Fitting Agents into the Sales and Marketing Mix

Much has been written recently about how marketing and sales processes change when human buyers and sellers are replaced by buyer and seller agents: abbreviated, inevitably, as “A2A” marketing. It’s a fascinating topic but just one model that will coexist in the near future with human (or, more precisely, non-agentic) buyers interacting with agentic sellers, agentic buyers interacting with human sellers, and, lest we forget, humans interacting with humans. Any consultant will immediately recognize that this cries out for a 2x2 matrix, or perhaps a pair of 2x2 matrices if you want to distinguish business marketing from consumer marketing. For the moment, let’s stick with the single matrix model:

It’s worth making these admittedly-obvious distinctions because each situation raises separate issues, which are otherwise easily jumbled into a confusing heap. Let’s look at each situation in turn.

Human to Human (H2H)

Beyond the literal situation of one seller talking to one buyer, I’d argue this also includes humans interacting with traditional broadcast media, web search, and even non-agent websites. The common thread is that the human buyer does most of the work of asking questions and processing answers. The seller is largely reactive, although there are some situations where she makes choices such as selecting a personalized “next best action”, embedding dynamic content in a website, and setting up conventional search engine optimization. Those choices may be informed by predictive models or some other type of AI, but every step in the workflow is ultimately managed by humans, not agents.

I can’t point to specific data but am pretty sure that H2H interactions still account for the vast majority of today’s sales and marketing activity. This means that marketing and sales teams should still give significant amounts of attention to improving them, even though agentic interactions are vastly more fun to think about. If you absolutely must bring AI and agents into the picture, you can use them behind the scenes to speed up workflows, optimize performance, and analyze results.

Agentic Buyers to Human Sellers (A2H)

This is probably the situation that gets the most attention today. It includes true “buyer agents” (controlled directly by buyers) and “buyer-supporting” agents such as AI search engines and browsers. I call these “buyer-supporting” because they’re not controlled by the buyer, but instead by a company like OpenAI or Google which provides them to buyers at little or no cost.

The distinction matters because companies that offer “buyer-supporting” agents have their own agendas, which don’t necessarily align with the interests of actual buyers. In particular, these companies are increasingly interested in monetizing their products by serving ads within AI search and browser results. Some of these ads will be clearly labeled while others may be subtly embedded in the results themselves. These ads are an opportunity for marketers but may be problematic for users, who could be led to question the objectivity of the AI results.

Concern about biased AI search results could in turn lead to significant interest in true “buyer agents” that consumers pay for themselves. History suggests this will be an uphill battle: as we’ve seen with streaming video, large majorities of consumers typically chose free, ad-supported services over paid, ad-free subscriptions. Still, as streaming video has also shown, a significant fraction of consumers will pay for subscriptions in return for a better experience. This could be a large enough market to support a profitable business. Business buyers are even more likely to purchase agent subscriptions, since they don’t pay with their own money and can easily justify the expense based on better quality results. The precedent here is ad-supported versions of office productivity apps, which have never been broadly successful. There’s a chance that agents could be funded by charging advertisers for access to their owners, although such models have also failed in the past.

Advertising aside, most A2H discussions in martech and adtech circles focus on how sellers can adapt their systems to get the best results from buyer-side agents. This often involves advice on optimizing website design to accommodate search and browser agents, so a given brand receives the best possible treatment. Traditional SEO vendors are frantically expanding their products to meet this need and new AEO (AI Engine Optimization) specialists are also appearing. So far, the solutions are pretty basic: systems run sample queries to measure how often a given brand is mentioned in AI search results and vendors offer design tips to expose the kinds of data that AI agents are looking for. The next level is to look beyond measuring and influencing whether the brand is presented, to how it’s presented in terms of positioning and value. We’ll surely see more of that.

The thing to remember about “buyer-supporting” AI search and browser agents is they are generally driven by a big LLM model that draws from the same information for all users. True “buyer agents” would supplement the more-or-less static LLM models with custom research that visits seller websites to find answers to buyers’ specific questions. For example, one buyer might be interested in pricing details while another cares more about product quality. Beyond exposing all possible information, a seller might aim to present its product differently depending on what appear to be the buyer’s priorities. This is largely similar to today’s (non-agentic) website personalization. What’s more intriguing is the possibility that sellers can find a way to identify individual buyers’ agents over time, perhaps by requiring registration in exchange for detailed information. This would let the seller build a buyer profile and tailor responses to this profile. Piercing the buyer agents’ veil of anonymity would be hugely valuable.

There is a third situation: where the “H” in “A2H” is an actual human, not a non-agentic system. One current example is humans responding to agent-generated Requests for Proposals, which will likely be joined by other formats such as email inquiries or even telephone surveys. The growing volume of agent-generated requests is already a nightmare for business sellers faced with the cost of responding to them. The obvious solution is to let seller agents respond to the buyer agents, but it may be a while before most firms can deploy this capability. In the interim, sellers will be increasingly pressed to qualify buyers before deciding how to respond. Insofar as responding to qualification questions requires effort by the buyer, this imposes a cost on the buyer that should help to eliminate frivolous requests. At some point it might make sense for sellers to impose a literal cost – that is, to charge a fee – for responding to agent-generated sales queries. A less obvious concern is that buyers who rely on agent-generated research questions may fail to understand their true needs, removing a substantial portion of the value gained from a good purchasing project.

Human Buyers to Agentic Sellers (H2A)

Traditional websites may use AI-driven personalization but they are still non-agentic systems. In the future, we can expect true agentic interactions to become increasingly common. The best current example would be chat interfaces connected to an agentic back-end, enabling them to engage in true conversations with potential buyers. These have already evolved in some situations to full-scale agentic business development reps (who send those those super-annoying emails complementing your latest blog post and asking for an appointment) and sales reps (engaging in lengthy dialogs).  Agentic customer support reps are even more common and, often, better than humans at many tasks. While the distinction between AI-based and agent-based interactions can be vague, it’s fair to say that agentic interactions will be significantly more responsive to individual situations. This, in turn, makes them more reliant on capturing real-time data, both for customer behaviors and surrounding context.

Letting autonomous agents interact directly with customers raises major concerns about governance, output quality, and risk. These are widely recognized, as are the challenges of integrating agent-based systems with existing infrastructure. That being the case, I won’t rehash them here, apart from noting that they currently present substantial barriers to adoption of H2A models.

Agentic Buyers to Agentic Sellers (A2A)

Agents selling to other agents is the obvious endpoint of agentic adoption. It’s appealing if only for the amusing prospect of agents merrily jabbering with each other without any human involvement. But apart from a few highly structured interactions, such as programmatic advertising, it’s still largely in the future. A2A can’t become more common until the industry first solves the separate challenges of agentic buyers and agentic sellers. It must then overcome the additional challenges of connecting the two. Once the plumbing issues are addressed, there will be another level of adoption as buyers and sellers work to turn the interactions to their advantage. How will price negotiations work when buyers want the lowest price possible and sellers want the highest price? How will sellers discover the actual needs of buyers so they can make the best recommendations – and is what’s best for the seller necessarily what’s best for the buyer? How will seller agents decide which information to offer and which to exclude? How will agents build trust with each other? And how will companies manage the computing costs of agent-to-agent interactions, which could be substantial if the interactions are extensive?

