Will ChatGPT Destroy Martech?

Like everyone else, I’ve been pondering what generative AI means for martech, marketing, and the world in general.  My crystal ball is no clearer than yours but I’ll share my thoughts anyway.

Let’s start by looking how past technology changes have played out.  My template is the transition from steam to electric power in factories.  This happened in stages: first, the new technology was used in exactly the same way as the old technology (in factories, this meant powering the shafts and belts that previously were powered by waterwheels or steam engines).  Then, the devices were modified to make better use of the new technology’s capabilities (by attaching motors directly to machine tools).  Finally, the surrounding architecture was changed to take advantage of the new possibilities (freed from the need to connect to a central mechanical energy source, factories went from being small, vertical structures to large horizontal ones, which allowed greater scale and efficiency).  We should probably add one more stage, when the factories started to produce new products that were made possible by the technology, such as washing machines with electric motors.

During the earliest stages of the transition, attention focused on the new technology itself: factories had “chief electricians” and companies had “chief electricity officers”, whose main advantage was they were among the few people who understood new technology.  Those roles faded as the technology became more widely adopted.  The exact analogy today is “prompt engineer” in AI, and will likely be even shorter-lived as a profession.  

Most of the discussion I see today about generative AI is very much stuck in the first phase: vendors are furiously releasing tools that replace this worker or that worker, or even promising a suite of tools that replace pretty much everyone in the marketing department.  (See, for example, this announcement from Zeta Global https://zetaglobal.com/press-releases/zeta-introduces-generative-ai-agents-powered-by-zoe/  .)  Much debate is devoted to whether AI will make workers in those jobs more productive (hurrah!) or replace them entirely (boo!)   I don’t find this particular topic terribly engaging since the answer is so obviously “both”: first the machines will help, and, as they gradually get better at helping, they will eventually take over.  Or, to put it in other terms: as humans become more productive, companies will need fewer of them to get their work done.  Either way, lots of marketers lose their jobs.  

(I don’t buy the wishful alternative that the number of marketers will stay the same and they’ll produce vastly more, increasingly targeted materials.  The returns on the increasing personalization are surely diminishing, and it’s unrealistic to expect company managers to pass up an opportunity to reduce headcount.)

While the exact details of the near future are important – especially if your job is at stake – this discussion is still about the first stage of technology adoption, a one-for-one replacement of the old technology (human workers) with the new technology (AI workers).   The much more interesting question is what happens in the second and third stages, when the workplace is restructured to take full advantage of the new technology’s capabilities.

I believe the fundamental change will be to do away with the separate tasks that are now done by specialized individuals (copywriters, graphic designers, data analysts, campaign builders, etc.).  Those jobs have evolved because each requires complex skills that take full time study and practice to master.  The division of labor has seemed natural, if not inevitable, because it mirrors the specialization and linear flow of a factory production line – the archetype for industry organization for more than a century.  

But AI isn’t subject to the same constraints as humans.  There’s no reason a single AI cannot master all the tasks that require different human specialists.  And, critically, this change would bring a huge efficiency advantage because it would do away with the vast amount of time now spent coordinating the separate human workers, teams, and departments.  There would be other advantages in greater agility and easier data access.  Imagine that the AI can get what it needs by scanning the enterprise data lake, without the effort now needed to transform and load it into warehouses, CDPs, predictive modeling tools, and other systems.  Maintaining those systems takes another set of specialists whose jobs likely to vanish, along with all the martech managers who spend their time connecting the different tools.  

Of course, the vision of “citizen developers” using AI to create sophisticated personal applications on the fly is entirely irrelevant when the citizen developers themselves no more have jobs.  Thousands of independent applications that make up today’s martech industry may vanish, unless the marketing Ais build and trade components among themselves – which could happen.

So far, I’ve predicted that monolithic AI systems rather than teams of (human or robotic) specialists will create marketing programs similar to today’s campaigns and interactions.  But that assumes there’s still a demand for today’s types of campaign and interactions.  This brings us to the final type of change: in the outputs themselves.

Again, we may be in for a very fundamental transformation.  The output of a marketing department is ultimately determined by the how people buy things.  It’s a safe bet that AI will change that dramatically, although we don’t know exactly how.   For sake of argument, let’s assume that people adopt AI agents to manage more of their personal lives for them (pretty likely) and that they delegate most purchasing decisions to those agents (less certain but plausible, and again already happening to a limited degree).  If that happens, our AI marketing brains will be selling to other Ais, not to people.  To imagine what that looks like, we again have to move beyond expecting the AI to do what people do now, and look at the best way for an AI to achieve the same results.

