Subscribe: Customer Experience Matrix
Added By: Feedage Forager Feedage Grade A rated
Language: English
abm  account  accounts  based  customer  data  marketing automation  marketing  messages  system  systems  target  vendors 
Rate this Feed
Rating: 3.1 starRating: 3.1 starRating: 3.1 starRate this feedRate this feed
Rate this feed 1 starRate this feed 2 starRate this feed 3 starRate this feed 4 starRate this feed 5 star

Comments (0)

Feed Details and Statistics Feed Statistics
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: 2016-09-29T17:03:20.508-04:00


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 proclai[...]

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. __________________________________________________________________________________* Here's the actual Cabinet[...]

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. Or they can retain their agility and support new, innovative martech vendors, recognizing that near-term effic[...]

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 delivery of messages in a single channel (Evergage, GetSmartContent, Kwanzoo, Terminus, Triblio). Marketers [...]

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 a better job of prioritizing accounts within the list, often by incorporating event and intent information to[...]

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, etc.)special methods to associate personal and business emails, attach leads to accounts, find social media h[...]

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 only 1 1 1 1 1 [...]

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.    The final step was the trickiest because this was where I was actually classifying vendors. F[...]

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 audience of people who have shown interest in one or more topics and YesPath machine learning builds a model that[...]

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 accounts, with some adjustments based on deal size. Pricing starts at $30,000 per year for 500 accounts. The[...]

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 activities, gaps, and outcomes. These correlations will be the basis for recommending the next best action for each a[...]

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 has attracted $22 million in venture funding since it was founded in 2013, and why its investors waited until [...]

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:

  1. 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.
  2. 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.
  3. Choose triggers for your messages, based on time, behavior or interaction with other messages.
  4. Then simply rank them by priority, with the most important message listed first.
  5. 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.(image)

#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 immediate transaction.  Also remember that a personalized experience is relatively easy to implement in t[...]

Demandbase Buys Web Data Collector Spiderbook to Expand Its Account Based Marketing Footprint Yet Again


I’ve been writing about Demandbase since 2009, when they had already begun their climb from compiling company profiles to enhancing Web site visitor records to personalizing Web content to targeting Web display ads. This has landed them at the center of today’s Account Based Marketing excitement which, in turn, paved the way further developments such as last month’s announcement that Demandbase data and account scores would be part of Oracle Eloqua’s ABM solution.  That solution, in case you missed it, links leads to accounts and makes account data available for segmentation, campaign rules, and personalization.* But the Oracle announcement was last month’s news and the question with Demandbase is always, what’s next? The answer came yesterday with the announcement that Demandbase is buying Web data collector Spiderbook. As usual with Demandbase, this is both a logical extension of their current business and major increase in the value offered to its clients.As its name implies, Spiderbook scans Web and social sites for information about company and individuals' behaviors and events. This can be refined into several types of data including enhanced business and individual profiles, buying intent, topics of interest, and personal relationships with a company’s own staff. Demandbase will combine these inputs with its own data to give clients with lists of target accounts and individuals within those accounts. At the other end of the funnel, Demandbase will help salespeople choose messages by feeding them information about the likely interests of target individuals. These are both new functions for Demandbase – and selling net new account and contact names is a big leap. In the finest software marketing tradition, Demandbase accompanied its announcement with a new graphic that shows it is “now the only end-to-end platform”  in the ABM category (having added "identify" and "close"). Those are carefully chosen words that shouldn’t be misread as claiming to be a complete platform – as the Oracle Eloqua deal so clearly illustrates, there are still ABM functions that Demandbase doesn’t provide, although it certainly supports them. Content creation, journey orchestration, and email would be high on the list.In a way, I’m pleased to know Demandbase still doesn't do everything.  It lets me look forward to seeing what they add next. _________________________________________________________________________________________________*Had you assumed Eloqua already did that?  Now you know better…and hopefully won’t make the same assumption about other marketing automation systems.  Some do, most don't.[...]

CRM Evolution Conference: Mobile Really Does Change Everything About Marketing


I snuck down to Washington DC yesterday for a few hours at the CRM Evolution conference, where a critical mass of industry experts triggered a chain reaction of interesting thoughts.  The first was that customer systems should read most data directly from the system that created it rather than loading that data into a master database. This isn’t really a new idea – it’s called federated access and has been around for decades.  But I’ve always considered it problematic because source systems might not be easily accessible and source system owners often worry that direct external access would slow their systems’ performance. Moreover, operational source systems often don’t keep old versions of important data that changes over time (such as lead scores or contract expiration dates), making historical analysis difficult if that data isn’t stored elsewhere. Despite these issues, several practitioners and vendors at the conference said they were using the approach and had found it more practical than moving all customer data into a central repository.I’ll guess that more open system designs and higher performance technology have made direct access to source systems more practical than it used to be.  It’s certainly true that the sheer volume of customer-related data has increased to the point where replicating it all into a central system would be a massive project. Indeed, I’ve been telling clients for some time now that they will need a mix of consolidated and federated sources, with federation clearly the right choice for contextual information that is only relevant in a small number of situations. For example, you wouldn’t store the minute-by-minute history of weather in every location if it were only relevant at times and places of customer interactions. Instead, you’d look up the weather in the customer’s location when an interaction began and store it as part of the interaction history. It’s true you might miss some interesting patterns – perhaps raincoat sales spike the weekend after a big storm, which you wouldn’t know if you hadn’t tracked weather during the preceding week. But such insights are probably uncommon and there would be other ways to find most of those patterns without storing massive quantities of largely-irrelevant detail.But the argument I heard this week was stronger than that.  It was that even information core customer information such as purchases should be referenced rather than copied. The ultimate expression of this would be a central customer record that only stores the identifiers needed to find customer data in external systems. I heard at least one vendor say her system worked this way and it's just fine, although I suspect it may have a little more central storage than she described. Other people took a more moderate approach, stating they will copy data into a central system but only if there's specific use for it. But treating replication as an exception is still a reversal from the traditional approach of treating replication as the default.  In practical terms, it means marketers need to look more closely at the federated access capabilities of systems they consider and at how those systems will deal with history data and cross-channel identity matching, which often relies heavily on historical information. So a bit of attitude adjustment may be in order.A more profound (or, at least, less technical) chain of thought started[...]