Plenty of smart people are surely working through these issues. We already see some technical foundations being laid in protocols such as MCP and Google’s A2A. But it’s probably too soon for most marketers to put much energy into worrying about A2A deployment. Mastering the intermediate steps of A2H and H2A should come first and will put them in a better position to deal with A2A when the time is right.

Summary

The impact of AI in general, and agentic AI in particular, is overwhelming. While this piece offers some ideas and makes some prediction, my real goal is much simpler: to suggest that distinguishing the different types of human and agent interactions is a way to split the topic into smaller, more tractable pieces. I hope that helps.

Is Peak Martech Approaching At Last?

Contrary to popular belief, forecasting is easy: tomorrow is nearly always like today. What’s hard is predicting when something will change: a snowstorm, stock market crash, or disruptive technology. Of course, predicting change is also where forecasting is most useful.

In marketing technology, we’ve seen a long succession of sunny days. Every year, the number of systems grows, fed by a proliferation of channels, declining development costs, and easily available funding. The safe bet is the number will grow next year, too. But I think one day soon the pendulum will reverse direction.

I can’t point to much data in support of my position. Surveys do show that marketers don’t use the full capabilities of their existing stacks, which might mean they’re inclined to take a break before making new purchases. But marketers have never used every feature in their old systems before buying new ones. The pandemic probably led to a temporary dip in martech purchases but budgets appear to be opening up again. So the appetite for new martech will likely reappear. 

(Update, August 2021: a Mulesoft survey released in August 2021 did show the average number of apps used by large enterprises had fallen from a peak of 1,020 in 2018 to 843 in 2021, and Gartner's July 2021 CMO Spend Survey reported a sharp drop in marketing spending from 11% of company revenue in 2020 to 6.4% in 2021, which would imply a drop in martech spending too.  I'm skeptical of the Gartner data but both studies support the idea that martech proliferation may decrease.)

My prediction is based less martech trends than a general impression that many people feel the world is spinning out of control and want to rein it in. Tech in particular is having impacts that no one fully understands. Concerns about disinformation, social media-induced radicalization, lost privacy, and biased artificial intelligence are all part of this. Even in the narrower spheres of martech and adtech, many users feel their systems are too complicated to really understand. Worries about ad fraud, ads appearing in the wrong places, inappropriate personalization, and unintended campaign messages all come down to the same thing: people worry their systems are making unseen bad decisions.

Technologists tend to feel the cure is more technology: smarter AI, systems checking on other systems, and democratized development to let more people build systems for themselves. But there’s an air of hubris to this. Stories from Daedalus to Frankenstein to Jurassic Park warn us advanced technology will ultimately destroy its creators. Every data breach and wifi outage reminds us no technology is entirely reliable and fixing it is beyond most people’s control.

As a result, non-technologists increasingly doubt that technology can solve its own problems. Some people will bury their worries, accepting technology’s risk as the price for its benefits. Others will take the opposite extreme, rejecting technology altogether, or at least to the great degree possible.

But there’s a middle ground between blindly surfing the net and leaving the grid entirely. This is to consciously seek technology that’s simpler and more controllable than current extremes, even if it’s also less powerful as a result. The key is willingness to make that trade-off, which in turn implies willingness to invest the effort needed to assess the relative value vs. risk of different technical options.

Making that investment is probably the biggest change from the current default of accepting technical progress as inevitable and trusting the technologists to appropriately balance risk against rewards when they decide which products to release. In many cases, the cost of assessment will probably be higher than the cost of using the diminished technology itself: that is, the difference in value between a more secure system and a less secure one may be less than the value of the time I spend comparing them. This means the main cost of making this adjustment isn’t the lost value from using safer technology, but the cost of assessing that technology.

In theory, the assessment cost might be reduced by splitting it among many people who would share their results. But here’s where trust comes back into play: if you can’t trust someone else to do accurate research, you can’t decide based on their results. Since loss of trust is arguably the defining crisis of today’s society, you can’t just wave it away with an assumption that people will trust others’ assessments of technology tradeoffs. Rather, the need will be to build technology that is self-evidently understandable, so that each person can assess it for herself. This will reduce the assessment cost that blocks them from choosing simpler solutions.

So here’s where I think we’re headed: away from ever-increasing, and increasingly opaque, technical complexity, and towards technology that’s simpler and more transparent. Remember: simplicity is the goal, and transparency is what makes it affordable. I call this the “new pragmatism”, although I doubt the label will catch on. As the word “pragmatism” suggests, it’s rather boring and a lot of hard work. But compared with the chaos or authoritarianism that seem to be the main alternatives, it’s about the best way we can hope our current chapter will end. After we turn the page, people may later learn to rebuild the presumption of trust that enables non-verifiable relationships.

If you’re still reading this, thanks for your indulgence; I know you don’t come to this blog for half-baked social theories. But these ideas do have direct implications for marketing and martech. If I’m right, both consumers and martech buyers will want simpler, more transparent products. For marketers, this means:

The time may finally have come when stripped-down versions replace feature-rich products, with a stress on ease of use rather than power. I know this idea has been tried before without success. But that was during the earlier age of techno-optimism.

Buyers may be more interested in products whose actual operation is transparent. This will usually mean status indicators, meters, and diagnostics to show’s happening. In some cases may literally mean see-through designs that let users watch, say, as the dishes are cleaned or the vacuum bag fills with dirt. Whatever it takes for a feeling of control.

Privacy will continue to gain importance, with particular emphasis on systems that are private by design rather than user choice. Privacy policies and options are poorly understood and mistrusted, so many consumers would rather buy a system that makes them unnecessary because it can’t collect data or connect to the internet. Of course, they need to be confident the system behaves as promised.

Marketing messages should also switch from promoting advanced technology to promoting simplicity, reliability, and clarity. Explanations about what’s inside a product, in terms of the technology, design and manufacturing processes, materials, and people may be more important to buyers looking for reasons to trust.

Marketing methods should match the claims, avoiding unnecessary personalization and staying away from mistrusted media. This is a tricky balance because few marketers will want to sacrifice the performance benefits that come from data-driven targeting. But they do need to weigh long-term brand value against short-term campaign results. For what it’s worth, relying more on basic branding and less on advanced technology is itself consistent with the return to simplicity.

Martech vendors will want to make all these adjustments in their own marketing. Other considerations include:

• Artificial intelligence must be understandable. It’s tempting to suggest discarding AI altogether, since it may be the ultimate example of complicated, opaque, and ungovernable technology. But the apparent benefits of AI are too great to discard. The pragmatic approach is to demand proof that AI really delivers the expected benefits. Then, assuming the answer is yes, find ways to make AI more controllable. This means building AI systems that explain their results, let users modify their decisions, and make it easy to monitor their behaviors. These are already goals of current AI development, so this is more a matter of adjusting priorities than taking AI in a fundamentally different direction.


• Reconsider the platform/app model. This may be blasphemy in martech circles, since the martech explosion has been largely the result of platforms making it easier to sell specialized apps. But the platform/app model relies on trust that apps are effectively vetted by platform owners. If that trust isn’t present, assessment costs will pose a major barrier to new app adoption. At best, buyers with limited resources (which is everyone) would be able to afford fewer apps. At worst, people will stop using apps altogether. So the pragmatic approach for platforms and app developers alike is to work even harder at trust-building. We already see this, for example, in Apple’s new requirements for data privacy labels and tracking consent rules. https://developer.apple.com/app-store/user-privacy-and-data-use/ What Apple hasn’t done is to aggressively audit compliance and publicize its audit programs. The dynamic here is that users will make more demands on platforms to prove they are trustworthy and will concentrate their purchases on platforms that succeed. Since selecting a platform carries its own assessment cost, we can expect users to deal with fewer platforms in total. This means the trend for every major vendor to develop its own platform ecosystem will reverse. Looking still further ahead: fewer platforms gives the remaining platforms have more bargaining power with the app developers, so we can expect higher acceptance standards (good) and higher fees (not so good). The ultimately is fewer app developers as well.