If you think about marketing today – and for all the yesterdays that ever were – it’s based on the fundamental fact that humans have a limited amount of attention.  Every aspect of marketing is ultimately aimed to capturing that attention and feeding the most effective information to the human during the time available.  

But AIs have unlimited attention.

If an AI wants to buy a product, it can look at every option on the market and collect all the information available about each one.  Capturing the AI’s attention isn’t an issue; presenting it with the right information is the challenge.  This means the goal of the marketing department is to ensure product information is available everyplace the AI might look, or maybe in just one place, if you can be sure the AI will look there.   Imagine the world as a giant market with an infinite number of sellers but also buyers who can instantly and simultaneously visit every seller and gather all the information they provide.  The classical economist’s fantasy – perfect market, perfect information, no friction – might finally come true.

And, also as the classical economists dream, the buyers will be entirely rational, not swayed by emotional appeals, brand identity, or personal loyalties.  (At least, we assume that AI buyers are rational and objective, although it won’t be easy to ensure that’s the case. That relates to trust, which is a topic for another day.)

If the role of marketing is to lay out virtual products on a virtual table in a virtual market stall, there’s no need for advertising: every buyer will pass by every stall and decide whether to engage.  With no need for advertising, there’s no need for targeting or personalization and no need for personal data to drive that targeting or personalization.  Privacy will be preserved simply because advertisers will no longer have any reason to violate it.

The key to business success in this world of omniscient, rational buyers is having a superior product, and, to a lesser extent, presenting product information in the most effective way possible.  There’s still some room for puffery and creativity in the presentation, although presumably mechanisms such as consumer reviews and independent research will keep marketers reasonably honest.  (Trust, again.)   There’s probably more room for creativity in developing the products themselves and constructing a superior experience that extends beyond the product to the full package including pricing, service, and support.

We can expect the AIs to play a major role in developing those new and optimal products and experiences, although I suspect the pro-human romantic in everyone reading this (except you, Q-2X7Y) hopes that people will still have something special to contribute.  But, wherever the products themselves come from, it will be up to the marketing AI to present them effective to the AI shoppers.

(Side note: today’s programmatic ad buying marketplace comes fairly close to the model I’m proposing.  The obvious difference is the auction model, where buyers bid for a limited supply of ad impressions.  It’s conceivable that the consumer marketplace would also use an auction.  Again, just because most of today’s shopping is based on a fixed price model, we shouldn’t assume that model will continue in the future.  Come to think of it, an auction would probably be the best approach, since buyers could adjust their bids based on their current needs and preferences, and sellers could adjust them based on inventory and current demand.  In the traditional marketplace, this would be called haggling, or negotiating, and it's the way buying has been done for most of history.  With perfect information on both sides, the classical economists would be pleased yet again. It could be fruitful to explore other analogies with the programmatic marketplace when trying to predict how the AI-to-AI marketplace will play out.)

(You could also argue that Amazon, Expedia, and similar online marketplaces already offer a place for virtual sellers to offer their virtual wares to all comers.  Indeed they do, but the exact difference is that searching on Amazon requires a painfully inefficient use of human time.  If Amazon evolves a really good AI-based search method, and can convince users to share enough data to make the searches fully personalized, it could indeed become the basis for what I’m proposing.  The biggest barrier to this is more likely to be trust than technology.  It’s also worth noting that traditional marketing barely exists on those marketplaces.  The travel industry, where most marketing is centered on loyalty programs, may be an early indicator of where this leads.)

So what role, exactly, do humans play in this vision?  

As consumers, humans are no longer buyers.  Instead, they receive what’s purchased on their behalf.  So, their main role is to pick an AI and train it to understand their needs.  Of course, most of that training will happen without any direct human effort, as the AI watches what its owner does.  (I almost wrote “master”, but it’s not clear who’s really in charge.)  For people with disposable income, purchases are likely to move away from basic goods to luxury goods and experiences.  Those are inherently less susceptible to purely rational buying decisions, so there’s a good chance that conventional buying and attention-based marketing will still apply.

