Future of Marketing Content: Reflections on the Content2Conversion Conference

I spent the early part of this week at Demand Gen Report's Content2Conversion conference. The event was superbly run, as usual, but I didn't sense any over-arching pattern until I was literally on my out the door and stopped for one last chat with some colleagues.  Then I knitted together – at least to my own satisfaction – what had seemed to be disconnected observations.

The first strand was the number of systems that offer detailed information about content consumption. Vendors including Highspot, SnapApp, Ceros, Uberflip, and ion interactive all let marketers track customer behaviors within a piece of content – such as how much time is spent on each page or even regions within a page. On reflection, it struck me as amazing that we have this level of detail available, given that just a few years ago marketers couldn’t even tell whether a given piece of content had been looked at. The uses for this information are obvious, including helping marketers to understand which topics are most appealing and giving salespeople insight into the interests of individual prospects. But I wonder how many marketers or content creators are ready to take advantage of this information. Of course, it’s clear that they should. But I suspect most are already overwhelmed by the less precise information available through less advanced technologies. This leaves them with little appetite for still greater detail.

Naturally, my own preferred solution to this technology-created flood of data is still more technology. Some of this involves advanced analytics to extract the significant needles of information from the hayfields of detail, although I don’t recall seeing vendors who do that type of analysis at the show or hearing speakers discuss them. But the more interesting response is to automate content creation and selection directly, using the detailed information to create new content and to send the most appropriate content to each individual. Again, there weren’t many solutions at the show that promised to do this, apart from Captora – which extracts keywords from a company’s Web site and its competitors’ sites, constructs draft landing pages for the most important topics, and deploys them (after some manual polishing) with links to CRM or marketing automation data capture forms. Captora is focused on paid and organic search marketing, so it can’t pick which ads to display to which prospects. But I also chatted with people from Adaptive Campaigns (which did not exhibit), whose system uses rules to generate highly customized programmatic display ads. And, on the way to the airport, I caught up with Idio, another system that automatically analyzes content and picks the best match for each individual – although Idio doesn’t do any content creation or dynamic customization.

As you know from the Machine Intelligence in Marketing Landscape in my last post, I’ve also identified a several other systems that use automated methods to generate and select content. I’ll even predict that machine generated content will be a major trend in the near future – precisely because it’s the only practical way for marketers to take full advantage of the detailed information now available on content consumption.

This connects to another theme that I did actually hear articulated at the conference: the need to move beyond “quality” content to appropriate content. That’s an interesting evolution, since recent discussions have often focused on the challenge marketers face in just getting the volume of content they need for increasingly segmented programs. That requirement hasn’t ended, but I heard more discussion of how to create the right content mix and how to create content that is compelling enough to attract attention. To some extent, this argues against the notion of machine-generated content, which will probably never be better than mediocre and formulaic. But I can easily imagine a world where humans create a few great pieces of tentpole content and use a lot of simple, machine-created messages to feed people to it.  The machine-based messages won't be brilliant but they'll be effective because they're highly tailored to their targets. This tailoring will be enabled by behavioral and intent data, which were also popular topics at the conference.

I also have one other observation, which was totally unexpected (the best type!).  It might be just my imagination, but I think I sensed a bit of overconfidence among marketers about their ability to buy new technology. This is certainly surprising, given that marketers until recently have been more frightened of technology than anything else. I’ll speculate that a new generation of marketers are more comfortable with technology in general and are now reaching positions where they have control over purchasing decisions. Mostly that's great: the industry can’t advance if marketers are afraid to try new things. But some of these buyers may not realize that they are unfamiliar with the full scope of products available or that deploying complex technology is much harder than signing up for a new software-as-a-service application. Let me be clear that this concern is is based on one conversation I had and one comment that a friend overheard.  So I might be overreacting. Still, it’s something to guard against; overconfidence can lead to cavalier decisions that are just as harmful as indecision based on fear.

LinkedIn Buys Fliptop: Why Account Based Marketing and Predictive Analytics Are a Natural Fit

Predictive analytics vendor Fliptop today announced its acquisition by B2B social network LinkedIn.  It's an interesting piece of news but I'm personally disappointed at the timing because I have been planning all week to write a post about the relationship between predictive analytics and account-based marketing (ABM).  I would have looked so much more prescient had they announced the acquisition after I had published this post!

The original inspiration for the planned post was a set of three back-to-back conversations I had last Friday with one ABM vendor and two predictive analytics companies (none of which were Fliptop or LinkedIn).  The juxtaposition highlighted just how much predictive and ABM complement each other.  In fact, the relationship is so obvious that it almost seems unnecessary to lay it out: predictive vendors help marketers find accounts to target; ABM helps marketers reach target accounts.  You can safely assume that both sets of vendors have noticed the relationship and that many are working to combine the two techniques.  The Fliptop/LinkedIn deal is just more evidence of the connection.

