Microsoft Buys LinkedIn for $26.2 Billion: Get Ready for Software Vendors as Data Owners

Microsoft surprised pretty much everyone today by announcing a $26.2 billion acquisition of LinkedIn. This is fascinating since Microsoft intersects with LinkedIn in several areas: Dynamics CRM software, Office productivity software, and Bing online advertising. It gives Microsoft access to a rich trove of personal and company information, something it didn’t have before (although Microsoft probably collected more personal and company data than most of us realize).

LinkedIn is primarily a social network with revenue from subscriptions, recruiting services, and advertising. But Microsoft’s announcement suggests it is primarily interested in using LinkedIn’s data for other purposes, such as enhancing the effectiveness of Office and CRM users by showing information about their contacts and potential contacts. This puts Microsoft at the center of the “third party data revolution” (a term I just made up and will probably never use again) that makes detailed information about everyone easily available from commercial sources. This is a trend that’s been clear for some time; it’s a big part of the intent data and predictive data excitement of the past year or two. It's also one foundation of the MadTech vision I offered last year.

It still feels odd to think of a software company owning a data business, although Salesforce.com bought Jigsaw (now Data.com) in 2010 and Oracle purchased the BlueKai and Datalogix in 2014. The prospect of seamlessly integrating third party data with a company’s own sales and marketing products is intriguing, although neither Salesforce nor Oracle has done much with it. Other vendors like Nimble and HubSpot have done a better job of simplifying access to third party data about an individual or company. Those features are immensely appealing and become even more important in the world of Account Based Marketing, where knowing who to reach at your target customers is everything. Done correctly, integration of LinkedIn with Dynamics CRM could provide a major boost to that product’s utility while creating a new barrier to competition.

We’ll see what happens next: Microsoft might be able to reset expectations among CRM (and Outlook) users for having prospect and company data immediately available. That would force other CRM and marketing automation vendors to follow suit, although it's hard to imagine them matching the depth of LinkedIn's data.

If nothing else, this confirms the foundational role of data and data management in marketing and sales technologies.  That's important because companies that start by planning a stable data layer are best positioned to manage the accelerating changes in decision and delivery systems.

Design Your Best Marketing Technology Stack and Plan the Transition: Sneak Peek at FlipMyFunnel Conference

Picture posted by Terminus

I can’t decide which is more exciting about next month’s FlipMyFunnel conference in Atlanta (register here and use the code DR50 for a 50% discount): the opportunity to interact with a great collection of speakers and attendees or seeing what the conference organizers at Terminus do with the notion of MarTech Stack Jenga. Based on one cryptic Twitter picture, they’re up to something big.

My own contribution will be a presentation on designing your marketing stack. This is something I’ve done for years as a consultant but it’s now an especially hot topic. Here are some of the key points I’ll be making:

- the stack is based on your business and marketing strategies. I’ve described the importance of strategy before but have now refined my explanation to show how marketing programs, business requirements, and functional requirements connect over-all marketing strategy with martech. The picture below also highlights the importance of planning for future business, marketing, and martech developments.

And I’ve provided a sample template for organizing your requirements by system.

- a winning stack is efficient as well as functional. I'll present a checklist for evaluating your stack design along those dimensions.

- how you draw the stack makes a difference. I’ll argue that a diagram which shows relationships between systems is more helpful than one that simply lists the different components. In the example below, the flow highlights the isolation of sales and service from the rest of the stack – a critical weakness that isn’t apparent when you look at the systems only.

- transition planning must be systematic as well. Companies struggle with transition planning even more than they struggle with stack design.  The goal is to sequence the stack changes so that each new system adds the greatest value with the least disruption. This requires understanding which system changes support each improvement.  This lets you figure out which improvements would be supported by changing any one system, what would then be possible after changing a second system, what is possible after changing a third system, and so on.  The worksheet lets you explore different sequences so you can pick the best one.

This will be easier to understand in person than in writing. Don't take my word for it: join us in Atlanta and see for yourself.

Does Future Marketing Technology Require Perfect Data?

I mentioned in my last post that I’ve started to think in terms of three realities: today (the next two years), tomorrow (two to five years out), and later (after five years). Like the famous New Yorker magazine cover that showed a detailed knowledge of Manhattan and increasingly vague view of more distant regions, our picture of the immediate future is much more nuanced than what happens farther out. One result is an apparent assumption that future technology will work much better than today’s technology – not because anyone really thinks that future technology will be perfect, but because we can’t see where its imperfections will appear.

I’ve been thinking about this because so my own predictions are premised on increasingly detailed knowledge about customers and prospects. Both the “madtech” vision of broad access to third-party data and the “robotech” vision of delegating decisions to machines assume that effectively complete data will be available about each customer. But a quick look at today’s data shows that is far from true. Here are some factoids I’ve been gathering to illustrate the point:


- 37% of mobile ad locations are accurate to within 100 meters (Thinknear)

- 30-55% match rates for B2C individual-level onboarding (LiveRamp)

- 16-29% match rates for B2B individual-level data enrichment: (Raab Associates client tests)

- 14% match rates and low predictive value for B2B account-level intent data: (Infer)

And this doesn’t even begin to address predictive modeling, where even a 10x lift vs average still implies many errors at the individual level.

Contemplating these results does give me pause. At some point, poor data means that theoretically possible approaches are not practical because of low coverage or insufficient performance. Those constraints won’t magically vanish in the future, even though they’re not visible at this distance.