• Rebirth of suites. That’s not quite the right label since suites never died. But the point is that buyers looking for simplicity and facing higher assessment costs will find suites more appealing than ever. Obviously, the suites themselves must meet the new standards for simplicity, value and transparency, so integrated-in-name-only Frankensuites don’t get a free pass. But once a buyer has decided a suite vendor is trusthworthy, it’s far more attractive to use a module from that suite than to assess and integrate a best-of-breed alternative. Less obvious but equally true: building a system in-house also becomes less attractive, since in-house developers will also need to prove that their products are effective and reliable. This will necessarily increase development costs, so the build/buy balance will be tilted a little more towards buying – especially if the assessment costs of buying are minimal because the purchased option is part of a trusted suite. It’s true that this doesn’t apply if companies require users to accept whatever their in-house developers deliver. But that doesn’t sound like a viable long-term approach in a world where the gap between poor in-house systems and good commercial products will be larger than ever.


• Limits on citizen developers. If blasphemy comes in degrees, this takes me to the professional-grade, eternal-damnation level. On one hand, nothing is more trusted than something a citizen developer creates for herself: she certainly knows how it works and can build in whatever transparency and monitoring she sees fit. So the new-pragmatic world is likely to see more, not fewer, user-built systems. But if we’re learned anything from decades of using Excel, it’s that complex spreadsheets almost always contain hidden errors, are opaque to anyone except (maybe) the creator, and are exceedingly fragile when change is required. Other user-built solutions will inevitably have similar problems. So even if users trust whatever they’ve built for themselves, everyone else in the organization will, and should be, exceedingly cautious in accepting them. In other words, the assessment cost will be almost insurmountably high for all but the simplest citizen-developed applications. This puts a natural, and probably shrinking, limit on the ability of citizen-developed systems to replace commercial software or in-house systems built by professional developers. In practice, citizen development will be largely limited to personal productivity hacks and maybe some prototyping of skunkworks projects. This doesn’t mean that no-code and low-code tools are useless: they will certainly be productivity-enhancers for professional developers. Don’t sell those Airtable options just yet.

I’ll caution again that the picture I’m drawing here is far from certain to develop. I could be wrong about the change in social direction – although the alternative of continued disintegration is ugly to contemplate. Even if I’m right about the big shift, I could be wrong about its exact impact marketing and martech. Still, I do believe that current trends cannot continue indefinitely and it’s worth considering what might happen after their limits are reached. So what I’ll suggest is this: keep an eye out for developments that fit the pattern I’m suggesting and be ready with suitable marketing and martech strategies if things move in that direction. 

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Addendum: The core argument of this post is “people feel the world is spinning out of control and trust will solve that problem”. That feels like a non sequitur, since it’s not obvious how trust creates control. It also feels uncomfortably hierarchical, and perhaps elitist, if “control” implies a central authority.  (Note: you might read “control” as referring to people controlling their own personal technology and data. But fully self-sovereign individuals can still cause chaos if there’s not some larger control framework to constrain their actions.)

But it's not a non sequitur because there is in fact a clear relationship between trust and control. Specifically:

• Trust can be defined as the belief that someone will act in the way you want them to
• Control is a way to force someone to act in the way you want. 
• Thus, trust and control are complementary: the greater trust you have in someone, the less control you need over them (to still ensure they act the way you want).

Although power-hungry people might enjoy control for its own sake, most people will care only about achieving the desired result. So the solution to a world “spinning out of control” isn’t necessarily reinstating hierarchical, elite authority; it can also be generating trust.  Both yield the same outcome of predictable desired behaviors. 

This applies in particular to the discussion of citizen development and no-code software, which seems to imply that applications can only be used by more than one person if there’s a central authority to coordinate and approve them.  This is where "governance" comes in.  It's correct that self-built software needs to meet certain standards to be safely used and shared.  But "governance" can be achieved either through control (a central authority enforces those standards) or through trust (convincing users to apply those standards by themselves).  Either approach can work but trust is clearly preferable.

 

Software Has Stopped Eating the World

This August will see the tenth anniversary of Marc Andreessen’s famous claim that software is eating the world. He may have been right at the time but things have now changed: the world is biting back.

I’m not referring to COVID-19, although it’s fitting that it took an all-too-physical virus to prove that a digital bubble of alternate facts could not permanently displace reality. Nor am I juxtaposing the SolarWinds hack with the unexpectedly secure U.S. election, which showed a simple paper trail succeed while the world’s most elite computer security experts failed.

Rather, I’m looking at the most interesting frontiers of tech innovation: self-driving vehicles, green energy, and biosciences top my list. What they have in common is interaction with the physical world. By contrast, recent years haven’t seen radical change in software development. There have certainly been improvements in software, but they’re more about architectures (cloud, micro-services) and self-service interfaces than fundamentally new applications. And while most physical-world innovations are powered by software, the importance of those innovations is that they are changing physical experiences, not that they are replacing them with software-based virtual equivalents.

Even the most important software development of all – artificial intelligence – measures much of its progress by its ability to handle physical-world tasks such as image recognition, autonomous vehicle navigation, and recognizing human emotion. Let’s face it: it’s one thing for a computer to beat you at Go, but quite another for it to beat your dance moves.  Really, what special talent is left for humans to claim as their own?

The shift is well under way in the world of marketing. One of the more surprising developments of the pandemic year was the boom in digital out-of-home advertising, which includes outdoor billboards and indoor signage. The growth seemed odd, given how much time people were forced to spend at home. But the industry marched ahead, spurred in good part by increased ability to track devices as they move through the physical world. It’s a safe bet that out-of-home ads will grow even faster once people can move about more freely.

Indeed, the industries hit hardest by the pandemic – travel and events – also show that virtual experiences are not enough. Whatever their complaints before the pandemic, almost everyone who formerly traveled for business or attended business events is now eager to return to seeing people and places in person. The amount of travel will surely be reduced but it’s now clear that some physical interaction is irreplaceable.

In a similarly ironic way, the pandemic-driven boost to ecommerce has been accompanied by a parallel lesson in the importance of physical delivery. Almost overnight, fulfillment has gone from a boring cost center to a realm of intensive competition, innovation, and even a bit of heroism. Software plays a critical role but it’s a supporting actor in a drama where the excitement is in the streets.

Still closer to home for marketers, we’ve seen a new appreciation for the importance of customer experience, specifically extending past advertising to include product, delivery, service and support. If the obsession of the past decade has been targeted advertising, the obsession of the next decade will be superior service. This ties into other trends that were already under way, including the importance of trust (earned by delivering on promises through fulfillment, not making promises in advertising) and the shift from prospecting with third party data to supporting customers with first party data. Even at the cutting edge, advertising innovation has now shifted to augmented reality, which integrates real-world experiences with advertising, and away from virtual reality, which replaces the real world entirely.

This shift has substantial implications for martech.

