MarTech Stack Jenga: Official Rules

I spent some time in Atlanta last week, including a Friday afternoon visit with Sangram Vajre at Terminus to discuss his upcoming FlipMyFunnel conference. I’ll be keynoting the part of the conference devoted to technology stacks. Our conversation naturally turned to MarTech Jenga, my recent random thought of using the popular stacking game to illustrate how marketers assemble their technologies. Lacking adult supervision, Sangram and I spent too much time on the actual game design and came up with a seemingly workable idea. We're still debating the details – Sangram favors simplicity and my style is more complex* – but here are the official rules for MarTech Jenga** at the moment. Public comment is welcome:

Object of the game: assemble the most complete marketing stack before everything collapses.

Equipment: standard set of Jenga blocks, divided into nine groups of six. Groups are numbered 1-9 and (optionally) assigned a color and type of marketing system. Blocks are marked on each end with their group number and color. Individual blocks can also be marked with the logo of a specific vendor*** although this has no effect on the game play. Numbers 1-4 are marked with an asterisk to indicate that those groups are required for a complete stack.****

Setup: blocks are stacked three-across in alternating directions, as in standard Jenga. 

Game play: each player in turn may remove one block from the stack or pass. Players retain all blocks they remove in their own stack, whose contents must remain visible to other players. Play continues until the stack falls or all players have passed in sequence.

Scoring: the player who causes the stack to collapse loses. If at least one player has acquired all the required blocks, then players who have not acquired all the required blocks lose (if this rule is adopted). Remaining players are given one point for each group that is present in their stack. There are no points for additional blocks within the same group. Player with the most points wins.

Sangram promises me that he’ll have some version of the game available at FlipMyFunnel, but I’m not holding him to it. On the other hand, this seems the perfect thing to have at a vendor booth. The opportunities for customization are self-evident, as are things like a leaderboard for high scores through the conference.  I'll leave the drinking game versions up to the Internet.


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*No surprise there: he's a practitioner and I'm a consultant.

**The name Jenga is actually trademarked so we’ll have to pick something else if this ever gets beyond the blogging stage.

***a sponsorship opportunity.

**** Whether to have required blocks is a particular point of debate. It originally reflected the reality that certain types of systems are essential in a marketing stack. But from a game design point of view, it adds strategy considerations such as picking the required blocks sooner and blocking other opponents from completing their stack. That makes the game considerably more interesting.  The question is whether that takes too much thinking for a casual game. The only real way to resolve this is through play testing, where we could fiddle with a number of variables such as number of groups in total, number of required groups if any, and whether to play topless (i.e., allowing players to remove blocks from the top of the stack).

Campaign Management Is Dead. Here's What Next-Generation Marketing Automation Looks Like.

Scientists tell us that the attention span of the average human is now shorter than the attention span of a goldfish.(1)  In such a world, the chances of anyone reading this 2,000 word blog post are pretty much nil. But I think the topic is extraordinarily important, so here is a summary in sushi-sized bites:

- the conventional flow-chart model of marketing campaigns can’t capture the complexity of today’s  disjointed customer journeys.

- a new approach is emerging that identifies stages in the customer journey and picks “plays” (small, highly targeted sets of treatments) to execute in specific situations within each stage

- this approach is easier for marketers to manage because it lets them think in smaller, more comprehensible units

- it will eventually lend itself to greater automation as machines take over more of the marketer's job in a “madtech” world

You can stop here if you need to watch an important cat video.  But if you want to understand my thinking in more detail, please read on.

The first modern campaign manager, Third Wave Network’s MIND, was released in 1991. What made it modern was a standard relational database(2) and multi-step campaigns that that sent users down different paths depending on their actions during the campaign. The system displayed each campaign as a set of boxes connected with lines to represent movement of customers from one step to the next. This flow chart interface has been fundamentally unchanged ever since and remains the gold standard of marketing automation(3).

The significance of the flow chart is what it replaced. The previous standard was a list of segments, each described on one row of a (paper) spreadsheet, with characteristics including selection criteria, description, key code, promotion materials, and quantity. Marketers filled out these sheets and handed them to programmers to execute. This was how direct mail marketers worked for decades, and often still do: it’s an efficient way to manage dozens or hundreds of cells within a large outbound campaign. It supports multiple contacts, such as mailing a second catalog to high-performing segments several weeks after the initial drop. But the list for the second mailing is pulled at the same time as the first, so it doesn’t allow changes in treatment based on subsequent customer behavior. This adjustment is what branching flow charts provide.

A quarter century after its introduction, the flow chart is now ripe for replacement. Flow charts assume that customers will follow a small number of predefined paths. This was realistic when interactions were limited to a few company-controlled touchpoints.  But it doesn’t describe today’s self-directed, random-walk journeys through an ever-shifting media landscape. In this environment, the best a company can do is react intelligently wherever a customer appears, taking into account both the current situation and whatever it knows about the customer’s past. Even company-initiated messages, while not wholly reactive, must be consistent with other treatments.

The basic features of a better approach have been obvious for years. I’ve long described it as “do the right thing, wait, and do the right thing again.” What’s changed is visibility: marketers now can see vastly more data about their customers and can interact with them through vastly more channels. In the “madtech” vision I’ve been articulating recently, this translates to assuming that all data about each customer’s demographics, interests, behaviors, locations, intentions, and other attributes is available to everyone. This includes both data a company gathers through direct interactions and data aggregated by third parties and offered for sale. Indeed, third party data is essential for building a complete picture of each customer’s experience.(4)

The vision similarly assumes that messages can be delivered through channels the company does not own directly. These extend beyond conventional advertising to private channels that other companies have opened to external messages. A concrete example would be third party offers delivered through a company’s Web site. My shorthand for this is “everything is biddable”: meaning that marketers can pay to embed a message within every interaction the customer has with anyone. Since all marketers have the same opportunity to bid on all impressions, a corollary is that buying the right messages at the right price is the key to success – or, in another catchphrase, “the smartest bidder wins”.

Sadly, neither you nor I can make a living by repeating clever phrases. Someone has to do the hard work of figuring out what treatments to deliver in each situation. A flow chart can’t come close because the situations are far too varied for any one chart to capture all the alternatives. What’s needed is a system that will assess each situation as it arises and come up with a custom-tailored solution.

If the only goal were maximizing immediate response, this would be pretty simple. Existing recommendation engines and predictive models can easily tell you which content a person is most likely to pick or which product they are most likely to buy. Today's products often do this with limited information about the individual being targeted, but that’s just a reflection of what data is currently available: at least some current systems could incorporate individual details and history without a major revision. There are practical details of speed, scope, and accuracy but the broad design of such a system is straightforward.  It connects with every touchpoint (including exchanges that deliver external opportunities up for bidding), receives information about each interaction, and returns an optimal message along with the value to bid for the right to deliver it.

