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.

History of Marketing Technology and What's Special about Journey Orchestration

I delivered my presentation on the history of marketing technology last week at the Optimove CONNECT conference in Tel Aviv. Sadly, the audience didn’t seem to share my fascination with arcana (did you know that the Chinese invented paper in 100 CE? that Return on Investment analysis originated at DuPont in 1912?) So, chastened a bit, I’ll share with you a much-condensed version of my timeline, leaving out juicy details like brothel advertising at Pompeii.

The timeline* traces three categories: marketing channels; tools used by marketers to manage those channels; and data available to marketers.  The yellow areas represent the volume of technology available during each period. Again skipping over my beloved details, there are two main points:

• although the number of marketing channels increased dramatically during the industrial age (adding mass print, direct mail, radio, television, and telemarketing), there was almost no growth in marketing technology or data until computers were applied to list management in the 1970’s. The real explosions in martech and data happen after the Internet appears in the 1990’s.

• the core martech technology, campaign management, begins in the 1980’s: that is, it predates the Internet. In fact, campaign management was originally designed to manage direct mail lists (and – arcana alert! – itself mimicked practices developed for mechanical list technologies such as punch cards and metal address plates). Although marketers have long talked about being customer- rather than campaign-centric, it’s not until the current crop of Journey Orchestration Engines (JOEs) that we see a thorough replacement of campaign-based methods.

It’s not surprising the transition took so long. As I described in my earlier post on the adoption of electric power by factories (more arcana!), the shift to new technology happens in stages as individual components of a process are changed, which then opens a path to changing other components, until finally all the old components are gone and new components are deployed in a configuration optimized for the new capabilities. In the transition from campaign management to journey orchestration, marketers had to develop tools to track individuals over time, to personalize messages to those individuals, identify and optimize individual journeys, act on complete data in real time, and to incorporate masses of unstructured data. Each of those transitions involved a technology change: from lists to databases, from static messages to dynamic content, from segment-level descriptive analytics to individual-level predictions, from batch updates to real time processes, and from relational databases to “big data” stores.

It’s really difficult to retrofit old systems with new technologies, which is one reason vendors like Oracle and IBM keep buying new companies to supplement current products. It’s also why the newest systems tend to be the most advanced.** Thus, the Journey Orchestration Engines I’ve written about previously (Thunderhead ONE , Pointillist, Usermind, Hive9 ) all use NoSQL data stores, build detailed individual-level customer histories, and track individuals as they move from state to state within a journey flow.

During my Tel Aviv visit last week, I also checked in with Pontis (just purchased by Amdocs), who showed me their own new tool which does an exceptionally fine job at ingesting all kinds of data, building a unified customer history, and coordinating treatments across all channels, all in real time. In true JOE fashion, the system selects the best treatment in each situation rather than pushing customers down predefined campaign sequences. Pontis also promised their February release would use machine learning to pick optimal messages and channels during each treatment. Separately, Optimove itself announced its own “Optibot” automation scheme, which also finds the best treatments for individuals as they move from state to state. So you can add Optimove to your cup of JOEs (sorry) as well.

I’m reluctant to proclaim JOEs as the final stage in customer management evolution only because it’s too soon to know if more change is on the way. As Pontis and Optimove both illustrate, the next step may be using automation to select customer treatments and ultimately to generate the framework that organizes those treatments. When that happens, we will have erased the last vestiges of the list- and campaign-based approaches that date back to the mail order pioneers of the 19th century and to the ancient Sumerians (first customer list, c. 3,000 BCE) before that.




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*Dates represent commercialization, not the first appearance of the underlying technology. For example, we all know that Gutenberg’s press with moveable type was introduced around 1450, but newspapers with advertising didn’t show up until after 1600.

** This isn’t quite as tautological as it sounds. In some industries, deep-pocketed old vendors with big research budgets are the technical leaders. 

How Quickly Is the MarTech Industry Growing?

Everyone in marketing knows there’s a lot of new marketing technology, but how quickly is martech really growing? Many people cite changes in Scott Brinker’s iconic marketing technology landscape, which has roughly doubled in size every year since Brinker first published it in 2011. Brinker himself is always careful to stress that his listings are not comprehensive, and anyone familiar with the industry will quickly realize much of the growth in his vendor count reflects greater thoroughness and broader scope rather than appearance of new vendors. But no matter how many caveats are made, the ubiquity of Brinker’s chart leaves a strong impression of tremendously quick expansion.