- The endless proliferation of martech tools may well continue, especially if the definition of “tools” stretches to include self-built applications. But the importance of tools that only interact with other software will diminish. What will grow will be tools that interact with the real world, and it’s likely those tools will be harder to find and (at least initially) take more skills to use. It’s the difference between building a flight simulator game and an actual aircraft. The stakes are higher when real-world objects are involved and there’s an irreducible level of complexity needed to make things work right.

- As with all technology shifts, the leaders in the old world – the big software companies and audience aggregators like Facebook and Google – won’t necessarily lead in the new world. Reawakened anti-trust enforcement comes at exactly the worst moment for big tech companies needing to pivot. So we can expect more change in the industry landscape than we’ve seen in the past decade.

- New skills will be needed, both to manage martech and to do the marketing itself. The new martech skills will involve learning about new technologies and tighter integration with non-marketing systems, although fundamentals of system selection and management will be largely the same. The marketing skill shift may be more profound, as marketers must master entirely new modes of interaction. But, again, the marketer’s fundamental tasks – to understand customer motivations and build programs that satisfy them – will remain what they always were.

It’s been said that people overestimate short-term change and underestimate long-term change.  The shift from software to physical innovation won’t happen overnight and will never be total. But the pendulum has reversed direction and the world is now starting to eat software. Keep an eye out for that future.

CDP Myths vs Realities

A few weeks ago, I critiqued several articles that attacked “myths” about Customer Data Platforms. But, on reflection, those authors had it right: it’s important to address misunderstandings that have grown as the category gains exposure. So here's my own list of CDP myths and realities. 

Myth: CDPs are all the same.
Reality: CDPs vary widely. In fact, most observers recognize this variation and quite a few consider it a failing. So perhaps the real myth is that CDPs should be the same. It’s true that the variation causes confusion and means buyers must work hard to ensure they purchase a system that fits their needs. But buyers need to match systems to their needs in every category, including those where features are mostly similar.

Myth: CDPs have no shared features.
Reality: This is the opposite of the previous myth but grows from the same underlying complaint about CDP variation. It’s also false: CDPs all do share core characteristics. They’re packaged software; they ingest and retain detailed data from all sources; they combine this data into a complete view of each customer; they update this view over time; and they expose the view to other systems. This list excludes many products from the CDP category that share some but not all of these features. But it doesn’t exclude products that share all these features and add some other ones. These additional features, such as segmentation, data analysis, predictive models, and message selection, account for most of the variation among CDP systems. Complaining that these mean CDPs are not a coherent category is like complaining that automobiles are not a category because they have different engine types, body styles, driving performance, and seating capacities. Those differences make them suitable for different purposes but they still share the same core features that distinguish a car from a truck, tractor, or airplane.

Myth: CDP is a new technology.
Reality: CDPs use modern technologies, such as NoSQL databases and API connectors. But so do other systems. What’s different about CDP is that it combines those technologies in prebuilt systems, rather than requiring technical experts to assemble them from scratch. Having packaged software to build a unified, sharable customer database is precisely the change that led to naming CDP as a distinct category in 2013.

Myth: CDPs don’t need IT support.
Reality: They sure do, but not as much. At a minimum, CDPs need corporate IT to provide access to corporate systems to acquire data and to read the CDP database. In practice, corporate IT is also often involved in managing the CDP itself. (This recent Relevancy Group study put corporate IT participation at 49%.)   But the packaged nature of CDPs means they take less technical effort to maintain than custom systems and many CDPs provide interfaces that empower business users to do more for themselves. Some CDP vendors have set their goal as complete business user self-service but I haven’t seen anyone deliver on this and suspect they never will.

Myth: CDPs are for marketing only.
Reality: It’s clear that departments outside of marketing can benefit from unified customer data and there’s nothing inherent in CDP technology that limits them to marketing applications. But it’s also true that most CDPs so far have been purchased by marketers and have been connected primarily to marketing systems. The optional features mentioned previously – segmentation, analytics, message selection, etc. – are often marketing-specific. But CDPs with those features must still be able to share their data outside of marketing or they wouldn’t be CDPs.

Myth: CDPs manage only first party, identified data.
Reality: First party, identified data is the primary type of information stored in a CDP and it’s something that other systems (notably Data Management Platforms) often handle poorly or not at all. But nothing prevents a CDP from storing third party and/or anonymous data, and some CDPs certainly do.  Indeed, CDPs commonly store anonymous first party data, such as Web site visitor profiles, which will later be converted into identified data when a customer reveals herself. The kernel of truth inside this myth is that few companies would use a CDP to store anonymous, third party data by itself.

Myth: Identity resolution is a core CDP capability.
Reality: Many CDP systems provide built-in identity resolution (i.e., ability to link different identifiers that relate to the same person).  But many others do not.  This is by far the most counter-intuitive CDP reality, since it seems obvious that a system which builds a unified customer profiles should be able to connect data from different sources.  But quite a few CDP buyers don’t need this feature, either because they get data from a single source system (e.g., ecommerce or publishing), because their company has existing systems to assemble identities (common in financial services), or because they rely on external matching systems (frequent in retail and business marketing). What nearly all CDPs do have is the ability to retain links over time, so unified profiles can be stitched together as new identifiers are connected to each customer’s master ID. One way to think about this is: the function of identity resolution is essential for building a unified customer database, but the feature may be part of a CDP or something else.

Myth: CDPs are not needed if there’s an Enterprise Data Warehouse.
Reality: It’s a reasonable simplification to describe a CDP as packaged software that builds a customer-centric Data Warehouse. But a Data Warehouse is almost always limited to highly structured data stored in a relational database.  CDPs typically include large amounts of semi-structured and unstructured data in a NoSQL data store. Relational technology means changing a Data Warehouse is usually a complex, time-consuming project requiring advanced technical skill. Pushing data into a CDP is much easier, although some additional work may later be required to make it usable. Even companies with an existing Data Warehouse often find a CDP offers new capabilities, flexibility, and lower operating costs that make it a worthwhile investment.

Myth: CDPs replace application data stores.
Reality: Mea culpa: I’ve often explained CDPs by showing separate silo databases replaced by a single shared CDP.  But that’s an oversimplification to get across the concept. There are a handful of situations where a delivery system will read CDP data directly, such as injecting CDP-selected messages into a Web page or exposing customer profile details to a call center agent. But in most cases the CDP will synchronize its data with the delivery system’s existing database. This is inevitable: the delivery systems are tightly integrated products with databases optimized for their purpose. The value of the CDP comes from feeding better data into the delivery system database, not from replacing it altogether.

Myth: CDP value depends on connecting all systems.
Reality: CDPs can deliver great value if they connect just some systems, or sometimes even if they only expose data from a single system that was otherwise inaccessible.  This matters because connecting all of a company's systems can be a huge project or even impossible if some systems are not built to integrate with others.  This shouldn't be used as an argument against CDP deployment so long as a less comprehensive implementation will still provide real value.

Myth: The purpose of CDP is to coordinate customer experience across all channels.
Reality: That's one goal and perhaps the ultimate.  But there are many other, simpler applications a CDP makes possible, such as better analytics and more accurate data shared with delivery systems.   In practice, most CDP users will start with these simpler applications and add the more demanding ones over time.

Myth: The CDP is a silver bullet that solves all customer data problems.
Reality: There are plenty of problems beyond the CDP's control, such as the quality of input data and limits on execution systems.  Moreover, the CDP is just a technology and many obstacles are organizational and procedural, such as cooperation between departments, staff skills, regulatory constraints, and reward systems.  What a CDP will do is expose some obstacles that were formerly hidden by the technical difficulty of attempting the tasks they obstruct.  Identifying the problems isn't a solution but it's a first step towards finding one.

