Genoo Adds SEO To Web Site Management and Marketing Automation

I had an earful last week from Genoo president Kim Albee, who told me that I’ve misclassified her target customers as “micro businesses” (under $5 million revenue) for the past two years. She tells me nearly all of her clients are larger than that. I’ve revised my big list of demand generation vendors to reflect this.


The main cause of my misunderstanding was Genoo’s starting price of $199 per month, which is below any small or mid-size business system. But the lowest price for Genoo with CRM integration is $599 per month, and I consider CRM integration a required feature in a B2B marketing automation product. This is still low for small-to-mid systems but not wholly out of line. On the other hand, I should have been warned by the fact that Genoo doesn’t provide a built-in CRM system, which is pretty much the key defining characteristic of a micro business system.

The good news is the error doesn’t seem to have crimped Genoo’s growth, although Albee tells me it was used against them in several competitive situations. The company now has about 75 clients, compared with around 35 at the time of my initial review nearly eighteen months ago.

The system itself has also grown although the general approach remains consistent. This approach extends beyond most marketing automation products to incorporate full Web content management and marketing services. The most interesting recent enhancements are products for search engine optimization (SEO): “competitive intelligence” runs a multivariate analysis to identify factors that contribute to competitive pages’ ranking; and “content relevancy analyzer” scans high-ranking pages find theme words that search engines will use to determine relevance. The goal in both cases is to recommend changes that will improve search marketing results.

Other enhancements allow deeper visitor tracking, including tracking tags on Web pages not hosted at Genoo; improved customer scoring; progressive profiling; unlimited custom fields on the lead table (Albee said no client has ever asked for custom fields anywhere else); and an expanded Application Program Interface (API) to accept leads from other systems. The February release will add features to capture social media activities and encourage social book-marking. These join a respectable set of social media features already in place, including sharing buttons, share tracking, and traffic reporting.

Albee said the company has also developed anonymous visitor look-up, based on IP address. But internal tests found additional filtering is needed to avoid wasting users’ time, so full deployment is on hold until this can be added.

This concern about users’ time reflects Genoo’s unusually deep involvement with its clients’ marketing programs. Professional services have always been a major part of the company’s offering and constitute its fastest growing line of business. The company is setting up alliances with external consultants to help meet the demand.

Albee also pointed to Genoo’s pricing as a reaction to customer requirements. The model is more complicated than many small business oriented marketing automation systems.  It has three product levels and gives users within each level the choice to pay either for emails (with unlimited leads in the database) or by database size (with unlimited emails). The two lower price levels also require additional charges for the SEO tools, which are licensed from an external developer.  Free phone support is always included and professional services are always billed separately. There’s also a salesperson access tool billed at $9.95 per month per user. Clients can change their plans each month and are billed for overages on a cost per thousand basis. While conventional wisdom among marketing automation vendors is that buyers prefer a fixed fee and no surprises, Albee said her clients appreciate the flexibility of Genoo’s approach.

Albee said her clients are a mix of small and mid-size businesses, including some divisions of large enterprises.  Genoo's low price may have scared off a few buyers who felt a system that cheap simply couldn't be adequate.  It's an easy mistake to make, but companies of all sizes who want to combine conventional marketing automation with Web site and SEO should still take a look.

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Oracle Buys ATG: Bad News for Marketing Automation?

So…Oracle bought ATG today for $6.00 per share or, as the press release puts it with charming nonchalance, “approximately $1.0 billion”. I can’t exactly say I told you so, since this particular pairing never crossed my mind. But if you look back at my “doughnuts and pizza slices” post on software acquisitions, it does make perfect sense. ATG is a specialist in e-commerce (the ERM doughnut in the online operations pizza slice), an area where Oracle’s traditional ERM products are weak. As my model suggests it should, ATG also encompasses online CRM and online marketing, where Oracle’s Siebel line is also a little thin.

Since Oracle is already strong in offline ERM and offline analytics, ATG leaves Oracle just one slice short of a pie. In other words, Oracle needs a Web analytics product. With Omniture, CoreMetrics and Unica already gone, only Webtrends is an option…unless Oracle gobbles up Adobe. ‘nuff said.

So much for the obvious. What I really care about is the implications for marketing systems. I’d say the ATG purchase lessens the odds of Oracle buying a marketing automation vendor. The logic is this: buying ATG suggests that Oracle, like IBM (which put Unica in its WebSphere organization), is focusing on online marketing rather than marketing automation in general. Since ATG itself provides substantial online marketing functionality, there’s a smaller gap for Oracle to fill with a separate marketing automation purchase. Nor have I forgotten that Oracle already bought marketing automation vendor Market2Lead, plugging a different set of holes.

If anything, Oracle (and IBM) need to strengthen their position in online advertising. I'd look for them to buy tools to manage banner ads, search ads, and search engine optimization. This in turn could point towards investments in content management and digital asset management systems. That also leads further away from standard marketing automation.

The day-to-day impact of all this on marketers is slight. They still need marketing automation tools to do their jobs. If anything, they’re better served by having some marketing automation vendors remain independent, since this keeps prices down and encourages competitive innovation. A less-helpful result may be to further isolate digital marketing from other channels, when what we need is to integrate them more closely. Perhaps digital marketing systems will grow to the point that they take over offline marketing as well. I hadn't expected such a role reversal, but it’s certainly possible. Just ask Oedipus. Not that that turned out so well.

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IBM Buys Unica: Will Acquisitions Now Shift to B2B Marketing Automation?

IBM announced this morning that it was purchasing enterprise marketing automation leader Unica for $480 million, more than double the company’s current stock market valuation. This is wholly unsurprising: as the last and only big independent left in its space, Unica was obvious acquisition bait. It was also a motivated seller, since it faced an increasingly impossible struggle to fund the product enhancements necessary to compete with the likes of SAS, Teradata and Siebel / Oracle. Conversely, IBM is on a customer intelligence acquisition spree that has already included Coremetrics Web analytics, Sterling Commerce B2B integration and Cognos and SPSS business analytics.

