected. A quick call to Zoho support pointed us to a rollback function that should have cleaned up the problem in a few seconds. Sadly, it rolled back one set of records but not the other (remember, there had only been one import).  I spoke again with Zoho support, who promised to look into it but hadn’t accomplished anything several hours later.  At that point, I realized – duh – that it would take about two minutes to delete the records manually (you can only delete 100 at a time, but it’s three keystrokes for each batch, so you can probably do about 50 batches per minute). Once I figured that out, I cleaned out the old records and reimported everything, and we had a clean set of data.

Susan has been working with the system for the past two days, and I’ve been peeking over her shoulder and poking around a bit myself.  ZohoCRM is certainly not perfect – there are bunch of little things she would like to do, such as preview a template-based email with the variables populated. There are also some oddities like two unrelated sets of email templates, a vestige of Zoho's earlier separate systems for CRM and mass mailings. Those quirks take a bit of getting used to but are far from show-stoppers. There are some other tasks that cumbersome at the moment, but I suspect we’ll be able to automate once we have time to explore those functions. And, yes, there are some things it doesn’t do that Susan would like, such as associating multiple email addresses with the same contact. I wouldn’t exactly say they’re trivial – certainly not to Susan – but she can live with them.

We're generally satisfied with customer support: phone calls aren’t always answered immediately, but after about a minute on hold, a very nice lady picks up the line and offers to take a message. I appreciate the human touch, and, more important, the opportunity to get immediate help if something is truly urgent. We do get callbacks in an hour or two and the agents have been pleasant and helpful, which is about all I can ask. There’s a “how’d we do?” email after each interaction, which is a good sign that Zoho is trying to do a good job.

Bottom line: We’re still in the honeymoon period, so I may find Zoho isn’t really as great as I think.  On the other hand, I proposed to Susan almost immediately after meeting her and that's worked out just fine.  So I'd say ZohoCRM is worth a close look for small business CRM, even for people who think it may be too simple for their needs.

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LeadSpace Offers A No-Memory Approach to B2B Lead Scoring

My discussion last week of Infer, Mintigo, and Lattice Engines raised the question of what other B2B data vendors might be considered Customer Data Platforms. It’s easy to exclude companies that provide basic B2B lists (D&B, Data.com, Netprospex, ZoomInfo, etc.) since they’re clearly in a different business. But there’s another set of vendors that look very much like Mintigo, Infer, and Lattice Engines building detailed profiles by extracting data from Web sites, social networks, and other sources. This group includes InsideView, OneSource, SalesLoft and LeadSpace. So far as I know, none of them maintains a permanent copy of a client’s own customer file, which is the essence of being a Customer Data Platform. But if you’re a marketer needing to identify and score B2B prospects, you’d still want to give them a look.

I bring this up because a colleague suggested reconsider classifying LeadSpace as a CDP, which prompted me to learn more about them. Here’s what I found.

- LeadSpace, like the other vendors, scans Web sites, blogs, Twitter feeds, LinkedIn profiles, job hunting sites, and other sources to build a picture of a company’s business, managers, technologies, and similar attributes. Of course, every vendor argues it does this better than anyone else.  I  suspect there are indeed significant differences.  But I haven’t done any testing or seen anyone else’s test results – so all I can say is that wise buyers will test for themselves before making a choice.

- LeadSpace does build lead scores, something its Web site doesn’t reflect. This is one of the major points of differentiation among vendors in this space, so it’s worth understanding exactly what kind of scores each company provides. In LeadSpace’s case, the company builds “ideal buyer profiles” that measure how similar a lead is to a sample of existing customers provided by a client. Most clients have multiple profiles for different products or customer segments. Other companies in this group build different types of scores: say, for response to a specific campaign, or becoming a sales accepted lead, or having a high lifetime value. Some also estimate the incremental financial value of taking an action. It’s easy for buyers to gloss over these differences, but that would be a big mistake: they largely what kinds f applications a system can support. So be sure to explore them in detail (or read our explanations once we release the CDP Report itself.)