Of course, everyone knows there are no silver bullets but there's always that tiny spark of hope that one will appear.  I hesitate to quench that spark because it's one of the reasons people try new things, CDPs included.  But I think the idea of CDPs is now well enough established for marketers to absorb a more nuanced view of how they work without losing sight of their fundamental value.  Gradual deflation of expectations is preferable to a sudden collapse.  Let's hope a more realistic understanding of CDPs will ultimately lead to better results for everyone involved.

Will GDPR Burst the Martech Bubble?

Some people have feared (or hoped) that the European Unions’ General Data Protection Regulation would force major change in the the marketing and advertising ecosystems by shutting off vital data flows. I’ve generally been more sanguine, suspecting that some practices would change and some marginal players would vanish but most businesses would continue pretty much as they are. The most experienced people I’ve spoken with in recent days have had a similar view, pointing to previous EU privacy regulations that turned out to be mostly toothless.

But even though I respect those experienced opinions, I’m beginning to wonder GDPR might have a much greater than most of us think. The reason isn’t that GDPR requires major changes in how data is collected or used: by and large, consumers can be expected to grant consent without giving it much thought and most accepted industry practices actually fall within the new rules. Nor will the limited geographic reach of GDPR blunt its impact: it looks like most U.S. firms are planning to apply GDPR standards worldwide, if only because that’s so much easier than applying different rules to EU vs non-EU persons.

What GDPR does seem to doing is create a shake-out in the data supply chain as big companies reduce their risks by limiting the number of partners they’ll work with. The best example is Google’s proposed consent tool for publishers, which limits consent to no more than twelve data partners. This would inevitably lead to smaller firms being excluded from data acquisition.  Some see this as a ploy by Google to hobble its competitors, and maybe they're right. But the real point is that asking people to consent to even a dozen data sharing options is probably not going to work. So even though publishers are free to use other consent tools, there’s a practical limit on the number of data partners who can succeed under the new rules.

A similar example of market-imposed discipline is contract terms proposed by media buying giant GroupM , which requires publishers to grant rights they might prefer to keep. GroupM may have the market power to force agreement to its terms, but many smaller businesses will not. With less legal protection, those smaller firms will need to be more careful about the publishers they work with. Conversely, advertisers need to worry about using data that wasn’t acquired properly or has been mistreated somewhere along the supply chain before it reached them. Since they can’t verify every vendor, many are considering cutting off smaller suppliers.  Again, the result is many fewer viable firms as a handful of big companies survive and everyone else is shut out of the ecosystem.  (Addendum: see this Marketing Week article about data supplies being reduced, published the day after I wrote this post.)

There’s nothing surprising about this: regulation often results in industry consolidation as compliance costs make it impossible for small firms to survive. The question I find more intriguing is slightly different: will a GDPR-triggered reduction in data processing will ramify through the entire adtech and martech ecosystem, causing the long-expected collapse of industry growth?

So far, as uber-guru Scott Brinker recently pointed out, every prediction of consolidation has been wrong.  Brinker argues that fundamental structural features – including low barriers to entry, low operating costs of SaaS, ever-changing needs, micro-services architectures, and many more – favor continued growth (but carefully avoids making any prediction).  My simplistic counter-argument is that nothing grows forever and sometimes one small jolt can cause a complex system to collapse. So something as seemingly trivial as a reluctance of core platforms to share data with other vendors could not only hurt those vendors, but vendors that connect with them in turn. The resulting domino effect could be devastating to the current crop of small firms while the need to prove compliance could impose a major barrier to entry for new companies.

I can’t say how likely this is. There’s a case to be made that GDPR will have a more direct impact on adtech than martech and adtech is particularly ripe for simplification.  You could even note that all my examples were from the adtech world. But it’s always dangerous to assume trends will continue indefinitely and it’s surely worth remembering that every bubble is accompanied by claims that “this time is different”. So maybe GDPR won’t have much of an impact. But I suspect its chances of triggering a slow-motion martech consolidation are greater than most people think.

Eager to Sell Your Personal Data? You'll Have to Wait

Should marketers pay consumers directly to access their personal data? The idea isn’t new but it’s become more popular as people see the huge profits that Google, Facebook, and others make from using that data, as consumers become more aware of the data trade, and as blockchain technology makes low cost micro-payments a possibility.

One result is a crop of new ventures based on the concept has popped up like mushrooms – which, like mushrooms, can be hard to tell apart. I’ve been mentioning these in the CDP Institute newsletter as I spot them but only recently found time to take a closer look. It turns out that these things I’ve been lumping together actually belong to several different species. None seem to be poisonous but it’s worth sharing a field guide to help you tell them apart.

Before we get into the distinguishing features, let’s look at what these all have in common. They’re all positioned as a way for consumers to get value from their data. I’ve also bumped into a number of data marketplaces that serve traditional data owners, such as Web site publishers and compilers. They can often use some of the same technologies, including micro-payments, blockchain, and crypto-currency tokens. Some even sell personal data, especially if they’re selling ads targeted with such data. Some sell other things, such as streams from Internet of Thing devices. Examples of such marketplaces include Sonobi, Kochava, Narrative I/O, Datonics, Rublix and IOTA. Again, the big difference here is the sellers in the traditional marketplaces are data aggregators, not private individuals.

Here’s a look a half-dozen ventures I’ve lumped into the personal data marketplace category (which I suppose needs a three letter acronym of its own).

Dabbl turns out to be a new version of an old idea, which is to pay people for taking surveys. There are dozens of these: here's a list.  Dabbl confused me with a headline that said “Everyone’s profiting from your time online but you.” Payment mechanism is old-school gift cards. On the plus side: unlike most products in this list, Dabble is up and running.

Thrive pays users for sharing their data, but only in the broad sense that they are paid to fill out profiles which are exposed to advertisers when the users visit participating Web sites. The advertisers are paying Thrive; individual users aren’t deciding who sees their data or paid to grant access on a buyer-by-buyer basis. Payments are made via a crypto-token which is on sale as I write this. The ad marketplace is scheduled for launch at the end of 2018. That sequence suggests there’s at least a little cryptocurrency speculation in the mix. (Another hint: they’re based in Malta. Yet another hint: the U.S. Securities Exchange Commission won’t let you buy the tokens.)

Nucleus Vision is also in the midst of its token sale.  But they’re much more interested in discussing a propriety technology that detects mobile phones as they enter a store and shares the owner’s data using blockchain as an exchange, storage, and authorization mechanism. Store owners can then serve appropriate offers to visitors. This sounds like a lot of other products except that Nucleus’ technology does it without a mobile app. (It does apparently need some cooperation from the mobile carrier.) Rewards are paid in tokens which can be earned for store visits, by using coupons or discounts, by making purchases, or by selling data. Each retailer runs its own program, so this isn’t a marketplace where different buyers bid for each consumer’s data.  Sensors are currently running in a handful of stores and the loyalty and couponing systems are under development.

Momentum is an outgrowth of the existing MobileBridge loyalty system.  It rewards customers with yet another crypto-token (on sale in late April) for marketer-selected behaviors. Brands can play as well as retailers but it’s still the same idea: each company defines its own program and each consumer decides which programs to join. The shared token makes it easy to exchange or pool rewards across programs. The published roadmap is ambiguous but it looks like they’re at least a year away from delivering a complete system.