There’s been some comment (I’m looking at you, Jonathan Block of SiriusDecisions) relating the IBM/Unica deal to consolidation with the B2B marketing automation industry. Sorry, but I don’t see a connection. As I discussed in my own post on industry consolidation, Unica belongs to the class of marketing systems that serve consumer marketers. Its acquisition is basically the completion of the consolidation of that space, not the start of consolidation among B2B marketing automation vendors. (I’m overstating a bit: there are a couple of B2C vendors left including Neolane, Alterian and SmartFocus, although the latter two use proprietary database engines that would make them difficult to integrate into a larger enterprise suite. Probably the most prominent survivor is Aprimo, but they’re more B2B.)

If there’s any connection at all, it’s that this acquisition may spur Web content management vendors to accelerate their own acquisition of marketing automation capabilities. I discussed this a bit in my post on Adobe’s acquisition of Day Software and in the industry consolidation post. Given that there are so few B2C marketing automation vendors left, the Web content management players are almost forced to consider buying a B2B marketing automation system. (The other option would be email vendors like ExactTarget and Responsys.)

This isn’t really a bad thing: the B2B marketing automation products have pretty much all the capabilities of the B2C systems and then some. On the other hand, most B2B systems are designed for smaller data volumes and have less flexible data structures.

The bottom line is probably that the upper tier B2B marketing automation vendors (Eloqua, Silverpop, Aprimo, possibly Marketbright) are next in line to be bought. But you already knew that.

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Pegasystems Buys Chordiant to Help Coordinate Customer Treatment Decisions

Summary: Pegasystems purchased Chordiant last week, adding a sophisticated cross-channel decision engine to its stable. It's been hard for independent decision engines to survive, even though it seems an independent product should make it easier for marketers to unify their customer treatments.

Business process technology vendor Pegasystems announced on Monday that it was purchasing Chordiant, which offers a central decision engine for customer interactions. Although the news is interesting in its own right, it also triggered a twinge of personal regret because I’ve been meaning to write about Chordiant for nearly a year. At that time, they had just added some slick simulation capabilities that estimated outcomes if a different set of rules had been applied to historical interactions.

This type of simulation allows business managers, rather than technicians, to directly assess the impact of alternative business rules. It's an important sign of maturity, showing that the vendor has shifted resources from primary system functions (making things work) to supporting functions (making things work better).

If you’re not familiar with the Chordiant decision engine, its primary function is to apply business rules that guide real-time customer treatments. It has been deployed primarily in call centers, although it is designed to work across multiple touchpoints. To accomplish this, the system must accept inputs from each touchpoint about a current interaction, apply rules to select an offer, and feed the selection back to the touchpoint. Tracking results also requires a second loop for the touchpoint to report whether the offer was actually delivered and whether it was accepted.

The business rules can use both data provided by the touchpoint and data from other systems such as transaction and marketing databases. The rules frequently include predictive models that can either be built within Chordiant or imported from other systems such as SAS or SPSS. Chordiant also supports self-adjusting models that monitor outcomes and modify future recommendations based on the results of different offers.

The appeal of a stand-alone decision engine like Chordiant is that companies can coordinate treatments without using a single vendor for all their touchpoint systems. This makes perfect sense, since in practice most firms do use different products for different touchpoints. In particular, Web interactions are often managed outside of the CRM system.

Yet it’s still been difficult for stand-alone decision engines to survive. Most firms use whatever interaction management features are built into the separate touchpoint engines and coordinate the rules administratively (if at all). Or they rely on interaction management features provided by their marketing automation system.

A few independent decision engine vendors remain, notably thinkAnalytics (another product I’ve been meaning to write about for months) and eGlue (which I wrote about here [update: a week after this post was written, eGlue was apparently purchased by interaction management vendor NICE Systems, although I've yet to see a formal announcement]). But it’s ultimately not surprising that Chordiant should end up as part of Pegasystems, with which Chordiant had already been integrated. The new relationship will let Pegasystems offer added value to its clients and better compete with CRM vendors.

As an aside, it's interesting to compare the position of decision management vendors with execution vendors like Conversen (which I wrote about last month) and ClickSquared (yet another vendor I hope to review shortly). Both sets of products unify a single function that is otherwise spread across multiple systems: offer selection for decision engines and message delivery for execution engines.

The challenges faced by independent decision engines may suggest that the execution engines will face similar problems. But the execution engines sit at the end of the messaging sequence, rather than in its middle: that is, they process outputs from marketing systems and send them elsewhere, rather than feeding them back into the same systems for delivery. This may make it easier for them to survive.

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Eloqua SmartStart Speeds Marketing Automation Deployment, But It's Still Work

Summary: Eloqua's SmartStart gets marketers rolling in less than one week. It does require extensive preparation, but Eloqua leads you through that too. Let's face it, folks: putting a good demand generation program in place is real work.

Eloqua last week announced a money-back satisfaction guarantee for clients who participate in its SmartStart deployment program. Skeptical creature that I am, I wanted to hear the details before writing about it. By happy coincidence (OR WAS IT?), Eloqua Director of Key Accounts Jill Rowley scheduled a talk with me a few days later and filled me in.

SmartStart is a two-to-five day paid consulting engagement that helps new Eloqua clients fully deploy their systems. It’s not to be confused with the free QuickStart program (which I wrote about last May) which provides a smaller set of services. More than 150 Eloqua clients have now completed the SmartStart process, which is delivered by both Eloqua’s own professional services group and certified consulting partners.

The scope of SmartStart is indeed impressive. By the end of the program, marketers have initial email, forms, landing pages, Website tracking, CRM integration, reporting, and either lead scoring or nurturing programs. One key is preparation – the on-site sessions are preceded by extensive information gathering and technical groundwork, guided by Eloqua templates. This covers CRM integration, adding Web tracking scripts to company Web pages, assembling images and email formats, data cleansing, landing page subdomain set-up, specifying forms content and designing the lead scoring matrix. The process also includes a marketing maturity assessment that helps to define long term plans for improving the client’s marketing operations.

