Martech in the Apolocalypse

I once read that the most accurate weather forecast is tomorrow will be the same as today. That may or may not be true, but it doesn’t matter.  What’s really important is predicting when the weather will change. That’s what warns you to bring an umbrella for the afternoon when it’s sunny in the morning, or buy milk before a blizzard, or evacuate before a hurricane.

The marketing industry is about to face not one but several of those seemingly sudden brutal changes.

Privacy

Privacy is the change that gets the most industry attention.  Our long season of customer data raining freely from the heavens is being replaced – seemingly overnight – by a customer data drought. This is one change we can’t blame on global warming, although it is man-made.

• It’s people’s concerns about privacy that prompts governments to pass laws like GDPR and CCPA, and their counterparts around the globe. 

• It’s people’s concerns about being tracked that leads Chrome, Safari, Firefox, and every other major browser to block third party cookies, although you all know that Google just deferred that on Chrome yet again.

• It’s people’s concerns about controlling their data that leads Apple and Android to shut down unauthorized data sharing by smartphone apps. 

• And it’s people’s concerns about identity theft and fraud, that leads companies to impose ever-tighter tighter controls on how they collect, use and share customer data, even when it is legally permitted.

The immediate impact of the data drought falls on advertisers.  They now find it much harder to assemble audiences, target individuals programmatically, and to connect media impressions with sales results.

Big Tech Under Siege

But there’s an arguably larger, secondary impact.  Privacy changes threaten the flow of third-party data into the surveillance marketing engines of Google and Facebook, which between them capture nearly 70% of global digital ad revenues and 50% -- half – of all global advertising revenues.

And privacy is just one of the challenges facing Google, Facebook, and other Big Tech companies like Apple and Amazon.  Governments everywhere are reining in the almost unbridled power they’ve let those firms accumulate. Power over not just advertising, but news and commerce, and future technologies like cloud hosting and artificial intelligence. 

• Europe recently enacted its Digital Services Act and Digital Markets Act, which are squarely targeted at reducing the power of Big Tech. 

• The U.S. Congress may or may not pass new privacy and anti-trust laws, but anti-trust legal action is ramping up either way. 

• China has had its own Big Tech clampdown under way for several years now, and Russia has recently followed suit.

What these changes mean is more work for marketers.  In recent years, they've been happy to shovel dollars into one end of the Big Tech black box and pipe revenue out from the other, without worrying much about what happened in between.  In fact, Big Tech channels have already become less efficient and harder to use, and their volumes are falling.  The Duopoly’s share of US digital ad market actually peaked in in 2017, if we believe eMarketer. 

This means marketers have to look at new channels, which indeed are flourishing. Retail media, connected TV, podcasts, in-game ads, in-app ads, social commerce, even out-of-home media and humble direct mail, are all gaining attention as marketers are forced to scramble for alternatives to walled gardens that they weren’t really all that eager to escape.

But there’s more.

Pandemic

You may have heard of this little thing called the pandemic?

It certainly accelerated the growth of digital media, contributing to marketers’ search for outlets to supplement Google and Facebook. But the larger impact was a growth in digital commerce, as consumers were forced to buy more online than they had ever intended. This, in turn, accelerated companies’ interest in digital transformations of all sorts: in ecommerce, in hybrid retail like curbside pickup of online purchases, and in remote work for employees.

The result was a crisis-driven acceleration in the pace of change for corporate systems and processes, and dare I even say culture, as companies were forced to adapt to ever-changing customer expectations, working conditions, and supply chain bottlenecks. For truly deep thinkers, I’ll throw in a power shift towards junior employees, who were more attuned than their doddering elders to digital technologies and less committed to old school, top-down management styles.

Fortunately for doddering elders like myself, the kids were trapped at home with us, so they could explain how to use that damned remote.

These changes have very specific technical implications.

• Adoption of no code and low code systems, which make it easier for business users to make changes without help from corporate IT teams.

• Broader access to corporate data to feed the low/no code systems, and to provide feedback about how the new processes were working out.

• Growth of the Customer Data Platform industry, as companies quickly realized that a foundation of clean, accessible customer data was essential to reach many of their new business goals.