But immediate response isn’t the only goal. Each interaction is embedded in the context of a customer’s relationship with the company. The best message is the one that maximizes the long-term value of that relationship. This won’t necessarily be the message with the highest response rate or greatest immediate financial value. Automated systems can incorporate long-term value in their recommendations but only if they are tracking long-term outcomes and analyzing what changes them. I believe – and this is the main point of this entire post – that automated analysis can only optimize for long-term outcomes if customer data is classified by stages in the customer journey. In other words, you can’t just randomly test all possible treatments in all situations and let the best approaches bubble to the top. There are simply too many variables for that to work. Rather, marketers must assign customers to journey stages and use those stages as inputs when selecting treatments and evaluating results. The stages themselves can be adjusted over time in the light of experience. But without a journey map as a starting point, marketers and their machines will flounder endlessly in a sea of big data.

Maybe you're unimpressed.  Maybe that cat video beckons.  Maybe you're thinking, "All you've done is change the labels.  A map of journey stages looks a lot like a campaign flow, and for that matter an old-style campaign funnel."  I understand your doubts. But there are significant differences:

- journey stages are not associated with specific messages, while campaign steps are.  In fact, the whole purpose of campaign steps is to define which messages are delivered when. So a journey stage is a significantly higher level of abstraction. Put another way, journey stages are just one factor in deciding how to treat a customer, while campaign steps are the only factor.

- journey stages are inherently random, while campaign flows and funnel stages are relentlessly linear. The goal of a campaign or funnel is push customers from one stage to the next as quickly as possible. Journey stages do track a funnel-type motion but they’re more intended to capture a set of customer needs and interests. In conformance with the general notion that customers will control their own movement, journey stages are more descriptive than directional. It’s no problem for a journey map if customers move back to an earlier stage or if they stay in one stage indefinitely.  For stages such as “satisfied customer”, that’s actually a good thing.

I do recognize that “journey stage” implies motion, which means it's probably not the best term for what I have in mind.  A more neutral term like "state" would be better.  But I’ll stick with journey for now because it will intuitively make more sense to most people.

So let’s assume, at least for sake of argument, that you’re convinced marketing systems should consider  journey stage when they’re picking the best message for a specific customer in a specific situation. Does that mean campaign flows can be replaced by any recommendation engine that adds journey stage to its list of customer attributes?

I think not. The system needs an intermediate level between broad journey stages such as “interested prospect”, “active buyer”, “satisfied customer”, and “advocate”, and actual treatments during single interactions. That level is needed because marketers have to create the content that the machines will choose from during those treatments. Marketers will decide what content to create by envisioning experiences that span multiple interactions and then creating content for a complete experience. The best analogy I’ve found is “plays” in a sport like football: tightly choreographed sets of actions that serve a narrow purpose. Teams prepare plays in advance and pick the right play for the moment but they don’t try to set the sequence of plays before the game begins. They know that games, like customers, follow their own unpredictable course and all the team can do is react appropriately in each situation. It’s true that each play is directed towards a long-term goal, but execution of the play is really a self-contained project. What’s critical is that the marketing play can incorporate multiple interactions over time; this allows a more coherent, richer customer experience than treating each interaction as wholly independent.

Good marketers and sales people already think this way, although they usually describe it in terms of tactics to handle different types of buyers, personas, or situations. I’ve also recently heard several innovative marketing system vendors describe approaches that I believe are fundamentally similar to this concept, again in different terminology. I’ll tentatively adopt the term “plays” precisely because it’s vendor-neutral and because I think most people are familiar enough with sports plays for the analogy to be helpful.

To clarify, then, I believe that marketers of the future will think in terms of broad customer journey stages (i.e., states), such as “active buyer” or “satisfied customer”. Each stage has strategic objectives, which might be to move an active buyer closer to purchase or to strengthen the relationship with a satisfied customer. Marketers will pursue those objectives through “plays” that are appropriate in specific situations, such as “active buyer requests a demonstration” or “satisfied customer has a service problem” and take into account other context such as location, device, and recent behaviors. They will create content and process flows to execute those plays.  These plays will resemble current multi-step campaigns but on a smaller scale and with narrower goals. This limited scale is exactly what makes plays so useful, because marketers can easily visualize each play as a whole. This lets them construct coherent sets of messages, rules and metrics to execute each play from start to finish, measure its impact, and make changes to optimize its effectiveness. Today’s massive nurture campaigns are too large and complicated to do the same.

Marketing systems of the future will also be designed to support this model. It may even happen that systems designed on this model come first and marketers adopt the model as they come to appreciate the systems. Or, because the stage/play model is especially well suited to automated campaign design and the general “madtech” world, perhaps it will be embedded in automated systems and grow as such systems are adopted.

Whatever the mechanism, the traditional campaign flow model has reached the end of its useful life.  I believe the stage/play model will be its successor.

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(1) The study actually measured the attention span of average Canadians.  It didn’t specify the nationality of the goldfish.

(2) The preceding generation of campaign managers, called MCIF systems, used proprietary columnar databases to wring adequate performance from the PC hardware available at the time. The first of these, MPI Max$ell, was introduced in 1984; other major products included OKRA Marketing (1986), Customer Insight (1987), and Harte-Hanks P/CIS (1988). Interestingly, the height of sophistication among these products was a feature called “matrix marketing”, which identified the best offer to make each customer after each (monthly) update. Sound familiar?

(3) click here for my 1994 review of MIND and a couple significant contemporaries. If you add a reference to digital media, I could be describing any of today’s cutting-edge marketing automation products:

“At the core is a very powerful campaign management function that allows a marketer to define sequences of marketing events–each including a mix of direct mail, telephone contacts and personal sales efforts–to be followed in different circumstances, and then to automatically execute these sequences.

“The system uses an efficient graphical interface to lay out the alternate sequences that can be followed within each campaign, the tasks associated with each step in each sequence, and even the specific promotional materials used with each task. As a result, the marketer gains extremely precise control over the marketing approach used with each customer–including the ability to switch the customer to a different sequence depending on actions during the campaign.”

(4) I'm perfectly aware that reality will be messier than the vision implies.  Coverage will be incomplete, people won’t always be recognized across devices, and some predictions will be wrong. But perfection isn’t necessary for success: systems using this data only have to be more effective on average than systems that don’t.

Tealium Grows from Tag Manager to Customer Data Platform

It took me an embarrassingly long time to recognize why Tealium’s AudienceStream is just a bit odd. The oddity itself I saw immediately: while most marketing systems focus on sending messages to customers, AudienceStream is organized around overwriting data and sending it to other systems. The reason took longer to grasp: eventually I recalled that Tealium started as a tag management system and passing updated attributes to other systems is what tag managers do. So it’s perfectly natural for AudienceStream to offer precise control over which attributes are sent to which systems in which situations.

To some degree, the difference is just a matter of presentation. The data that Tealium sends to other systems can trigger customer messages from those systems.  So Tealium does support customer messaging.  But AudienceStream's heritage does give useful insights into what might otherwise seem a random set of strengths and weaknesses.

Let’s step back a bit. Like other tag management vendors, Tealium recognized several years ago that its core competency at capturing customer behavior could be applied to build unified customer profiles. This meant it could serve as a Customer Data Platform to support many other marketing applications. In fact, Tealium explicitly calls its product a CDP, using the tagline “build your own marketing cloud” to stress that it can connect systems from any vendor, not just components within a single vendor’s suite. That is indeed the essence of the CDP value proposition.