Rowley said most small companies can assemble the necessary information in a few days, although larger organizations take longer. Similarly, the SmartStart process itself works best for firms with relatively simple marketing operations, which Rowley said has less to do with size than numbers of regional offices and lead scoring programs, CRM integration, and existing automation. The single biggest challenge is the complexity of rules that govern CRM data synchronization, which can get very detailed when companies want different treatments in different situations.

The other key to the program is concentration during the SmartStart execution itself. The primary system administrator must devote full time to the project, while other users are brought in as needed. Because most policy decisions are made in advance, the company’s chief marketer doesn’t need to be constantly present.

The price of SmartStart varies from $4,000 to $19,000 depending on the version of Eloqua and type of CRM integration. Although that particular bit of information isn’t published, Rowley did point out to me that Eloqua’s Web site now shows basic price data, which used to be a closely-guarded secret. Pricing rules have also been vastly simplified.

That money-back guarantee? It’s good for six months and applies only to future portions of a subscription: so if you pay for a year and cancel after four months, you get refunded for the remaining eight months. That’s not quite a full refund, but it puts Eloqua on par with competitors who allow month-to-month agreements without an annual contract.

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Net-Results Simplifies Demand Generation for Small Business

Summary: Net-Results is simpler to use than comparable demand generation systems because it applies the same features to many tasks. The system is aimed at small business but offers an interesting design lesson for everyone.

When Net-Results’ showed me their marketing automation system, the demonstration ended so quickly that I wondered what was missing. But on reflection I realized that Net-Results offers a full set of demand generation functions. The demonstration was short because the system uses only a few features to deliver them. In an industry where every competitor is striving for grater ease of use, stand-out simplicity is an impressive achievement.

The key to Net-Results’ approach is to build everything around segments. Email campaigns are targeted at segments; Web visitors are classified into segments; behavior alerts are triggered by segments; lead scores are assigned to segments; leads are sent to the sales system based on segments; reports are run against segments. This simplifies the system in two ways: marketers have fewer features to learn, and they can reuse their work across many functions.

Let’s run through the standard demand generation process to see how this works in practice. This process has five functions: send emails to prospects; capture responses on landing pages; score leads; send qualified leads to sales; and nurture non-qualified leads with multi-step campaigns.

Prospects enter Net-Results from external Web forms (more about that later), file imports, manual data entry, or Salesforce.com synchronization. They’re assigned to campaigns by defining entry conditions for campaign steps, which the system calls “actions”. These conditions are not themselves segments but can be copied from existing segment definitions or built with the standard segment-creation interface.

Campaigns can have multiple actions, each with its own entry conditions. Actions can be arranged hierarchically with several "children" attached to the same "parent". Each lead is assigned to the first "child" action whose entry conditions it meets. This allows leads to follow different paths within the same campaign.

The approach imposes some limits, since different branches cannot be reunited. But it will meet the needs for most marketers. Net-Results plans to remove the limits by allowing actions to send leads directly to other actions, within or across campaigns.

Users can also specify a waiting period between actions, and whether to send alerts when a lead qualifies for an action. The actions themselves can send an email, adjust a lead score, or send the lead to Salesforce.com. Since entry conditions can also accept leads into nurture campaigns, the Net-Results actions by themselves account for four of the five core demand generation functions.

The fifth core function, capturing Web response, is Net-Results’ main deviation from standard demand generation techniques. Nearly all demand generation systems let marketers create and deploy landing pages outside of the company Web site. Net-Results does not. Rather, it copies data captured on existing Web forms and posts it to the Net-Results database. This requires users to add a bit of Javascript to company Web pages.

Loading data from existing Web forms requires mapping the original form fields into the Net-Results databases. Net-Results makes this as simple as possible by reading field names on the existing form and suggesting Net-Results fields that are likely to match. Such mapping may sound a scary to serious technophobes, but it’s less work than building a form from scratch.

Net-Results argues that its approach avoids the “vendor lock-in” that comes from using forms hosted by the demand generation vendor. I guess that’s true, but doubt it’s important to most marketers. On the other hand, the Net-Results approach means marketers cannot create new forms without help from whoever runs the company Web site. This strikes me as a significant drawback, which other demand generation systems are expressly designed to avoid.

I wouldn’t be surprised to see Net-Results add a form builder fairly soon, although they didn’t say they were planning to. The system already has an email authoring tool, which includes a graphical editor and works from user-defined templates. Extending this to build Web forms should be pretty simple.

The Javascript tracking code also allows Net-Results to capture the behavior of Web visitors. This is another standard feature for demand generation systems. Here’s where segments reappear, since Web behavior can be used in segment definitions and system reports are run against segments.

Running reports against segments may not sound too exciting, but it greatly simplifies marketing analysis. Practical applications include reports to salespeople about their own accounts and reports on campaign results. Each report can run and emailed to specified users on a user-specified schedule. Reports include graphs as well as tabular data. The system's main reports all relate to Web behavior: visitors, traffic source, search terms, and pages viewed.

The Web visitor report is particularly impressive: it's almost a separate application, similar to the tools that other demand generation vendors use to give salespeople a view of Web activity. Users start with a list of visitors (within a segment, of course) showing key information including source, name, email address, telephone, company, most recent visit date, pages viewed, and visit duration. They can then select a lead and drill into the details of current and previous visits. They can also take actions including sending the lead to Salesforce.com and issuing an alert. Marketers could easily extend direct access to salespeople, since system security could restrict the salesperson to her own leads. An incremental user costs just $25 per month.

Net-Results can also issue automatic alerts, again based on entrance into a segment. Alerts can be directed to one or more email addresses and are summarized in a periodic report.