Everything Else

The pandemic has faded into the background while our attention shifts to new crises: war, political unrest, inflation, recession, and the unignorable symptoms of accelerating climate change. It feels a bit silly to do this, but let’s look only at how these affect marketing.

• From a tech vendor perspective, uncertainties make it harder to raise new capital or increase prices, and we’ve begun to see some scattered industry layoffs as a result.

• From a tech buyer perspective, company belts are being tightened. While we haven’t yet seen a slowdown in martech revenues, we do know that buyers are more cautious than before. 

• Consumers are feeling the pinch of inflation and higher interest rates today, and worry about a recession tomorrow.

It’s an environment where marketing plans are less a roadmap than a deck of contingency cards, any one of which might be played depending on how things develop.

Welcome to the Future

So welcome to the apocalypse: a chaotic present on an unstable path to an uncertain future. What’s a marketer to do?

The best advice anybody can give comes from The Hitchhiker’s Guide to the Galaxy: Don’t Panic. 

The second-best advice is to recognize that change right now is both inevitable and unpredictable. So rather than picking one most likely future, preparing for it, and hoping you’re right, your best bet is to be agile, so you can adapt effectively to whatever the future may be.

This is more than just a platitude. We don’t know which channels will dominate as the roles of Facebook and Google continue to diminish. But we do know that there will be a lot of channels, certainly in the immediate future, as media fragmentation grows.

Fragmented Media

There are specific things you can do to deal with that fragmentation.

• Build more adaptable customer data systems. This means systems that can easily connect to any new channel, both to collect data from the channel as customers interact, and feed data to the channel to support personalized interactions. Easy connection implies open APIs, schema-free data storage, and low- and no-code interfaces.

• Build scalable systems for higher volume and variety of data. Schema-free data stores are part of the solution, but so are cloud platforms that allow effortless, affordable growth on demand.

• Even more important, rely on artificial intelligence to make sense of all this new, ever-changing data.  AI removes, or at least eases, the critical bottleneck of using human experts to map each new data source. Remember you might be able to capture data in a raw form without mapping it to a schema, but you still need to identify the data elements before you can use them in a customer profile.  

And AI can go beyond just identifying a data type, to extracting meaning from unstructured or semi-structured contents. This might be understanding relations among entities mentioned in call notes, or classifying the sentiment expressed in a social media post.  This sort of tagging is critical to extracting value from unstructured and semi-structured data.

Doing those things automatically, at scale, and with a minimum of set-up for new data feeds and types, is critical to adapting to change as it happens.

• Find ways to create more content. You’ll almost certainly need a greater variety of messages, as you communicate to customers in a greater variety of circumstances – which is what happens when you have more change.  You’ll also need to distribute each message in more channels, because everything no longer goes to just Facebook and Google.

Our friend artificial intelligence will be critical here as well. It might actually create messages, although so far those capabilities are limited. But it will definitely to convert messages from one format to another, either entirely unsupervised or as a productivity multiplier for skilled humans.

• Finally, you’ll need to do a better job of measurement. That’s always been a challenge for marketers, although third-party cookies and convenient if self-serving attribution reports from the walled garden vendors made it easier in recent years. But those cookies and those attribution reports are exactly what we’re losing as we enter the new era.  So we’ll have to work harder to find measurement methods that apply to different situations: some where we can identify each customer from start to finish, others where each audience is entirely anonymous, and everything in between.
  

Anonymous measurement is not a new problem. Some of the old-school methods will still apply, like test/control experiments and media mix models. You’ll also have new options, like data clean rooms and probabilistic models, that old-school marketers couldn’t imagine. The key here is to find techniques that apply to many different channels, because the number of channels will continue to increase.

More First Party Data

The second thing we know for certain is that in the future your own data – first-party data – will become more important.

In part, that’s a natural consequence of the loss of third-party data: you make the best use of what’s available.  When you can no longer just buy and test different prospect lists to see which work, you build look-alike models based on your existing customers and have media partners use those models to select your best prospects from their own lists.  Or you use a data clean room to match your customers with their customers, which is a more effective way of doing the same thing.

But there’s more to it than that. Consumers are increasingly wary of sharing their data with strangers, but your customers don’t see you as a stranger. They see you as someone they’ve chosen to do business with, in part because they think they can trust you to handle their data properly, and to use that data in their interest.