YourBlock gets closer to what I originally had in mind: it stores personal data (in blockchain, of course), uses the data to target offers from different companies, and lets consumers decide which offers to accept. Yep, there’s a crypto-token that will be used to give discounts. Sales started yesterday (March 12) and are set to close by April 23. Development work on the rest of the platform will start after the sale is over, with a live product due this August.

Wibson calls itself a “consumer-controlled personal data marketplace” and, indeed, they fit the archetype: users install a mobile app, grant access to their data, and then entertain offers from potential buyers to read it. Storage and sharing are based on blockchain but payments are made via points rather than a crypto-token. At least that’s how it works at the moment: in fact, Wibson has just completed its initial mobile app and you can’t download it quite yet. During the initial stage, only Wibson will be able to buy users’ data and they’ll just use it for testing. If they’ve published a schedule for further development, I can’t find it.

So, that’s our little stroll through the personal data marketplace. Less here than meets the eye, perhaps – most players offer more or less conventional loyalty programs, although they use blockchain and crypto-tokens to deliver them.  True marketplaces are still in development. But it’s still an interesting field and well worth watching. As with mushrooms, look carefully before you bite.

Collapse of Civilization Makes Marketers' Jobs Harder

Political situations come and go but trust is the foundation of civilization itself. So I was genuinely shaken to see a report from the Edelman PR agency that trust in U.S. institutions fell last year by a huge margin – 17% for the general public and 34% for the “informed public,” placing us dead last among 27 countries. All four measured institutions (business, media, government, and non-governmental organizations) took similar hits, although government fell the most.

You won’t be surprised to learn that concerns about fake news and social media are especially prominent. What’s less expected is that trust in traditional journalism actually increased in the U.S. The over-all decline in media trust resulted from falling confidence in news from search engines and social media. Similarly, world-wide trust increased in traditional authorities such as technical, academic, and business experts.  So there are rays of hope.

Digging deeper, the sharp fall in U.S. trust levels follows two years when levels were exceptionally high. The current U.S. trust level is roughly the same as the four reports before that. Maybe you shouldn't head for that doomsday cabin quite yet.

Still, other reports also show tremendous doubts about basic questions of truth. A Brand Intelligence study comparing brand attitudes of Democrats vs. Republicans found that eight of top 10 most polarizing brands were news outlets. World-wide, 59% of people told Edelman they were simply not sure what is true and just 36% felt the media were doing a good job of guarding information quality.

The social implications of all this are sadly obvious.  But this blog is about marketing. How can marketers adapt and thrive in a trust-challenged, politically-polarized world?

• Protect privacy. Consumers can be remarkably cavalier in practice about protecting their data: this McAfee report found 41% don’t immediately change default passwords on new devices and 34% don’t limit access to their home network at all. But they are adamant that companies they do business with be more careful: Accenture studies have found that 92% of U.S. consumers feel it’s extremely important for companies to protect their personal information while 80% won’t do business with companies they don’t trust. Similarly, a Pega survey found that 45% of EU respondents said they would require companies to erase their data if they found it had been sold or shared with other companies. 

• Personalize wisely. Accenture also found  that 44% of consumers are frustrated when companies don’t deliver relevant, personalized shopping experiences and 41% had switched companies due to lack of personalization or trust. So there’s clearly a price to be paid for not using the data you do collect. Similarly, an Oracle report found 50% of consumers would be attracted to offers based on personal data while just 29% would find them creepy.  In fact, consumers have a remarkably pragmatic attitude toward their data: 24[7] survey found their number one reason for sharing personal information is to receive discounts. This has two implications: ask consumers whether they want personalized messages (or any messages), and be sure the value of your personalization outweighs its inherent creepiness. 

• Use trusted media. Consumers’ attitudes towards media in general, and social media in particular, are complicated. We’ve already noted that Edelman found growing distrust in online platforms. Other studies by Kantar and Sharethrough found the same. But consumers still spend most of their time on search engines and social media, which GlobalWebindex found remain by far the top research channels. Yet when it comes to building awareness, a different GlobalWebIndex report found that social ranked far behind search engines, TV, and display ads. Further muddying the waters, social and ecommerce companies (Facebook, Amazon, and eBay) topped NetBase’s list of most loved brands while Google ranked just 29th. But love isn’t the same as value: a LivePerson survey of 18-to-34 year olds – presumably the most enthusiastic social media users – found most would delete social apps from their phones before they'd give up practical apps for banking, ride-sharing and shopping. Similarly, The Verge found that Amazon and Google were significantly better liked and trusted than Facebook or Twitter. Taken together, this suggests that marketers need social channels for scale but can’t rely on them for credibility. Indeed, that’s precisely the conclusion of this Trusted Media Brands report about branded video.

• Consider brand safety. The problems with social and display channels extend beyond general mistrust to actively offensive environments. GumGum found that 68% of brands knew their ads have been placed in objectionable environments, with fake news, divisive politics, and disasters heading the list. A Dun & Bradstreet report on programmatic B2B ads similarly found that 66% of brands have found brand safety increasingly important.  Ad fraud is also a major concern – the two are related because they both reflect brands’ loss of control over their ad placements. This Forrester report on mobile advertising found 69% of marketers felt at least 20% of their budgets were exposed to found mobile ad fraud. Yet all three studies found marketers were plunging ahead with just limited efforts at brand safety and fraud prevention. In a world where consumer trust is tenuous to begin with, this is a very high-stakes gamble.

• Be careful about politics.  Edelman found that 64% of people want business CEOs to lead change rather than waiting for government to impose it. Sprout Social reported a similar finding:  65% of U.S. consumers felt brands should take a stand on social/political issues and 59% felt CEOs in particular should step in. But Euclid found the opposite: 78% said brands should avoid making political statements. Even more extreme, Bambu found just 2.3% of consumers said posting political content would make them more likely to buy from a salesperson while 34.9% said posting political content was a deal breaker, regardless of whether they agreed.  Still, the real danger is taking a position the customer dislikes: Bambu, Euclid and Sprout all found consumers are likely to boycott firms based on their positions. Sprout noted some compensating gain from people who agree but the net benefit is questionable at best: people are slightly more likely to praise a brand when they agree (28%) than criticize when they disagree (20%). But fewer will recommend it (35%) than warn friends and family (38%) and, most critically, fewer will purchase more (44%) than purchase less (53%).   In short, the data here are wildly conflicting: people want businesses to lead but they’ll punish behaviors they don’t like as often as they’ll reward choices they agree with. Of course, widely popular positions are still safe but many issues today have large numbers of people on both sides.  And remember it’s still possible to annoy everyone: Brand Intelligence found that Democrats, Independents, and Republicans all had Diet Pepsi (Kendall Jenner commercial, presumably) and Diet Mountain Dew (I don’t know why) on their most disliked lists. The ultimate result is probably that business leaders can justify being as active or inactive as they personally prefer.

So there you have it. Assuming we avoid complete social collapse, marketing in today’s polarized, anxiety-ridden world poses unprecedented challenges. Ironically, the loss of trust is happening at the precise moment when physical products are being replaced by trust-based services such as subscriptions and automated recommendations. The stakes couldn’t be higher.  Choose carefully and good luck.

Wizaly Offers a New Option for Algorithmic Attribution

Wizaly is a relatively new entrant in the field of algorithmic revenue attribution – a function that will be essential for guiding artificial-intelligence-driven marketing of the future. Let’s take a look at what they do.

