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Preview: Customer Experience Matrix

Customer Experience Matrix

This is the blog of David M. Raab, marketing technology consultant and analyst. Mr. Raab is Principal at Raab Associates Inc. The blog is named for the Customer Experience Matrix, a tool to visualize marketing and operational interactions between a comp

Updated: 2016-10-26T12:23:54.489-04:00 Offers A Customer Data Platform for B2B Marketers


The need for a Customer Data Platform – a marketer-controlled, unified, persistent, accessible customer database – applies equally to business and consumer marketing. Indeed, many of the firms I originally identified as CDPs were lead scoring and customer success management vendors who serve primarily B2B clients. But as the category has evolved, I’ve narrowed my filter to only consider CDPs as companies that focus primarily on building the unified data.  This excludes the predictive modeling vendors and customer success managers, as well as the big marketing clouds that list a CDP as one of many components. Once you apply that filter, nearly all the remaining firms sell largely to B2C enterprise is an exception. Its clients are mostly small, B2B companies – exactly the firms that were first to adopt software-as-a-service (SaaS) technologies including marketing automation and CRM. This is no accident: SaaS solves one problem by making it easy to acquire new systems, but that creates another problem because those systems are often isolated from each other. Hull addresses that problem by unifying their data, or, more precisely, by synchronizing it.How it works is this: Hull has connectors for major customer-facing SaaS systems, such as Salesforce, Optimizely, HubSpot, Mailchimp, Facebook custom audiences, Slack, and Zendesk. Users connect with those systems and specify data elements or lists to synchronize. When data changes in one of customer-facing products, the change is sent to Hull which in turn sends it to other products that are tracking that data. But, unlike data exchanges such as Zapier or Segment, Hull also keeps its own copy of the data. That’s the “persistent” bit of the CDP definition. It gives Hull a place to store data from enhancement vendors including Datanyze and Clearbit, from external processes called through Javascript, and from user-defined custom variables and summary properties, such as days since last visit. Those can be used along with other data to create triggers and define segments within Hull.  The segments can then be sent to other systems and updated as they change.In other words, even though the external systems are not directly reading the data stored within Hull, they can still all work with consistent versions of the data.* Think of it as the martech equivalent of Einstein’s’ “spooky action at a distance”  if that clarifies things for you.To extend its reach even further, can also integrate with Zapier and Segment allowing it to exchange data with the hundreds of systems those products support. Three important things have to happen inside of to provide a unified customer view. First, it has to map data from different sources to a common data model – so that things like customer name or product ID are recognized as referring to the same entities even if they come from different places. simplifies this as much as possible by limiting its internal data model to two entities, customers and events.  Input data, no matter how complicated, is converted to these entities by splitting each record into components that are tagged with their original meaning and relationships. The splitting and tagging are automatic, which is very important for making the system easy to deploy and maintain.  Users still need to manually tell the system which elements from different systems should map to the same element in the shared data. The second important thing is translating stored data into the structure needed by the receiving system. This is the reverse of the data loading process, since complex records must be assembled from the simplified internal model. What’s tricky is that the output format is almost always different from the input format, so the pieces have to be reassembled in a different format.  While we’re making questionably helpful analogies, think of this as the Jive Lady translating for the sick passenger in the movie Airplane. The third key thing is that data relating to the[...]

Datorama Applies Machine Intelligence to Speed Marketing Analytics


As I mentioned a couple of posts back, I’ve been surveying the borders of Customer Data Platform-land recently, trying to figure out which vendors fit within the category and which do not. Naturally, there are cases where the answer isn’t clear. Datorama is one of them.At first glance, you’d think Datorama is definitely not a CDP: it positions itself as a “marketing analytics platform” and makes clear that its primary clients are agencies, publishers, and corporate marketers who want to measure advertising performance. But the company also calls itself a “marketing integration engine” that works with “all of your data”, which certainly goes beyond just advertising. Dig a bit deeper and the confusion just grows: the company works mostly with aggregated performance data, but also works with some individual-level data.  It doesn’t currently do identity resolution to build unified customer profiles, but is moving in that direction. And it integrates with advertising and Web analytics data on one hand and social listening, marketing automation, and CRM on the other. So while Datorama wasn’t built to be a CDP – because unified customer profiles are the core CDP feature – it may be evolving towards one.This isn't to say that Datorama lacks focus. The system was introduced in 2012 and now has over 2,000 clients, including brands, agencies, and publishers. It grew by solving a very specific problem: the challenges that advertisers and publishers face in combining information about ad placements and results. Its solution was to automate every step of the marketing measurement process as much as it could, using machine intelligence to identify information within new data sources, map those to a standard data model, present the results in dashboards, and uncover opportunities for improvement. In other words, Datorama gives marketers one system for everything from data ingestion to consolidation to delivery to analytics.  This lets them manage a process that would otherwise require many different products and lots of technical support. That approach – putting marketers in control by giving them a system pre-tailored to their needs – is very much the CDP strategy.Paradoxically, the main result of Datorama’s specialization is flexibility. The system’s developers set of goal of handling any data source, which led to a system that can ingest nearly any database type, API feed or file format, including JSON and XML; automatically identify the contents of each field; and map the fields to the standard data model. Datorama keeps track of what it learns about common source systems, like Facebook, Adobe Analytics, or AppNexus, making it better at mapping those sources for future implementations. It can also clean, transform, classify, and reformat the inputs to make them more usable, applying advanced features like rules, formulas, and sentiment analysis. At the other end of the process, machine learning builds predictive models to do things like estimate lifetime value and forecast campaign results. The results can be displayed in Datorama’s own interface, read by business intelligence products like Tableau, or exported to other systems like marketing automation. Datorama’s extensive use of machine learning lets it speed up the marketing analytics process while reducing the cost. But this is still not a push-button solution. The vendor says a typical proof of concept usually takes about one month, and it takes another one to two months more to convert the proof of concept into a production deployment. That’s faster than your father’s data warehouse but not like adding an app to your iPhone. Pricing is also non-trivial: a small company will pay in the five figures for a year’s service and a large company's bill could reach into seven figures. Fees are based on data volume and number of users. Datorama can also provide services to help users get set up or to run the system for them if they prefer. [...]