Let me be clear: this isn’t an option: customers know you have their data and they expect that you’ll use it to help them.

Now, your customers’ definition of ‘help’ isn’t to send them personalized advertisements: in fact, survey after survey shows that most customers say they don’t want any advertisements at all.  

What they do want is better service, which in their minds means greater convenience for things like placing an order, receiving a delivery, or making a return. The good news is they also want marketing that looks like good service, such as suggesting a useful product upgrade or more suitable pricing plan. If you do that kind of marketing really well, they’ll rave about how great it is to be your customer.

Let me make my point more directly: the only way you can give really terrific service is to have really terrific customer data. That data is what lets you understand and anticipate what each customer wants and how they want it delivered.

You’ll also need powerful analytics to make sense of that data, which brings us back once more to our friend artificial intelligence.

The Story So Far

Let’s stop here for a quick recap.

• Change is accelerating. And while we can’t predict the specifics, we do know that channels will fragment, and personal data will be harder to get. 

• Agility is the key to dealing with both those changes. Agility allows you to exploit whichever channels turn out to be important. And agility lets you take full advantage of whatever data you’re able to collect.

Building for Agility

The final question, then, is how do you build for agility?    And that’s a paradox, right? How can you build a solid, stable platform that allows you to be flexible?

Well, it’s not really a paradox.  Imagine you want to build a ballet theater.

The stage of that theater will feature the world’s most agile, flexible, dynamic ballerinas, appearing in a wide variety of shows. In fact, that same stage may also host operas, orchestras, musical theater, and maybe even a circus or two. The theater has to be flexible and adaptable to accommodate these diverse uses, but also strong and solid enough to support leaping ballerinas and trudging elephants without collapsing under the strain.

It’s the same for your marketing stack. The customer-facing systems in your stack are ballerinas: talented, hard-working, and exciting, they’re the stars of the show. 

But they’re also replaceable. Dancers come and go from season to season and even one performance to the next. Lose any single dancer, and the show will go on without missing a performance. Lose the entire corps de ballet, and at worst you’re delayed a few weeks as you rehearse their replacements.

But if the theater burns down, you’re out of business for a very long time.

As you’ve no doubt figured out by now, your Customer Data Platform is that theater. It’s the repository of accumulated knowledge and resources that enable your business applications to adapt and thrive in the face of change.

We’ve already listed technical features that make this possible:

• easy connectivity to new sources and destinations
• flexible, scalable data storage
• efficient content creation and conversion from one channel to another
• alternative measurement techniques for different situations
• privacy-supporting technologies such as data clean rooms and policy enforcement, and
• enabling technologies like artificial intelligence, composable architectures, and no code/low code tools

The key point here is you need a clear separation between your stable, central customer data system and the applications which depend on that system.  That separation is what makes it relatively easy to add or change applications without disrupting other parts of the operation. Violating the separation is dangerous because any application that also stores centralized data cannot easily be replaced.

The ballet analogy for that is a principal dancer who’s also the director and choreographer.  Losing that person shuts down the operation, or at least requires a long period of retooling while you recruit replacements for both jobs. 

There may be times where you want to take this risk, because the one person, or one system, is so good at both roles that you build your organization around it. But you should at least recognize what you’re doing is dangerous, and be sure it’s really worth the taking the chance. You’ll also want to take out the technology equivalent of key person insurance, doing whatever you can to minimize your reliance on that dual purpose system and to separate the platform from application functions.

I should also stress that separating the platform from applications doesn’t free you to use any applications you want. You still need applications that integrate well with your platform, and ideally will complement the strengths and weaknesses of the applications already in place.

We could say it’s like hiring ballerinas who fit the company style, but let’s give those poor ballerinas a rest.

Agility Beyond Technology

You should also recognize that technology is just one part of agility. People and process are at least as important. You have to support the technology with training, governance, organizational alignment, leadership, recruiting, and reward systems that empower staff respond effectively as new conditions arise.

A few guidelines include:

• Decide on data, not intuition. That’s been the goal forever. But it’s much more important when intuitions are less reliable because they've developed under conditions that no longer exist. 