As workers, humans are in trouble.  Farming and manufacturing have been shrinking for decades and AI is likely to take over many of the remaining service jobs.  Some conventional jobs will remain to do research and supervise the AI-driven machines, and there may more jobs where it matters that the worker is human, such as sports and handcrafts, and where human interaction is part of the value, such as healthcare.  But total employment seems likely to decrease and income inequalities to grow.  It’s possible that wealthy nations will provide a guaranteed annual income to the under-employed.  But even if that happens, meaningful work will become harder to find.

I’ll admit this isn’t a terribly pleasant prospect.  The good news is, predictions are hard, so the odds are slim that I’m right.   I’m also aware that we’re at the peak of the hype cycle for ChatGPT and perhaps for AI in general.  Maybe what I’ve described above isn’t technically possible.  But, given how quickly AI and the underlying technologies evolve, I wouldn’t bet on technology bottlenecks blocking these changes indefinitely.  Quantum AI, anyone?

All that said, of the three major predictions, I’m most confident about the first.  It’s pretty likely that a monolithic marketing AI will emerge from the specialized AI bots that are being offered today.  The potential benefits are huge, the path from separate bots to an integrated system is an incremental progression, and some people are already moving in that direction.   (Pro tip: it’s easier to predict things that have already happened.)

The emergence of an AI-driven marketplace to replace conventional human buying is much less certain.  If it does happen, the delegation will emerge in stages.  The first will cover markets where the stakes are low and buying is boring.  Groceries are a likely example.  How quickly it spreads to other sectors will depend on how much time people have and how much intrinsic enjoyment they derive from the shopping itself.

The role of humans is least predictable of all.  Mass under-employment probably isn’t sustainable in the long run, although you could argue it’s already the reality in some parts of the world.  The range of possible long-term outcomes runs from delightful to horrific.  Where we end up with depend on many factors other than the development of AI.  The best we can do is try to understand developments as they happen and try to steer things in the best directions possible.

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.

MarTech Plot Lines for 2021

“Apophenia” – seeing patterns where none exist – is both occupational hazard and job requirement for an industry analyst. The CDP Institute Daily Newsletter provides a steady supply of grist for my pattern detection mill. But the selection of items for that newsletter isn’t random. I have a list of long-running stories that I follow, and keep an eye out for items that illuminate them. I’ll share some of those below.

Feel free to play along at home and let me know what stories you see developing. Deep State conspiracy theories are out of bounds but you’re welcome to speculate on the actual author(s) of the works attributed to “Scott Brinker”. 

Media

Everyone knows the pandemic accelerated the shift towards online media that was already under way. A few points that haven’t been made quite so often include:

- connected TVs and other devices allow individual-level targeting without use of third-party cookies. As online advertising is increasingly delivered through those channels,  the death of cookies becomes less important. Nearly all device-level targeting can also include location data, adding a dimension that cookies often lack.

- walled gardens (Facebook, Google, Amazon) face increasing competition from walled flower pots – that is, businesses with less data but a similar approach. Retailers like Walmart, Kroger, Target, and CVS have all started their own ad networks, drawing on their own customer data. Traditional publishers like Meredith have collected their formerly-scattered customer data to enable cross-channel, individual-level targeting.  Compilers like Neustar and Merkle are also entering the business. None of these has the data depth or scale of Facebook, Google, or Amazon but their audiences are big enough to be interesting. The various “universal ID” efforts being pursued by the ad industry will enable the different flow pots to cross-pollinate, creating larger audiences that I’ll call walled flower beds unless someone stops me.

- shoppable video is growing rapidly. Amazon seems unstoppable but it faces increasing competition from social networks, streaming TV, and every other digital channel that can let viewers make purchases related to what they’re watching. The numbers are still relatively small but the potential is huge. And note that this is a way to sell based purely on context, so targeting doesn’t have to be based on individual identities. That will become more important as privacy regulations become more effective at shutting off the flow of third-party personal data.

- digital out-of-home ads will combine with augmented and virtual reality to create a fundamentally new medium. The growth of digital out of home advertising is worth watching just because DOOH is such a great acronym  . But it’s also a huge story that doesn’t currently get much attention and will explode once people can travel more freely post-pandemic. Augmented and virtual reality are making great technical strides (how about an AR contact lens?) but so far seem like very niche marketing tools. However, the two technologies perfectly complement each other, and will be supercharged by more accessible location data. Watch this space.