- LeadSpace doesn’t maintain its own permanent master database of all companies on the Internet. Rather, it conducts a fresh scan as each client requests research into its target audience.  This is another big difference from its competitors, who do run continuous scans and keep the results. LeadSpace argues that its approach avoids outdated information, saves the cost of storing and updating a persistent database, and lets the system collect precisely the right attributes for each situation – which can’t be known in advance. The company also points out that even a new scan will capture some history: the public Twitter feed goes back one year, as do job site listings. I have doubts about these arguments – I think older data can show important trends, am sure there’s plenty of outdated information on current Web pages, and suspect there’s the important attributes are pretty similar from one project to another.  Perhaps LeadSpace is really making the subtler argument that the incremental value of older information doesn’t justify the incremental cost of scanning and storing it, which is perfectly possible.  The company does store some old information, such as common job titles, to help analyze and classify inputs.

- LeadSpace doesn’t load a copy of its clients’ customer names, either. That’s essential for a CDP, which by definition has the potential of evolving into a primary marketing database. But it's not essential for LeadSpace's primary business of lead scoring, where even can be built on just a sample of a few hundred records. The arguments for and against the permanent master database also apply here, so I won’t repeat them. In addition, LeadSpace says its clients care more finding prospects with the right attributes, such as industry, company size, and technology fit, than trends in their behaviors or new job titles. Again, I’m not sure I agree, but should point out that LeadSpace mentioned combining their own scores with behavior data captured in marketing automation: so LeadSpace itself is at least implicitly acknowledging that behaviors are important.. LeadSpace's approach also means it can’t monitor a set of names and issue alerts when they do something interesting.  This is definitely something salespeople like to do. LeadSpace is closing that particular gap by developing a service, soon to enter beta testing, that will do a monthly scan of a client’s customer records.  It will feed the results back to the client's CRM or marketing automation, which themselves will highlight any changes.

- LeadSpace provides both prospect lists (i.e., new names) as well as data enhancement (i.e., information on names provided by the client). Most of its competitors also do both, but some do only enhancement. Again like its competitors, LeadSpace provides an interface for sales people to view the details associated with an existing customer. This is where its on demand approach comes in handy, since the interface can present information in categories tailored to each client’s needs. The system also lets sales people rate each lead with a thumbs up or thumbs down, providing feedback to fine tune the scoring model. I haven’t seen that particular feature in competitive systems but it’s not something I’ve specifically researched.

LeadSpace was founded in 2007 as a prospecting tool that let salespeople enter a company name and receive a list of individuals and their associated information and social conversations. The evolutionary path from there to the current system , launched in 2012, is fairly obvious. The company currently has more than 50 clients, mostly large B2B technology vendors. Pricing is based on the number of records either enhanced or provided in prospect lists, and starts around $25,000 per year.

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Infer Keeps It Simple: B2B Lead Scores and Nothing Else

I’ve nearly finished gathering information from vendors for my new study on Customer Data Platform systems and have started to look for patterns in the results. One thing that has become clear is that the CDP vendors fall into several groups of systems that are similar to each other but quite different from the rest. This makes sense: most of the existing CDP systems were built to solve specific problems , not as general-purpose data platforms. Features will probably converge as vendors extend their products to attract more clients. But right now the groups are quite distinct.

One of these categories is systems for B2B lead scoring. I found three CDPs in this group: Lattice Engines (which I reviewed in April), Mintigo (reviewed in June), and Infer, which I'm reviewing right now.

Like the others, Infer builds a proprietary database of pretty much every company on the Internet by scanning Web sites, blogs, social media, government records, and other sources for company information and relevant events.  It then imports CRM and marketing automation data from its clients' systems, enhances the imported records with information from its big proprietary database, and builds predictive models that score companies and individuals on their likely win rate, conversion rate, deal size, and lifetime revenue.

The models are applied to new records as they enter a client’s system, creating scores that are returned to marketing automation and CRM to use as those systems see fit. The most typical application is deciding which leads should go to sales, be further nurtured by marketing automation,  or discarded entirely. But Infer customers also use the scores to prioritize leads for salespeople within CRM, to measure the quality of leads produced by a marketing program, assess salesperson performance based on the quality of leads they received, and even adjust paid search campaigns based on the quality of leads generated by each source and keyword.