That doesn’t mean the Tealium is merely offering a relabeled tag manager. The company’s own diagram illustrates this clearly: the tag manager, Tealium iQ TMS (circled in red) is just one component of its CDP.  Here are key points to understand:

- scope is beyond Web channels. Conventional tags go on Web pages.  Tealium now offers connectors to gather data in other ways including APIs, batch file transfers, and system development kits (SDKs) for mobile apps.

- speed is real-time when possible. This is one benefit of the company’s tag management legacy, where real-time execution is required. It’s an important differentiator compared with other Customer Data Platforms, which often don’t support true real-time interactions.

- data storage is extensive. As the Tealium diagram implicitly indicates, a pure tag manager doesn’t need to permanently store much data. But Tealium incorporates several layers of persistent data, including semi-structured raw data in AudienceStore and EventStore (using Amazon Redshift), structured data in EventDB and AudienceDB, and an access layer in the AudienceStream iDMP (Data Management Platform).

- the system tracks individuals. The “i” in “iDMP” stands for individual, which pretty much says it all. Traditional DMPs are based on cookies, which are theoretically anonymous although they can often traced to specific individuals in practice. Tealium’s iDMP stores both cookies and personal identifiers such email addresses. It builds profiles by linking people to devices they have used for identity-revealing tasks such as opening an email or logging into an account. Devices used by the same person are then linked to each other. The system does not apply “probabilistic” or “fuzzy” matching to infer linkages using other data, such as simultaneous activities, shared locations, or similar names.  Users who want such links can import them from external services such as LiveRamp.  Tealium does link its cookies with major ad networks through other DMPs, although it won’t pass on individual identifiers.

- sophisticated rules support enhancement, segmentation, and triggers. This is another outgrowth of tag management, which sends different information to different partners. Derived attributes can be calculated with complex formulas including current data, time periods, ratios, rolling averages, random splits, and other functions. Similarly complex rules can assign people to audiences or segments and can set up triggers that change data values and send messages to external systems. Users can give higher priority to more important actions and can limit how many times an action is executed for the same person (for example, to avoid sending too many messages or repeating the same message). Again, this all happens in real time as data flows into the system.

- lots of connectors. Tealium cites integration with nearly 1,000 other systems. Most of these are for tag management but about 20 are to message delivery systems including ecommerce, email, marketing automation, CRM, and advertising vendors. Users can connect with additional systems through Webhooks, SDKs, or APIs. Reporting and analytical systems can query the underlying data directly, receive file extracts, or subscribe to a live stream from the collection server.

- it doesn’t do everything. Tealium offers a remarkably powerful data-layer CDP.  It extends a bit into decisions by applying segments, creating derived variables, setting up triggers, and sending audiences to execution systems. But it doesn’t do multi-step campaigns, predictive analytics, offer tracking, and supporting functions like content management. So, although AudienceStream can connect directly with message delivery systems to manage some types of campaigns, most users will combine it with a more robust decision system to properly manage relationships. It probably makes the most sense to view system’s segmentation, enhancement and (to a lesser degree) trigger capabilities as part of creating a rich customer database, not as directly managing customer relationships. This is perfectly consistent with Tealium’s own “do it yourself marketing cloud” goal of letting its clients work with whatever decision and delivery tools they wish.

Tealium introduced AudienceStream in late 2013.  It is currently used by about 100 of its 550 clients.  Pricing is based on events processed and starts around $12,000 for a small implementation. Mid-size and bigger customers can expect to pay more..

Mautic Offers Free, Open Source Marketing Automation

The only real question about free, open source marketing automation from Mautic is what took so long. The core features of B2B marketing automation have been well understood for nearly ten years and prices have been dropping steadily for about the same time. Open source has been successful in related applications including analytics (R, Jaspersoft, Pentaho), CRM (SugarCRM, vTiger) and Web content management (WordPress, Joomla, Drupal). Small businesses constantly cite cost as a major roadblock to adoption. So the opportunity seems obvious.

Mautic campaign builder

The reason for the delay may be as simple as the generous funding available to marketing automation start-ups.  This made commercial products more financially attractive to potential developers and probably scared off others who couldn’t compete for attention on an open-source shoestring. Or perhaps people felt that the real barriers to adoption were lack of time and skills, so even a free product would not unlock a large new segment of customers. The failure of freemium (though not open source) offerings from LoopFuse and Genius are strong evidence that being free is not enough. (See this post for more on that.)

Or maybe it’s just a matter of timing. David Hurley, the open source industry veteran behind Mautic, argues that marketing automation has just now become widely enough understood for many marketers to purchase it without a lengthy sales cycle.  I suspect that’s true but still wonder whether those buyers will know how to use a system effectively once they get it. Hurley's hope is that the user community will largely support itself through public forums and that service professionals such as consultants, agencies, and Web developers will fill the rest of the gap. Mautic certainly has an appeal for service vendors, since it removes the cost of payments to a HubSpot or Infusionsoft. Mautic will encourage such support by building a marketplace for users to sell or share resources such as workflows and templates. It is also putting together its own set of templates and workflows to help users get started.

What about the product itself? I promise I’ll get to that in a minute. But first let me cover one more business issue, which is that there are two ways to get Mautic. You can download the source code for free at Mautic.org, install it on your own server, and modify it as you please. Hurley said this has appealed to some large firms and government agencies who want to modify source code for themselves and to run an on-premise deployment. Or, you can sign up at Mautic.com, which will host a system with up to 2,500 contact names, one user, and three integrations for free. Mautic.com runs an enhanced version of the system called AllydeMautic, provided by Allyde, a for-profit business also run by Hurley. Starting this week, users can also buy a Pro version of AllydeMautic for $12 per month. This gets them further enhancements including unlimited database size, custom domains, additional integrations, and a second user. Allyde will eventually add other packages with more features. But pricing will remain well below standard marketing automation products.

None of this would matter if the actual Mautic product were no good. After all, the real cost of marketing automation is the time spent running the system and creating content and the real value comes from improved business results. In other words, a free system that wasted users’ time or produced substandard results would be a very costly investment. To judge whether Mautic is really a good deal, I set up a free account at Mautic.com and tested it for a bit.

My general impression was positive.  The interface is straightforward and intuitive, using tabs to make advanced features available without intimidating clutter. I do have some quibbles – most annoyingly, objects like emails and contact records open in a “view” rather than “edit” mode, so an extra mouse click is almost always needed to get real work done. But, for the most part, things worked efficiently and about as I expected.

The functions cover all the marketing automation bases: you can import contacts or enter them manually; assign them to lists; create emails, Web pages, and Web forms; upload other assets; assign points for lead scoring; build multi-step, branching campaign flows; and integrate with CRM systems.  I started with the lead import feature, which was rather barebones.  You can import a CSV file but not an Excel spreadsheet or other format; map the input file to database fields but not see a sample of the results; create custom fields but not custom tables; apply tags during the import but not link people within the same organization using a company field.  On the other hand, the system automatically imported pictures of people on my test list, presumably from public social media profiles. It apparently could have imported more social data if I had connected with my own Facebook, Twitter, or LinkedIn accounts.