Marketing Technology

- data will become more accessible. That marketers want to be “data-driven” is old news. What’s changing is that years of struggle are finally yielding progress toward making data more available and providing the tools to use it. As with digital advertising, the pandemic has accelerated an existing trend, achieving in months digital transformations that would otherwise have taken years.  Although internal data is the focus of most integration efforts, access to external data is also growing, privacy rules notwithstanding. Intent data has been a particular focus with recent announcements from TechTarget, ZoomInfo, Spiceworks Ziff Davis, and Zeta Global.

- artificial intelligence will become (even more) ubiquitous. It seems just yesterday that we were impressed to hear that a company’s product was “AI-powered”. Today, that’s as exciting as being told their offices have “electric lights”. But AI continues to grow stronger even if it doesn’t get as much attention (which the truly paranoid will suspect is because the AIs prefer it that way). Marketers increasingly worry that AI will ultimately replace them, even if it makes more productive before that happens. The headline story is that AI is taking on more “creative” tasks such as content creation and campaign design, which were once thought beyond its capabilities. But the real reason for its growth may be that interactions are shifting to digital channels where success will be based more on relentless analytics than an occasional flash of uniquely human insight.

- blockchain will quiet down. I’ll list blockchain only to point out that’s been an underachiever in the hype-generation department. Back in 2018 we saw it at least as often as AI. Now it comes up just rarely.  There are many clear applications in logistics and some promising proposals related to privacy. But there’s less wild-eyed talk about blockchain changing the world. Do keep an ear open, though: I suspect more is happening behind the scenes than we know.

- no-code will continue to grow. If anything has replaced AI as the buzzword of the year, it’s “no code” and related concepts like “self-service” and “citizen [whatever]”. It’s easy to make fun of these (“citizen brain surgeon”, anyone?) but there’s no doubt that many workers become more productive when they can automate processes without relying on IT professionals. The downside is the same loss of quality control and integration posed other types of shadow IT – although no-code systems are more often governed than true shadow IT projects.  In addition, no-code’s more sophisticated cousin, low-code, is widely used by IT professionals.  It’s possible to see no-code systems as an alternative to AI: both improve productivity, one by letting workers do more and other by replacing them altogether. But a more realistic view is to recognize AI as a key enabling technology inside many no-code systems. As the internal AIs get smarter, no-code will take on increasingly complex tasks, making it more helpful (and more threatening) to increasingly skilled workers.

Marketing

The pandemic has changed how marketers (and everyone else) do their work. With vaccines now reaching the public, it’s important to realize that conditions will change again fairly soon. But that doesn’t mean things will go back to how they were.

- events have changed forever. Yes, in-person events will return and many of us will welcome them with new appreciation for what we’ve missed. But tremendous innovation has occurred in on-line events and more will surely appear in coming months. It’s obvious that there will be a permanent shift towards more digital events, with in-person events reserved for situations where they offer a unique advantage. We can also expect in-person events to incorporate innovations developed for digital events – such as enhanced networking techniques and interactive presentations. I don’t think the significance of this has been fully recognized.  Bear in mind that live events are often the most important new business source for B2B marketers, so major changes in how they work will ramify throughout the marketing and sales process.

- remote work is here to stay. Like events, marketers’ worksites will drift away from the current nearly-all-digital mode to a mix of online and office-based activities. Also like events, innovations developed for remote work, such as improved collaboration tools, will be deployed in both situations. The key difference is that attendance of most events is optional, so attendees can walk away from dysfunctional changes. Workers have less choice about their environments, so harmful innovations such as employee surveillance and off-hours interruptions are harder for them to reject. Whether these stressors outweigh the benefits of remote work will depend on how well companies manage them, so we can expect a period of experimentation and turmoil as businesses learn what works best. With luck, this will mean new attention to workplace policies and management practices, something many firms have handled poorly in the past. Companies that excel at managing remote workers will have a new competitive advantage, especially since remote work lets the best workers choose from a wider variety of employers.

- privacy pressures will rise. The European Union’s General Data Protection Regulation (GDPR) wasn’t the first serious privacy rule or the only reason that privacy gained more attention. But its enforcement date of May 25, 2018 does mark the start of an escalating set of changes that impact what data is available to marketers and how consumers view use of their personal information. These changes will continue and companies will find it increasingly important to manage consumer data in ways that comply with ever-more-demanding regulations and give consumers confidence that their data is being handled appropriately. (A closely related subplot is continued security breaches as companies fail to secure their data despite best efforts.  Another is the continued misbehavior of Facebook and other social media firms and increasing resistance by regulators and consumers.  That one is worth a channel of its own.)  Marketers will need to take a more active role in privacy discussions, which have been dominated by legal, security, and IT staffs in businesses, and by consumer advocates, academics, and regulators in the political world. Earning a seat at that crowded table won’t be easy but making their voice heard is essential if marketers want the rules to reflect their needs.

