Monday, September 19, 2016

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 – aracana 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 aracana!), 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. 

Thursday, September 15, 2016

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.







Wednesday, September 07, 2016

Will Marketing Technologists Kill Martech?

I’ll be giving a speech next week on the evolution of marketing technology, which doesn't follow the path you might think. The new channels that appear on a typical “history of marketing timeline”, such as radio in the 1920’s and TV in the 1950’s, didn’t really trigger any particular changes in the technology used by marketers: planning was still done on paper spreadsheets and copy was typed manually up to the 1970’s. Similarly, marketers up that time worked with the same data – audience counts and customer lists – they had since Ben Franklin and before.

It was only in the 1960’s, when mailing lists were computerized, that new technologies begin to make more data available and marketers get new tools to work with it. Those evolved slowly – personalized printing and modern campaign managers appeared in the 1980’s. The big changes started in the 1990’s when email and Web marketing provided a flood of data about customer behaviors and vendors responded with a flood of new systems to work with it. But it wasn't until the late 2000’s that the number of vendors truly exploded.

I can’t prove this, but I think what triggered martech hypergrowth was Software-as-a-Service (SaaS). This made it easy for marketers to purchase systems without involving the corporate IT department, allowing users to buy tools that solved specific problems whether or not the tools fit into the corporate grand scheme of things. Major SaaS vendors, most notably Salesforce.com, made their systems into platforms that provided a foundation for other systems. This freed developers to create specialized features without building a complete infrastructure. Building apps on platforms also sharply reduced integration costs, which had placed a severe limit on how many systems any marketing department could afford. Easier development, easier deployment, and easier acquisition created perfect environment for martech proliferation.

But every action has a reaction. The growth of martech led to the hiring of marketing technologists, as marketing departments realized they needed someone to manage their burgeoning technology investments. That might seem like a good thing for the martech industry, but it introduced a layer of supervision that restrained the free-wheeling purchases that marketers had been  making on their own. After all, the job of a martech manager is to rationalize and coordinate martech investments, which ultimately means saying “no.”

The quest for rationalization leads to long-term planning, vision development, architecture design, corporate standards, and project prioritization: all the excellent practices that made corporate IT departments so unresponsive to marketers in the first place. The scrappy rebels in martech departments hear the call of order-obsessed dark side and find it increasingly hard to resist.

And it only gets worse (from the martech vendor point of view). As marketing technologists discover just how many systems are already in place, they inevitably ask how they can make things simpler. The equally inevitable answer is to buy fewer systems by finding systems that do more things. This leads to integrated suites – marketing clouds, anyone? – that may not have the best features for any particular function but offer a broad range of capabilities. When the purchase is made by individual marketers focused on their own needs, the best features will win and small, innovative martech vendors can flourish. But when purchases are managed by the central martech department, integration and breadth will weigh more heavily in the decision.  This gives bigger, most established firms the advantage.


In short, martech today is at a crossroads. Martech managers can follow the natural logic of their positions, which leads to greater centralization, large multi-function systems, and increasingly frustrated marketers. Or they can retain their agility and support new, innovative martech vendors, recognizing that near-term efficiency will suffer. Put so starkly, it’s obvious that agility is the better choice, and there is plenty of discussion in the industry of how to maintain it. But the dark side is powerful, relentless, and seductively rational. Martech managers – and the marketers they ultimately serve – must tread carefully to stay on the right path.

Thursday, August 25, 2016

ABM Vendor Guide: Differentiators for Result Analysis

...and we wrap up our review of sub-functions from the Raab Guide to ABM Vendors with a look at Result Analysis.


ABM Process
System Function
Sub-Function
Number of Vendors
Identify Target Accounts
Assemble Data
External Data
28
Select Targets
Target Scoring
15
Plan Interactions
Assemble Messages
Customized Messages
6
Select Messages
State-Based Flows
10
Execute Interactions
Deliver Messages
Execution
19
Analyze Results
Reporting
Result Analysis
16

As the Guide points out, this category focuses on measurements unique to account-based programs:

Nearly every system will have some form of result reporting. ABM specialists provide account-based result metrics such as percentage of target accounts reached, amount of time target accounts are spending with company messages, and distribution of messages by department within target accounts.