• Experiment, don’t optimize. Optimization is based on fine-tuning parameters over time. This only works when the underlying conditions are stable. In a period of rapid change, it’s more important to uncover big new opportunities than to squeeze small improvements out of the old ones. 

• Measure outcomes, not inputs. When conditions are stable, the relation between inputs and outcomes can be discovered.  This means you can measure one to predict the other. In unstable conditions, the old assumptions don’t apply. So you’ll have to put in the extra effort it takes to measure outcomes directly. 

• Manage customers, not departments. What I really mean here is to manage the customer experience across all departments. Customers don’t know or care which department controls which part of their experience. They only judge the company on their experience as a whole.  Departments working in isolation from each other cannot see the ultimate results of their actions. The only way to deliver good outcomes is to cooperate and manage from the customer’s view.
  

Let me focus on that last point for a moment, because it’s especially important.

Agility cannot be just random leaping about. It needs a purpose, so you know which direction to leap. And in business, the purpose of agility is to deliver great customer experience.

Summary

I hope the core message is clear:

• In uncertain times, agility is the key to success. 

• Agility is more than being flexible. Quick, effective response to change requires strong, stable base of technical and human resources – including a solid Customer Data Platform. 

• The purpose of agility is meet the one goal that never changes: delivering a great customer experience.

Game of Thrones Meets Big Bang Theory: Welcome to CDP Industry's Next Phase

The CDP Institute just published its latest Industry Update, our semi-annual overview of CDP vendors with data on employment, funding, locations, and more. (Download here.)  There were three pieces of information that stood out:

• Only four new vendors were added, compared with an average of fifteen in past reports.

• four companies reported funding rounds over $100 million, compared with one round that size across all past reports

• nearly all employment growth (85%) came from previously listed vendors, compared with just 36% in past reports

Of course, it makes sense that most growth would come from existing vendors if we added few new ones. But industry growth over-all was in line with past trends, and actually a bit stronger: up 12% over the past six months. This meant that the growth rate of existing vendors was high enough to compensate for the “loss” of new vendors. In fact, the existing vendor growth of 11% was the highest since 2018, when the industry was just taking off.

Connecting these dots reveals a clear picture: an industry that has stopped attracting new entrants but is now growing strongly on its own – with leading vendors stockpiling funds to compete against each other an elimination round where only a few can emerge as winners. Think Survivor meets Game of Thrones with a dash of Big Bang Theory.

It’s a picture that makes a lot of sense. Customer Data Platforms are now widely accepted as an essential component of a modern data architecture, so it’s a market worth fighting for. But the situation facing potential entrants is daunting:

• the leading independent CDP vendors now have mature products, big customer bases, high brand recognition, and lots of funding. 

• enterprise software companies, including Salesforce, Adobe, Oracle, Microsoft, and SAP, are chipping away at the market by selling CDPs as part of their packages. 

• marketing automation, customer support, ecommerce, and other vendors increasingly offer CDP modules baked into their own systems
  

• IT teams show growing interest in building their own CDP equivalent, supported by a growing array of components that make the job easier.

Some mid-tier CDP vendors have already given up the fight and been acquired, most often by firms needing a CDP to anchor a multi-channel customer experience suite. The acquisition wave may have peaked, since there were just three acquisitions in the latest report, compared with a dozen over the previous two. 

Among the remaining firms, some may compete successfully as generalists.  But the more promising path in most cases will be to offer specialized products that can be the best in a particular niche. Those niches might be defined by a particular industry, region, company size, or CDP function.

The functional niches are most intriguing because they serve the growing market for CDP components. We’ve seen some movement in that direction, as vendors offer parts of their CDP as stand-alone modules for identity resolution, data collection, data distribution (“reverse ETL”), and campaign management. Those vendors see their modules as a point of entry into clients who will later buy more of their products. They may be right, but I wonder how many companies that buy best-of-breed components will reverse course by favoring components from a single source. What’s certain is that this approach exposes the CDP vendors to competition from point-solution specialists in each area while discarding the advantage of that comes from purchasing a CDP with a full range of pre-integrated functions.  Here's a sampling of that competitive landscape:

I also suspect that companies like Snowflake, Amazon Web Services, and Google Cloud Services, which now position themselves as providing one piece of a “composable” solution, will eventually add features that match what the independent component providers now offer. Actually, that’s already happening, so I don’t get much credit for predicting it. It’s a dynamic we’ve seen repeatedly in other markets: primary vendors expand their features to secure their position with clients by adding more value (hooray!) and increasing the cost of switching (boo!).