First a bit of background: Wizaly is a spin-off of Paris-based performance marketing agency ESV Digital (formerly eSearchVision). The agency’s performance-based perspective meant it needed to optimize spend across the entire customer journey, not simply use first- or last-click attribution approaches which ignore intermediate steps on the path to purchase. Wizaly grew out of this need.

Wizaly’s basic approach to attribution is to assemble a history of all messages seen by each customer, classify customers based on the channels they saw, compare results of customers whose experience differs by just one channel, and attribute any difference in results to that channel   For example, one group of customers might have seen messages in paid search, organic search, and social; another might have seen messages in those channels plus display retargeting. Any difference in performance would be attributed to display retargeting.

This is a simplified description; Wizaly is also aware of other attributes such as the profiles of different customers, traffic sources, Web site engagement, location, browser type, etc. It apparently factors some or all of these into its analysis to ensure it is comparing performance of otherwise-similar customers. It definitely lets users analyze results based on these variables so they can form their own judgements.

Wizaly gets its data primarily from pixels it places on ads and Web pages. These drop cookies to track customers over time and can track ads that are seen, even if they’re not clicked, as well as detailed Web site behaviors. The system can incorporate television through an integration with Realytics, which correlates Web traffic with when TV ads are shown. It can import ad costs and ingest offline purchases to use in measuring results. The system can stitch together customer identities using known identifiers. It can also do some probabilistic matching based on behaviors and connection data and will supplement this with data from third-party cross device matching specialists.

Reports include detailed traffic analysis, based on the various attributes the system collects; estimates of the importance and effectiveness of each channel; and recommended media allocations to maximize the value from ad spending.  The system doesn't analyze the impact of message or channel sequence, compare the effectiveness of different messages, or estimate the impact of messages on long-term customer outcomes. As previously mentioned, it has a partial blindspot for mobile – a major concern, given how important mobile has become – and other gaps for offline channels and results. These are problems for most algorithmic attribution products, not just Wizaly.

One definite advantage of Wizaly is price: at $5,000 to $15,000 per month, it is generally cheaper than better-known competitors. Pricing is based on traffic monitored and data stored. The company was spun off from ESV Digital in 2016 and currently has close to 50 clients worldwide.

Will Privacy Regulations Favor Internet Giants?

Last week’s MarTech Conference in San Francisco came and went in the usual blur of excellent presentations, interesting vendors, and private conversations. I’m sure each attendee had their own experience based on their particular interests. The two themes that appeared the most in my own world were:

- data activation. This reflects recognition that customer data delivers most of its value when it is used to personalize customer treatments. In other words, it’s not enough to simply assemble a complete customer view and use it for analytics.  “Activation” means taking the next step of making the data available to use during customer interactions, ideally in real time and across all channels. It’s one of the advantages of a Customer Data Platform, which by definition makes unified customer data available to other systems. This is a big differentiator compared with conventional data warehouses, which are designed primarily to support analytical projects through batch updates and extracts.  Conventional data warehouse architectures load data into a separate structure called an “operational data store” when real-time access is needed. Many CDP systems use a similar technical approach but it’s part of the core design rather than an afterthought. This is part of the CDPs’ advantage of providing a packaged system rather than a set of components that users assemble for themselves. CDP vendors exhibiting at the show included Treasure Data, Tealium, and Lytics.

- orchestration. This is creating a unified customer experience by coordinating contacts across all channels. It’s not a new goal but is standing out more clearly from approaches that manage just one channel. More precisely, orchestration requires a decision system that uses activated customer data to find best messages and then distributes them to customer-facing systems for delivery. Some Customer Data Platforms include orchestration features and others don’t; conversely, some orchestration systems are Customer Data Platforms and some are not. (Only orchestration systems that assemble a unified customer view and expose it to other systems qualify as CDPs.) Current frontiers for orchestration systems are journey orchestration, which is managing the entire customer experience as a single journey (rather than disconnected campaigns), and adaptive orchestration, which is using automated processes to find and deliver the optimal message content, timing, and channels for each customer. Orchestration vendors at the show included UserMind, Pointillist, Thunderhead, and Amplero.

Of course, it wouldn’t be MarTech if the conference didn’t also provoke Deeper Thoughts. For me, the conference highlighted three long-term trends:

- continued martech growth. The highlight of the opening keynote was unveiling of martech Uber-guru Scott Brinker’s latest industry landscape, which clocked in at 5,300 products compared with 3,500 the year before. You can read Brinker’s in-depth analysis here, so I’ll just say that industry growth shows no signs of slowing down.

- primacy of data. Only a few presentations or vendors at the conference were devoted specifically to data, but nearly everything there depends on customer data in one way or another. And, as you know from my last blog post, the main story in customer data today is the increasing control exerted by Google and Facebook, and to a lesser degree Amazon, Apple, and Microsoft. If those firms succeed in monopolizing access to customer information, then many martech systems won’t have the inputs they need to work their magic. That could be the pin that bursts the martech bubble.

- new privacy regulations. As Doc Searles (co-author of The Cluetrain Manifesto) pointed out in the second-day keynote , new privacy regulations also threaten to cut off the data supply of marketing and advertising systems, creating an “extinction level event”. Searles announced a “customer commons” that lets consumers share data on their own terms . It’s an interesting concept but I suspect few consumers will put that much work into personal data management.

My initial inclination was to agree with Searles about the implications of new privacy rules, but I’ve since adjusted my view.  It’s just inconceivable that an economic force as powerful as Internet marketing will let regulations put it out of business. It's much more likely that companies like Google and Facebook will learn to work within the new regulations, which after all don’t ban personal data collection but merely require consumer consent. Surely firms with products that are literally addictive can gain consumer consent in ways that will satisfy even the most determined regulators. More broadly, big companies in general should be able to make the investments needed to comply with privacy regulations with minimal harm to their business.

Small businesses are another matter.  Many will lack the resources needed to understand and comply with new privacy regulations.  In other words, privacy regulations will have the unintended consequence of favoring big businesses – which can afford to find ways to comply – over small businesses – which won’t.   Google and Facebook will spend whatever they must to protect their businesses, in the same way that auto manufacturers found ways to comply with safety and pollution regulations. Indeed, as the auto industry illustrates, the actual cost of compliance is likely to be slight and may even result in better, more profitable products. The impact on small businesses will be to push them to use packaged software – yes, including Customer Data Platforms – that have regulatory compliance built in by experts. The analogy here is with financial and human resources packaged software, which similarly provides built-in compliance with government and industry standards.

Of course, if Google, Facebook, and a handful of others take near-total control over access to customers, there won’t be much data for anyone else to manage. But it seems likely that companies will find ways around those toll booths, especially when dealing with customers who have already purchased their products. Ironically, this would return marketers to the situation that existed before the Internet, when data on prospects was limited but customers could be reached directly. That might put a small crimp in martech growth but would still leave plenty of room for innovation.

Martech Vendors Can't Avoid Ad Audience Battles

It’s been said that sports are soap operas for men. You can see business news the same way: a drama with heroes, villains, intertwining story lines, and endless plot twists. One of the most interesting stories playing out right now is online advertising, where the walled gardens of Google, Facebook, and other audience aggregators are under assault by insurgent advertisers who, like most rebels, aspire as much to replace their overlords as destroy their power. What they’re really fighting over is control of the serfs – oops, I meant consumers – who create the empires' wealth.