News from Krux, Demandbase, Radius: Customer Data Takes Center Stage


If Dreamforce seems a little less crowded than you expected this week, perhaps it's because I didn’t attend. But I’m still tracking the news from Salesforce and other vendors from my cave in Philadelphia. Three announcements caught my eye, all highlighting the increasing attention being paid to customer data.Salesforce itself had the biggest news yesterday, with its agreement to purchase Krux, a data management platform that has expanded well beyond the core DMP function of assembling audiences from cookie pools. Krux now has an “intelligent marketing hub” that can also load a company’s own data from CRM, Websites, mobile apps, and offline sources, and unify customer data to build complete cross-channel profiles. Krux also allows third party data owners to sell their data through the Krux platform and offers self-service data science for exploration and predictive models. The purchase makes great strategic sense for Salesforce, providing it with a DMP to match existing components in the Oracle and Adobe marketing clouds. But beyond the standard DMP function of generating advertising audiences, Krux gives Salesforce a solid customer data foundation to support all kinds of marketing management.  In particular, it goes beyond the functions in Salesforce ExactTarget, which was previously the designated core marketing database for Salesforce Marketing Cloud. To be clear, there’s no campaign management or journey orchestration within Krux; those functions would be performed by other systems that simply draw on Krux data. Which is exactly as it should be, if marketers are to maintain maximum flexibility in their tools.Demandbase had its own announcement yesterday: something it calls “DemandGraph,” which is basically a combination of Demandbase’s existing business database with data gathering and analytical functions the Spiderbook system that Demandbase bought in May 2016. DemandGraph isn’t exactly a product but rather a resource that Demandbase will use to power other products. It lets Demandbase more easily build detailed profiles of people and companies, including history, interests, and relationships. It can then use the information to predict future purchases and guide marketing and sales messages. There’s also a liberal sprinkling of artificial intelligence throughout DemandGraph, used mostly in Spiderbook’s processing of unstructured Web data but also in some of the predictive functions. If I’m sounding vague here it’s because, frankly, so was Demandbase. But it’s still clear that DemandGraph represents a major improvement in the power and scope of data available to business marketers.Predictive marketing vendor Radius made its announcement last week of the Radius Customer Exchange.  This uses the Radius Business Graph database (notice a naming trend here?) to help clients identify shared customers without exposing their entire files to each other. Like Spiderbook, Radius gathers much of its data by scanning the public Web; however, Radius Business Graph also incorporates data provided Radius clients. The client data provides continuous, additional inputs that Radius says makes its data and matching much more accurate than conventional business data sources. Similarly, while there’s nothing new about using third parties to find shared customers, the Radius Customer Exchange enables sharing in near real time, gives precise revocable control over what is shared, and incorporates other information such as marketing touches and predictive models. These are subtle but significant improvements that make data-driven marketing more effective than ever. The announcement also supports a slight shift in Radius’ position from “predictive modeling” (a category that has lost some of its luster in the past year) to “business data provider”, a category that seems especially enticing after Microsoft paid $26.2 billion for LinkedIn. Do these announcements reflect a change in industry focus from marketing applicati[...]

Reltio Makes Enterprise Data Usable, and Then Uses It


I’ve spent a lot of time recently talking to Customer Data Platform vendors, or companies that looked like they might be. One that sits right on the border is Reltio, which fits the CDP criteria* but goes beyond customer data to all types of enterprise information. That puts it more in the realm of Master Data Management, except that MDM is highly technical while Reltio is designed to be used by marketers and other business people. You might call it “self-service MDM” but that’s an oxymoron right up there with “do-it-yourself brain surgery”.

Or not. Reltio avoids the traditional complexity of MDM in part by using the Cassandra data store, which is highly scalable and can more easily add new data types and attributes than standard relational databases. Reltio works with a simple data model – or graph schema if you prefer – that captures relationships among basic objects including people, organizations, products, and places. It can work with data from multiple sources, relying on partner vendors such as SnapLogic and MuleSoft for data acquisition and Tamr, Alteryx, and Trifacta for data preparation. It has its own matching algorithms to associate related data from different sources. As for the do-it-yourself bit: well, there’s certainly some technical expertise needed to set things up, but Reltio's services team generally does the hard parts for its clients. The point is that Reltio reduces the work involved – while adding a new source to a conventional data warehouse can easily take weeks or months, Reltio says it can add a new source to an existing installation in one day.

The result is a customer profile that contains pretty much any data the company can acquire. This is where the real fun begins, because that profile is now available for analysis and applications. These can also be done in Reltio itself, using built-in machine learning and data presentation tools to provide deep views into customers and accounts, including recommendations for products and messages. A simple app might take one or two months to build; a complicated app might take three or four months. The data is also available to external systems via real-time API calls.