Fortunately, other data is available. Venture Scanner recently published the the number of companies founded by year for 1,295 martech firms in its database. This shows growth of around 12% per year from 2000 through 2012. (Figures for 2013 and later are almost surely understated because many firms started during those years have not yet been included in the data.)

A similar analysis from CabinetM, which has a database of 3,708 companies, showed a slightly higher rate of 14.5% per year for the same period.* Both sets of data show a noticeable acceleration after 2006: to about 16.5% for Venture Scanner and just under 16% for CabinetM.
 

These figures are still far from perfect. Many firms are obviously missing from the Venture Scanner data. CabinetM has apparently missed many as well: Brinker reported that comparison between CabinetM’s list and his own found that each had about 1,900 vendors the other did not. All lists will miss companies that are no longer in business, so there were probably more start-ups in each year than shown.

But even allowing for such issues, it’s probably reasonable to say that the number of vendors in the industry has been growing at something from 15% to 20% per year. That’s a healthy rate but nothing close to an annual doubling.

Note also that we’re talking here about the number of companies, not revenue.  I suspect revenue is growing more quickly than the number of vendors but can't give a meaningful estimate of how much.

Are particular segments within the industry growing faster than others? CabinetM provided me with a breakdown of starts by year by category.** To my surprise, growth has been spread fairly evenly across the different types of systems. Adtech grew a bit faster than the other categories in 2006 to 2010 and content marketing has grown faster than the average since 2006. But the share of marketing automation and operations have been surprisingly consistent throughout the period covered. So while the number of marketing automation vendors has indeed grown quickly, other categories seem to growing at about the same pace.

So what, if anything, does this tell us about the future?  It's certainly possible some of the drop-off in new vendors since 2013 reflects an actual slowdown in addition to the lag time before new vendors appear in databases. Funding data from Venture Scanner suggests that 2015 may have been a peak year for investments, although 2016 data is obviously incomplete.

 

Another set of funding data, from PitchBook, suggests 2014 was a peak but shows much less year-on-year variation than Venture Scanner. The inconsistency between the two sets of data makes it hard to accept either source as definitive.

So, what does this all mean?  First of all, that people should calm down a bit: the number of martech vendors hasn't been doubling every year.  Second, that industry growth may indeed be slowing, although it's too soon to say for sure.  Third, whatever the exact figures, there are plenty of martech vendors out there and they're not going away any time soon.  So marketers need to focus on a systematic approach to martech acquisition, balancing new opportunities against training and integration costs.
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* Here's the actual CabinetM data.  I'm mostly showing this to clarify that my "growth rate" is comparing the number of new companies vs. total industry size, and not the number of new companies this year vs new companies last year.

**CabinetM actually tracks 30 categories.  I combined them into the seven groups used here.

Guide to ABM Vendors: What's in a Complete ABM Stack?

Yesterday’s post announced our new Guide to ABM Vendors, which helps marketers make sense of the confusing variety of ABM-related systems. The post describes our framework of four ABM process steps, six system functions that support those steps, and six sub-functions that are hardest to find. These are summarized in the table below:

ABM Process	System Function	Sub-Function
Identify Target Accounts	Assemble Data	External Data
	Select Targets	Target Scoring
Plan Interactions	Assemble Messages	Customized Messages
	Select Messages	State-Based Flows
Execute Interactions	Deliver Messages	Execution
Analyze Results	Reporting	Result Analysis

You might assume that a complete ABM stack would include systems that do all of the functions and sub-functions, but that’s only half right. All the functions are indeed required, but the sub-functions are optional. Customized Messages and Execution can be found in non-ABM systems such as Web site personalization or marketing automation.* The rest, including External Data, Target Scoring, State-Based Flows, and Result Analysis, make ABM better or easier but you can do ABM without them.

I know you really want to learn which vendors do which functions, but, as I also explained yesterday, that's not a simple question to answer.  Instead, let’s start with an overview of how the vendors as a group matched up against the sub-functions.