Not surprisingly, most of vendors who do ABM Result Analysis also do some sort of Execution (12 out of 16, to be exact). Another two (Everstring and ZenIQ) didn't fall into the Execution group but came close.  Of the final two, one supports measurement with advanced lead-to-account mapping (LeanData) and one attribution specialist (Bizible). It's important to recognize that many of the Execution vendors will report only results of their own messages.  This is certainly helpful but you'll want to see reports that combine data from all messages to get a meaningful picture of your ABM program results. 

Differenatiators for this group include:

  • lead-to-account mapping to unify data
  • corporate hierarchy mapping (headquarters/branch, parent/subsidiary, etc.) to unify data
  • marketing campaign to opportunity mapping to support attribution
  • combine data from marketing automation, Web analytics, and CRM
  • track offline channels such as conferences, direct mail, outbound hone calls
  • capture detailed interaction history for each Web visit (mouse clicks, scrolling, active time spent, etc.)
  • capture mobile app behaviors with SDK as well as Web site behaviors with Javascript tag
  • use device ID to link display ads, Web site visits, and form fills to revenue, even when visitors don’t click on ad or Web page
  • report within Salesforce CRM on combined information about leads, contacts, accounts, opportunities, campaigns, and owners
  • apply multiple attribution methods including first touch, last touch, fractional, etc.
  • show account-level descriptive metrics including coverage, contact frequency, visitors, contacts by job title
  • show account-level result metrics including reach, engagement, influence, velocity
  • show reach, engagement, influence, velocity by campaign, content, persona, segment, etc.
  • identify gaps in coverage, reach, or engagement by account and recommend corrective actions
One final reminder: the just-published Guide to ABM Vendors helps marketers understand what tools they need to complete their ABM stack.  It provides detailed profiles of 40 ABM vendors, with contents including:
  • introduction to Account Based Marketing
  • description of ABM functions
  • key subfunctions that differentiate ABM vendors
  • vendor summary chart that shows who does what
  • explanations of information provided in the report
  • vendor profiles including a summary description, list of key features, and detailed information covering  37 categories including data sources, data storage, data outputs, target selection, planning, execution, analytics, operations, pricing, and vendor background.
For more information or to order, click here.

ABM Vendor Guide: Special Features to Deliver ABM Messages

Our tour of sub-functions from the Raab Guide to ABM Vendors has now reached Execution.


ABM Process
System Function
Sub-Function
Number of Vendors
Identify Target Accounts
Assemble Data
External Data
28
Select Targets
Target Scoring
15
Plan Interactions
Assemble Messages
Customized Messages
6
Select Messages
State-Based Flows
10
Execute Interactions
Deliver Messages
Execution
19
Analyze Results
Reporting
Result Analysis
16



This has a very broad definition:

These are systems that actually deliver messages in channels such as email, display advertising, social media advertising, the company Web site, or CRM. As used in this Guide, execution may include direct integration with a delivery system, such as adding a name to a marketing automation campaign, sending a list of cookies and instructions to an ad buying system, or pushing a personalized message to a company Web site.

That definition could apply to almost any system that delivers marketing messages, but the ABM Guide includes only ABM specialists. This narrows the field drastically. Most Execution firms in the Guide specialize in a particular channel, such as display advertising, social media advertising, Web content, or email. Many can also push messages to other channels via marketing automation or CRM integration.

Differentiators include:

  • channels supported (display advertising, social advertising, CRM, marketing automation, email, direct mail, telemarketing, text, mobile apps, content syndication, etc.)
  • channels supported directly vs. via integration with external systems
  • targeting at account and/or individual levels
  • targeting based on external data assembled by the vendor
  • maintain central content library
  • present externally-hosted content without losing control over the visitor experience
  • integrated a/b and multivariate testing
  • vendor provides content creation and program management services
  • capture detailed content engagement data across multiple content types and deliver to external systems (e.g. marketing automation or CRM)
  • capture detailed behavior data and deliver to external systems
  • analyze content consumption to identify visitors with specific interests or surge in consumption volume and pass to external systems
  • send alerts to CRM regarding behavior by target accounts
  • assign tasks in CRM to sales reps
  • salespeople can create custom content streams for specific accounts
  • support for channel partner marketing (lead distribution, gamification, marketing development fund management, pipeline optimization, etc.)
  • user can specify which ads are seen by each account 
  • set up ad campaigns within the system and transfer to external vendors to execute
  • buy and serve ads using the vendor's own technology (in particular, platforms that can buy based on IP address or device IDs rather than cookies)
  • pricing for ad purchases (some vendors pass through actual costs; some charge fixed CPMs or monthly flat fees and may profit from effective buying)
  • tele-verify, gather additional information, and set appointments with leads identified by the vendor
  • fees based on performance vs. program costs
  • self-service features vs. vendor managed services
  • program reporting and analytics
As with the Customized Messages and State-Based Flow subfunctions, Execution functions can also be delivered many non-ABM specialists.  If you want to go this route, be sure to check how well the system can integrate with your messaging and flow management systems and be sure they can work at the account level.  Those are the places where ABM specialists are most likely to shine.