Reltio is a cloud service, meaning the system doesn’t run on the client’s own computers. Pricing depends on the number of users and profiles managed but not the number of sources or data volume. The company was founded in 2011 and released its product several years later. Its clients are primarily large enterprises in retail, media, and life sciences.

* marketer-controlled; multi-source unified persistent data; accessible to external systems(image)

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 [...]

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. [...]

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, 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. O[...]

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 datacorporate hierarchy mapping (headquarters/branch, parent/subsidiary, etc.) to unify datamarketing 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 callscapture 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 taguse device ID to link display ads, Web site visits, and form fills to revenue, even when visitors don’t click on ad or Web pagereport within Salesforce CRM on combined information about leads, contacts, accounts, opportunities, campaigns, and ownersapply multiple attribution methods including first touch, last touch, fractional, account-level descriptive metrics including coverage, contact frequency, visitors, contacts by job titleshow account-level result metrics including reach, engagement, influence, velocityshow reach, engagement, influence, velocity by campaign, content, persona, segment, etc.identify gaps in coverage, reach, or engagement by account and recommend corrective actionsOne 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 Marketingdescription of ABM functionskey subfunctions that differentiate ABM vendorsvendor summary chart that shows who does whatexplanations of information provided in the reportvendor 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 systemstargeting at account and/or individual levelstargeting based on external data assembled by the vendormaintain central content librarypresent externally-hosted content without losing control over the visitor experienceintegrated a/b and multivariate testingvendor 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 systemsanalyze content consumption to identify visitors with specific interests or surge in consumption volume and pass to external systemssend alerts to CRM regarding behavior by target accountsassign tasks in CRM to sales repssalespeople can create custom content streams for specific accountssupport 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 executebuy 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 vendorfees based on performance vs. program costsself-service features vs. vendor managed servicesprogram 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 [...]

ABM Vendor Guide: Features to Customize Messages


Moving along with our series on sub-functions described in the Raab Guide to ABM Vendors, let’s take a look at Customized Messages. 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 According to the Guide:Vendors in this category build messages that are tailored to the recipient. This tailoring may include insertion of data directly into a message, such as “Dear {first name}.” Or it may use data-driven rules to select contents within the message, such as “show a ‘see demonstration’ button to new prospects and a ‘customer service’ button to current customers”. Systems may also use predictive models rather than rules to select the right message. Customized messages can appear in any channel where the audience is known to some degree – as an identified individual, employee of a particular company, or member of a group sharing particular interests or behaviors. The Guide lists just a half-dozen vendors in this category. That’s not because there are so few systems that do this: to the contrary, nearly any email, marketing automation, or Web personalization tool would fit the definition. What is rare is ABM specialists who provide this function. That’s because, ultimately, message customization for ABM is pretty much the same as message customization for any other purpose. So the customization vendors in the Guide either provide customization to support a different ABM function such as display advertising (Demandbase, Kwanzoo, Vendemore) or have a broadly-usable customization tool they have targeted at ABM applications (Evergage, SnapApp, Triblio). Some differentiators to consider when assessing a customization system include:types of data made available to use in customization rules (behind the scenes) and in presentation (actually displayed). ability to work with individual and account level data for rules and presentationcomplexity of rules that can be used to create customized contentuse of machine learning or predictive models to create customized content (either to select content directly or to use scores within rules that select content)channels supported  (emails, Web site messages, display ads, etc.)effort and skills needed to set up customized content ability to use the same content definition in multiple locations or promotions (some systems tie the content definition directly to a single Web page location or email template; others store the content definitions separately and let any message call them).generation of messages in real time during interactions, using data gathered during the interactioncustomization level (are messages unique to each contact, same for all contacts in an account, same for all contacts in a segment such as account industry and/or contact role)complexity of created content (single page, multiple pages, interactive content, etc.)ability to coordinate messages received by different individuals within an accountability to recognize individuals, accounts, locations, etc.Only a few of these differentiators apply specifically to ABM. Many marketers will be able to use an existing customization system to generate their ABM messages. But for marketers whose current messaging systems lack adequate customization features, a specialized ABM customization system may make sense.[...]

ABM Vendor Guide: Features to Look for in Target Scoring Vendors


My last post used data from our new Guide to ABM Vendors to describe differentiators among companies that provide external data for account based marketing. Let’s continue the series by looking at differentiators related to Target Scoring, the second sub-function related to the ABM process of identifying target accounts. 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 While External Data is one of the broadest sub-functions described in the Guide, Target Scoring is one of the narrowest. Target Scoring isn’t just any use of predictive analytics, which can also include things like finding surges in content consumption (used to identify intent) or recommending the best content to send an individual. As the Guide defines it:Vendors in this category use statistical techniques to select target accounts. The models most often predict whether an account will make a purchase, but sometimes predict events such as renewing a contract or becoming an opportunity in the sales pipeline. Scores can be built for individuals as well as accounts, although account scores are most important for ABM. Many scoring vendors gather external data from public or commercial sources (or both) to gain more inputs for their models. They may or may not share this data with their clients, and they may or may not provide net new records. Target scoring is more than tracking intent surges, which do not capture other factors that contribute to likelihood of purchase. The vendors in this category include the specialized scoring firms (Infer, Lattice Engines, Leadspace, Mintigo, Radius) plus companies that do scoring as part of a data offering (Avention, Datanyze, Dun & Bradstreet, InsideView, GrowthIntel) or for message targeting (Demandbase, Everstring, Evergage, Mariana, MRP, The Big Willow). Beyond those fundamental differences in the vendor businesses, specific differentiators include:the range of data used to build models, including which data types and how much is proprietary to the vendor amount of client data (if any) loaded into the system and retained after models are built advanced matching of unaffiliated leads to accounts (an important part of preparing data for account-level modeling) tracking movement of accounts and contacts through different segments over time (as opposed to simply providing scores or target lists on demand)self-service model building (as opposed to relying on vendor staff to build models for clients)separate fit, engagement, and intent scores (as opposed to a single over-all score)range of model types created (fit, engagement, behavior, product affinity, content consumption, etc.)limits on number of models included in the base feeimplementation time (for the first model) and model creation time (for subsequent modelssales advisory outputs including talking points, intent indicators, product recommendations, content suggestions, etc. Even though target selection is obviously a core ABM process, target scoring is distinctly optional.  Most firms already have target account lists that were built by sales teams based on their own marketing knowledge.  An ABM program can easily get started using that list.  Chances are, though, that target scoring will find some high-potential accounts that aren't on the old list and find some low-potential accounts that are on the old list but shouldn't be.  Scoring can also do [...]

