Marketo Adds Custom Objects. It's a Big Deal. Trust Me.

My first question when Marketo announced its new mobile app connector this week wasn’t, “What cool new things can marketers do?” but “Where is the data stored?”

It's not that I'm obsessed with data.  (Well, maybe a little.)  But one of Marketo’s biggest technical weaknesses has always been an inflexible data model. Specifically, it hasn’t let users set up custom objects (although they’ve been able to import custom objects from Salesforce.com or Microsoft Dynamics CRM). This was a common limitation among early B2B marketing automation products but many have removed it over the years. Indeed, even $300 per month Ontraport is about to add custom objects (and does a good job of explaining the concept in a typically wry video).

Sure enough, when I finally connected with Marketo SVP Products and Engineering Steve Sloan, he revealed that the mobile data is being managed through a new custom objects capability – one that Marketo didn’t announce prominently because they felt Marketing Nation attendees wouldn’t be interested. I suspect that underestimates the technical savvy of Marketo users, but no matter.

For people who understand such things, the importance is clear: custom objects open the path to Marketo supporting new channels and interactions, removing a major roadblock to competing as the core decision engine of an enterprise-grade customer management system. This will be more true once Marketo finishes its planned migration of activity data to a combination of Hadoop and HBase.  This will give vastly greater scale and flexibility than the current relational database (MySQL). Sloan said that even before this happens, data in the custom objects will be fully available to Marketo rules for list building and campaign flows.

The strategic importance of this development to Marketo is high. Marketo is increasingly squeezed between enterprise marketing suites and smaller, cheaper B2B marketing automation specialists. Its limited data structure and scale were primary obstacles to competing in the B2C market, where custom data models have always been standard. Even in B2B, Marketo’s ability to serve the largest enterprises was limited without custom objects. While this one change won’t magically make Marketo a success in those markets, its prospects without the change were bleak.

All that being said, the immediate impact of Marketo’s new mobile and ad integration features is modest. The mobile features let Marketo capture actions within a mobile app and push out messages in response. This is pretty standard functionality, although Marketo users will benefit from coordinating the in-app messages with messages in other channels. Similarly, the advertising features make it simpler to export audiences to receive ads in Facebook, LinkedIn, and Google and to find similar audiences in ad platforms Turn, MediaMath, and Rocketfuel. Again, this is pretty standard retargeting and look-alike targeting, with the advantage of tailoring messages to people in different stages in Marketo campaigns. The actual matching of Marketo contacts to the advertising audiences will rely on whatever methods the ad platform has available, not on anything unique to the Marketo integration.

In fact, I’d say the audience reaction to the announcement of these features during the Marketing Nation keynote was pretty subdued. (They were probably more excited that they can now manage their email campaigns from their mobile devices.) So maybe next time, Marketo should make the technical announcements during the big speech: at least the martech geeks will be on their chairs cheering, even if everybody else just keeps looking at their email or cat videos or whatever it is they do to amuse themselves during these things.

Note: for an excellent in-depth review of what Marketo announced, look at this post from Perkuto.

New Frontiers in Data Driven Marketing

I recently gave a talk on New Frontiers in Data Driven Marketing, which managed to incorporate Barbie, Fred Astaire and Ginger Rogers, General Winfield Scott, and The Three Stooges. Let’s just say you had to be there. But even without celebrities, I think the list is worth a quick look as you start planning for next year’s marketing programs.

New Challenges

• Integrate ad tech and martech. We’ve seen this coming for some time but it’s now much more obvious as marketing automation vendors like Oracle and Adobe, display ad targeters like Bizo (now part of LinkedIn) and Demandbase, and even tag managers like Signal (formerly BrightTag) and Tealium come at the challenge from different directions. The core issue is that marketing campaigns in advertising, traditional outbound media, and new social and inbound media all target increasingly-identifiable audiences rather than anonymous cookies, site visitors, viewers, or prospect lists. This makes it more possible to work across all media to improve targeting, to coordinate messages for each individual, and to measure the incremental impact of each promotion. This, in turn, requires integrated systems to gather the necessary data in a single location, track interactions with individuals, send appropriate messages, and monitor results. Look for more integration along those lines from big platform players and for cooperation among specialized solutions as they seek to participate in the consolidated approach.