The first obvious question is how many vendors deliver each sub-function. The table below gives the answers, and they’re pretty much what you’d expect: lots of vendors use external data and do execution, a fair number who do target scoring and result analysis, and relatively few do customized messages and state-based flows. Bear in mind that customized messages and state-based flows (which are roughly equivalent to multi-step campaigns) are widely available in general purpose systems not covered in the ABM Guide, so the low counts here don't mean they are really hard to find.

Sub-Function	Number of Vendors
External Data	28
Target Scoring	15
Customized Messages	6
State-Based Flows	10
Execution	19
Result Analysis	16

 
The preceding table implies that most vendors deliver multiple sub-functions (otherwise, the numbers would total to 40). In fact, just eleven vendors qualify for a single category. On the other hand, none deliver all six sub-functions and only seven deliver four or five. The table below shows the details. What it means in plain English is that no vendor provides a complete ABM solution and that most are quite specialized. In still plainer language, it means you'll need more than one ABM vendor.

Number of Sub-Functions	Number of Vendors
1	11
2	14
3	8
4	4
5	3
6	0

This is even truer when you recognize that the sub-functions themselves are very broad categories, so vendors who qualify for the same sub-function may be delivering significantly different products. You may not need to catch ‘em all, but you certainly need to work with several.**


Right now, the more analytically inclined among you are probably wondering if there’s any pattern to which sub-functions are provided by the same vendor. There sure is! The table below shows values for individual vendors (names removed), sorted by sub-function.

	Identify Target Accounts		Plan Interactions		Execute Interactions	Analyze Results
	Assemble Data	Select Targets	Assemble Messages	Select Messages	Deliver Messages	Reporting
	External  Data	Target Scoring	Customized Messages	State-Based Flows	Execution	Result Analysis
data only	1
	1
	1
	1
	1
	1
	1
	1
data and delivery	1		1		1	1
	1				1	1
	1				1	1
	1				1
	1				1
data and target scoring	1	1
	1	1
	1	1
	1	1
	1	1
	1	1
	1	1
	1	1
	1	1
	1	1				1
data, scoring, and delivery	1	1	1		1	1
	1	1		1	1	1
	1	1			1	1
	1	1			1
custom messages		1	1	1	1	1
			1	1	1	1
			1	1	1	1
			1		1
journey management				1	1	1
				1	1	1
				1	1	1
				1	1
				1	1
	1			1		1
					1
measurement						1
						1

As the color coding shows, I see the vendors falling into seven clusters having similar combinations of sub-functions. These are

• data-only vendors, who accumulate account data to resell to others. They often specialize in particular data types such as intent or technology use. The ABM Guide refers to these as “commercial” data vendors.

• data and delivery vendors, who accumulate account data and use it to deliver targeted messages. They often specialize in channels such as display ads.

• data and target scoring vendors, who accumulate account data and use it to build models that select target accounts. Many of these vendors combine data they gather themselves (which they often consider a competitive advantage) with data they purchase from commercial vendors.

• data, scoring, and delivery vendors, who accumulate data, build targeting models, and deliver messages based on those models. 

• custom message vendors, who create and deliver messages that are dynamically tailored to specific accounts or segments. Most of these vendors also qualify for the State-Based Flow sub-function because they automatically move accounts into new segments over time. But their focus isn’t on comprehensive journey management and they don’t orchestrate messages delivered by other systems. So they're not quite as powerful as all those checkmarks might suggest.

• journey management vendors, who don’t create custom messages but do move accounts into new segments based on behaviors and external data. This cluster includes several single-channel vendors, who are really quite different from multi-channel vendors but get checks in the same boxes. Even the true multi-channel journey managers send messages in just a few channels and rely on external delivery systems for the rest.

• measurement vendors, who specialize assembling account-level data and reporting on it.

You’ll notice the clustering isn’t perfect – there are a few cells with a 1 outside the highlighted areas and a few blank cells within the highlights. There’s nothing wrong with that. In fact, the exceptions are quite interesting although I won’t be discussing them here. My point at the moment is that we’re starting to make sense of the vendor chaos.

The following table collapses each of the seven segments to a single row, with the numbers showing how many vendors in each segment qualified for each sub-function. This shows even more clearly how the pieces of an ABM stack should fit together.