ABM Vendor Guide: What to Look for in External Data Sources


Last week’s posts introduced our new Raab Guide to ABM Vendors (buy it here) and introduced a framework four process ABM steps, six system functions, and six key sub-functions. The idea was that functions define major categories of systems, while the sub-functions differentiate systems within each category. The world isn’t really quite this simple, if only because many systems provide more than one function. But the sub-functions are still important for stack design and vendor selection.My plan this week is to follow up with a sequence of posts that go through each sub-function in some depth.  Let’s start with the first sub-function, External Data.  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 Vendors that support this sub-function gather account and contact information from the Internet, private, and government sources and purchase it from other vendors. They may resell the data to marketers or use it themselves to support tasks such as account scoring or ad targeting.  (To put things in a broader context, “external data” can be contrasted with “internal data”, which comes from a company’s own systems for CRM, marketing automation, Web analytics, order processing, customer support, etc. Internal data is most important later in the sales cycle, when prospects and customers are interacting with the company directly. External data is most important at the start, when the company hasn’t identified its target accounts or established direct relationships with them.) External data may seem like a commodity – after all, all vendors have access to pretty much the same sources. Yet there’s probably more variety among the vendors in this category than any other. Some key differentiators identified in the ABM Guide include:types of data provided (companies, contacts, events, intent, technology used) number and types of data sources (company Web pages, publisher Web pages, ad exchanges and networks, job posting sites, social networks, IP directories, financial reports, government files, industry and professional directories, news feeds, etc.  Different sources provide different data types.)depth of data (to measure this, get a list of data elements)quality of data (harder to measure: review some sample records and have the vendor explain their quality methods)coverage by region, company size, industry, etc. (depends heavily on data types and sources)coverage by language (many systems extract data using natural language technology that can only read English) how often data is refreshed (which involves two issues: how often are sources revisited and how quickly do changes get communicated to clients)on-demand updates for individual accounts or contacts (to get up-to-the minute information on a new or existing account)add new data sources to meet specific client needs (e.g., reports of new research contracts in the client's industry)custom research to supplement public information (in particular, some vendors do custom research to identify the IP addresses used by target accounts)custom taxonomies for intent analysis (because standard taxonomies may not be precise enough for specialized client needs)maturity of data management processes (how long they’ve been evolving, size of team, etc.)data verification methods (phone call, test for email bounces, compare against other sources, [...]

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 60 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 onl[...]

Just Released: ABM Vendor Guide Gives Detailed Comparison of 40 ABM Vendors


It’s nearly a month since my last blog post, which I think is the longest gap in the ten years I've been blogging.  Some of pause was due to vacation, but mostly it was because I’ve been working feverishly to finish the Raab Guide to Account Based Marketing Vendors, which I’ve released today and you can purchase here. This was a huge project with the almost insanely ambitious goal of making sense of the ABM vendor landscape. What made it challenging wasn’t just gathering detailed information on 40 vendors but the variety of the vendors serving ABM needs. This meant I had to cover many different topics in my research, drilling into different sets of details for different kinds of products. I won’t whine-brag about the amount of work involved but rest assured it was substantial.*Hopefully the result is worth the effort. You, Dear Reader and Potential Buyer at the Bargain Price of $495 Which You Can Order Here, will be the final judge of that. But if nothing else, the report helped to clarify my own thinking about ABM technologies. The fundamental challenge was two-fold. First, I needed to define a reasonably complete set of ABM functions: this gives marketers a framework to identify gaps in their ABM stack. Second, I needed to identify differentiating features within each function: those are what buyers should consider when deciding which vendors to use. What made this tricky is that different vendors provide different combinations of functions, so I couldn’t just classify the vendors themselves.After much pondering, I ended up with a three level approach. The first was to define an ABM process – and note that I wrote "an ABM process" not "the ABM process", since more than one process is possible. My process had four steps, which I think should be self-explanatory. (If you’re underwhelmed by the sophistication of this approach, bear in mind that many people have published detailed and rigorous descriptions of how ABM works. I didn’t see any point in duplicating their efforts.)Identify Target AccountsPlan InteractionsExecute InteractionsAnalyze ResultsThe next step was to define the system functions that support each process step. This was still pretty high level because I didn’t want to be prescriptive. I ended up with six functions**:  ABM Process System Function Comment Identify Target Accounts Assemble Data Data includes existing accounts and contacts from marketing automation and CRM, external information including events and interests, and information about net new accounts and contacts.  It may also include analysis of account data to identify gaps in contact lists and understand current engagement levels.  Select Targets This includes predictive model scores to rank potential targets ,  conventional account profiling, and behavior analysis.  Plan Interactions Assemble Messages This includes building marketing messages and importing messages built in external systems.  Message creation is largely generic but some vendors specialize in account-based messages. Select Messages This includes both systems that automatically select messages and those where the user selects a specific message for each list. Execute Interactions Deliver Messages This includes systems to deliver emails, personalized messages on the company Web site, display advertisements, and includes alerts and advice to sales reps.  Most delivery systems serve all kinds of marketing but a few are ABM specialists. Analyze Results Reporting This includes systems that analyze behaviors and program results at the account level.&n[...]