Being a technology optimist, I assume that data will get better over time. But I can’t cite much evidence to support my optimism.  If anything, the number of new data sources is outstripping improvements in existing sources. The true core challenge is identity resolution, which means associating data from different sources with the right individual profile. Cross-device matching is the current focus of this discussion but covers just part of the problem.

It’s a safe bet that perfect data won’t be available in two years or five years or probably ever. But the real question is whether enough good data will be available to support the futures I’ve been forecasting.

I think a realistic view is that some data will be more available than other data, and, as a result, some portions of the visions will happen while others do not. Customer data is likely to be richer than prospect data, since customers will grant permission to link with external data sources (or take actions that make linking easier even without their permission). Sharing among complementary companies – for example, airlines and hotels – will be easier to negotiate than sharing with anyone through public exchanges. Data about objects, such as cars or groceries or homes, should be less sensitive than data about individuals (even though there’s obviously a close relationship between objects and their owners). Data about public behaviors, such as travel and store visits, is less sensitive than data about private matters such as health care.  (See this recent Altimeter Group report for more information on consumer attitudes to privacy.)

In short, the future will remain unevenly distributed, as William Gibson observed. Marketers and the technologists who support them need both the ideal vision of how things would work in a world of perfect data (which isn’t the same as a perfect world!) and the realistic understanding of what’s likely to be practical within their planning horizon. They can then aggressively pursue opportunities revealed by the vision without chasing chimeras that will never appear. This pursuit is essential: tomorrow always comes, but the future won’t happen by itself.

Marketing Beyond MadTech: What Happens When The Robots Take Over?

I’ve recently found myself bouncing between three worlds:

- today’s world, where I spend my time reviewing software and helping marketers choose martech products. Since most of that discussion is currently phrased in terms of building a marketing stack, let’s call it the world of “stacktech”.

- tomorrow’s world, about two to five years in the future.  This is dominated by the merger between martech and adtech, a.k.a. “madtech”. The trends shaping this world are well known and many people agree on the broad outlines of what it will look like. I spend my time filling in the details since details will determine which tools and skills marketers need for success.

- the future world, out five years and beyond.* There’s much less agreement on how this will look and it’s arguably too far away for most marketers to worry about. But I do have a vision which I think may be useful to vendors and managers making long term plans. Since the dominant feature of this world will be an expanded role for machines, I’ll call it “robotech”.

Each of these worlds is very different from the others. Today’s marketing stack is still largely about tools to manage direct interactions between customers and the company. It works with the company’s own data and primarily through company-owned media like email and Web sites. Customer activities with anyone other than the company are largely invisible to the company.

By contrast, advertising and social messages in the "madtech" world are tightly integrated with company-owned channels and all customer behaviors are visible (for a price). The technical symbol of this transition is the change I wrote about last week from linear, company-driven campaign flows to customer-triggered experience plays.

The "robotech" world brings yet another radical shift.  In this future, humans have delegated increasing numbers of day-to-day decisions to their machines. My recent speeches have illustrated this with a vignette about a person in headed home in her self-driving car: she works quietly in her virtual office while her devices debate whether to stop for fuel, buy her coffee, avoid donuts, and get milk for breakfast. Only once the machines have reached a consensus do they inform her of the decision.

The example is trivial but the implications are profound. When machines buy on behalf of their owners, then marketers will sell to the machines. Since the machines will decide on the basis of algorithms, marketing becomes a matter of understanding and appealing to those algorithms. We already do this today in specialized areas like search engine optimization (“selling” to Google for a higher ranking) and programmatic media buying (providing more data about impressions so they earn higher bids). This sort of marketing is fundamentally different from both stacktech and madtech. My rough calculations show that nearly half of all consumer expenditures could eventually shift to machine control.

Humans still play an important role in the robotech world. It’s not just that they’re paying the bills for purchases by autonomous agents – a relationship familiar to any parent of a teenager. It’s also that humans are choosing the agents themselves. This is essentially a subscription: people will pay for a service that manages individual purchases. Since the details of each agent’s algorithm will be too hard to evaluate directly, the subscriptions will ultimately be purchased on the basis of trust.  This is a classic goal for traditioinal brand marketing but quite a change from the madtech focus on optimizing shorter-term metrics such as response or immediate revenue.

I don’t think the rebirth of brand marketing will mean a return to the simple-minded glories of the Mad Men era – we’ll still have all that data and all those channels to work with. But it might just possibly mean a less frantic urge to respond to every twist in the customer journey, replaced by broader, more stable messages aimed at building brand trust and a long-term relationship. In a world where customers increasingly filter out marketing messages and rely on machines to manage many steps in their customer journey, marketing approaches that deliver a few general messages may ultimately be the best use marketers can make of the limited customer attention they have available.

In sum, the transition madtech to robotech will be just as wrenching as the transition from stacktech to madtech.  Marketers should recognize that both are coming, even if it's too soon to prepare for the robotech world.  The time for that will come very quickly and it's always good to have at least thought about it in advance.

_____________________________________________________________________
* Serious planners think much further out, in terms of decades. But I don’t think anything usefully concrete can be predicted that far in advance.

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.


____________________________________________________________________________
*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.

______________________________________________________________________________

(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.

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.

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.

_________________________________________________________________
*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.