	Identify Target Accounts		Plan Interactions		Execute Interactions	Analyze Results
	Assemble Data	Select Targets	Assemble Messages	Select Messages	Deliver Messages	Reporting
	External  Data	Target Scoring	Customized Messages	State-Based Flows	Execution	Result Analysis
data only	8
data and delivery	5		1		5	3
data and target scoring	10	10
data, scoring, and delivery	4	4	1	1	4	3
custom messages		1	4	3	4	3
journey management	1			6	6	4
measurement						2

To put this table in a more narrative format:

• ABM starts with data, since the goal is to identify target accounts in advance instead of marketing to anyone you happen to reach. Your internal data won’t be adequate so external data is essential. So data from one or more vendor will be the foundation of your ABM effort.

• You can jump directly from data to messaging if you want to do all the intermediate steps by yourself (target selection, message creation, and journey management). Clearly this is more work but it does lead to simplest ABM stack possible. The data-and-delivery vendors tend to be channel-specific, so you might need more than one and you may struggle to coordinate their campaigns.

• If you do want help, you can start by hiring a scoring vendor to identify your best target accounts. They’ll usually bring their own data and combine it with your own information.

• Most scoring vendors simply hand over a scored target list plus maybe some data.  But others add delivery services, providing the most comprehensive single vendor solution available. As with the data-and-delivery vendors, most data-scoring-and-delivery vendors serve a subset of channels, so you’ll need to supplement them with other execution systems.

• Somebody has to create your messages. If you want advanced customization, you’ll probably need a content building specialized tool. That product won’t necessarily be limited to ABM applications, though, so you’ll have options beyond the vendors listed in the ABM Guide. Most systems that create customized messages can also deliver them, although again you'll need several systems to cover all your channels.

• Messages need to change as the account moves through its journey. Some of the messaging and delivery products do this within specific channels, which still leaves you to manually coordinate messages across channels. Multi-channel journey managers built especially for ABM are just starting to appear; you can buy one today if you’re willing to be an early adopter. More will show up as vendors from other clusters extend their products and as generic marketing automation systems and journey orchestration engines add features to meet ABM requirements.

• Most systems that deliver messages also report on the results. Again, the trick is finding a solution that combines data from all channels. If your delivery systems are fragmented, you'll probably need a separate cross-channel reporting system to build a full picture of your ABM program.

So…maybe this clears things up and maybe it leaves you more confused than ever. When you cut through all the pesky details, it comes down to two main points:

• at a minimum, you need data to pick target accounts and delivery systems to send them messages. Everything in between is optional but helps to make your work easier and more effective.

• there are a lot of single-channel ABM solutions. There’s nothing wrong with that but remember that you’ll ultimately want to coordinate across channels.  So don’t think one single channel solution can fill all your needs by itself.

Finally, as a reward for reading this far and because you’ve now absorbed enough nuance to understand how tentatively such lists must be read***, here is a table showing which vendors I’ve placed in each cluster:

Cluster	Vendors
data only	Bombora, Data.com, DiscoverOrg, HG Data, InsideView, Orb Intelligence, ReachForce, ZoomInfo
data and delivery	Azalead, LinkedIn, Madison Logic, True Influence, Vendemore
data and target scoring	Avention, Datanyze, Dun & Bradstreet, GrowthIntel, Infer. Lattice Engines, Leadspace, Mintigo, Radius, Everstring
data, scoring, and delivery	Demandbase, Mariana, MRP, The Big Willow
custom messages	Evergage, Kwanzoo, SnapApp, Triblio
journey management	Engagio, GetSmartContent, LookBookHQ, Terminus, Uberflip, YesPath, ZenIQ
measurement	Bizible, LeanData

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*You could argue that those should be considered ABM systems, especially when they’re used for ABM programs. I might even agree. But I had to draw the line somewhere when deciding which systems to include in the ABM Guide, and there are too many general purpose products to include. That said, marketers looking for ABM solutions should certainly assess whether non-specialist products can meet some portion of their needs. The assessment framework in the ABM Guide applies quite nicely.

**Which in turn implies a Pokémon Go-style vendor collecting game. The really good conference organizers are probably working on this already. I’m looking at you, Scott and Nikki.

***and therefore why you need to buy the Guide to ABM Vendors to make a wise choice.