To move past the very obvious, ABM vendors – whose basic business is selling ads targeted to specific companies – could also use predictive analytics to refine their ad targeting.  This could mean selecting the best people to reach within targeted accounts or selecting the most effective ad placements to reach those accounts.  This requires integration of predictive analytics within the ABM product, not just using predictive before ABM begins.  I expect LinkedIn will use Fliptop's capabilities in these ways among others.

But, getting back to last week's conversations, what really struck me was a less obvious connection of ABM and predictive to content.  Two of the vendors described using their systems to select which content to send to specific accounts or individuals.  These selections are based on previous behavior, something that certainly makes sense.  But I don't generally recall hearing ABM or predictive vendors discussing as one of their applications.  It's an important idea because it promises to improve results by delivering more relevant content for the same price.  The same data gives marketers insights into broader trends in the types of content that buyers find interesting.

Content analysis requires the ABM or predictive system to be aware of the topics of the content being consumed.  This is only possible if someone specifically goes to the trouble of tagging the content and capturing the tags.  So content analysis is not quite a natural byproduct of the ABM or predictive analytics: it takes some intentional effort.  A corollary is that not all ABM and predictive systems can deliver this benefit.  So it's something to specifically ask prospective vendors about if you think you'll want it.

To put things in a still broader perspective, targeting content with ABM and predictive systems is part of a broader trend of using advanced technology to help marketers create, manage, and optimize content.  This is something that vendors like Captora, Persado, and Olapic do in terms of content creation, and Jivox, OneSpot, Triblio, and BloomReach do in terms of personalized content creation.  I've been looking at a lot of those systems recently although I haven't written much about them here.  New targeting technologies create unprecedented demands for more content, which only new content technologies can meet.  So you can expect to hear more about technology-based content creation, whether I write about it or not.

LeadLiaison Helps Marketing Automation Users Break the Content Bottleneck

You may have noticed that there are many B2B marketing automation systems available. So it’s not surprising that LeadLiaison prefers to be called something else – in their case, “revenue generation software”. I’m not sure exactly why they chose that term, apart from the fact that everybody likes revenue. But they do go far enough beyond standard marketing automation to justify a different label.

In particular, LeadLiaison helps marketers create content, a critical bottleneck that is not addressed by most marketing automation systems.  The most impressive feature is an outsourced content creation service, which lets marketers build an online creative brief for a particular item and then send it to a network of writers who agree to produce it for a fixed price in a few days. Identities are hidden in both directions, so the parties can’t easily circumvent the service to work together directly in the future. But prices are either reasonable (if you’re a buyer) or ridiculously low (if, like me, you're a sometime content creator), starting around $50 for a blog post. There are several third-party networks that offer this sort of service, but I’m not aware of any other marketing automation vendor that has developed their own.

LeadLiaison takes good advantage of this feature by closely integrating the resulting content into other operations. The outsourced content can be loaded directly into the marketer’s content library with access controls based on date range, number of downloads, or whether the requestor provides an email address.  There is also an option to request an email address but then grant access even if it's not provided. Content can be linked to a social media publishing process that can release it immediately, schedule it for the future, or add it to a “buffer” of materials that are released at predefined intervals. The system warns users when the buffer inventory is dangerously low, so they have time to replenish. Content is served through short URLs to track consumption and sharing. The system also tracks consumption by individuals using cookies and by companies based on IP address.

Marketers who want to build their own content are also covered.  They get powerful tools for email, landing page, Web form, and survey creation, including templates with drag-and-drop editing for different types of components.  The system can also extract the HTML of an existing Web page, insert new content such as a Web form or survey, and deploy the modified page in place of the original. That's a big deal: it means marketers can add their content into existing Web pages and forms without recreating them from scratch.

Forms can generate an alert or take another action after they are submitted. They can also include progressive profiling rules to avoid asking people questions they have already answered. LeadLiaison is adding a marketing content map that will help planning by showing the inventory of available content by buyer type of purchase stage.

Although content creation is probably LeadLiaison's most unusual set of features, the system also does an above-average job at the standard marketing automation functions: email, multi-step workflows, behavior tracking, lead scoring, CRM integration, and analytics. To look at each of these in turn:

• emails can be sent by the vendor itself or through integrations with third parties including Constant Contact, MailChimp, SendGrid, and others. Salespeople can also send through Gmail, Salesforce.com, or Microsoft Dynamics CRM. Users can preview how their emails would look in different clients. 