So what about building the segments themselves? There’s no truly easy way to define complex selections, but Net-Results does a reasonable job of balancing simplicity with power. Segments can have general attributes including security (specifying which user groups can access the segment), automatic exclusion of known Internet Service Providers (so reports can only show visitors from identifiable companies), automatic inclusion of only known contacts (to report only on previously-identified individuals), and parsing of “get” variables from the incoming Web address (to capture information passed within the URL). Treating these selections as attributes reduces the complexity of the segmentation statement itself.

Users build the segmentation statements by selecting data categories (visit activities, contact attributes, campaigns, lists, Web forms, traffic source) and then choosing attributes relevant to each category. For example, attributes for Web visits include pages visited and duration, while attributes for contacts include name, company and job title. Many vendors use a similar approach, which I consider the best method for helping non-technical users to create complex segmentations.

Users can group multiple criteria into blocks. All conditions within a block must be met for a lead to qualify; a lead must qualify for at least one block to qualify for the segment. (In more technical terms: the system uses "and" conditions within each block, and "or" conditions between blocks.) Although some subtle queries can’t be created with this approach, it should meet the vast majority of marketers’ needs. Few demand generation systems offer more power, and many offer less.

Once a segment is defined, users can view the records it selects to check that it works as intended. They can then save the segment and assign it to alerts or reports.

Is Net-Results really simpler than other demand generation systems? To some extent it depends on your definition. Net-Results supports many marketing functions with relatively few features. This is one type of simplicity. But different features tailored to different functions could, at least in theory, make other systems more efficient at each task. This is another kind of simplicity. In practice, I felt that Net-Results’ shared features were just as efficient as specialized features used in other systems. So, yes, I ultimately think Net-Results will be simpler for most users.

Net-Results’ drive for simplicity is based on its target market of small businesses. Many of its clients have just one marketer on staff. These people don’t have the time or resources to use a complicated system, and may not need the refinements, such as rule-driven dynamic content within emails, that Net-Results doesn't provide.

Pricing is also aimed at small businesses. [Note: the following is revised price information provided by the vendor as of December 2009.] Fees are based on a combination of page views, email volume, support hours and length of commitment. A client with 60,000 page views, 20,000 emails, and 5 hours of support would pay about $700 per month on a month-to-month basis and just over $600 for an annual agreement. Half of those numbers (30,000, 10,000, 2 hours) would run $400/$350 based on agreement length. The company reports its average billing per client is around $500 per month. No contract is required and Net-Results offers a 14 day free trial.

The Net-Results system was launched in April 2009 and the vendor says it now has “hundreds” of clients. Some have converted from a simpler predecessor product that was launched in 2006.

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New White Paper and Eloqua Prospect Profiler

Eloqua yesterday announced Eloqua Prospect Profiler , which makes it easier for salespeople to review prospect behaviors that are captured by the demand generation system. In honor of the event, they sponsored a white paper by Yours Truly on the general topic of, um, why it’s important to make it easier for salespeople to review prospect behaviors that are captured by the demand generation system. The paper, Restoring the Balance: Why Marketing Holds the Key to Effective Selling in a Changed Business World, is available for free on the Raab Guide site and will eventually show up on the Eloqua site as well.

Despite its origins, the paper itself is quite generic and I think makes a valid argument: basically, that salespeople have less contact with prospects today because the prospects can gather so much information on their own. This makes it harder for salespeople to understand prospects and build relationships with them. The behavior data captured by marketing automation systems restores the balance by providing an alternate source of insights into prospect interests and intentions.

Replacing the relationship-building is more difficult, but demand generation systems can help somewhat by responding appropriately to prospect behaviors. This gives prospects a generally positive feeling towards the company even if no personal relationships are created. At a minimum, it keeps the company in the consideration set during the early stages of the buying cycle. Once the prospect has been assigned to an actual salesperson, the demand generation system can also send a stream of emails “signed” by the salesperson, building something of a one-to-one relationship. Obviously those emails must be appropriate, but this is what clever campaign design and good predictive modeling are about, per my last two posts.

Back to Eloqua Prospect Profiler. There’s nothing new about demand generation systems making prospect behavior available to sales people. Pretty much every major system on the market does this, and in roughly the same way: they pass activity headers over to the sales automation system, where they can be viewed as part of the normal interface, and let salespeople drill into details that are stored in the demand generation system itself. This may sound a bit awkward but it’s seamless from the user’s perspective, and moving all the details into the sales automation system isn’t practical.

Prospect Profiler’s claim to fame, so near as I can tell, is that it also presents summaries and trends of the prospect activity, per this very nice screenshot from Eloqua:

I don’t recall the other vendors doing that. The idea is to make it easier for the sales person to see patterns and then to drill into the details. This seems like a nice enhancement, and perhaps (I’m speculating here; Eloqua didn’t mention it) will be followed by additional of other marketing-gathered data with the salesperson’s interface. That would seem to be the general path that Eloqua and the rest of the industry are headed down, as part of the larger trend towards more closely intertwining marketing and sales activities.

If this isn't clear, think in terms of data from directories (D&B, Hoovers, OneSource), news feeds (Google Alerts, Lexis-Nexis, Reuters), social networks (Jigsaw, Linked-In), and social media (blogs, Facebook, Twitter). These are already assembled by various vendors, so all that’s needed is a relatively simple integration. The demand generation / marketing automation system is the obvious place to do this, rather than asking each sales person to do it for herself. The sales automation system could also be an option, but marketing already needs the data for its own purposes so it’s arguably a stronger contender.

Anther feature of Prospect Profiler is that salespeople can define their own rules for behavior-related alerts. Again, other demand generation systems also allow alerts, but I don't think they allow each salesperson to configure the rules for herself. It would be done by system administrators instead. But whether this is truly unique to Eloqua, I can't say.

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More on the Future of Demand Generation Systems

Summary: let's not forget that most companies are still not even doing simple demand generation. Systems for that might succeed even though advanced integration between sales and marketing is the long-run trend. And, my car hit a deer.