Infer differs from its competitors in many subtle ways: the scope of its data sources, its matching processes to assemble company and individual data, the exact types of scores it produces, its modeling techniques, and reporting.  It also differs in one very obvious way: it returns only scores, while competitors return both scores and enhanced profiles on individual prospects.  Infer gathers the individual detail needed for such profiles, but has decided so far not to make them available. Its reasoning is that scores provide the major value from its system and profiles would detract from them – perhaps because sales people might ignore them scores in favor of profile data. Focusing on scores alone also makes Infer simpler to set up, operate, and understand.

Infer might be right, but it’s hard to imagine they'll will stick with this position once they start selling directly against competitors that offer scores plus profiles.  They will surely lose many deals for that reason alone.  On the other hand, Infer’s initial clients have been companies where free trials versions generate huge lead volumes, including Box, Tableau, NitroPDF, Zendesk, Jive and Yammer. Scores that accurately filter non-productive leads are more important to those companies than individual lead profiles.  Perhaps there are enough such firms for Infer to succeed by selling only to them.

Whether or not Infer expands its outputs, it faces another challenge: convincing buyers that its scores and data are better than its competitors. This might well be true: based on the information I’ve gathered, Infer seems to have a richer set of data sources and more sophisticated identity matching than at least some competitors. But my impressions may be wrong, and most buyers will won’t dig deeply enough to form an opinion.  Instead, their eyes will glaze over when the vendors start to get into the details, and they’ll simply assume that everybody’s data, matching, and modeling are roughly equivalent.

The only real way to measure relative quality is through competitive testing of which scores work better.  Each buyer needs to run her own tests since results may vary from business to business. How many buyers will take the time to do this, and which vendors will agree to cooperate, is a very open question.

That said, I did speak with some current Infer users, who were quite delighted with how easy it had been to deploy the system and with results to date. This is hardly a random sample – these were pioneer users (the system was only launched about a year ago) and hand-picked by the vendor. But their experience does confirm that performance is solid.

Infer pricing is based on the number of records processed and connected systems.  The vendor doesn’t reveal the actual rates but did say it is looking at options to make the system more affordable for smaller clients.

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NitroMojo and Marketing Advocate Specialize in Marketing Automation for Channel Partners

As I noted in a post last year, there is a universe of specialized marketing automation systems for companies that sell through channel partners. These products address several interrelated challenges: distributing leads to partners without losing track of performance; distributing partner-customized versions of company-created content; and helping partners run their own marketing campaigns. Here are two more vendors with related offerings:

NitroMojo focuses primarily on lead distribution and tracking. Its particular strength comes from sending follow-up email surveys directly to leads to find out what happened: were they contacted by the channel partner? did they eventually buy? is there someone else at their company to talk to? is there something else they might purchase? This addresses one of the central dilemmas of selling through partners, which is losing contact with the leads and, as a result, not being able to measure effectiveness of corporate lead generation programs. NitroMojo says about 60% of leads reply to the surveys, giving enough information for meaningful analysis of program, partner, and salesperson performance.

The system also provides sales reps and sales managers with basic sales automation, including abilities to enter and rate new leads, review and prioritize existing leads, track call results, send materials from a central library, and schedule future calls. Corporate marketers can build campaigns with multiple events, create landing pages, capture revenues and costs, distribute leads with complex routing rules, score leads on behaviors and salesperson ratings, and measure performance.  Pricing starts around $3,000 per year plus $100 per user per month, which is usually less than the cost of marketing automation and sales automation systems that NitroMojo would replace. The current version of NitroMojo system was introduced about a year ago and had three global clients with more than 150 users when I spoke with the company in April.