• Extract meaning from big data. Everybody loves big data but few people talk about the downside: sloshing huge buckets of information into a giant data lake means that everybody has to do their own refining before they can do anything useful. Of course, analysts have always spent a lot of time on data prep and veterans will scoff at the implication that most data warehouses are pristine. But the ease of adding new feeds to big data stores, especially of unstructured data, means that users now face a “do it yourself data quality” challenge that's much greater than before. To make things even harder, direct access to data has expanded to many business users who don’t have the data management skills or sensitivity of expert analysts. This is a problem I haven’t seen discussed very much, but you can be certain it is coming to a desktop near you.

• Translate offers across media and campaigns. All that cross-channel coordination means marketers have more ways to present the right message to each individual, which turn means each message much be available in the format of each touchpoint. “Responsive design” addresses one piece of the problem, making it easy for the same Web content to render effectively on different devices. But there are plenty of other touchpoints that responsive design doesn’t reach, including display ads, call centers, and social media. So far, most of the energy related to this issue has been spent in making it easier for a single system to send messages to multiple channels, not in automatically adjusting messages to account for different amounts of content or user mindset in a given context. This is another area that has received little attention so far, especially in terms of refinements like testing and optimization.

New Technologies

• Predictive everywhere.  Most marketers are now familiar with basic predictive modeling applications like lead scoring and content recommendations. But big data and multiplying channels offer them opportunities to do so much more – and, given the alternative of poor customer treatments, they really have no choice.  Happily, the technology to build predictive models has kept up with marketer needs, so it’s increasingly possible for automated systems to build and deploy dozens or hundreds of models with almost no marketer input. This means programs can be designed to incorporate predictive models in all kinds of treatment decisions, from content recommendations to sales call prioritization to banner ad selection. In fact, the technology in this area is probably ahead of marketers, who need to learn how to identify modeling opportunities, to structure programs to use models effectively, and to monitor model results.

• Natural language processing for unstructured data management. Natural language processing (or NLP, as the cool kids say) and unstructured data are different things and both relatively established. I’m listing them here because unstructured data must become at least semi-structured to be useful, through processes such as tagging and indexing. Doing this efficiently at big data volumes requires automated solutions, which is where NLP comes into play. There are plenty of other NLP applications, such as sentiment analysis, speech processing, data gathering, and even some slick “copy generation” methods (for example, Persado and Captora, which I described briefly last June ). But I think making sense of unstructured data is NLP’s killer app.

New Opportunities

• Mobile/local marketing. Okay, maybe not so new. But still at the frontiers, since marketers are struggling to take advantage of what’s unique about mobile systems rather than just treating them as tiny desktops. Mobile apps are one part of this, since they’re separate from regular Web sites and emails. Location- and context-aware programs are another aspect: the potential is obvious even though it’s not yet clear how to best exploit it. There are some pretty serious privacy concerns to address here, although it’s never clear whether those will be real obstacles or evaporate as customers overcome their initial surprise at how much marketers can tell about them and get back to playing Clash of Clans.

• Advanced attribution. I’m talking here about attribution based on a nearly complete view of all customer interactions with a brand: Web and email messages, of course, but also search, display, broadcast and print advertisements, in-store and near-store* interactions, purchase and service histories, social messages and networks, device telemetry, and only the NSA knows what else. Once you have all that data and have managed to link identities across different sources, you can apply some truly whiz-bang analytics to estimate the incremental impact of different messages on short- and long-term customer behaviors. This goes beyond the simplifying assumptions of first-touch, last-touch and fractional attribution approaches. If it works properly, it promises to revolutionize how marketing budgets are managed and to give a substantial business edge to companies that master it first.