Content building was more impressive. Users are presented with a free-form canvas to build emails or Web pages, with the usual controls for fonts, colors, inserted images, URL links, etc. Emails can include personalization tokens such as {first name}. Predesigned email templates give more structure in the form of blocks for headlines, body text, footers, unsubscribe messages, and viewing the email as a Web page. Template-based emails can also be linked to a landing page. Advanced tabs for emails let users specify the sender name, sender address, reply address, BCC address, and attachments. Emails and other content can be assigned to categories and given dates when they are published and unpublished. Forms can be attached to campaigns and users can specify where to send the visitor after a form is submitted. Forms support a variety of input types including fancier options such as radio buttons , checkboxes, and Captcha validation. There are some other features too: the set is pretty complete.

The campaign builder was better still. It offers a real drag-and-drop interface to build a flow chart with a modest list of actions (send email, update lead, push lead to integration, add or remove lead from a list, change campaigns, and adjust lead points).  The flow can branch on a few lead behaviors (downloads asset, opens email, submits form, visits page). Movement can be triggered by user behavior, happen after a specified number of days, or be scheduled for a specific date and time. The real power will come from pushing leads to external integrations with CRM, email, social media, and cloud storage. There are about twenty of these, including major vendors in each category. More will be added over time.

Lead scoring is also fairly powerful.  Scores can be adjusted by actions or triggers. The actions can be generic or specific: that is, being sent any email or being sent a specific email.  That’s better than some commercial systems, but doesn’t include advanced lead scoring features like limiting the number of points that generated by repeating an action or reducing points for past behaviors over time.  Triggers can be linked to reaching a specified point total.

All told, these features are enough to run a reasonable marketing automation program.  Unfortunately, my experience was marred by considerable bugginess: features to select a list and upload an image weren’t working when I tried them, although a note to tech support received prompt human response – under 15 minutes – and the issues were resolved within an hour. I might have been testing on a particularly bad day, but it still seemed odd that such basic features could be broken without anyone else noticing. Hurley said that Mautic has accrued 9,000 users since its first stable release in January 2015, but apparently few of them were active that evening.

Despite my positive impression, I have mixed feelings about Mautic.  I love the idea of open source marketing automation and think its time may finally have come. I generally liked the product, which combines simplicity with considerable power under the hood. The bugginess worries me a little but I assume it will be straightened out over time – and know that commercial products have bugs too.

My problem is two-fold. I missed the richness of commercial marketing automation products – even though I can’t identify a particular missing feature missing that Mautic needed to be effective. The glaring omissions such as CRM, ecommerce, and social are provided through integrations. But, even though I know that, the basic nature of Mautic leaves me uncomfortable.

The other issue is more concrete. I think new marketing automation users will need more hand holding than Mautic or Allyde can afford to provide or that the community will offer for free. The product should definitely help service vendors by reducing costs for their clients, assuming the service vendors decide it’s powerful enough for their purposes.  But I worry that unassisted small business users won’t know what to do with Mautic and won’t take the time to figure it out.  Remember that screening out uncommitted users is exactly why firms like Infusionsoft charge a substantial implementation fee – although the economics of Mautic are different because Allyed will invest almost nothing in acquiring or supporting new customers.

Then again, there are an awful lot of small businesses out there. Even a small fraction could be enough to support Mautic until it adds the features needed to serve a broader audience. So while I won’t necessarily recommend Mautic to many of my own friends and clients, I’m glad to have it as an option and will root for its success.

Highspot Sales Enablement Helps Sales People Find Content and Marketers Measure What Works

“Sales enablement” is something of a catch-all term for a wide range of solutions that help sales people do their jobs better. Highspot has staked out the corner of this world occupied by systems that help sales people find the right marketing materials. It grew out of the pain that co-founder Robert Wahbe felt which running marketing for Microsoft’s Server and Tools Division, where he found no good tools to help sales people and channel partners find the right materials when they needed them.

When Highspot was founded in 2012, it focused on better content discovery for sales people. But the firm soon learned that this wasn’t enough. It has now redefined its core mission as improving results by showing which content is working.  This is currently measured by tracking how often each item is used by sales people and read by recipients. The July release will supplement this with opportunity information from Salesforce.com CRM, allowing correlation of content usage with funnel stage conversions and revenue.

Highspot mostly does what you’d expect from this sort of system: it lets users load content and sales people, tracks who sends which content to which prospects, and reports on results. Users can set up collections (called “spots”) of materials for a particular product, sales team, funnel stage, region, or any other purpose. They can find content by looking in a spot, by filtering on sales stage, industry, product, and other attributes, or by doing an “intelligent semantic search” that recommends content based on past choices by the user and others. Users can view, download, bookmark or email the selected content or do a live pitch to a prospect. The system automatically adds pitches and emails to the prospect history in Salesforce.  It can also track when a piece of emailed content is opened by the prospect, how long they kept it open, and which pages they viewed. A dashboard can highlight new and featured content. The system will also analyze the inventory of available contents to find gaps or redundancies by sales stage, product, region, etc.

The operational details are all nicely executed, which is probably the most important consideration for a sales enablement system: if it's not easy, sales people won’t use it. But from a technology standpoint, what’s most interesting about Highspot is what the vendor calls “content genomics”. This uses machine learning to examine each piece of content – such as each slide in a Powerpoint deck – and identify properties including text, color, graphs, and images. Different pieces are then compared to find similarities and grouped into “content families”. This approach lets Highspot recognize when a piece has been modified and reused, for example by taking a slide from one deck and adding it to another with some reformatting along the way. Identifying these relationships gives a much more accurate understanding of how often each item is used and how well it is performing. Without this grouping, results from the system could be highly misleading.

Highspot now has more than 100 paying customers. The system is now sold primarily to marketing departments as the system of record for marketing content. There’s a limited function Business Edition and a full function Enterprise Edition, which includes Salesforce integration. Pricing for the Enterprise Edition isn’t published but the vendor says that, once volume discounts are included, it is usually less than the $30 per month per user charged for the Business Edition.

Blueshift Offers a Simple B2C Customer Data Platform

[Note: this post is from 2015.  Click here for a newer post about BlueShift.]

It’s just over two years since I started writing about Customer Data Platforms. One thing that’s become clear since then is that only big companies will purchase a marketing database by itself. Everyone else wants to combine the database with a practical application. B2B CDPs have favored analytical applications like lead scores and churn predictions. B2C CDPs have often included campaign engines that manage triggers, query-based segmentations, and multi-step program flows in addition to predictive models. But even the B2C CDPs rely on external systems such as email agents and Web content managers to deliver the campaign messages.

Blueshift fits nicely into the B2C CDP mold: it builds a multisource database, incorporates machine learning-based predictive models, uses filters to create segments, and runs multi-step campaigns that are executed by external systems in email, SMS, mobile apps, and display and Facebook retargeting. What sets Blueshift apart – and this is typical of later entrants to a new market – are a lower price point and simpler operation than early B2C CDPs like RedPoint  and AgilOne.