I hit a deer last Thursday while driving to Boston for the Sales 2.0 conference. I'm treating the four hour delay that followed as field research into customer relationship management, which ranged from great (the family-run auto shop that towed my car and took me in) to poor (the Enterprise car rental office that kept me waiting nearly two hours before admitting they didn’t have a vehicle available). It ended with the retired dad of the auto shop owners driving me into the next town to pick up a rental car there. The finishing touch was waving as we passed the local traffic cop, who had earlier stopped at the auto shop to chew the fat in true Mayberry RFD style. All told, my little visit to Plantsville (the actual town name) was straight from a grade B movie—city slicker makes an unplanned stop in a small town and learns about real life—except that I didn’t fall in love with a local shop girl.

The net result, beyond some new anecdotes, was that I missed much of the conference. My only extended conversation was with a sales manager who was just recognizing that cold calls were not the most efficient use of his time, and was quite excited to learn that many vendors can provide qualified lists and do the appointment setting for him. He was also starting to think that maybe the company Web site could play a role in attracting leads. In other words, he far behind the times. Yet he also appeared to be seasoned, competent and generally successful. In its own way, this conversation was as much an intrusion of the real world into my bubble as the stopover in Plantsville.

But then it was back to the bubble (so much more interesting than reality) with vendor meetings. Much of the conversation related to my blog post of the day before, which argued that self-adjusting statistical models will replace manually-generated business rules for alerts, lead scores, segmentation and message selection in demand generation systems. Discussing this idea let me to refine the presentation, which I now describe in terms of marketers catching up with changes in their role. That is, most marketers understand their job as generating leads and are just starting to implement systems to do this better. But their job today is actually to manage relationships deep into the buying process. Industry leaders have recently recognized this. The next step, which has barely begun, is for demand generation vendors to deliver solutions that support the new role. My specific argument is that self-adjusting models must replace rules because only models let the systems handle their expanded responsibilities and still be simple enough for marketers to actually use them. My broader argument is that self-adjusting models, rather than a variety of other new capabilities, will be the really important features for vendors to add.

My vendor discussions also touched on the other side of this coin, which is that demand generation systems to perform the original marketing tasks are quickly becoming commodities. Interestingly, I’ve spoken with more than one vendor who sees this as an opportunity, so long as they get to be the dominant commodity provider. Whether these vendors know how to make this happen is another question. I don't know myself, although I’m guessing the primary requirement (beyond a suitable product) is deep pockets for extensive marketing to grab share quickly.

Nor am I convinced that commoditization is a very good strategy, since even the dominant player may not make much money. But, with my little reality check still fresh in mind, I don’t want to underestimate the market for old-style demand generation systems. Perhaps a simple, low-priced system really can be a major success even though marketers will eventually need something more advanced. (Yes, the obvious strategy is to offer one product that can start simple and expand. But I’m very skeptical that this is actually possible.) It’s an interesting strategic puzzle. Fortunately for me, I don’t have to solve it. I just observe and report.

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Prediction: Statistical Methods Will Replace Conventional Rules for Marketing Decisions

Summary: basic demand generation features are close to a commodity. Vendors who replace conventional decision rules with automated statistical methods may gain a key competitive advantage because the automated methods produce substantially and measurably better results.

One of the most popular posts ever on this blog is Low Cost Systems for Demand Generation, which listed several options that started at under $500 per month. But it seems that nearly every day brings yet another possibility to my attention. Some really frugal alternatives include Genoo starting at $199 per month; Net-Results starting at $79 per month; and Nurture starting at $495 per month. I haven’t looked closely at any of these but they all seem to promise the core demand generation capabilities of email, landing pages, automated nurturing, lead scoring, and sales system integration.

The question this raises in my mind is where the industry goes from here. Basic demand generation is on the verge of becoming a commodity if it isn’t one already. The more sophisticated vendors will of course continue to add features, but it’s not clear that most marketers will be interested in the additional capabilities or be able to handle the added complexity. Perhaps the key competitive battleground is the ability to add that complexity without making the systems harder to use. But even though there are certainly substantial differences in usability among today’s systems, it’s hard to see why everyone won’t eventually be able to do roughly equal jobs of simplifying their interfaces.

Another possibility is that vendors will compete on their ability to help marketers use their systems – that is, by providing marketing training, usage reviews, and professional services. In other marketing automation segments, including MCIF systems and campaign management for consumer marketers, the ability to provide such services was the single most important difference between winners and losers. The same applies to CRM systems – it was Siebel’s partnerships with big system integrators that ultimately let it pull away from the pack. I do think these services will be a key success factor in the demand generation market, but there’s a big difference: because demand generation systems are offered as on-demand services rather than on-premise software, the actual deployment is much simpler. This means independent consulting firms can more easily learn to work with multiple systems. Because it’s much harder for vendors to build a loyal, locked-in base of resellers, it’s easier for new players to duplicate the service infrastructure of established competitors.

This brings us back to features. Certainly there is a list of hot items right now: Webinar integration, digital asset management, dedicated IP addresses for outbound email, APIs to post data from external forms, integration with Google Adwords, providing contact names from external databases when a visiting company is recognized by its IP address, pulling data from social networks to flesh out a prospect’s profile, and interacting through social media in addition to traditional channels.

The question is which of these features will turn out to be really essential. The only one I personally see as important to a large number of marketers in the immediate future is the Webinar integration, because Webinars are widely popular and integration makes the marketer’s life significantly easier. Everything else on that list strikes me as either of interest to a relatively small fraction of marketers or as simple enough to add that it won’t be a competitive advantage.

So is there something else that could be really important? Well, I wouldn’t ask the question if I weren’t leading up to something.

My particular insight, if it is one, is that consensus has crystallized within the past month that marketing now remains dominant much deeper into the buying cycle, and that sales and marketing must work much more closely together as a result. The idea itself isn’t new, but I suddenly see it referenced everywhere I turn. Part of the reason may be that I’m paying more attention because I wrote a paper on the topic myself (see When Best Practices Go Bad: New Rules for Sales and Marketing Management) although I’m under no illusion that my paper was anything other than one voice among many. It’s simply one of those ideas whose time has come.