Marketing Advocate is designed to help technology resellers who lack in-house marketing skills. It provides a resellers with a vendor-sponsored microsite that gives them access to marketing content, prospect lists, acquisition email campaigns, and automated nurture emails.  Resellers define their target prospects when they set up the system and then purchase suitable lists from suppliers including NetProspex, Jigsaw, and Harte-Hanks. These prospects, and other names uploaded by the reseller, receive standard campaign emails at three week intervals until they respond by visiting a landing page. The system then sends them personalized emails offering contents related to their behaviors. The leads are also scored and, when ready, can be passed to a telephone lead qualification service or directly to the vendor’s sales automation system. The sponsoring vendor doesn’t see the lead names until the reseller enters them into the system.

The point of all this is to minimize the effort that the resellers themselves must put into marketing. Marketing Advocate typically builds 25 to 30 prospecting campaigns tailored to different customer segments, and lets the resellers select the campaign and segments they want to pursue. The company also assembles and selects content to offer in the emails, has negotiated arrangements with the list providers, gives reports that analyze program response quantity and quality, and offers a concierge service to review results with resellers and discuss improvements. The system can also integrate with event management software and Google AdWords. Partner agencies are available for telephone lead qualification, search engine optimization, and paid search.

Marketing Advocate typically costs $500 to $700 per month per reseller, with some portion of the expense usually subsidized by the sponsoring vendor. Marketers pay $1 per name for prospects. The system is used by divisions at several major technology vendors including IBM, Microsoft, and HP.

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NICE Buys Causata to Extend Its Customer Experience Management Position

So, there I was around 7:30 Eastern time this morning, sending out reminder notices to vendors I need to interview for an upcoming report on Customer Data Platforms. I received an immediate response from the Kevin Nix of Causata, offering to talk that very morning. This seemed a bit odd – Causata is based in San Francisco, so it was 4:30 a.m. local time and most people need more notice to schedule a call. But I had Things To Do, so I didn't give it much thought. Then, at the end of another call, a participant casually mentioned that Causata had just been purchased by Israel-based NICE Systems.  At first I was struck by the coincidence, and then realized what had happened: Nix was up because he had been talking to the folks in Israel, and he replied because he wanted to discuss his acquisition, not my report. [Insert image of deflating self-importance].

Sure enough, when I did dial in, I was treated to a prepared briefing on why NICE had made the deal.

There’s really nothing wrong with that. NICE is little-known in marketing circles, although I had bumped into them previously when they bought decision management vendor eGlue in 2010. But NICE is a major player in contact center systems, with nearly $1 billion revenue and $2.5 billion stock market capitalization. So I was pleased to connect with them directly and learn a bit more.

The briefing itself was interesting too. It turns out that while NICE still sells primarily to contact center managers, it is working hard to expand to clients in marketing, sales, compliance (it bought Actimize in 2007) and other areas related to customer experience. Its interest in Causata related to all  that, and in particular to that fact that Causata can capture Web interactions in real time and present them with related recommendations to contact center agents and other systems. This pumped me back up a bit, since it can be read as validation of the Customer Data Platform concept that I’ve been developing, which is about exactly this need to make customer data easily available across platforms. In fact, Causata was the original example I used to introduce the idea.

But enough about me, at least for the moment. The idea of NICE expanding to become an all-channel, all-department customer experience vendor immediately raises the question of how they’ll compete with all those other omni-everythings approaching from digital marketing (Adobe), B2B CRM (Salesforce.com), and general enterprise systems (Oracle, SAP, IBM). The contact center world has actually been a font of decision management systems, most notably Chordiant (now part of Pegasystems) and Infor Epiphany. So it’s certainly possible that they will be another source of competitors converging on the market for integrated customer experience management solutions. Like the CRM and Web content management vendors, the contact center firms start from a strong customer and financial base, making them formidable contenderss in what will surely be a long battle for high stakes.

I haven’t formed a solid opinion yet on how NICE in particular or contact center vendors in general are likely to fare in this new arena. But they are definitely something to factor into future assessments.