How low? Pricing for the most basic version of Blueshift starts at $999 per month.  The most advanced version starts at $3,999 per month for all features and up to 1 million “active users” across all channels.  (The company says that most clients are in fact larger than one million users, with the largest at 100 million.)  The fact that prices are published is itself a mark of a later entrant.

How simple? Well, one measure is implementation time.  Blueshift says can be operational in as one day (if data is loaded through an existing Web page tag or push-button integration with Segment) or under two weeks if some work is required.  Technically, this is plausible: the system has JSON API that can accept pretty much anything and will put it into MongoDB and/or Postgres with minimal data modeling.

Another measure of simplicity is the campaign building interface.  Blueshift lets users specify a sequence of steps by filling out forms to define the segment, channel, and content template for each step and time between steps. This is nowhere near as pretty or flexible as graphical flow charts, but does qualify as simple.

Segments are also built using forms to define one or more filters.  Again, nothing fancy but it gets the job done. What’s more important is that the segments can use a wide range of data including online behaviors, attributes from CRM and other systems, predictive model scores, and product information from catalogs.  This is what gives the system its power.  Content templates do incorporate some visualization, as well as tokens for personalization and machine learning-based product recommendations. Split testing, ecommerce integration, and predictive models for activation, churn and repeat purchase are available in advanced versions of the system. Reports show model performance and attributes, segment counts, and campaign results using several basic attribution methods.

So, apart from some missing bells and whistles, what doesn’t Blueshift do? The main limit is that it works only with known individuals (i.e., those reachable through an email or SMS address, app registration, or similar identifier) and primarily in outbound channels. This means that Web display ads, site personalization, and anonymous visitor targeting aren’t part of the mix, aside from retargeting. And, while data and models are updated continuously, the system isn’t designed to manage real-time interactions.

Blueshift was launched earlier this year.  It has more than ten clients, who are mostly multi-channel marketers with a majority of revenue from mobile payments.

In sum, Blueshift isn’t the fanciest marketing system available but it provides a solid mix of highly usable features at a reasonable price. B2C marketers will find it worth a look.

suitecx Offers Industrial-Strength Customer Journey Maps and More

Customer journey mapping is now the buzziest of buzz words.  Every self-respecting marketing automation system offers something called a “customer journey map,” even if it’s exactly the same as last year’s campaign designer or does nothing more than connect functionless icons on a virtual whiteboard. Journey mapping is equally popular among agencies and consultants, although it also often is little more than a new label for the old sales funnel.

None of this affects me personally, but if I were a real customer experience expert I'd be annoyed at cartoon versions being presented as the real thing.  Sophisticated journey mapping has been around for more than a decade*.  It involves not just listing interactions or displaying them in a diagram, but also analyzing their contents, results, and supporting systems. Most customer experience teams have struggled to do this with spreadsheets, graphics programs, or generic flow charts. I’ve done it that way myself and, trust me, it’s painful.

suitecx is to static customer journey diagrams as Google Maps is to the Rand McNally Road Atlas: an interactive alternative with almost boundless functionality. Built by a team of customer experience veterans at the east bay group,** it’s clearly the system its designers always wanted but could never find elsewhere. The resulting sophistication makes it a bit scary at times, with more data crammed onto some screens than casual users can digest. But it also means the system is hugely flexible and will make serious users vastly more productive.

Customer journey mapping is just one feature within suitecx, which is sold as three modules: diagnosticcx to gather and organize customer experience information; visualizecx to display maps, findings, and recommendations; and precisioncx, which defines contact strategies and campaign flows.

The tool is designed around its own intended user journey. This would start in diagnosticcx, which collects information about a company’s business, customers, and processes using customer and employee surveys, interviews, and direct experience. The findings are organized into a list of customer interactions, which are classified by journey stage, channel, department, emotional outcome, segment, and other properties. The interactions are then linked to recommendations, which are themselves classified, prioritized on four dimensions (customer impact, company impact, cost, and feasibility), mapped onto a matrix, set on a timeline, and ultimately converted into detailed project plans.

visualcx supports the project by displaying the data in formats including story maps, process flows, interaction grids, a virtual wall with virtual sticky notes, and various summaries. The grids are automatically generated from the interaction list developed in diagosticcx or imported via spreadsheet.  Grids can be filtered on different interaction attributes and users can drill into individual interactions to see the underlying details. The story maps and process flows are built manually, alas, but still use the same drillable interactions.

precisioncx completes the project by letting users design new customer experiences.  These include over-all contact strategy and multi-step campaigns with segments, creative, triggers, metrics, and other attributes for each step. The system can’t execute the campaigns but an API is available that could export the campaign designs to execution systems.

This is all industrial-strength stuff, aimed at corporate customer experience departments, agencies, and consultants. Pricing is similarly industrial, starting at $15,000 per module for up to three users. suitecx also offers single-function “primer” products for a much more affordable $699 each (and a seven day free trial). Current modules include grid diagrams, story flows, and the virtual wall with sticky notes. Process maps and campaign flows are under development.

Early versions of suitecx have been used by the east bay group in their own work for years. The commercial product was formally released in December 2014 and currently has about 20 paid clients for the major modules.

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* the earliest reference I could find on Google was in a business school course syllabus from 2001.  Apparently the term had already been around long enough by then to be picked up in academia.

** which seems to have an issue with capital letters.

A Tale of Two Sittings: Best of Times with HubSpot and Teradata

Yes, that title is a pun on Dickens’ Tale of Two Cities. Just be glad I don’t review housewares, or this could be about rating knives and forks: A Tale of Two Settings: It Was The Best of Tines, It Was The Worst of Tines.

But I digress. Where was I? Ah yes, in Las Vegas, at ONE: Teradata Marketing Festival, which is Teradata's conference for users of its marketing applications. Despite the location and title, the program did not include jousting.

What the conference did offer was a detailed look at Teradata’s current marketing applications, vision, and product roadmap. These were solid and comprehensive, although Teradata continues to make an unfashionable distinction between “omni-channel” marketing, which is conventional relationship marketing across all channels, and “digital” marketing, which is Web and email marketing. Teradata argues that many digital marketing departments still function independently of relationship marketing groups and therefore want their own tools. That’s probably true, especially at the big enterprises who are Teradata’s primary clients. But the trend is towards closer integration and you’d think Teradata would rather lead than follow. I do suspect that at least part of the reason for the distinction is internal: the omni-channel products are based on Teradata’s original marketing automation products, Aprimo and Teradata Relationship Manager, while the digital products are based on eCircle email the company purchased in 2012. To avoid misunderstanding, let me stress that Teradata does let users integrate the omni-channel and digital products if they want to and that digital includes text messages, mobile apps, social media monitoring and publishing, and Web landing pages as well as email.