As I and others have written, the immediate implication of this change is that marketing systems should provide salespeople with more information about prospect behaviors – what Steve Woods of Eloqua elegantly calls “digital body language”. This gives the salespeople insights into customer interests, replacing to some extent the information that they previously gathered for themselves when dealing with prospects directly.

But those direct interactions also built a relationship between the salesperson and the prospect. Watching their behaviors doesn’t do that. To the extent that anything does build the early relationship today, it’s the automated nurturing programs and behavior-driven responses executed by marketing systems. I don’t really believe that even the cleverest marketing systems can really replace the trust built by a good salesperson, but at least the automated programs can educate prospects and leave a positive impression about the company’s responsiveness to their needs.

I haven’t seen much written about the burden that this change places on the marketing systems. We’re not talking about some simple drip marketing to keep leads warm and educate them a bit until they move closer to their purchase. Rather, marketing must come as close as possible to simulating the interactions between a prospect and a good salesperson to build an essential relationship. This means that the marketing system has to be really smart. And I think providing this sort of intelligence might be a major competitive battleground for the vendors.

That last sentence was a bit of a leap, so let me fill in the blanks. Today’s demand generation systems are largely rule-driven when it comes to selecting prospect treatments. Whether those rules are embedded in list definitions, campaign flows or dynamic content doesn’t matter. The problem is that rules are hard to build and remain unchanged until somebody writes a new one. They’re generally based on somebody’s best guess about how the world works and they tend to be fairly simple. As a result, rule-driven systems just can’t be very smart, in the sense of reacting appropriately to subtle clues or changes in behaviors.

The limits of rule-driven systems don’t matter when there isn’t much data to work with and there aren’t many choices to make. That was arguably the case in the past when lead management systems worked with only a small amount of data from a postal reply card or brief telephone survey. But today’s demand generation systems are dealing a flood of behavioral data related to emails and Web visits. Rules can’t deal optimally with that much information. In addition, the demand generation systems have many more decisions to make, since every personalized email and Web page involves many choices for information to display. No one can create enough rules to handle all the possibilities.

Nor is the challenge limited to rules for selecting messages. Demand generation systems also use rules to decide when to alert salespeople about prospect behaviors. Lead scoring formulas are essentially rules as well. In addition to the fact that these rules are all defined manually and pretty much arbitrarily (that is, based on users’ best judgments), there is little feedback to check whether they are effective.

All of this absolutely guarantees that demand generation systems will produce suboptimal results. That would be annoying under any circumstances, but if the demand generation system takes on the primary responsibility for early relationship building, it’s more than merely annoying. It could destroy your company.

There is an alternative. Marketing systems can deploy automated statistical techniques to select messages, issue alerts and send leads to sales. Consumer marketers have used such methods for years with proven success. In addition to dealing with many more options than rules can handle, such systems can automatically learn from past results to improve their accuracy and adjust to changes in behaviors. Nicer still, marketers and salespeople can actually observe the success or failure of the decisions by watching objective criteria such as return visits and close rates. This last point is critical because it means marketers have a way to actually compare the value of decisions made by different systems. This means that vendors can meaningfully compete to offer the best decision-making capabilities, and marketers can choose the system that does a better job. And, unlike a feature that appeals to just a small fraction of marketers, better decisions are important to everyone. A system that could show it made better decisions would therefore have a very major competitive advantage.

So far, everything I’ve written here is just my private little theory. I haven’t heard any vendor, pundit or client suggest anything similar. This could well mean that I’m wrong; after all, I do like fancy automated systems with their cool bells and whistles. But I think maybe I’m right. Demand generation systems are getting more and more complicated, and something is needed to radically simply them before they collapse into chaos. Given that the stakes are nothing less than the sales process itself, allowing this to happen is unthinkable.

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Demand Generation Deployment Survey: Preparation Saves Two Months

Summary: My survey of demand generation deployments found that some companies deploy many features immediately, while others take two or three months to reach the same stage. A fast start depends on ample preparation. A white paper on the Raab Guide site explores the results in detail.

**************************************

I finished my detailed analysis of the demand generation deployment survey results yesterday and posted it to the resource library of the Raab Guide site. This turned out to be a major project (the analysis, not the posting) because I revisited the data from a company perspective. The analysis in my earlier blog posts looked at average deployment rates by feature, without relating those to particular companies.

As often happens, averages gave misleading results. For example, one of the original factoids that most impressed me was that 80% of features ever deployed are deployed by the second month. This seems to suggest that people deploy quickly and then are largely done. But analyzing data by company, I found a very wide divergence in behaviors: some companies deploy nearly all features immediately, while others start very slowly.

Specifically, I grouped the companies into four quartiles, ranked by the number of features they deployed during the first week. This figure itself varied hugely, from 0.6 features per company in the lowest quartile to 8.8 features/company in the highest. But what I found is the companies who start with very few features will add them steadily over time, while the ones who deploy many features immediately quickly reach the maximum. So, that average of 80% deployment by the second month really is a combination of rates ranging from 57% to 97% for the different quartiles.

table 10 [table numbers refer to tables in the paper]
% of final features deployed by time period (companies stay in original quartile over time)
quartile	first week	first month	second month	third month	later
1 (tortoise)	0.05	0.32	0.57	0.72	1.00
2	0.28	0.55	0.70	0.77	1.00
3	0.49	0.76	0.84	0.86	1.00
4 (hare)	0.68	0.93	0.97	0.97	1.00
average	0.39	0.66	0.80	0.84	1.00

In other words, we have a classic tortoise vs the hare race, with some fast starters and others moving slow but steady. Looking at the number of features per company rather than percentages, we see the tortoises (quartile 1) never quite catch up, but do greatly narrow the gap.

table 9
average features per company by quartile (companies stay in original quartile over time)
quartile	first week	first month	second month	third month	later
1 (tortoise)	0.6	3.4	6.1	7.7	10.7
2	3.0	6.0	7.7	8.3	10.9
3	5.6	8.7	9.7	9.9	11.5
4 (hare)	8.8	12.0	12.6	12.6	12.9
average	4.5	7.6	9.2	9.6	11.5

My fundamental interpretation is that the companies who deploy many features immediately have done their homework and are ready to go from day one, while those who start slowly did little advance preparation. The figures above suggest it takes the tortoises about three months to approach the initial deployment levels of the hares - so it seems this is the length of the delay from lack of preparation.