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Acquisitions Reshape the Marketing Automation Industry: Growth at the Bottom, Room in the Middle, Fog at the Top

Raab Associates officially released the new edition of our B2B Marketing Automation Vendor Selection Tool (VEST) yesterday. This is our flagship report on the industry, with nearly 200 data points on 23 vendors and separate ratings for micro-business, small to mid-size companies, and enterprise marketing departments. There are quite a few vendor comparisons out there, but none come close to the level of detail in the VEST – and details are what you really need to select a system. I personally suggest that anyone interested in the industry buy a copy for themselves and another for someone they love. See www.raabguide.com/vest for details.

I genuinely enjoy catching up with the vendors while preparing the VEST, but must admit that my favorite part of the process is analyzing the data once it’s assembled. Sadly, the wave of acquisitions that swept the industry in the past year has made this harder: many major vendors are now part of a public company, which severely restricts the information they can share. We’ve probably passed a tipping point where so much information is hidden that I can’t draw a clear picture of industry growth rates or competitive positions.

The table below shows the data available and highlights the holes. I’ve grouped the vendors into three buckets based on the market sectors they serve: micro-business (under $5 million revenue), small to mid-size business ($5 to $500 million), and large enterprises (over $500 million).

You’ll immediately see that the “not reported” information is concentrated among companies serving mid-size and enterprise clients, which is where all the acquisitions to date have taken place. Neolane is an exception but only because they provided the VEST information just before Adobe acquired them in June. I doubt we’ll see new numbers from them in the future. Marketo was mostly missing until they provided key figures in their earnings call this afternoon. Thanks, guys.

I've summarize my thoughts on this data with three oh-so-catchy phrases: growth at the bottom, opportunity in the middle, and fog at the top.

Growth at the Bottom: the green shading in the client growth column highlights companies reporting a year-on-year increase of 60% or more. What jumps out is the concentration at the top of the chart, in the micro-business sector. Four of the five micro-business vendors grew more than 60% and the fifth (Venntive) grew at a far-from-shabby 54%. There’s too much missing data in the other sectors to say for certain that the micro-business vendors are growing the fastest, but it sure looks that way. My interpretation is that the micro-business sector is the least mature and still presents the greatest untapped opportunity – even if buyers are still limited to the small proportion of business owners who are “tech geeks”.

Room in the Middle: Marketo's client count increased just 36% from mid-2012 to mid-2013 (although they’re projecting 54% revenue growth for 2013 vs. 2012).  We can no longer see the growth rates for mid-market heavy weights Pardot and Eloqua, but I’d be surprised if they beat Marketo.  They're certainly not close to the 67% to 90% rates reported by LeadFormix, Act-On, and eTrigue. I suspect Pardot, Eloqua and Marketo will increasingly focus on selling to enterprises, and in Marketo’s case on expanding footprint within existing clients. If so, this might open the way to faster growth by the next tier of mid-market vendors, who are mostly still private.  (LeadFormix is the exception, but seems to be pretty much left alone by its corporate parent). The clear winner in this scenario is Act-On, which has ample venture funding and has indeed been growing very rapidly. They are already the first vendor since Pardot to break the 150-employee barrier (blue shading). Silverpop and HubSpot might also benefit but neither is fully focused on standard B2B marketing automation. Other vendors would need outside funding to squeeze through what will probably be a briefly open window.

Fog at the Top: My visibility into enterprise B2B marketing automation was always clouded because of cross-over by B2C vendors including IBM, SAS, Teradata, and Neolane. It is now completely obscured except for sporadic glimpses of details that vendors choose to reveal. But even if everyone shared all their data with me, the enterprise picture would remain foggy because enterprises are increasingly integrating marketing automation with advertising , sales, service, and Web management. This makes it increasingly meaningless to treat marketing automation as a distinct category. Of course, that integration is exactly why the enterprise vendors purchased all those marketing automation systems in the first place.

If integration really happens at the top then we'll end up with a bizarre symmetry, since the enterprise market will be mirroring the integrated sales / CRM / Web / ecommerce products already bought by micro-businesses.  This would leave stand-alone marketing automation as a niche product for mid-tier companies. It would be a very large niche, but squeezed between broader suites from above and below and, eventually, challenged from within by integrated suites built for mid-market companies. The obvious response from marketing automation vendors is to build those broad suites themselves or to create platforms that are the foundation of such suites. That’s exactly what the larger mid-tier companies are doing, but it’s an expensive proposition. Any small mid-market companies who want to play must grab whatever fleeting opportunity the market offers today for growth, before they are locked out for good.