- trust is under fire. This is a broad trend spanning continents and stretching back for years (see Martin Gurri’s uncannily prescient The Revolt of the Public, published in 2014),  Socially, the trend presents itself as a loss of trust in institutions, the benefits of technology, and credentialed experts in general. In marketing, it shows up as companies voicing disappointment with data-driven analytics and personalization, as consumers not trusting companies to manage or protect their data, as workers' fear that AI systems will harm creativity and codify unfair bias, as widely-noted gaps between what customers want and companies deliver, as “citizen developers” preferring to build their own systems, and as buyers preferring peers, Web searches, social media, and pretty much any other information source to analysts reports.  

Trust is the theme that connects all the stories I’ve listed above.  Without trust, consumers won’t share their data, respond to marketing messages, or try new channels; governments will push for more stringent privacy and business regulations; workers will be less productive; and all industry progress will move more slowly. The trust crisis is too broad for marketers fix by themselves. But they need to account for it in everything they do, adjusting their plans to include trust-building measures that might not have been needed in a healthier past.  The pandemic will end soon and technologies come and go.  But trust will be a story to follow for a long, long time.

Twilio Buys CDP Segment for $3.2 Billion

Friday afternoon brought an unconfirmed Forbes report that communications platform Twilio is buying CDP Segment for $3.2 billion. (The all-stock deal was officially announced on Monday.)  It's Twilio’s third acquisition this year, following much smaller deals in January for telephony platform Teravoz and in July for IoT connector Electric Imp.  It comes two years after Twilio’s $3 billion purchase of email platform SendGrid.

The deal is intriguing from at least three perspectives:

Valuation: the $3.2 billion price is impressive by any standard. Segment’s current revenue isn’t known, although one published estimate put it at $180 million for 2019.  That sounds a bit high for a company with 450 employees at the time, but let's go with it and assume $200 million for 2020 revenue.  This has Twilio is paying 16x revenue, which is less than the 20x that Salesforce paid for Mulesoft ($6.5 billion on roughly $300 million) but in line with the 15x that Adobe paid for Marketo ($4.7 billion on $320 million)  or 14x that Twilio itself paid for SendGrid ($2 billion on $140 million when the deal was announced; the $3 billion price reflects the subsequent rise in Twilio’s stock). Note that these prices are well above the run-of-the-mill SaaS valuations, which are below 10x revenue.

Twilio: the SendGrid acquisition marked a major movement of Twilio beyond its base in telephone messaging to support a broader range of channels. If they’re to avoid the fragmentation that has plagued the larger marketing clouds, which also grew by acquisition, they need a CDP to unify their customer data. The big clouds (Oracle, Adobe, Salesforce, Microsoft, SAP) all chose to build their CDPs internally, but Twilio is much smaller and lacks the resources to do the same in a timely fashion. (Even the big clouds struggled, of course). On the other hand, Twilio’s surging stock price makes acquisition much easier. So buying a CDP they can deploy immediately gains them time and a mature product. It also offers entry to 20,000 accounts that might buy other Twilio products, especially given Segment’s position at the heart of their customer data infrastructure.

Of course, if Twilio really wants to compete with the marketing clouds, it will need to support other channels, most notably Web site management and ecommerce. Note that vendors beyond the clouds are pursuing the same strategy, including Acquia (which bought CDP AgilOne), IBM-spinoff Acoustic, MailChimp, and HubSpot. So the strategy isn’t unique, but it may be the only way for companies like Twilio to avoid being marginalized as apps that depend on major platforms controlled by other vendors. By definition, apps are easily replaced and are therefore easily commoditized. That’s a position to escape if you have the resources to expand beyond it.