Teradata’s marketing applications also extend beyond standard marketing automation to include marketing resource management and analytics. Indeed, there’s a case to be made that the company’s scope is superior to most competitive “marketing clouds”, which are usually pretty light on MRM and analytics and are often barely integrated. On the other hand, Tereadata seems to have something of a blind spot regarding advertising and anonymous customers: I got mixed messages from the various Teradata presentations about whether it considers support for paid media as part of its marketing applications.  The clearest statement I can extract from my notes is that they will store anonymous identifiers such as cookies in their database but not use them until they are linked with an identifiable individual. As readers of this blog know well, I feel all media (owned, earned, paid) and all users (anonymous and known) should be managed together.

Teradata itself sees its primary differentiator as analytics. It presents an appealing vision of “adaptive self-learning marketing automation” that combines historical data, predictive models, and prescriptive models. By “prescriptive”, it means recommending the types of marketing programs to create, as opposed to predicting which existing marketing campaigns are best for an individual customer. This all strikes me as correct, if not downright futuristic.

But down at the practical level, Teradata’s near-term roadmap was considerably less visionary. Maybe that’s the nature of roadmaps. Perhaps inspired by the venue, the Teradata folks did a lot of (metaphorical) kimono opening at the event, detailing their product plans in ways I rarely see in public. What they revealed were mostly incremental enhancements such as improved user interfaces to make marketing activities easier and more nimble. There were some more fundamental promises, including better integration across suite components, more open access by external systems, and a more unified view across campaigns of the customer journey. It’s solid but not flashy, which is a pretty good summary of the Teradata style.

I was barely home from Vegas before I headed up to Boston for HubSpot’s Open House, a small event for primarily for business partners. (HubSpot’s main user conference is the INBOUND show in September. No jousting there, either.) Although the Open House style was low key, there were a couple of substantial announcements: the company’s free CRM system is now generally available (and still free); expansion of its $50/month Sidekick sales productivity tool; and eleven new integration partners including some predictive technologies (BrightInfo and Infer) and paid media retargeting (PerfectAudience). These are interesting extensions beyond the current set of partners, who mostly support operational tasks such as content creation, events management, analytics, and CRM integration. There was also some modest boasting about HubSpot’s continued growth – which actually accelerated slightly to 58% year on year in the most recent quarter – and other achievements including 15,000+ customers, 2,300 partners, 900+ employees, and top satisfaction rating in industry surveys.

HubSpot was less forthcoming than Teradata about future directions, perhaps because they see little change from the current course. Their general intention is to continue serving their existing target market (companies from 10 to 2,000 employees) with marketing and sales tools. There is a bit of redefinition to being a “growth engine” that solves additional marketing and sales problems, but this is an incremental change at most. The company’s announced focus is the improve the existing product with a mantra of faster, lighter, and easier, not to lead major changes in how businesses interact with their customers. Or perhaps HubSpot feels that most companies have virtually no automation in how they market and sell, getting more companies to adopt the existing HubSpot tools and best practices would itself be a major change. Fair enough.

On the other hand, co-founder and CTO Dharmesh Shah did tell me HubSpot is about a year away from supporting custom objects in its data model, which would open up some major new opportunities for the software. So perhaps there’s a bit more vision than they’re talking about publicly. People in Boston aren’t quite so free about opening their kimonos.

MDC DOT Provides Marketing Automation for Direct Salespeople

I briefly mentioned MDC Dot in an earlier blog post about giving sales people access to marketing automation capabilities. This may not have done them justice since they are more specialized than that general description implied. What MDC Dot really does is serve organizations that wrangle a herd of independent sales people, like financial advisors or direct sales representatives. Those firms have very specific requirements for balancing central control over content and brand image with the agents’ desire for flexibility and personal client relationships. It’s actually a crowded space, with companies like Balihoo and MindMatrix competing aggressively for sales. What sets MDC Dot apart is that it targets organizations where the sales people have modest skills, modest needs, and even more modest budgets.  Pricing starts at $15 per month per salesperson for a database of up to 500 active contacts and reaching a still-modest $90 per month for 10,000 active contacts.

Beyond low price, MDC Dot offers two key capabilities to suit its target audience. The first is a structure that links customers to their original salesperson, even if they later contact the organization through a Web search or visit to the corporate Web site. This is especially important for businesses that pay commissions to the customer’s original salesperson. MDC Dot does this by tagging each customer with the original salesperson’s ID and then ensuring that customer is sent back to the salesperson’s own microsite when they return. The initial tagging and subsequent redirection both require inserting some MDC Dot code onto whatever content the salesperson uses for the initial customer interaction. The redirection works best when the corporate Web site and the salespeople’s microsites are all on a subdomain hosted by MDC Dot.

The second key feature makes it easy for sales people to use content created by the central marketing team by automatically inserting tags such as the salesperson’s name and contact information. This customized content can include entire microsites, landing pages, emails, campaign flows, and social media posts. Salespeople can also set up their own contents.

Beyond these special features, MDC Dot also provides basic contact management including notes, tasks, tags, attachments, and activity tracking. Users can send emails, which come from the user’s own domain. Reports show email and Web activities, campaign results, and customers at each stage of the sales funnel. Screens are designed for simplicity and mobile devices. The interface is designed with the target of no more than three mouse clicks to accomplish any one task.

Corporate users can see the list of salespeople they are managing, along with performance statistics for each user. They can’t see the actual customers in the salesperson’s database. Corporate users also have tools to build email and social content and to set up contact sequences. These sequences are what puts the "dot" in MDC Dot: they’re built by connecting “Qualification Dots” to assign sequence members (all contacts, by contact type, via campaign manager, via sequence group), “Activity Dots” to react to behaviors within the sequence (opened an email, clicks an email, or visited a Web page, or not), and “Action Dots” to either send an email or transfer customers to another sequence group.

Sequences are built by connecting the dots (get it?). As you may have noticed, the set of available dots is pretty limited, although they are adequate to create basic email campaigns. At the moment each sequence can contain just one “Activity Dot” split, but multiple splits should be available later this year. Several sequences can be assigned to the same campaign and execute in priority order. This allows more complex treatments despite the simple design of the individual sequences.

The system also can do basic lead scoring and assign prospects to sales funnel stages. It lacks segmentation tools, although users can build an on-screen list of customers based on tags and then add the listed customers to campaigns. Landing pages must currently be built by the vendor. Tools to let users build their own filters and landing pages are under development.

MDC Dot was introduced in December 2014 and currently has more than 2,000 paying end users.

Will Machines Replace Marketers? Artificial Intelligence Isn't Ready Yet But Watch Your Back

Anyone who has chatted with me in recent months knows that I’ve added the impending domination of humans by intelligent machines to my usual list of obsessions. This most definitely applies to marketing, where I found many artificial intelligence-based solutions once I began looking for them. After accumulating a list, I’ve decided it’s time to pull together an overview of the topic.

My thesis was that AI-based systems already exist for most tasks that marketers perform, but are not yet connected into a single robo-marketer that (or is it who?) could do the job from start to finish. To test this, I listed the tasks that go into building a marketing program and matched these against my list of AI-based products.

Quite to my surprise, the machines haven’t risen so far after all. Of the three broad tasks I defined – planning, content creation, and execution – only content creation is served by what I consider to be strong AI* solutions. Some AI options are available for execution, but most are conventional predictive modeling products that I don’t count as strong AI because they still require humans to deploy their results. Marketing planning, which includes the all-important task of campaign design, is almost wholly untouched by AI.