• Journey mapping. Another old concept, but one that’s gaining a lot of new attention. I’ll give a shout-out to my friends at SuiteCX who have built some slick mapping tools that I never quite get around to reviewing. If I had to speculate why journey mapping is suddenly so popular, I’d guess it’s because it’s become so obvious that the traditional purchase funnel has exploded into maze of hopscotch courts, with customers leaping from one spot to the next like crickets on a frying pan. Journey mapping is one way to make sense of it all, or at least apply a bit of order to the natural chaos. It relates closely to multi-channel programs, attribution and mobile/local marketing as well, if you think about it. No wonder it’s climbing to be king of the buzz hill.

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* I just made that up.

BlueConic Selects Targeted Messages Using a Cross-Channel Marketing Database

This blog has mentioned BlueConic in passing a couple of times but never quite gotten around to reviewing it in detail. Until now.

The delay may seem surprising, since BlueConic qualifies as a Customer Data Platform, a type of system I’ve been arguing will play an increasingly central role in marketers’ futures. For those of you who haven’t been paying close attention, a CDP is defined as a

• marketer-controlled system that
• supports external marketing execution based on
• persistent, cross-channel customer data.

This definition distinguishes CDPs from traditional marketing automation products, which do their own execution, and from real-time interaction managers, which lack persistent data stores. CDPs are important because few marketers have been able to build adequate cross-channel databases and because connecting those databases with execution systems has been difficult. The databases and connections are needed because today's customers expect personalized, coordinated treatments across all channels.  Call it the “Amazon fallacy”: customers believe that since Amazon.com can give them highly personalized treatments, so can everyone else.

Anyway, back to BlueConic. The system has two main capabilities, which are to maintain customer profiles and to deliver targeted messages. The profiles can be based on data imported from other systems via batch processes or APIs or captured by BlueConic itself.  It does this with “listeners” that can read data from forms or monitor behaviors via Javascript tags on Web pages, emails, and other media. “Listeners” can also create interest rankings and scores based on user behavior. All of these become available as attributes on the customer profile, which in turn can create customer segments and drive targeted messages.

The messages are delivered by what BlueConic calls “dialogues”, each of which sends a single message to a single location (email, text message, section on a Web page, etc.) to a specified customer segment.  Messages tailored to customer interests would require creating a separate segment for each message.  Similarly, presenting a sequence of messages would require a separate dialogue for each step in the sequence.  If a customer is eligible for several dialogues at once, the system currently relies on an optimizer to pick best-responding option and will soon let users create rules to further guide the results. There is no built-in predictive modeling but the optimizer can continuously test alternative messages within a dialogue and automatically deploy the winner. Users can also apply a frequency cap to dialogues to limit the number of times any customer sees the same message.

BlueConic’s integration features are more extensive than its decision management. The system can capture data entered into forms even if the form ise not submitted. Users can insert message contains into an existing Web page without writing HTML code. Profiles capture  customer identifiers provided by other systems and are automatically merged when two profiles are linked to the same external ID.  Data is exchanged with other sources and execution systems via REST APIs. There are standard integrations with Twitter, Facebook, and Salesforce.com, as well as a system development kit for integration with mobile apps. The underlying data store is Apache Cassandra running on Amazon Web Services, which is highly flexible and scalable at moderate cost.

Integration and data management are what make BlueConic most interesting from a CDP perspective, since those are the core CDP functions. A “pure" CDP would provide only those services while leaving decision management and message delivery to other systems. I expect “pure” CDPs to appear, but most marketers prefer a broader solution, like BlueConic, to assembling the components for themselves. Pure CDPs will become more attractive as integration becomes easier through more standard APIs and connectors, a promise that cloud-based systems often make but are just starting to deliver.

BlueConic’s pricing is already data-centric: fees are based on numbers of profile and channels, not interactions or messages. Prices start around $1,000 per month although most clients pay more. Current implementations are mid-size and enterprise firms in B2C industries including retail, publishing, financial services, utilities, telecommunications, sports and travel. The system has about 70 current customers and is sold both directly and to partners such as ad agencies, other software vendors, and marketing service providers.