Aprimo Brand Re-emerges as Marketing Operations Specialist and Merges with Revenew Distributed Marketing System


When we last left Teradata Marketing Applications, it had just been sold to Marlin Equity Partners, whose major previous investment in marketing technology was SaaS email provider BlueHornet. At the time, I expected Marlin would merge the Teradata applications (mostly the old Aprimo product line, plus eCircle email and some other bits) with BlueHornet and was puzzled by why Marlin thought this would result in a good business.

Well, it turns out I was half right: Marlin announced this morning that it is splitting up the business it bought and merging the marketing execution pieces (email, campaign management, etc.) with BlueHornet. The other part – marketing operations functions including planning, workflow, asset management, content distribution, and analytics – will reemerge under the Aprimo brand and be merged with distributed marketing specialist Revenew, which Marline also announced today it has just acquired.

This makes a lot of sense to me. Mrketing operations was Aprimo’s original product and greatest competitive strength. It’s about as unsexy a business as you can imagine, and one that has mostly been merged into larger marketing suites by vendors like SAS, IBM, Adobe, SAP, Oracle, and Infor.  It has also been strangely divided between enterprise systems, like Aprimo’s, and specialists in distributed marketing (basically sharing assets with branch offices and channel partners such as distributors, agents, franchisees, etc.) such as Zift Solutions, BrandMuscle and Sproutloud. Revenew competes in the latter arena, so it’s a nice complement to Aprimo’s marketing operations features. In a conversation yesterday, Marlin and Aprimo management told me they hope that an offering that combines enterprise and distributed marketing operations management will be appealing to companies that now do them with separate systems.

It’s a reasonable bet, although far from a certain winner.  Separate fiefdoms within large companies don’t always want to cooperate and the big marketing suites will still be hovering over it all, claiming to do everything (or integrate with partners who fill their gaps). There’s also a question of whether Aprimo’s product, first released in 1999, still meets the needs of today’s marketing operations – although Aprimo management pointed out that the system was built as Software as a Service from the start, and further promised quick innovation now that they are an independent business again.

Anyway, I’m no longer puzzled by Marlin’s strategy with the acquisition and see how it could turn out well for them. Good luck to all concerned!(image)

YesPath Takes Its Own Route to Managing ABM Journeys


Account based marketing is clearly an important technique for B2B marketers, but I don’t see it displacing all other approaches. For exactly that reason, I also don’t see specialized ABM systems replacing the core marketing databases and decision engines that coordinate all marketing efforts. Today, the core roles are most often filled by marketing automation, although there are emerging alternatives such as Customer Data Platforms and Journey Orchestration Engines. Most of these tools will eventually add ABM features if they don’t have them already.But marketers whose current tools don’t support ABM will need to something new if they want to participate in the ABM gold rush. This gives ABM database and orchestration specialists an opportunity to sell to clients who would otherwise be uninterested in new core systems. The long term, though often unstated, goal of most ABM specialists is to replace the incumbents as their clients’ primary marketing platforms.* But before they can do that, they need to get their foot in the door by making ABM easier than it would be with clients’ existing tools.The main function provided by ABM specialists is account-level data aggregation.  This in turn makes possible account-level analytics and orchestration. But data and analytics don’t create revenue by themselves, so vendors naturally stress their orchestration features. Hence “plays” from Engagio (discussed here ) and “recipes” in ZenIQ (discussed here).  It’s important to recognize that both those systems also build an account-oriented database and provide ABM analytics. They are also similar in relying primarily on external systems to deliver the messages they select.  This is a primary difference from conventional marketing automation products, which deliver email and often other types of messages directly. (Special bonus: by relying on marketing automation to deliver their messages, the ABM orchestrators also show that they don’t intend to replace existing marketing automation systems, removing one objection to their purchase. What they don’t say is they are diminishing the role of marketing automation from the central marketing platform to a simple delivery system, clearing the way for the ABM vendors to eventually take over the central role.  But don’t tell anyone I told you.)YesPath is another ABM orchestrator (ABMO?). It too builds an account-oriented database, provides ABM analytics, and selects messages to be delivered by other systems. Of course, every system is unique.  Here are some important details that distinguish YesPath:- focus on unknown prospects. YesPath relies heavily on Bombora intent data to find companies and individuals (or, more precisely, anonymous cookies) that are interested in topics relevant to a marketer’s products. This lets YesPath programs (which are rather unimaginatively called “programs”) determine which companies on the client’s target list are in active buying cycles, even before they have visited the company Web site or responded to an outbound promotion. YesPath can then reach those companies and individuals through a just-announced integration with the Madison Logic display ad network.- persona-based programs. Marketers set up YesPath programs by uploading a list of target accounts and then defining the selection criteria for individuals to enter the program. These criteria can be considered a persona definition because they identify a set of similar individuals: in this sense, each program relates to a single persona. To create the selection criteria, users select a target audi[...]