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Marketo's Engagement Engine Simplifies Complex Marketing Automation Campaigns

I’ve long said that the best campaign design would be one circle: the system executes the best treatment for each customer, waits a day, and repeats. My point is that elaborate, branching flows are too complex for most marketers to build and maintain, and – because reality is infinitely messier than even the most sophisticated flow chart – will often give customers a sub-optimal treatment.


It’s probably just as well that no vendor has ever built a system based on my design. But the good folks at Marketo have taken a step in that direction with their latest enhancement, which they call an “engagement program”. It has more than one step but does get away from the idea of a rigid, branching campaign flow. Instead, it is organized in terms of “streams” that contain pools of content. Once a customer is added to a stream, the system will offer the next piece of content whenever a contact is due according to the campaign cadence. What’s next is set by the order of content within the stream: users just drag content into the container and put on top the ones they want sent first. The system will go through the content in this order during each execution (which Marketo calls a “cast”), and send each person the first item they have not already received. This avoids duplicate messages and lets the system deliver a defined series of messages without explicitly setting up a sequence. It also makes it almost effortless to insert a high priority message that goes to everyone or to swap out contents as new materials become available.

As you’ll immediately notice, sending the next thing isn’t quite the same as sending the best next thing.  In my ideal world, the content would be selected by calculating the value of each item for each individual and taking the highest. This would only take a small tweak in the current approach, which is one reason I like what Marketo has done. Marketo does in fact plan to apply predictive modeling to the system, although I think they're trying to find the most effective content sequence for all customers, rather than scoring content at the individual level.

There’s quite a bit more to the new Marketo feature than I’ve described so far. Content can actually be a multi-step program of its own, such as a sequence of messages to promote and manage a Webinar. Content can also have availability dates that are enforced automatically, so future messages can be added at any time and obsolete messages are automatically discontinued. One thing that’s missing is eligibility rules on content, to let users specify who is allowed to receive it. This is a key feature in traditional decision management systems, permitting customer-level customization within a fixed priority sequence. But Marketo users can achieve the same thing by embedding content within programs, which do have such rules, and adding the programs to the stream instead of the content itself. This is Marketo’s recommended approach because it also provides better data for reporting.

Users can further tailor treatments to customers by setting up multiple streams within one engagement program.  Each stream has “transition rules” that pull in qualified customers from other streams.  This is less rigid than having selection rules in one stream push customers to another stream.  It's not quite clear what happens if someone qualifies for more than one stream: Marketo's position is that would only happen if you make a mistake.  I think reality is not so tidy.  Marketo is considering letting marketers prioritize the transitions based on the position of the streams, just as they prioritize contents within a stream.

In any event, customers can only be in one stream at a time, so they won’t receive multiple messages from the same engagement program. Whether they receive messages from multiple programs is controlled by Marketo’s standard communication limit features, which can set a maximum number of messages per day and per week. Users can decide whether those limits apply to any particular program. The system also lets salespeople or other programs pause messages from an engagement program to an individual customer.

The new package includes a good set of reports that track content usage and results.  They also provide an “engagement score” that combines several success metrics into a single value. Other reports show how many people in the program have run out of content – a good way to ensure the company doesn’t lose touch with them. Surprisingly, there's no report on movement from one stream to the next. But Marketo says this can be set up using their revenue performance management module, which tracks movement of customers across other types of stages.

The engagement engine is included in all Marketo versions, although lower-level versions have some limits.  Adding a more powerful version to the Standard edition of Marketo starts at $295 per month.

Added Thought: Marketo engagement programs are a type of state-based system, an idea that has been tried from time to time in marketing systems and is currently the basis of Whatsnexx.   As the name implies, state-based systems assign customers to categories and then define treatment rules within each category.  Unlike the sequential flow of a traditional multi-step campaign, customers in a state-based system remain in the same category so long as they meet its membership conditions. State-based systems typically reclassify all members at the start of each cycle, which is different from Marketo's approach of relying on transition rules to pull customers from one stream to the next.  This means that someone could remain in a stream even though they no longer met its entry criteria.  This is something Marketo might want to reconsider.