Let’s be clear: both the added value and the switching costs are the result of integration cost. No matter how many promises are made about easy integration, the fact remains that any non-trivial connection between two systems takes skill to plan, deploy, and maintain. Integration is often the top-ranked vendor selection criterion in surveys, which some see as showing that problem is well understood. I draw the opposite conclusion: people list integration as a consideration because they know it’s poorly understood.  This forces them to invest time in trying to avoid integration problems and even sacrifice other benefits to achieve it. If integration were really easy, no one would worry about it.

Right now, someone reading this is saying, “Ah, but no-code changes everything”. I don’t think so. No-code works best when automating simple processes with a few users where flaws are acceptable. The more complex, widely-deployed, and mission-critical a process is, the more important it is to deploy professional-grade design and quality control. Prebuilt components doesn’t change this: configuring those components and connecting them to each other still takes great care and understanding.

This isn’t (just) a cranky-old-man digression. CDP functions rank high in complexity, scale, and risk, so they are poor candidates for no-code development. CDPs certainly can have no-code interfaces that empower business users to do things that might otherwise require a developer. But those interfaces will control carefully defined and constrained tasks, not create core functionality. Assembling CDP-equivalent systems from composable functions is a different matter, and, yes, that should become increasingly possible for people with the right integration skills. What I doubt is that selling modules with those functions will be good business for CDP vendors: they are likely to commoditize their products and ultimately to be pushed aside by platform developers who integrate key functions directly.

I'm not saying that CDP vendors who can’t raise several hundred million dollars are doomed.  I am saying that most will have to pick a niche to succeed. One promising option is building customer data profiles, especially for big enterprises. It’s a single function that incorporates enough separate components for CDP vendors to provide value by avoiding integration costs.

The other big niche, or set of niches, is integrated customer experience solutions. Our latest report already shows systems that campaign and delivery CDPs, our name for systems that do this, account for two-thirds of the industry vendor count and funding, and nearly three-quarters of employment. Their actual share may be greater still: immediately after completing the latest report, I happened to glance at G2 Crowd’s list of CDPs and found our reports doesn't include several large, fast-growing retail marketing automation or messaging specialists (Insider, Listrak, SALESmanago, Klaviyo, and Ometria) that offer what looks like CDP-grade multi-source profile building.

Whether those vendors are true CDPs depends on whether they make those profiles available to other systems. Either way, the point is that there’s a large and growing market for cross-channel retail marketing systems with unified customer profiles at their core. There are similar markets outside of retail, where we already see specialist campaign and delivery CDPs in hospitality, financial services, telecommunications, healthcare, education, and elsewhere.

The value of industry-specific systems is, once again, reduced integration cost. In this case, the key integration is with industry-specific operational systems such as airline reservations, core banking, phone billing, health records, and student management. Vendors in these niches compete primarily on the marketing functions they offer, which makes them more departmental than enterprise solutions and pushes them to add marketing features tailored to their particular industry. Systems like this are hard to dislodge once they’re deployed because they are populated with many complex, difficult-to-replicate campaigns, reports, predictive models, and content libraries. This stickiness is what enables many successful vendors to co-exist in each niche, and what makes it hard for non-specialists to enter.

The division of the CDP industry into enterprise-level data CDPs and industry-specific, department-level campaign and delivery CDPs is not a new trend. What is new is the maturity of the competitors within many niches, which will make it increasingly difficult for new entrants to succeed. What’s also new is that building CDP-style customer profiles is increasingly common, making it a standard feature rather than a product differentiator. This encourages vendors to position themselves as something other than a CDP, even though they need to show buyers that their CDP features are first-rate.

My final conclusion is this: the CDP industry will continue grow, and it will remain important for buyers to find the right CDP, even as the CDP itself slips from the spotlight.

Build vs Buy in a Nutshell