CDP Industry: Segment is/was the largest independent CDP vendor, although Tealium and Treasure Data are close. Other recent CDP acquisitions were mostly mid-tier vendors (AgilOne, Evergage, QuickPivot, Lattice Engines, SessionM). Of these deals, only AgilOne seemed central to the product strategy of the buyers. Segment’s decision to sell rather than try to grow on its own may signal a recognition that it will be increasingly difficult to survive as a general-purpose independent CDP. We’ve already seen much of the industry shift to more defensible niches, including integrated marketing applications and vertical industry specialization. There’s certainly still a case to be made for an independent CDP as a way to avoid lock-in by broad marketing clouds. But there’s no doubt that the marketing cloud vendors’ own CDPs will grab some chunk of the market, and more will be lost to CDPs embedded in other systems (email, ecommerce, reservations, etc.), offered by service vendors (Mastercard, Vericast, TransUnion, etc.) and home-built on cloud platforms like Amazon Web Services and Google Cloud.

Given these pressures, we’re likely to see additional purchases of CDPs by companies who are trying to build their own complete marketing platforms, including Shopify, MailChimp, HubSpot, and a number of private-equity backed roll-ups. Faced with a daunting competitive situation, many CDP vendors will be interested in selling, even at prices that might not be as high as they once hoped.

Ironically, none of this bodes ill for the fundamental concept of the CDP itself. Companies will still need a central system to assemble and share unified customer profiles. It is indeed the platform on which the other platforms are built. Whether their CDP is stand-alone software or part of a larger solution doesn’t really matter from the user’s perspective: what matters is that clean, consistent, complete customer data is easily available to any system that needs it. Similarly, companies will still need the skills to build and manage CDPs.  Marketing, data, and IT departments will wrestle with customer data long into the future, and the winners will be best positioned to achieve business success. 

Don't Misuse Proof of Concept in System Selection

Call me a cock-eyed optimist, but marketers may actually be getting better at buying software. Our research has long shown that the most satisfied buyers base their selection on features, not cost or ease of use. But feature lists alone are never enough: even if buyers had the knowledge and patience to precisely define their actual requirements, no set of checkboxes could capture the nuance of what it’s actually like to use a piece of software for a specific task. This is why experts like Tony Byrne at Real Story Group argue instead for defining key use cases (a.k.a. user stories) and having vendors demonstrate those. (If you really want to be trendy, you can call this a Clayton Christensen-style “job to be done”.)

In fact, use cases have become something of an obsession in their own right. This is partly because they are a way of getting concrete answers about the value of a system: when someone asks, “What’s the use case for system X”, they’re really asking, “How will I benefit from buying it?” That’s quite different from the classic definition of a use case as a series of steps to achieve a task. It’s this traditional definition that matters when you apply use cases to system selection, since you want the use case to specify the features to be demonstrated. You can download the CDP Institute’s use case template here.

But I suspect the real reason use cases have become so popular is that they offer a shortcut past the swamp of defining comprehensive system requirements. Buyers in general, and marketers in particular, lack the time and resources to create complete requirements lists based on their actual needs (although they're perfectly capable of copying huge, generic lists that apply to no one).  Many buyers are convinced it’s not necessary and perhaps not even possible to build meaningful requirements lists: they point to the old-school “waterfall” approach used in systems design, which routinely takes too long and produces unsatisfactory results. Instead, buyers correctly see use cases as part of an agile methodology that evolves a solution by solving a sequence of concrete, near-term objectives.

Of course, any agile expert will freely admit that chasing random enhancements is not enough.  There also needs to be an underlying framework to ensure the product can mature without extensive rework. The same applies to software selection: a collection of use cases will not necessarily test all the features you’ll ultimately need. There’s an unstated but, I think, implicit assumption that use cases are a type of sampling technique: that is, a system that meets the requirements of the selected use cases will also meet other, untested requirements.   It’s a dangerous assumption. (To be clear: a system that can’t support the selected use cases is proven inadequate. So sample use cases do provide a valuable screening function.)

Consciously or subconsciously, smart buyers know that sample use cases are not enough. This may be why I’ve recently noticed a sharp rise in the use of proof of concept (POC) tests. Those go beyond watching a demonstration of selected use cases to actually instal a trial version of a system and seeihow it runs. This is more work than use case demonstrations but gives much more complete information.

Proof of concept engagements used to be fairly rare. Only big companies could afford to run them because they cost quite a bit in both cash (most vendors required some payment) and staff time (to set up and evaluate the results). Even big companies would deploy POCs only to resolve specific uncertainties that couldn’t be settled without a live deployment.

The barriers to POCs have fallen dramatically with cloud systems and Software-as-a-Service. Today, buyers can often set up a test system with a just a few mouse clicks (although it may take several days of preparation before those clicks will work). As a result, POCs are now so common that they can almost be considered a standard part of the buying process.