Recent complaints about ad measurement. audience transparency, and even placement near objectionable Web content are all tactics in the assault, aimed both at winning concessions and weakening their opponents. More strategically, support for letting broadband suppliers resell consumer data is an attempt create alternative suppliers who will strengthen the insurgents’ bargaining position.

Yet another front opened up last week with an announcement from a consortium of adtech vendors, including AppNexus, LiveRamp, MediaMath, Index Exchange, LiveIntent, OpenX, and Rocket Fuel, that they had created a standard identity framework to support personal targeting of programmatic ads. The goal was to strengthen programmatic’s position as an alternative to the aggregators by making programmatic audiences larger, more targetable, and more unified across devices.

The consortium was quite explicit on this goal. To quote the press release:

"Today, 48 percent of all digital advertising dollars accrue to just two companies – Facebook and Google," said Brian O'Kelley, CEO of AppNexus. "That dynamic has placed considerable strain on the open internet companies that generate great journalism, film, music, social networking, and information. This consortium enables precision advertising comparable to that of Google and Facebook, and does so in a privacy-conscious manner. That means better outcomes for marketers, greater monetization for publishers, and more engaging content for consumers."

But behind the rallying cry, the alliance between advertisers and programmatic ad suppliers is uneasy at best. After all, programmatic threatens the core ad buying business of the agencies and faces its own problems of measurement and objectionable ad placement. How the two groups cooperate against a common enemy will be a story worth watching.

Martech vendors have so far remained pretty much neutral in the ad wars, feeding audiences to both sides with the pragmatic indifference of merchants throughout history. But the new ad tech consortium brings the battle closer, since it involves the personal identities that have been the martech vendors’ stock in trade. In particular, LiveRamp (which links anonymous cookies to known identities) belonging to the consortium creates a connection that will likely pull in other martech players. Of course, the convergence between adtech and martech has long been predicted – it's more than two years since I oh-so-cutely christened it “madtech”  and the big marketing clouds started to  purchase data management platforms and other adtech components even earlier.  The merger is probably inevitable as programmatic advertising looks more like personalized marketing every day.  Martech vendors have growing reason to side with the programmatic alliance as it becomes clear that audience aggregators could threaten their own kingdoms by cutting off access to personal data and taking control of contact opportunities.

In short, what seems like a remote, and remotely entertaining, conflict in adland is more closely connected to the central martech story than you may think. So it’s worth watching closely and deciding what role your business will play when they call your cue
.

Teradata Adds a Data Management Platform To Its Marketing Cloud...Who Will Be Next?

Teradata on Tuesday announced it is adding a data management platform (DMP) to its marketing cloud through the acquisition of Netherlands-based FLXone.  This is interesting on several levels, including:

- It makes Teradata the third of the big marketing cloud vendors to add a DMP, joining Oracle DMP (BlueKai) and Adobe Audience Manager. I already expected the other cloud vendors to do this eventually; now I expect that will happen even sooner. I’m looking at you, Salesforce.com.

- Unlike Oracle and Adobe, Teradata has stated (in a briefing about the announcement) that it intends to use the DMP as the primary data store for all components of its suite. I see this as a huge difference from the other vendors, who maintain separate databases for each of their suite components and integrate them largely by swapping audience files with a few data elements on specified customers. (In fact, Adobe just last week briefed analysts on a new batch integration that pushes Campaign data into Audience Manager to build display advertising lookalike audiences. The process takes 24 hours.)

Of course, we’ll see what Teradata actually delivers in this regard.  It's also important to recognize that performance needs will almost surely require intermediate layers between the DMP's primary data store and the actual execution systems. This means the distinction between a single database and multiple databases isn’t as clear as I may be seeming to suggest. But I still think it’s an important difference in mindset.  In case it isn’t obvious, I think real integration does ultimately require running all systems on the same primary database.

- It is still more evidence of the merger between ad tech and martech. I know I wrote last week that this is old news, but there’s still plenty of work to be done to make it a reality. One consequence of "madtech" is complete solutions are even larger than before, making them even harder for non-giant firms to produce. That’s the primary lesson I took away from last week’s news that StrongView had been merged into Selligent: although StrongView’s vision of omni-channel “contextual marketing” made tons of sense, they didn’t have the resources to make it happen. (See J-P De Clerck's excellent piece for in-depth analysis of the StrongView/Selligent deal.)  I’m not sure the combined Selligent/StrongView is big enough either, or that Sellingent owner HGGC will make the other investments needed to fill all the gaps.

To be clear: I'm not saying small martech/adtech/madtech firms can't do well.  I think they can plug into a larger architecture that sits on top of a customer data platform and perhaps a shared decision platform. But I very much doubt that a mid-size software firm can build or buy a complete solution of its own.  If you're wondering just who I have in mind...well, Mom always told me that if I couldn’t say something nice, I shouldn’t say anything at all.  So I won’t name names.

Data Plus MarTech: HubSpot and Demandbase Join the Race

There were two industry announcements this week that were unexpectedly related. The first was HubSpot’s announcement yesterday that its CRM offerings would now include access to a 19 million account prospecting database. The second was Demandbase’s acquisition of data-as-a-service vendor WhoToo, which offers its own set of 250 million profiles relating to 70 million business professionals.

The WhoToo acquisition marks a big step in the continued evolution of Demandbase, since it's a change from targeting companies to targeting individuals (although DemandBase still won’t sell you their names). More precisely, WhoToo aggregates audience data from multiple sources and makes it available for selections based on company and individual attributes. The company does know the identity of some individuals and will use these to target email and Web advertising to names you provide. It will also let you market to audiences in those channels without providing their names. This is a nice extension of Demandbase’s existing account-based marketing capabilities. What makes WhoToo really special is it has the technology to access its data with the split-second speed needed to purchase display and mobile ads in real time.

The addition of individual-level targeting puts Demandbase on a more even plane with LinkedIn, which of course already sells advertising to its own huge database of more than 350 million profiles. The WhoToo deal won’t fully close that gap, but it does help to keep Demandbase competitive. (I’m sure Demandbase would argue it has its own advantages over LinkedIn.)

In this context, the HubSpot announcement is interesting mostly because it too recognizes the importance of giving marketers audience lists without acquiring the names for themselves. You could argue this makes HubSpot a player in the super-hot Account Based Marketing category, although they didn't use the term.  If they are, it's ABM-lite, in the sense that HubSpot will give CRM users basic profile information, usually including a phone number, but doesn't offer contact names or email addresses. It also pulls recent news stories.  This is pretty consistent with HubSpot's historic aversion to unsolicited outbound contacts.  The company does approach the line by giving enterprise users an option to find other people in their company who have a contact at target accounts and ask for a warm introduction.

On the other hand, HubSpot also announced integration with LinkedIn for paid ad campaigns and said a Google AdWords integration is in beta, which are definitely in outbound territory. Naturally, HubSpot says its LinkedIn and Google campaigns will be giving potential buyers information they want, so they are not at all like that bad old interruptive advertising that HubSpot has always opposed. No, not one bit.

Anyway, the point here is that both HubSpot and Demandbase are adding data to their marketing technology, something we’ve seen in other deals like Oracle buying Datalogix. There are still plenty of stand-alone data vendors, especially when it comes to B2B prospecting lists. And there are plenty of vendors who combine prospect data with predictive – including LinkedIn itself since its recent FlipTop acquisition. But I think we can add “data plus tech” to the tote board of martech horse races.