Let’s look at each task in turn.

Planning

The lack of AI-driven planning solutions is especially surprising since so many planning tasks lend themselves to an AI approach. These tasks include market analysis (identifying potential buyers for a product, defining the needs and interests of potential buyers, identifying competitors, calculating potential market size and adoption rates), selecting marketing strategy, and selecting tactics (which can be defined as campaigns, experiences, or – if you’re cool enough – stages in the customer journey).

It seems well within the capabilities of current technology to find people who indicate a specific need, based on their Web searches or social comments, and then to understand who those people are in terms of demographics, behaviors, and other attributes. But the closest I could find were a couple of products that build profiles of groups the marketer defines in advance, such as brandAnalyzer by brandAnalyzer from Global Science Research and Empirical Insights from SG360. The one system that does look like strong AI is Bottlenose.  It performs the relatively common function of identifying trends but uses enough advanced technology, including natural language processing, topic discovery, and sentiment analysis, to impress me.

Similarly, competitor analysis should be well within the capabilities of companies like Radius, Everstring and Growth Intelligence, which already ingest the contents of corporate Web sites to understand each company’s business. But those vendors focus on finding prospects for B2B sellers. If any of them offers a competitor identification service, I’m not aware of it.

Business and marketing strategies are often compared to chess, a game that AI systems can famously play better than humans. I think the analogy is sound: like chess, business and marketing strategy involves a relatively limited number of moves with easily predicted short term consequences and large databases of past competitions which computers can study to predict longer term results. Strategic planning can also be supported by a rich treasury of simulation and optimization techniques that are very familiar to AI developers. Yet the closest I can find to strategic planning is optimization of media plans by media mix model vendors like MMA , Analytic Partners , Nielsen  and IRI.  But that is so tactical I classify it as part of execution. Otherwise, I haven’t seen anyone use AI to recommend a marketing strategy.

Nor has anyone really promised to design marketing campaigns using an AI system. This is another area that seems a natural application: computers can certainly use past results to predict the short- and long-term results of individual messages and, with a bit less certainty, of streams of messages. But while there are plenty of systems that predict the “next best message” or recommend what content the user is most likely to select, no one seems to have taken the obvious next step of using AI to design the best message sequence and refine it over time through automated testing. The closest I’ve seen are Amplero and Insightpool.  But they both start with individual data, so I'll discuss them in the section on execution.

Content Creation

Ironically, the most common reaction when I bring up machine-based marketing seems to be “well, they'll never write copy”. In fact, writing is one task where machines have already demonstrated huge success. General purpose writing programs including Wordsmith from Automated Insights , Quill from Narrative Insights and Arria NLG already write over one billion newspaper articles and reports each year, specializing in data-rich topics like sports and financial reports. Wordsmith also writes other sorts of reports, including summaries of marketing campaign performance.

Still closer to home for marketers, InboundWriter and Acrolinx score marketing content for effectiveness before it is released.

Most impressive of all from an AI perspective, Persado and Captora actually create content on their own. Persado does this by selecting and then testing content derived from a huge database of marketing language, classified by emotional, descriptive, and formatting categories. It works across email, landing pages, text messages, social posts, Facebook ads, app notifications, and other media. Captora automatically analyzes the topics and performance of content from the client and its competitors, finds opportunities for new campaigns, and creates appropriate landing pages to attract search traffic.

I consider Persado and Captora to be true AI-based marketing because they can actually replace work done by humans. But, as both vendors would probably rush to point out, what they really do is expand the volume of work that gets done, enabling marketers to execute hundreds of campaigns with the same human effort as it previously took to do dozens. So they are less about reducing the number of marketers than expanding marketer productivity.

Execution

While planning and content are arguably the most strategically important tasks that marketers do, there’s no question that they spend most of their time on execution. This is especially true since my definition of execution includes measurement and optimization because all three are so closely intertwined.

I broadly divide execution into audience development, message selection, and attribution. Audiences include paid media (purchased ads and lists), earned media (public relations and social influencers), and owned media (company Web sites and email lists, in-store promotion, call centers, etc.). Message selection includes content recommendations and personalization. Attribution includes everything that measures the results of marketing efforts – although, in practice, much message selection also relies on simple attribution to improve selection results.

Each execution category is served by advanced technology. Paid audiences are built with predictive modeling for list selection, programmatic media buys for advertising, and automated content analysis to understand intent. Earned audiences are built through influencer identification and predictions of who will cover which stories. Owned audiences are refined through more predictive models and behavior analysis. Message selection also relies on advanced analytics to recommend the right content for each individual and to find the best-performing messages for groups. Likewise, attribution systems apply sophisticated methods to isolate the incremental impact of individual marketing actions on long-term results. This long-term perspective is what distinguishes attribution from the measurement built into message selection systems, which instead chase immediate results such as email click-through or Web page conversion.

These technologies are certainly impressive, but few of them actually remove marketers from the process – which you’ll recall is my definition of marketing AI.  Predictive model scores, for example, are usually plugged into marketer-created rules that decide who receives which treatments. Even the recommendation engines rarely do more than predict which messages an individual is most likely to select.  Human-built rules still determine which messages are available and when messages will be presented.

There’s a lot of gray in this picture. Model scores and recommendation engines often replace complex segmentation rules even though some other rules remain. They may not replace marketers altogether but they do enable marketers to run larger numbers of more refined programs.  And they're a supporting technology for true AI marketing systems even if they are not AI themselves.

On the other hand, I think programmatic media buying does rise to the level of true AI. Again, the critical distinction is whether they replace human marketers – and I think that media buyers are pretty much not needed to execute programmatic programs. Obviously a human still has to set up the programs and provide the creative, but the programmatic systems then make complex judgements on their own.  Are these “judgements” significantly more advanced than the “judgements” that go into a lead scoring predictive model or personalized content recommendation? I’m not really sure. Maybe I’m misled by the fact that “media buyer” is an established profession while “lead scorer” or “content personalizer” are not job titles that people had before computers were available.

There are a few execution products that approach the border of true AI and may actually cross it. These include:

- Amplero, a newly released system that uses tree analysis to find very small market segments and then identifies the best content for each segment. What separates it from other personalization tools is that it optimizes against long-term value, such as revenue over the 14 days following each message, and its decisions take into account messages previously presented. I’d definitely consider Amplero true AI if it could plan sequences of messages, which would be pretty much the same as building multi-step campaigns. The system doesn’t do this yet but the vendor tells me they’re working on it.

- Insightpool, which identifies social media influencers, predicts how likely they are to take a user-specified action, and then recommends multi-step campaigns to encourage that result. Influencer identification and activity prediction are impressive but not unique; what makes me classify Insightpool as AI is its ability to select campaigns. This is something that you’d ordinarily expect a human marketer to do, even if the marketer was working with lists that the other functions had prepared.