I actually tightened the analysis even more by looking separately at deployment rates for basic, advanced and optional features within each quartile. (Basic features are needed for simple email campaigns; advanced and optional features are more complex and less common. The paper describes the definitions in detail.) Looking just at the basic features, you'll see they're deployed sooner than average, and that even the tortoises finish implementing them by the second or third month. (You'll also note that, even among basic features, the tortoises never quite deploy as many as the hares.)

table E-1
cumulative features deployed by quartile (based on first period rank)
% of final features deployed by period							average features deployed
quartile	feature category	first week	first month	second month	third month	later
1 (tortoise)	basic	0.10	0.53	0.85	0.93	1.00	4.4
2	basic	0.52	0.79	0.88	0.90	1.00	4.7
3	basic	0.71	0.88	0.92	0.92	1.00	4.8
4 (hare)	basic	0.84	0.98	1.00	1.00	1.00	5.0
avg		0.56	0.80	0.91	0.94	1.00	4.7

The paper draws a number of other conclusions from the data and makes some helpful if generic recommendations (select the right system, prepare in advance, plan for expansion, test and measure). That's all good stuff but far from world-changing. What's really interesting is the details themselves - go ahead and dig into the paper and see what you find.

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Demand Generation Implementation Survey - Background Results

I've been having a dandy time analyzing the results of my Demand Generation Implementation Survey. Responses are still coming in but I thought I'd at least post some preliminary results to whet your appetite. Hopefully I'll be able to post a more substantive analysis tonight or tomorrow.

As of April 29, I've received 40 responses, of which I've discarded two as incomplete and two because they related to vendors I considered irrelevant (Zoho and Ad Giants PitchRocket). Obviously any survey based on 36 net responses (and self-selected at that) has little statistical value, but I still think the broad results are extremely interesting.

The survey was promoted on this blog and the Raab Guide site, but primarily via posts on Twitter. (Thanks to the many people who 'retweeted' the request). This introduces yet another source of sample bias. One measure of this is the distribution of vendors reported by the respondents, which clearly doesn't reflect the installed base of the industry. This distribution actually pleases me, since it means we have results from users of many different systems. (Obviously, however, the quantities are too small and sample bias too significant to break out results by vendor.)

nbr responses	vendor
8	Marketo
6	Eloqua
3	Genius.com
3	LoopFuse
3	Pardot
2	Market2Lead
2	Treehouse Interactive
1	eTrigue
1	Vtrenz (Silverpop)
7	No Response
36

Another intriguing bit of contextual information is the deployment date of the systems. Two respondents actually reported future dates -- I'd guess those were typos but, since responses were anonymous, I couldn't ask. There was actually another dated 6/01/2208, which I treated as 2008.

I was also curious to see the six responses for implementations during 3/09 and 4/09; obviously, these companies haven't gotten past their first or second month. Most of the answers for those entries reported features deployed within the first two months, or made the reasonable selection of 'later', so they could quite well be accurate. One repondent reported deployment on 4/24/09 (i.e., last week) but showed several features as deployed in month three. I assume represents their plans rather than reality. Fair enough.

In any case, the ten deployments in the first four months of 2009 (or 12 if you count the two future dates) and 12 in 2008 highlights the newness and fast growth of the demand generation industry. There were just five earlier deployments, including one for 1990, which is almost surely an error.

nbr responses	deployment date
1	10/09
1	8/09
3	4/09
3	3/09
1	2/09
3	1/09
12	2008
2	2007
2	2006
1	2005
1	1990
6	No Response
36

One final bit of more data, this more substantive: I asked how well their experience with deployment and their systems as a whole had met their expectations. Results strike me as extremely positive -- about two-thirds rated both experiences as better than expected, with just a bit more satisfaction with the systems than the implementation. Only a couple of responders felt things were worse than expected. Again, we have to consider sample bias. But even so, this seems to be a pretty happy set of campers.

I actually looked to see if there was any relationship between deployment year and satisfaction, and it newer customers may be a bit happier. But the numbers are very small, recency may also introduce some bias, and in any event even the earlier customers are highly satisfied. So I don't consider this more than a hint of what might be the case.

How would you rate your experience with...
%	better than expected	about as expected	worse than expected	total
system implementation	0.64	0.33	0.03	1.00
the system itself	0.67	0.28	0.06	1.00

How would you rate your experience with...
nbr responses	better than expected	about as expected	worse than expected	total
system implementation	23	12	1	36
the system itself	24	10	2	36

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OfficeAutoPilot: Simple, Powerful, Low Cost Demand Generation for Small Business

My personal definition of demand generations systems (see Introduction to Demand Generation Systems from the Raab Guide site) explicitly states that they do not incorporate sales automation. The division makes sense in most organizations, since marketing and sales are separate. (See Should Demand Generation and Sales Automation Be Separate Systems? for whether this will change.)

But small businesses are different. Sales and marketing are often handled by the same department, if not the same person, and owners want as few systems as possible to keep costs to a minimum. So it’s pretty common for small-business-oriented systems to support both marketing and sales. Of products I’ve written about recently, Infusionsoft is the best example of this, extending beyond sales and marketing all the way to order processing. (See my Infusionsoft review.)