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Vocus Marketing Suite: Still Mostly Social But Marketing Automation is On the Way

If you’ve heard of Vocus at all, it’s probably as vendor serving public relations professionals. Its core offerings include a huge database of media contacts; media monitoring and social listening; and press release distribution. But since late 2011 the company has also offered a suite aimed at marketers, which now has more than 4,000 paid clients.

Even in the small business sector, that count would make Vocus one of the largest marketing automation systems.  But Vocus doesn’t quite match the profile of standard marketing automation products.  It lacks the Salesforce.com integration of B2B systems (due early next year), the lightweight CRM of micro-business systems, and the lead scoring and distribution of both. On the other hand, it does offer email and landing pages, two marketing automation basics, as well as several features borrowed from Vocus PR software.  So it's best to treat Vocus Marketing Suite as a class unto itself.

The product's two most intriguing features draw on Vocus’ monitoring of social media and news outlets. “Recommendations” finds conversations on client-specified topics across 130,000 online outlets, 10,000 print outlets, 35 million blogs, and posts on Twitter, Facebook, and other social sites. It presents these to Vocus clients with an interface that suggests a reaction but lets users decide how to reply or repost across several social channels. Clients can have Vocus add new topics, a process that takes a couple of weeks to allow testing and fine-tuning of the selection mechanism. “Recommendations” will also identify influencers for a selected topic, based on actual influence (number of reposts or references) rather than the number of followers.

“Buying Signals” draws on Twitter only. It identifies Tweets with a dozen or so purposes related to a client’s product, such as fact checking, asking for recommendations, shopping, or reporting that something has been lost or broken. As with Recommendations, users are presented with a list of messages they can review and reply to individually as appropriate.

Other features include press release posting via Vocus’ PRWeb subsidiary; Facebook promotions such as sign-up pages, sweepstakes, and fan offers; a central image library; and management of local directory listings. I’ve already mentioned email, which is reasonably powerful, and landing pages. The email engine will be enhanced with multi-step campaigns by the end of this year. Other traditional marketing automation features will be added as well.

User rights are organized around “profiles”, which might relate to a company, brand, or product line. Users are either assigned to a profile or not; there are no finer divisions of rights for specific features. This approach makes sense for small businesses – the bulk of the system's current client base – and for marketing agencies who manage separate profiles for each client.

Pricing is defined in tiers ranging from $3,000 to $30,000 per year, based on the number of profiles, amount of content monitoring, email volume, press release formats, and other variables.

As both the pricing variables and features suggest, Marketing Suite is still mostly a social media monitoring and public relations tool. This will change as Vocus adds conventional marketing automation features. But until those features mature, companies who want to do much beyond email will find they need a separate marketing automation product as well.

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Optify Lets Agencies Provide Small Business with Marketing Automation, Distributed Marketing, and Sales Enablement

In case you were wondering, I see four themes emerging today in B2B marketing automation:

• services: vendors are bundling their systems with services to help marketers use them, either by offering the services themselves (LeadLife, MakesBridge, Ontraport (formerly OfficeAutoPilot), RightWave, SalesEngineInternational) or by creating versions that agencies resell to their clients (Optify, MindFireInc, many others).

• sales enablement: systems to share marketing information with sales (Genius, SalesFusion, LeadFormix, RightOn Interactive, Optify)

• distributed marketing: systems shared between central marketing organizations and local branches, dealers, distributors, sales agents, etc. (NitroMojo, Marketing Advocate, OptifiNow, Optify, Balihoo)

• new options for small business: systems targeted at very small businesses (Venntive, Optify, Vocus)

The first three trends strike me as defensive: small vendors need niches to compete against the huge resources of the big general purpose marketing automation products, who are all now part of larger companies (Eloqua, Pardot/ExactTarget), public (Marketo) or heavily funded (Act-On, HubSpot). By contrast, the fourth trend seems to be driven by recognition that small business presents a huge opportunity.