Future-Proof Your Marketing Technology Stack: Whitepaper and Webinar


Research sponsored by the Raab Associates Institute has recently uncovered the earliest known marketing technology – a cave painting that promotes a local barbecue restaurant. Key selling points included freshness of the meat and how excited the kids would be.  Archeologists disagree as to whether they also promised live music every Saturday night.

Stone age marketers could invest with little risk that their tools would become obsolete. Today’s marketing technologists don’t have that luxury. Think of it this way: at any point in the past thirty years, an architecture built around the leading technology of the day would have been utterly obsolete ten (and probably five) years later:

The obvious conclusion is that an architecture built on today’s leading technology, mobile, has no chance of surviving the next decade. This realization calls for a change from planning around specific technologies to planning around change itself.

In one word, the solution to this problem is modularity: build an architecture that lets you replace obsolete components without stopping the entire system from operating. I’ve just released a white paper, sponsored by Tealium, with specific suggestions for how to make this happen. You can download it here. We’ll be presenting the paper and related research in a Webinar tomorrow (Thursday) at 12:30 p.m. Eastern time. You can register here for the Webinar. I hope you’ll join us!(image)

Strikedeck Adds Automation to Customer Success Management


I first started paying attention to “customer success management” systems when I realized they were assembling data from multiple sources to build a consolidated customer view – something that could potentially serve other departments throughout the organization. This made them a fifth subtype of Customer Data Platforms (CDPs), along with systems based on marketing, lead scoring, sales advisory, and tag management. In practice, this classification is more potential than real because few if any customer success systems actually expose their data to other systems in true CDP fashion. On the other hand, several do use rules and/or predictive analytics to help manage the post-purchase portion of the customer relationship – making them possible Journey Orchestration Engines (JOEs). Again, though, they fall short on other parts of the definition, in this case the one related to journey mapping.  If you're wondering why I'm going through this, my point is that customer success systems share functions with other kinds of customer management systems and should be evaluated in that larger context.This brings us to Strikedeck, which emerged from stealth in April after about a year of development. Strikedeck is aiming squarely at the same market as customer success leaders Gainsight and Totango but includes more automated execution of recommended actions. In fact, the execution is fully automated: users define rules, called “recipes”, that listen for triggers such as support issues, new contracts, or late invoices and specify the action to take when the trigger occurs. Actions can include emails, surveys, and updating customer data or assigning a task in an external systems.  Actions can also initiate a "playbook", which is a sequence of recipes (some serious mixed metaphors there, alas).  This allows for standard treatments to be fully automated.Users can see the tasks they’ve been assigned in a list or on a calendar, as well as looking at account details and an overview of key metrics for all accounts. They can also define account segments to select accounts for playbooks or reporting. The system includes features to create and send emails, surveys, and in-app mobile messages. If this sounds like “marketing automation for customer success managers”, that’s not a bad way to think about it. In fact, Strikedeck referred to itself as “customer success automation” in an early discussion I had with them, although they don’t seem to be using that positioning at present.But Strikedeck goes beyond standard marketing automation in a couple of key ways. Most notably, it takes data from many sources: not just CRM and its own tracking codes, but also customer support, marketing automation, analytics tools, and any other system with a suitable API connector. It also stores this data in what it called a “polyglot” data model of several technologies (Solr, Redis, Mongo, and Cassandra, fronted by Kafka data collection) that allows vastly more flexibility than a conventional marketing automation product. And it embeds Spark machine learning to build churn and upsell predictions, soon to be extended to other predictions such as willingness to give references or participate in case studies. On the other hand, the playbook sequences lack the event-based branching available in most marketing automation nurture flows. Strikedeck says it takes one to two weeks to deploy at most firms, compared with months for a typical customer success management system.Strikedeck pricing is based primarily on the number of a[...]

Microsoft Buys LinkedIn for $26.2 Billion: Get Ready for Software Vendors as Data Owners


Microsoft surprised pretty much everyone today by announcing a $26.2 billion acquisition of LinkedIn. This is fascinating since Microsoft intersects with LinkedIn in several areas: Dynamics CRM software, Office productivity software, and Bing online advertising. It gives Microsoft access to a rich trove of personal and company information, something it didn’t have before (although Microsoft probably collected more personal and company data than most of us realize).

LinkedIn is primarily a social network with revenue from subscriptions, recruiting services, and advertising. But Microsoft’s announcement suggests it is primarily interested in using LinkedIn’s data for other purposes, such as enhancing the effectiveness of Office and CRM users by showing information about their contacts and potential contacts. This puts Microsoft at the center of the “third party data revolution” (a term I just made up and will probably never use again) that makes detailed information about everyone easily available from commercial sources. This is a trend that’s been clear for some time; it’s a big part of the intent data and predictive data excitement of the past year or two. It's also one foundation of the MadTech vision I offered last year.

It still feels odd to think of a software company owning a data business, although bought Jigsaw (now in 2010 and Oracle purchased the BlueKai and Datalogix in 2014. The prospect of seamlessly integrating third party data with a company’s own sales and marketing products is intriguing, although neither Salesforce nor Oracle has done much with it. Other vendors like Nimble and HubSpot have done a better job of simplifying access to third party data about an individual or company. Those features are immensely appealing and become even more important in the world of Account Based Marketing, where knowing who to reach at your target customers is everything. Done correctly, integration of LinkedIn with Dynamics CRM could provide a major boost to that product’s utility while creating a new barrier to competition.