Like the broader application of use cases, having more POCs is generally a good thing. But, also like use cases, POCs can be applied incorrectly.

In particular, I’ve recently seen several situations where POCs were used as an alternative to basic information gathering. The most frightening was a company that told me they had selected half a dozen wildly different systems and were going to do a POC with each of them to figure out what kind of system they really needed.

The grimace they didn’t see when I heard this is why I keep my camera off during Zoom meetings. Even if the vendors do the POCs for free, this is still a major commitment of staff time that won’t actually answer the question. At best, they’ll learn about the scope of the different products. But that won’t tell them what scope is right for them.

Anther company told me they ran five different POCs, taking more than six months to complete the process, only to later discover that they couldn’t load the data sources they expected (but hadn’t included in their POCs). Yet another company let their technical staff manage a POC and declare it successful, only later to learn the system had been configured in a way that didn’t meet actual user needs.

You’re probably noticing a dreary theme here: there’s no shortcut for defining your requirements. You’re right about that, and you’re also right that I’m not much fun at parties. As to POCs, they do have an important role but it’s the same one they played when they were harder to do: they resolve uncertainties that can’t be resolved any other way.

For Customer Data Platforms, the most common uncertainty is probably the ability to integrate different data sources.  Technical nuances and data quality are almost impossible to assess without actually trying to load each system.  Since these issues have more to do with the data source than the CDP, this type of POC is more about CDP feasibility in general than CDP system selection. That means you can probably defer your POC until you’ve narrowed your selection to one or two options – something that will reduce the total effort, encourage the vendor to learn more about your situation, and help you to learn about the system you’re most likely to use.

The situation may be different with other types of software. For example, you might to test q wide variety of predictive modeling systems if the key uncertainty is how well their models will perform. That’s closer to the classic multi-vendor “bake-off”.  But beware of such situations: the more products you test, the less likely your staff is to learn each product well.

With a predictive modeling tool, it’s obvious that user skill can have a major impact on results. With other tools, the impact of user training on outcomes may not be obvious. But users who are assessing system power or usability may still misjudge a product if they haven’t invested enough time in learning it.  Training wheels are good for beginners but get in the way of an expert. Remember that your users will soon be experts, so don’t judge a system by the quality of its training wheels.

This brings us back to my original claim.  Are marketers really getting better at buying software?  I’ll stand by that and point to broader use of tools like use cases and proof of concepts as evidence. But I’ll repeat my caution that use cases and POCs must be used to develop and supplement requirements, not to replace them. Otherwise they become an alternate route to poor decisions rather than
guideposts on the road to success.

Acquia Buys AgilOne CDP

Acquia, which is moving past its roots in Web content management to become a multi-channel “digital experience platform” (DXP), took a big step in that direction today with a deal
to buy the AgilOne Customer Data Platform. The deal follows Acquia’s May 2019 purchase of open source marketing automation platform Mautic  and September 2019 purchase of site building tool Cohesion.  Acquia itself was purchased in September by Vista Equity Partners  for $1 billion, which obviously supports their DXP strategy.

The logic behind this deal is so clear that there’s little need for comment. While the exact meaning of DXP is a bit fuzzy, it surely involves coordinating and personalizing customer experiences across channels. This certainly requires the unified customer data that a CDP provides.  Acquia’s heritage in Web content management doesn’t provide deep customer data unification experience, and neither does Mautic.  AgilOne is a particularly good fit because it’s better than many CDPs at identity matching, including offline as well as online data. It also provides lots of connectors to source and delivery systems, as well as advanced machine learning for segmentation and predictions. Acquia and Mautic lacked those, too.

AgilOne rebuilt its core technology fairly recently, giving it a highly flexible and scalable platform that should easily extend beyond the company’s current base in mid-tier retail.  In particular, it will be able to serve Acquia’s clients, who tend to be very large companies with multiple Web multi-sites around the world. At the same time, AgilOne gives Acquia a stronger story in retail and other B2C markets where it has been less active.  AgilOne will also gain by integrating with some of Mautic’s features, notably email and SMS delivery and complex customer journey management. And the deal gives AgilOne much deeper resources to fund growth than it had as an independent company.