OfficeAutoPilot from MoonRay LLC is another small-business-oriented product that combines marketing and sales automation. The latest version actually offers integration with Salesforce.com as well, making the system more suitable for larger organizations. Indeed, the functionality of OfficeAutoPilot compares favorably with conventional demand generation systems, while the pricing – starting at just under $600 per month for 50,000 contacts and 100,000 monthly emails – is hugely attractive. Business marketers who can’t afford to pay more or want a low-risk way to get started with demand generation will find OfficeAutoPilot an intriguing option.

The basics are certainly there. Users can create personalized emails, landing pages and Web forms with the system’s tools or import externally-created HTML. Emails are sent from the OfficeAutoPilot server while forms can either be hosted by OfficeAutoPilot or externally. Web pages are built by dragging and dropping objects in the way you build a Powerpoint slide. Although most demand generation systems use an interface more like building a Word document, OfficeAutoPilot’s approach is a fairly common alternative and perfectly acceptable. Some marketers may even find it easier.

OfficeAutoPilot actually does a better job at split testing than most demand generation systems. Users can define two versions of an email or Web page when they set it up, and the system will automatically alternate between them during execution. Standard reports compare the performance of the two versions. This is the easiest approach I’ve seen to split testing. The only other demand generation system I’ve seen use it is Marketo.

OfficeAutoPilot takes a straightforward approach to multi-step campaigns. Users lay these out as a series of steps timed relative to a single date. This can be the start of the sequences, a fixed date such as a birthday, or an activity such as the last purchase. Leads can enter a sequence when they fill out a Web form, are manually added by the user, or trigger the conditions specified in a “global rule”. The system checks each lead against all the global rules every time the lead’s data changes, allowing real time response to lead activities.

Each step in a sequence does one thing: send an email, postcard or voice message, add the lead to a fulfillment list, create a sales automation task or execute a user-defined rule that triggers an action if its conditions are met. Available actions include adding or removing the lead from a sequence, adding or removing a tag from the lead profile, changing a data field, sending the lead an email or post card, sending an email to someone else (a sales rep or program administrator), adding or removing the lead from a fulfillment list, and sending the lead to the sales system.

The rules let OfficeAutoPilot react to new behaviors even though the sequence itself is fixed in advance. There is no true branching within a sequence, in the sense of sending different leads down different multi-step paths. But this isn’t necessarily a problem: it’s how linear campaign designs work in most demand generation systems and, as I argued last week, helps to avoid confusion. If anything, OfficeAutoPilot’s combination of steps, rules and actions makes it more flexible than the average demand generation product.

Treatment of tasks illustrates the tight connection between marketing sequences and sales automation. Tasks can be scheduled relative to the date of the step in the sequence, and the system can pause the sequence until the task is complete. Tasks can be assigned to the lead’s salesperson or another owner; the system can notify the owner by email, telephone or adding the task to their to-do list; and the system can notify the owner’s manager if the task is not completed on schedule.

Messaging capabilities are designed to help small businesses whose own resources are limited. MoonRay has negotiated with VoiceShot for outbound recorded voice messages and with a network of printers for low-volume personalized post card printing and mailing. Users design the post cards with the same interface used to build Web pages. Fulfillment lists accumulate names in a queue and then periodically send them to a list for a call center, warehouse, or other destination. The user specifies how often the list is generated, who it will go to, and what data it contains. Emails and postcards can include a personalized URL to help with tracking. The system also provides a pool of telephone 800 numbers that can be assigned to different promotions and will automatically route to a central number.

Lead scoring in OfficeAutoPilot is handled by an independent process similar to the “global rules”. That is, users define point values to different conditions and the scores are recalculated whenever the lead has an activity or data change. There’s even a standard feature to reduce activity-based score values by a specified percentage for each day after the activity occurs. This is more than some conventional demand generation products provide.

Leads can be sent to sales by actions within a sequence or by a global rule triggered by the lead score or other conditions. The system provides standard sales automation features including lead routing (“round robin”, weighted and others), contact management, task scheduling, sales funnels, call notes and disposition tracking. Dispositions can be tied to rules to automate follow-up actions. Since these are the same rules used elsewhere in the system, the actions can encompass any option available to the automated sequences. This is another benefit of running marketing and sales on the same system.

The system also handles user rights like a sales automation system, which is to say, more precisely than imost demand generation products. The system administrator decides which functions are available to which users, and users see only their authorized features. This is an important usability benefit when companies have many different types of users.

OfficeAutoPilot’s sales automation is not as sophisticated as specialized sales automation systems like Salesforce.com or even Goldmine. For example, there is no separate account or company level.

MoonRay says about 20% of its clients use its bi-directional Salesforce.com synchronization. This shares data between the two systems and can display a history of the lead’s OfficeAutoPilot activities within the Salesforce.com screens. Another adapter lets users view and edit contact records from within Microsoft Outlook and will add Outlook emails to the OfficeAutoPilot history. Data from other sources can be posted to the system through a standard SOAP API or by importing structured email messages. This is most typically used to add purchase information. Users can add new fields to the lead profiles as needed.

The system tracks marketing results from the lead source all the way through the sales funnel, and on to revenue if available. A marketing dashboard provides standard reports on Web activity and emails. Users can also drill into the details of each campaign sequence, listing the leads in each stage and drilling further to see the individual messages they received.

Pricing of OfficeAutoPilot is based largely on the number of users, subject to some fairly generous volume constraints. A five-user system with up to 50,000 contacts and 100,000 emails per month costs $597 per month. A dedicated email IP address adds $147 per month for up to 500,000 messages.

MoonRay also offers simpler systems at lower costs, including a new product called SendPepper scheduled to launch today (March 30). SendPepper includes outbound email and postcards, Web forms and landing pages, and simple auto-response sequences. Two versions are available, priced at $29 per month and $79 per month.

The original version of OfficeAutoPilot was introduced in 2003. The system currently has more than 100 active accounts. It is sold directly by MoonRay and through partners.

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