We’ll see what happens next: Microsoft might be able to reset expectations among CRM (and Outlook) users for having prospect and company data immediately available. That would force other CRM and marketing automation vendors to follow suit, although it's hard to imagine them matching the depth of LinkedIn's data.

If nothing else, this confirms the foundational role of data and data management in marketing and sales technologies.  That's important because companies that start by planning a stable data layer are best positioned to manage the accelerating changes in decision and delivery systems.


ZenIQ Account Based Marketing System Maps Buying Centers, Finds Data and Exection Gaps, and Recommends Actions to Fill Them


When I first starting thinking about Account Based Marketing, I assumed that an ABM system would let marketers replicate at scale how sales teams manage key accounts: that is, to analyze each account in depth, set goals specific to that account, and then execute against those goals. But most vendors serving the ABM space have taken a much narrower approach, either in providing data about accounts, managing campaigns against externally-built account lists, or providing account-level metrics such as coverage, engagement, and funnel velocity. Vendors who offered these things told me that the account-specific planning I imagined wasn’t practical and, in fact, was rarely done even by account teams in sales.I was disappointed but figured it was just another case of expectations outpacing reality.Then I saw ZenIQ.ZenIQ assembles account data from a company’s CRM, marketing automation, and Web systems; supplements this with account and contact information from external sources; assesses the current state of each account; and takes actions to improve that state. At present, the actions are chosen by rules set up manually by marketers – although even this is a step ahead of having marketers directly assign accounts to specific campaigns.  Later this year, ZenIQ plans to release machine learning-based recommendations that will, in effect, generate the rules themselves.  Even automated recommendations in place, ZenIQ won’t select your target accounts or execute the recommended actions. But tools for both of those tasks are widely available and, when it comes to execution, most companies don’t really want to replace their existing email, Web, CRM, and other execution systems. So ZenIQ comes about as close as anyone could want to to providing a complete ABM system.Let’s take a closer look at how all this works:ZenIQ starts by importing accounts and contacts from a company’s marketing automation and CRM systems, including static attributes and behaviors. It also places a tag on the company Web page to capture visitor behavior directly. The system applies sophisticated matching to unify contact data and to link contacts to accounts. It then enhances the contact and account data with attributes, events, and intent from the usual ABM data vendors.Now things start get interesting.  Contacts in each account are assigned to one or more “buying centers” and then classified by their role and importance within each center. This classification relies on machine learning to map titles and interests to standard buying roles such as influencer and decision-maker. ZenIQ next examines each buying center to find coverage gaps – that is, standard roles for which no contact has been identified. The system then fills those gaps with contact records from external sources. This is the sort of work you’d previously have needed a pretty smart sales rep to handle properly. Once the machine learning pieces are fully operational, ZenIQ will look for accounts with unusually low message volume and engagement, relative to all accounts for that client.  It will also infer contacts’ personal interests, channel preferences, and optimal message frequency from their behaviors in marketing automation, CRM, and the Web site. Automated classifiers will tag CRM, Web, and marketing automation activities across multiple dimensions (channel, engagement level, initial vs later contact, etc.), assign stages to opportunities (early, middle, and late) and find correlations between activiti[...]

Accelerating Waves of Marketing Technology: My Interview on Scott Brinker's ChiefmartechTV


I had the pleasure last week of appearing as the first guest on Scott Brinker's chiefmartechTV, an internet broadcast that will features interviews on marketing technology topics.  The official topic was accelerating waves of marketing technology, although we did manage to sneak in Personalized Mona Lisa.  You can view the broadcast here.  See if you can count how many times my cat forces her way into the picture..(image)

Usermind Makes Journey Orchestration Simple


Maybe you’ve been waiting with increasing impatience for me to finish reviewing the set of Journey Orchestration Engines (JOEs) I first mentioned in March.  More likely, it slipped your mind entirely. But I do worry about such things so I’m especially pleased for finish out the set by telling you about Usermind.Usermind JourneyI'm not saying that Usermind calls itself a JOE.  Its self-description is “the first unified platform for orchestrating business operations”.  But the company uses the language of journeys and customer data stores. So although they see themselves as enabling all kinds of business processes, I think it’s fair to view them largely in the context of customer management. Usermind is all about simplicity.  Its main screen sets the tone by offering just three tabs: Analytics, Journeys, and Integration. Deploying the system actually starts with the last of these, Integration, which is where the user connects to external systems that are both data sources and execution engines. The company lists about a dozen standard integrations including major marketing automation, CRM, email, customer service, collaboration, and analytics systems. Another half-dozen are “coming soon.” A key feature of Usermind is it makes integration easy by reading the contents of the source systems automatically, so any custom data elements or objects are incorporated without user effort.  This also means it adjusts to changes in those systems automatically. Users do build maps that show which fields to use to link customers (or other entities) across systems: for example, a map might use email address to link marketing automation to CRM, and customer ID to link CRM to customer service. The system can also map on combinations of fields and do fuzzy matching on inconsistent data. There can be separate maps for individuals, companies, products, customers, partners, or whatever other entities the user wants to work with. Usermind figures out relationships among tables or objects within each source system, so users simply see a list of available fields without having worry about the underlying data structures.Once the maps are in place, Usermind copies selected data elements into its own database, where they are available to use in journeys. Each journey is a sequence of milestones, which can each contain one or more rules. Each rule has selection conditions and one or more actions to take if the conditions are met. Actions can push data or tasks to back to the source systems.  Rules can be triggered by events or executed on schedule.Usermind RuleAnd that’s pretty much it. The Analytics tab reports on movement of customers through journeys, providing counts, conversion rates and drop-out rates for each milestone. It also analyzes the impact of actions on results.  The system can be connected to business intelligence tools for more advanced reporting. But there’s no predictive analytics, content creation, or message execution. True to its description, Usermind is designed to orchestrate actions in other systems, not take actions itself.Don’t let that simplicity fool you. Usermind (and other JOEs) address the critical challenge of unifying customer data from different sources and coordinating customer treatments. Tools to make this easy are rare; tools to send emails and deliver other messages are not. So Usermind fills an important gap – which is why the company h[...]