I only mention this because I’ve recently been looking at a lot of new (to me) vendors and haven’t been able to write about many of them. Placing them in the larger industry perspective gives me a chance to at least drop all their names and makes it easier to decide which to profile next. I’ll use the extremely scientific approach of selecting Optify, since it appears in all four categories.

Optify was founded in 2008 and launched its original product, a search engine optimization (SEO) tool, about a year later. Its primary clients were then, and still remain, digital marketing agencies. Both the agencies and their clients have been mostly small businesses – in the survey for our soon-to-be-published VEST report, Optify reports that 60% of its clients have under $5 million revenue. This makes it a system for both small business and service vendors: two of my four themes. Its distributed marketing capabilities stem from its agency roots, since the fine-grained, hierarchical permissions needed to let one agency manage installations for multiple clients are similar to the permissions needed to distribute permissions between central marketers and local affiliates. That's theme number three.

Finally, Optify has expanded into conventional marketing automation over the past 18 months and most recently added basic contact management and distribution of lead information, scores, and alerts to sales people. This is enough sales enablement to complete its sweep of the four themes.  I guess I should send them a t-shirt or something.

You can also think of Optify as having worked its way down from the top of the funnel (SEO) to the middle (marketing automation) and towards the bottom (CRM). This will remind marketing automation aficionados of HubSpot, which has made a similar journey. The biggest obvious difference (if you ignore HubSpot’s $100 million or so in venture capital funding) is that HubSpot offers its own blogging and Web content management, while Optify provides WordPress and Drupal plug-ins for visitor tracking and landing pages.  This is the standard approach among marketing automation products – as Optify says on its Web site, “We know you already have a website and a favorite marketing CMS.” The system can also create Facebook landing pages and track visitors to them.

What Optify does offer inbound marketers is extensive support for search engine optimization.  This includes detailed research into keyword rankings for the client and competitors, analysis of Web pages for features that improve search ranks, an inbound link manager, and a Twitter client to publish posts and embed trackable URLs that measure campaign results.

Moving towards the middle of the funnel, Optify offers reasonably powerful email and landing page builders, based on templates or HTML. Landing pages can be attached to an auto-responder email, while standard fields on forms are automatically mapped to Salesforce.com. Emails are delivered through ExactTarget. Users can create lists and segments based on all contact properties, activities, email history, and custom fields. There are no real multi-step campaigns, however.

Sales enablement includes lead scoring, with multiple scores per lead; alerts based on search keywords and lead scores; a live ticker showing current Web site visitors with companies identified via reverse IP lookup; and appending of company data from Dunn & Bradstreet. The system can send each salesperson a daily email of newly qualified leads, selected with shared rules or separate rules for each salesperson.. Salespeople can view their contact list, drill down to individual profiles, aand drill further to see behavior details – even as far as each page viewed during a Web site visit. Users can send the contact a system email or add it to a list.

CRM integration is currently limited to sending data to Salesforce.com. A proper API for bi-directional integration with any CRM system is under development.

Reporting is a particular strength.  Optify can build a unified contact profile by connecting names, email addresses, social accounts, and multiple cookies for the same person, using site log-ins, email clicks, and form submits on different devices. This lets reports show Web visits, conversions and other subsequent activity from search, social, and email campaigns.  A dashboard lets users pick widgets to display selected information.  Data can be exported to Excel, which is what many of Optify's agency clients prefer.  The company is planning an API to let clients export data directly.

 All told, this is a pretty reasonable package for a small business marketing system. It’s broadly similar to the scope of small business leaders Infusionsoft and Ontraport, although those products offer more elaborate campaigns and process flows. Pricing is also competitive with other small business systems if not especially cheap: company marketers pay based on page views and emails sent; starting at $350 per month for 10,000 views and 25,000 emails, . Agency pricing is based on the number of Web sites and email volume. Distributed marketing also has its own pricing.

Optify has more than 400 agency clients and many more individual sites using the system.

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