• workflows support multiple steps, event-based triggers, wait stages, and a wide variety of actions including lead scoring, lead distribution, list management, alerts, and calls to external Web hooks. 

• behavior tracking captures email responses, Web site visits, form completions, downloads, and video viewing through Wistia. Users can define “buy signals” based on combinations of behaviors, which in turn can be trigger actions in workflows.

• lead scoring assigns separate scores for “fit” against a target buyer profile (which LeadLiaison calls “grading”), for recency, total activity, buying signals, and specific actions the user has assigned points. Users can prioritize leads by combining these elements in a single aggregate score according to user-assigned weights.  

• CRM integration includes native connectors for all editions of Salesforce.com (not just the Professional edition, as with most marketing automation products), soon to be supplemented by Microsoft Dynamics and Sugar CRM. A Zapier connector supports integration with other systems. Salespeople can receive alerts, hot lead reports, and detailed information about Web site visitors. The system can use IP address to identify the company of anonymous visitors and will look up possible contact names at those firms from external sources including Data.com and LinkedIn. There is also an integrated phone dialer.

• analytics tracks content usage, conversions, lead distribution, email results, Web visitors on internal and external pages, and return on investment. Enhancements for more advanced reporting are also planned for 2015.

In short, this is a very mature marketing automation system for a company that launched in 2013. My take is that they learned from the experience of older products. Pricing starts at $500 per month, up to 5,000, which makes the system affordable for small companies even though the features are robust enough for the mid-market and perhaps higher. A stand-alone visitor tracking product starts at $200 per month.

Vendor Selection Best Practices, Predictive Marketing Explained, Content Marketing Integration, and Other New Papers on Raab Web site

I've just gotten around to posting a  bunch of new white papers in the Resources section of the Raab Guide Web site.  Ordinarily you would need to register to view these, but I spent the whole afternoon putting this together, so I want as many people as possible to see them.  Here you go:

• Technology Evaluation Best Practices describes the challenges that marketers face in selecting the right systems and lists best practices to follow for success.

• What Matters Most In Selecting Marketing Automation explores survey results that highlight common errors when buying a marketing automation system and offers recommendations for how to avoid them.

• Why Modern B2B Marketers Need Predictive Marketing explains why predictive modeling has become essential to B2B marketers and describes specific applications.

• Defining Your Marketing Technology Strategy presents a framework for coordinating your marketing systems and links to an online tool that analyzes your current situations and recommends how to improve your systems.

• Content Marketing Integration Workbook provides a set of checklists to help marketers understand how to integrate content marketing with their other marketing programs at the strategic, operational, and technical levels.

• Questions to Ask When Selecting Your Customer Data Platform helps marketers to understand what a CDP is, whether they need one, and what specific questions to ask vendors when trying to pick the right system.

• The Customer Data Platform describes an emerging class of products that combine marketing database management, centralizing treatment decisions, and integration with execution systems.

B2B Content2Conversion Conference: Let's Get Strategic

I spent two productive days this week at Demand Gen Report’s B2B Content2Conversion Conference  in New York. As some who spends more time creating content than pondering it, I appreciated the opportunity to put content generation in a more broader perspective.

Sessions at the conference were consistently excellent, which isn’t the case at every show. Here some of the key points that stuck with me:

- Take control of the conversation. Brent Adamson of the CEB presented the Challenger Sales and Marketing models, which have been around for a few years now but still impress me. The gist of the model is that marketers and sales people need to disrupt the normal purchase process by convincing buyers that they’re doing something wrong, it’s costing them money, and the seller’s product can fix it. Without that disruption, buyers will view competitive products as commodities. This notion of disruption is what separates the Challenger approach from conventional solution selling. It gives a strategic focus to your content planning, and readers of this blog know I’m a sucker for strategic focus.

- Measure wisely. Jim Lenskold of the Lenskold Group offered a detailed framework for ensuring that content marketing measurements tie to actual business objectives. The critical point is those objectives vary in different situations, so marketers need to be very purposeful in deciding which measure apply in each situation. In case you’re wondering, yes, that’s a lot of work. But this is one of those “eat your vegetables” situations where there’s not much value in taking shortcuts.  I'm a big fan of other people eating their vegetables.

- Buying processes differ with market maturity. Matt Papertsian of SiriusDecisions refined the now-cliched notion of buyers taking control of the purchase process, pointing out that it really depends on how well they understand what they’re buying. Buyers are in control in an established market, where they can easily assess the alternatives. But when sellers are offering something new, they can – in fact, must – educate buyers about why they need what the seller is selling. This elevates the role of sales people and means they should be engaged earlier in the buying process. It also echoes the Challenger Sales process while suggesting that the Challenger approach will work better in some circumstances than others. Naturally, content should be adjusted to the type of buying process as well.