Intercom "Smart Campaigns" Replace Decision Trees: Interesting But Not Perfect


I got all excited when I saw this description from messaging vendor Intercom about new "smart campaigns" in marketing automation that automatically send the best message at the best time in the best channel to each person without pre-designed campaign flows.  Their critique of the current process -- essentially that fixed flows are too complicated -- is spot on.Alas, a deeper look left me a little disappointed.   Here's how Intercom describes the smart campaign process:First, choose the people you want to message and the goal you want to achieve, e.g. send a series of messages to people who start a trial to get them to become paying customers.Then decide how often you would like them to receive messages, e.g. you may want to send, at most, a message every two days.Choose triggers for your messages, based on time, behavior or interaction with other messages.Then simply rank them by priority, with the most important message listed first.When people are eligible to receive a new message, Intercom looks at all the messages in the campaign, identifies the ones the customer matches the rules for and sends them the highest priority message. My problem is step 4: messages are ranked by priority.  This means that everyone receives basically the same sequence unless there are triggers that interpose specific messages first.  So, the smart campaigns aren't really figuring out the best message to send; they are applying static rules to pick the messages.This is still pretty impressive but it puts most of the work back on the user to figure out those triggers.  It doesn't automatically adjust the core priority ranking (which drives the default message sequence) based on user attributes or behaviors.  I'm sure that clever trigger design could achieve pretty much any use case I could imagine, but it means all the thought I previously put into building clever campaign flows now goes into building clever triggers (and to predicting the customer experience resulting from interactions among those triggers).  So the Promised Land of fully automated, optimized campaign design still hasn't been reached.  Note: I haven't spoken with Intercom.  I'll try to find time for that and to write a real review.  But I did want to put this out because it's a good example of people thinking about alternatives to the current marketing automation campaign flows, even if they haven't found a perfect replacement.Further note: I did subsequently speak with Intercom to learn more about their system.  It turns out that my initial description, presented above, was accurate.  They do make it pretty easy to connect with Web, mobile app, and other data sources and to send messages by email, text, and in-app.  They also let users assign goals to each campaign as a whole and to individual messages, which helps to report on campaign success and to optimize treatments within a campaign.  So they do have some nice features that make effective messaging much easier than standard marketing automation systems.  But fully automated, self-optimizing campaigns, they are not.     [...]

#Personalized Mona Lisa #Marketing #Humor #Fail


Dear Internet,I was disappointed but not wholly surprised that you didn’t find Wednesday's post with Personalized Mona Lisa as self-evidently hilarious as I did. This isn’t the first time my sense of humor failed to match with yours. And, while I know that explaining something never convinces anyone that it’s really funny, I think Personalized Mona Lisa has enough serious content to justify further discussion. So here goes.Let’s start at the beginning. The idea of Personalized Mona Lisa is that someone decided to offer “personalized” versions of Mona Lisa by presenting each individual with a portion of the painting that was related to their interests. So a geologist was shown the rock formations, a hairdresser saw Mona's curls, an ophthalmologist saw her eye, and so on. The joke was it’s obvious that Mona Lisa must be seen as a whole to be appreciated, so whoever tried to improve it by showing only pieces was foolishly mistaken. We laugh at their mindless over-use of personalization, and, perhaps a bit, with relief that we weren’t the ones to make that mistake.Ok, maybe it’s not all that funny.But the notion of over-extending personalization is still important. By showing that there’s at least one situation where personalization is bad, Personalized Mona Lisa (PML) proves personalization isn't always the right thing to do. This means we need to think about when to use personalization and how to make those choices.  Given that most marketing discussions today treat more personalization as the unquestioned goal, this is a conversation worth having.So what are the problems with personalization? PML actually illustrates them quite nicely if you take a close look. We can view it from three perspectives: the consumer, the company, and society as a whole.From the consumer perspective, personalization reduces choice by determining in advance which options the consumer will find most helpful. Of course, there’s always the danger that the personalization system will get that wrong, but let’s put that aside: in PML terms, let’s assume that ophthalmologists really are most interested in eyes and not noses. Yet even an ophthalmologist’s experience is diminished if she only sees that part of Mona Lisa. More broadly, we can say that consumers might enjoy seeing things they didn’t expect and making discoveries for themselves. Personalization prevents this from happening. Also bear in mind that real people have multiple interests: some ophthalmologists are also art lovers, and indeed some are also interested in geology and hair dressing. So personalization may be correct about the user’s primary interest and still make the wrong choice about what they’d find useful in a particular situation.From the company perspective, personalization limits the value presented to the consumer. For PML, you might think of the painting itself as the “company” that has something to offer – presumably, a delightful aesthetic experience. This experience is diminished if the picture is presented in pieces, so it’s in Mona Lisa’s interest to present herself as a whole even if the consumer might prefer a narrower view. In more conventional business terms, the company wants consumers to understand the breadth of its products and services and the promises made by its brand. Personalization does not optimize for these because it focuses only on the im[...]