ABM Vendor Guide: State-Based Flows to Orchestrate Account Treatments

Next up in this series on ABM sub-functions described in the Raab Guide to ABM Vendors: State-Based Flows.

ABM Process
System Function
Sub-Function
Number of Vendors
Identify Target Accounts
Assemble Data
External Data
28
Select Targets
Target Scoring
15
Plan Interactions
Assemble Messages
Customized Messages
6
Select Messages
State-Based Flows
10
Execute Interactions
Deliver Messages
Execution
19
Analyze Results
Reporting
Result Analysis
16

Your first reaction that may well be, What the heck is a State-Based Flow?  That's no accident.  I chose an unfamiliar term because I didn’t want people to assume it meant something it doesn’t. The Guide states:

Vendors in this category can automatically send different messages to the same contact in response to behaviors or data changes. Messages often relate to buying stages but may also reflect interests or job function. Messages may also be tied to a specific situation such as a flurry of Web site visits or a lack of contacts at a target account. Flows may also trigger actions other than messages, such as alerting a sales person. Actions are generally completed through a separate execution system. Movement may mean reaching different steps in a single campaign or entering a different campaign. Either approach can be effective. What really matters is that movement occurs automatically and that messages change as a result.

In other words, the essence of state-based flows is the system defines a set of conditions (i.e. states) that accounts or contacts can be in, tracks them as they move from one condition to the next, and sends different messages for each condition. This is roughly similar to campaign management except that campaign entry rules are usually defined independently, so customers don’t automatically flow from campaign to campaign in the way that they flow from state to state. (Another way to look at it: customers can be in several campaigns at once but only in one customer state at a time.) Customers in multi-step campaigns do move from one stage to the next, but they usually progress in only one direction, whereas people can move in and out of the same state multiple times. Journey orchestration engines manage a type of state-based flow, but they build the flow on a customer journey framework, which is an additional condition I’m not imposing here.

This may be more hair-splitting than necessary. My goal in defining this sub-function was mostly to distinguish systems where users manually assign people to messages (meaning that the messages won’t change unless the user reassigns them) from systems that automatically adjust the messages based on behaviors or new data. This adjustment is the very heart of managing relationships, or what I usually call the decision layer in my data / decision / delivery model.

Speaking of hair-splitting, you may notice that I’m being a little inconsistent in referring to message recipients as accounts, customers, contacts, individuals, or people. A true ABM system works at the account level but messages may be delivered to accounts (IP-based ad targeting), known individuals (email), or anonymous individuals (cookie- or device-based targeting, although sometimes these are associated with known individuals). Because of this, different systems work at different levels. The ideal is for message selection to consider both the state of the account and the state of the individual within the account.

As with the Customized Message category I described yesterday, vendors who qualify for State-Based Flows fall into two broad groups: those whose primary function is cross-channel message orchestration (Engagio, MRP, YesPath, ZenIQ, Mintigo*) and those that do flow management to support delivery of messages in a single channel (Evergage, GetSmartContent, Kwanzoo, Terminus, Triblio). Marketers who are looking for a primary tool to manage account relationships will be most interested in the first group.

Differentiators to consider with this group include:

  • orchestrates activities at account level (doesn't treat each lead independently)
  • assigns Web site visitors to segments during each visit using current data
  • automated models to classify content, define segments, and select best content per segment
  • automated models to assign contacts to personas and select best content per persona
  • automated models to recommend best actions per account
  • present sets of content in sequence or all at once
  • continue same experience over time across different channels
  • prioritization to ensure highest value message is always presented
  • accounts can be in multiple programs simultaneously
  • contacts can be limited to one program at a time
  • limit number of messages sent to each contact within a specified time period
As you no doubt realize, this is the area that most directly overlaps with marketing automation and journey orchestration systems that are not ABM specialists.  They key feature to watch out for when evaluating those systems for ABM programs is the abillity to work at the account level.  That was not part of many older marketing automation systems, although several vendors have now retrofitted their products to support to some degree.
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* via its Predictive Campaign integration with Eloqua