- OneSpot converts existing pieces of content into multiple ad formats to permit reuse, classifies them (automatically, so near as I can tell) by purpose, and then delivers them to precisely targeted or retargeted individuals through programmatic ad exchanges in the optimal sequence to meet long-term goals. The reformatting, classification, and sequencing all strike me as things that humans would otherwise do manually, and of course I’ve already argued that programmatic media buying itself qualifies as marketing AI.

Final Thoughts

I know this post is too long to be effective but I wanted all to get this information down in one place. Artificial intelligence is an important topic in our general society and seems to attracting increased attention, even though Google Trends suggests otherwise. Marketers in particular are thinking about it as they adjust to rapidly changing technologies that increasingly rely on predictive analytics and other automation for effective management.

Given the hype that accompanies pretty much every new technical development, it’s helpful to see that AI-based marketing isn’t as far along as one might expect. But don't take that as a reason to relax: while it’s not time to panic, it’s definitely time to prepare. AI marketing systems already present some significant opportunities and their scope can only grow – perhaps exponentially as key techniques become more widely distributed. Now is the time to start building a realistic understanding of how these systems work, what they can and can’t do, and how they’ll fit into your future.


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* “strong AI” is used by AI experts to mean systems match or exceed human intelligence. I’m using it in that roughly sense, although with the more specific meaning of “systems that perform tasks that otherwise require human marketers”.

Are 70% of Marketing Automation Users Unhappy? Well, Not Exactly

A recent piece in TechCrunch quoted me as saying that “almost 70 percent of marketers are either unhappy or only marginally happy with their marketing automation software.” The author included a link to the source of that quote, but unfortunately it was broken (it has since been fixed).  This lead to enough questions about the data that it now seems worth a blog post on the topic.

To resolve the original mystery: the quote references a survey I conducted with VentureBeat and released in June 2014. You can buy it here if you’re interested.  Answers came from 159 marketing automation users.  The quote refers to a question about how well marketing automation software met satisfaction and improved results, on a 1 to 5 scale. About 18% gave a score of 1 or 2, 50% gave a 3, and 32% gave a 4 or 5. The 1 and 2 scores are clearly unhappy and I’d consider a 3 to show neutral or marginally satisfied. Hence the “almost 70 percent” quote.

Source: Raab Associates, 2014

For what it’s worth, the survey also asked a second, more pointed question about whether marketing automation benefits were worth the investment. We received fewer responses (only 87) but the distribution was similar. In fact, the dissatisfied group was a higher percentage: 25.8% and there’s no question how they felt: “we could have achieved similar results more cheaply”. The middle group, 44.1% “achieved our goals” which still sounds to me like marginal satisfaction. Only 23.7% felt they exceeded expectations.

Source: Raab Associates, 2014

I've never considered these results particularly remarkable because they are consistent with other surveys on the topic.  See my posts for October 13, 2013  and October 22, 2013 for a several other surveys.

Of course, that's all old data and you may wonder whether anything has changed.  The short answer is no. For example, a recent survey from Marketo and Ascend2  found that 14% of buyers rated marketing automation as clearly unsuccessful and only 25% rated it as very successful: again, there was a big intermediate group of 61% who said is was only “somewhat successful”.

Another survey, this one from Salesforce.com, is generally more optimistic, showing 37% of users rating marketing automation as very effective or effective. But it also shows a relatively high 31% rating it as not very effective or not at all effective. The real difference is an unusually small middle group, 29% rating marketing automation as “somewhat effective”.  What’s probably more disconcerting about this survey is that it shows that marketing automation has relatively low satisfaction and importance compared with other technologies. This suggests that marketers who must prioritize their spending will make other investments first.
 

I must say that I don’t find this topic particularly engaging at the moment. The point of the original TechCrunch article was the growth of open, predictive-based platforms that unify sales and marketing, a direction I agree the industry will take. B2B marketing automation in its current form of systems that primarily use email, landing pages, and visitor tracking to nurture leads before sending them to CRM is a subset of this much larger vision. The challenges of using current marketing automation systems are well known but they will simply make it easier for newer, more effective approaches to replace them. It’s more important and more interesting to focus on that future.

Bombora Feeds B2B Data to Everyone

One of the little patterns that caught my attention at last week’s Marketo conference was that several vendors mentioned using data from the same source: Madison Logic Data, which recently renamed itself Bombora*. The company was already familiar to me through clients who deal with it. But I had never gotten a clear picture of exactly what they do. Those additional mentions finally pushed me to explore further.

A couple of emails later and I was on the phone with Erik Matlick, Madison Logic founder and Bombora CEO. We went through a bit of the back story: Madison Logic was founded as a B2B media company six years ago. It built a network of B2B publishers to sell ads and gather data about their site visitors. Both businesses grew nicely, but the company found that selling media conflicted with finding partners to gather data. So last November it spun off the data piece as Madison Logic Data, keeping Madison Logic as the media business.  The change from Madison Logic Data to Bombora was announced on April 13.

To which you probably say, who cares? Fair enough. What really matters is what Bombora does today and, more pointedly, what it can do for you. Turns out, that’s quite a bit.

Bombora’s core business is assembling data about B2B companies and individuals. It does this through a network of publishers who put a Bombora pixel on their Web pages.  This lets Bombora track activities including article and video viewing, white paper downloads, Webinar attendance, on-site search, and participation in online communities. The company tags its publishers' content with a 2,300-topic taxonomy, allowing it to associate visitors with intent based on the topics they consume. It identifies visitors based on IP address, domain, and registration forms on the publisher sites. It also attaches demographic information based on information they provide on the registration forms and the publishers have in their own profiles. The volumes are huge: 4 billion transactions per month, more than 250 million business decision makers, and 85 million email addresses collected in a year.

All that information has many uses: [feel free to insert your favorite cliché about how data being important]. Like meat packers who use every part of the pig but the squeal, Bombora is determined the squeeze the most value possible from the data it assembles. This means selling it intent and demographic audience segments for display advertising, marketing automation and email segmentation, Web audience analytics, data enhancement, content personalization, media purchasing, and predictive modeling.  Different users get different data: sometimes cookies, device IDs or email addresses, and sometimes by company, individual, or segment. Publishers who contribute data are treated as part of a co-op and get access to all 2,300 intent topics. Others only can select from around 60 summary categories.

If you’re a B2B marketer, you’re probably drooling at the thought of all that data. So why haven’t you heard of Madison Logic and Bombora before? Well, like those thrifty meat packers, Bombora sells only at wholesale.  In each channel, partners embed the Bombora data within their own products. Sometimes it's baked into the price and sometimes you pay extra. It’s a “Bombora inside” strategy and makes perfect sense: everything’s better with data.

At the risk of beating a dead pig, I'll also note out that Bombora illustrates a point I've made before: that public sources of data will increasingly supplement and to some degree may even replace privately gathered data.  This is a key part of the "madtech" vision that says the data layer of your customer management infrastructure will increasingly reside outside of your company's control.  The risk to companies who use this data is that their competitors can access it just as easily, so there's still a need to build proprietary data sources in addition to adding value in other areas such as better analytics or customer experience.

Enough of that.  I'm hungry.

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*Something to do with surfing in Australia.  There's a sea of data metaphor in there somewhere, I think.

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.