What, if anything, does the deal tell us about the larger CDP industry? I’d argue it mostly reinforces the trends I described in October, of independent CDPs being purchased by companies that are not primarily marketing software vendors but need to add customer data capabilities. Nearly all major CDP purchases to date meet this description: Mastercard buying SessionM, Dun & Bradstreet buying Lattice Engines, Arm buying Treasure Data, and Informatica buying Allsight. The only partial exception is Salesforce buying Datorama, but CDP wasn’t the focus of that deal. None of the other companies trying to follow Acquia’s approach of expanding from Web content management to DXP has yet purchased a CDP. But they’ll all need CDP functions so don’t be surprised to see more deals along those lines.

Put in a broader context, adding a CDP as a module inside a DXP is an example of CDP as a component within large marketing or even operational systems, something I refer to as “CDP Inside”. I expect that to be increasingly common and, thus, a potential home for independent CDP systems as the market matures, competition heats up, and the big marketing cloud vendors release their own products.  Selling or merging to become part of a larger system is one escape path for the independent CDPs. Another path is to focus on specific industries or cost-sensitive segments where the big marketing clouds are at a disadvantage.  I expect to see current CDP vendors take both approaches, even as new entrants continue to appear.  The CDP market won't get any simpler but buyers should have increasingly clear choices, so buying a CDP may become a bit less complicated.

BadTech Is the Next New Thing

Forget about Martech, Adtech, or even Madtech. The next big thing is BadTech.

I’m referring to is the backlash against big tech firms – Google, Amazon, Apple, and above all Facebook – that have relentlessly expanded their influence on everyday life. Until recently, these firms were mostly seen as positive, or at least benignly neutral, forces that made consumers’ lives easier.  But something snapped after the Cambridge Analytica scandal last March.  Scattered concerns became a flood of hostility.  Enthusiasm curdled into skepticism and fear.  The world recognized a new avatar of evil: BadTech.

As a long-standing skeptic (see this from 2016), I’m generally pleased with this development. The past month alone offers plenty of news to alarm consumers:

• Google tracks location even after users turn off Location Sharing.
• telecom lobbyists have successfully blocked close to 70 privacy bills so far this year.
• big gaps in social media efforts to block even someone as clearly horrid as Infowars’ Alex Jones – famous for claiming the Sandy Hook school shooting was a hoax.
• Facebook reinstated social monitoring vendor Crimson Hexagon despite concerns that government agencies were using it to monitor public activities.
• Amazon’s facial recognition software falsely matched 28 U.S. Congress members with mugshots.
• Artifical intelligence training data often creates unintentional bias and there's no standard method to prevent this.
• Russian hackers continue to attack U.S. (and other) elections.

There's more bad news for marketers and other business people:

• clients' customer data was exposed by Salesforce.com (although there’s no evidence that anything was actually leaked).
• up to 70% of video spend goes to unauthorized sellers.
• fake followers comprise up to one-third of Instagram influencers’ audiences.
• few organizations are succeeding in their AI investments.

Not surprisingly, consumers, businesses, and governments have reacted with new skepticism, concern, and even some action:

• Americans fear losing their personal data more than losing their physical wallet
• only Amazon ranks in the top five of the 20 most trusted companies.  Google, Apple and Microsoft are six, eight, and nine.  Facebook isn’t anywhere on the list.
• European Union puts teeth in regulations that require removal of extremist content within one hour.
• California legislature passes strong data privacy law.
• advertisers infatuation with blockchain is starting to fizzle.

But all is not perfect.

• BadTech firms still plunge ahead with dangerous projects. For example, despite the clear and increasing dangers from poorly controlled AI, it’s being distributed more broadly by Ebay, Salesforce, Google, and Oracle. 
• Other institutions merrily pursue their own questionable ideas. Here we have General Motors and Shell opening new risks by connecting cars to gas pumps.  Here – this is not a joke – a university is putting school-controlled Amazon Echo listening devices in every dorm room

• The press continues to get it wrong. This New York Times Magazine piece presents California’s privacy law as a triumph for its citizen-activist sponsor, when he in fact traded a nearly-impossible-to change referendum for a law that will surely be gutted before it takes effect in 2020.

• Proponents will overreach. This opinion piece argues the term “privacy policy” should be banned because consumers think the label means a company keeps their data private. This is a side issue at best; at worst, it tries to protect people from being lazy. Balancing privacy against other legitimate concerns will be hard enough without silly distractions.

So welcome to our latest brave new world, where BadTech is one more villain to fear   It's progress that people recognize the issues but we can't let emotion overwhelm considered solutions.  Let’s use the moment to address the real problems without creating new ones or throwing away what’s genuinely good.  We can't afford to fail.