Other sessions offered practical advice and case histories on how to succeed with marketing content. They were all good, too, but I’m a big picture kind of person. I look forward to applying these concepts at Raab Associates over the coming year.

Incidentally, Demand Gen Report will be moving the conference from New York to Scottsdale, AZ for its next session, which will be February 16-19, 2015. Definitely worth the trip.

Moving On: Lessons from the B2B Marketing Trenches

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

LoopFuse Captures More Web Traffic Data

Summary: LoopFuse has extended its system to capture more Web traffic data, which lays the foundation for future analytics.

LoopFuse recently released its latest enhancements, which it somewhat grandiosely labels as making it “the First and Only Marketing Automation Solution with Inbound Marketing”. In fact, as the subhead to their press release states, what they’ve really done is somewhat more modest: add “real-time Web traffic intelligence” by providing features to capture search terms, referring sites and page views, and link these to individual visitors.

The new release also adds real-time social media monitoring (directly for Twitter and Facebook, and through Collecta for blogs, YouTube and other sources).

These features are certainly useful. But my idea of "inbound marketing" is more along the lines of HubSpot, which provides search engine optimization, paid search campaign management, social media monitoring and posting, blogging, and Web content management. Although LoopFuse might eventually add those functions, it hasn't yet and isn’t necessarily moving in that direction.

Accepting their labels for the moment, let’s look at what LoopFuse has added:

- “content marketing” is a set of reports that tracks Web traffic related to different assets. Users get a list of the assets ranked by number of page views. They can then drill into each item to see a graph of traffic over time and to see details such as the number of visitors, views per visitor, and referring domains and pages. Because the views are tied to individual visitors, users can also click on the referring domain to see what other pages people from that domain visited. This is essentially the same information as provided by...

- “inbound marketing”, which shows visitor sources by category (direct links, paid search ads, organic search) and details within each category (specific messages, ads or keywords). As just noted, users can drill down to see which Web pages were viewed by visitors from each source.

- “social monitoring” provides real-time monitoring of user-selected terms on the various social Web sites. Unlike the other Web traffic data, this information isn’t stored within the LoopFuse database and isn't tied to specific individuals. LoopFuse plans to provide some trending reports in the future. Of course, the real trick would be linking social media comments to lead profiles.

All of these are valuable reports. Having them within a single system is particularly helpful for the small businesses targeted by LoopFuse, where all channels are likely to be handled by a small department and possibly the same individual. Otherwise, the users would need switch among several systems to do their job. In larger firms, where different people would be responsible for different channels, each channel can be managed by a separate system without requiring anyone to use multiple products.

Saving effort is nice, but the real value of a unified marketing database is being able to coordinate marketing messages and relate all marketing contacts to sales results. LoopFuse hasn’t publicly revealed its approach to marketing performance measurement but definitely has something in the works. I’m particularly hoping they'll use the detailed behavior information to relate outcomes to specific marketing messages, rather than just looking at movement through purchase stages. Although stage data by itself can project future revenues, it must be tied to specific marketing programs to measure those programs’ value.

In case you’re wondering, LoopFuse is storing the new Web traffic data in denormalized tables that are separate from the operational marketing database. This enables much quicker response to ad hoc queries and, should eventually support the time-based views needed for trends and stage analytics.

For those of you keeping score at home, LoopFuse’s Roy Russo also told me that the company stores each client’s data in a separate database instance. Russo said this has proven more scalable and cheaper than the textbook Software-as-a-Service approach of commingling several clients’ data in a single instance. So far as I know, most (but not all) marketing automation vendors use same approach as LoopFuse.

Russo also said that all data in the system is accessible via standard API calls, something that’s also not always possible with competitive products. In fact, Russo said LoopFuse’s entire interface is built on using the published API, which means that technically competent clients could build alternative interfaces to embed LoopFuse data and functions within other systems. If nothing else, this gets them Geek Style Points.

Of course, no discussion of LoopFuse is complete without mentioning its freemium offer, launched last June amid considerable controversy. The company says that nearly 1,000 accounts have now signed up for this, which is impressive by any standard. No news yet on how many have converted to paid.

One side effect that I hadn't anticipated – although LoopFuse apparently did – is that agencies and consultants use the freemium to service new clients, who convert to paid when their volumes grow. This gives LoopFuse an edge in the competition for channel partners. The value of that edge is a bit uncertain, though, since an increasing number of service firms – including Pedowitz Group, Annuitas and LeftBrain Marketing – are now working with multiple marketing automation vendors.