FlipMyFunnel Conference on Account-Based Marketing Comes to Austin on June 7


I’ll be joining an all-star cast of Account Based Marketing experts when the FlipMyFunnel Festival visits Austin on June 7. You can register here - price is $200 but it's free if you use the promo code DAVIDRAAB100.  You're welcome. My own talk, not surprisingly, will be about the technology behind ABM – or, more precisely, how to build an ABM marketing technology stack. The joke in that is, there’s no such thing: you’d no more want a separate ABM stack than you’d want a stack for people in California or customers in the insurance industry. ABM needs an extension of your existing stack, just as ABM itself is an extension of your existing marketing strategies. Or at least that’s my take – I suspect some of the other speakers will take a more radical view. Fortunately, I’ll be leaving shortly after I speak so I won’t be around to hear them complain.As often happens with these presentations, putting together a coherent treatment of the topic forced me to think things through in a bit more detail than I had previously. For this one, I finally got around to listing the specific features that ABM requires that are not part of standard marketing automation. The most fundamental is an account-based view of your customer and prospect data: while traditional marketing automation systems are organized around individual leads, ABM demands organization around accounts. This may not sound very significant but many marketing automation databases didn’t even have a distinct account object until recently, making account-based analysis difficult and unreliable at best. Account based lead scoring is also a relatively recent improvement that still isn’t universally available. And lead nurture campaigns are still primarily organized around individuals.There are other, more subtle differences, such as tracking behaviors, interests, and funnel stages for an account rather than individuals. And there are brand new requirements, including measuring penetration and coverage of leads within an account and identifying missing individuals (in terms of roles on the buying team that are not associated with anyone known to the system). There are also some execution differences, such as creating content that is designed to elicit information about the account rather than content that’s aimed at attracting new leads from whatever company happens to show up. I could go on, but then you’d have no reason to attend in person.The other thing that often happens with these presentations is I spend way too much time picking images for my slides.  In this particular case, I wanted to start off by making the point that ABM isn’t about technology. This led to the general idea that people are easily distracted by bright and shiny technologies, which in turn branch in two directions: the “bright and shiny” one that ended with Gollum from Lord of the Rings, as the ultimate becoming obsessed with something shiny (and a powerful technology, come to think of it). The other branch started with the idea of people getting inappropriately excited about technology and led to classic 1950’s advertising images of housewives in ecstatic relationships with their appliances. It was a tough choice and I won’t tell you where I ended up. Instead I'llshare one final image that was useless for my immediate purposes but is still irresistible: an apparently actual advertisement showing a woman who is extremel[...]

Pointillist Journey Orchestration Discovers Customer Paths for Itself (Marketing Automation is Doomed, I Tell You)


This post will resume the tour I started in March of journey orchestration engines – our new friend JOE. But first I’ll interrupt myself to announce that I have officially decided to predict that JOEs will replace campaign management and marketing automation as the core system for marketing departments. I usually hedge my bets with this sort of prediction, but will abandon my typical caution because I’m convinced that campaign management and marketing automation are too deeply rooted in the old world of batch list generation to meet today’s need for continuous optimization of customer treatments. Their core architectures just aren’t up to it.I’m not saying that JOEs will have no competition.  Plenty of other vendors have the potential to make the transition – in particular, products developed for real time interactions and Web personalization. I am saying the competition won’t come from today’s campaign management and marketing automation leaders.*Now that I’ve shared this exciting bit of news with you, let’s get back to the topic at hand. That would be Pointillist, a just-released “customer intelligence platform” that has been incubating inside financial services technology vendor Altisource since 2014. As Pointillist’s self-chosen label suggests, its own roots are in customer data analytics, not execution. Sure enough, the system is built around a custom data structure that (if my notes are accurate) they describe as a “combination graph relational time series”, which certainly sounds like something out of Dr. Who. Put in terms simple enough for me to understand, Pointillist stores all data as events, which can have  attributes including customers, products and campaigns. Different event types contain different sets of attributes but there are no formal data tables or relationships among tables. This sounds broadly like Hadoop and other NoSQL data stores, although I’m sure there are Important Technical Differences that matter deeply to people care about such things. What matters from a marketer’s perspective is this approach makes it easy to add new types of information and to update information very quickly.Also as with Hadoop and friends, the Pointillist data store needs some added structure to allow fast access and analysis, and that structure imposes some limitations. Pointillist has optimized for customer analysis, meaning that customer behaviors can be analyzed almost instantly but combining information about customers is harder. For example, it could be tough to find out which products two customers bought in common. All data is stored persistently on a disk somewhere in the Amazon cloud, but accessible data is loaded into memory.  This makes things really quick.That’s probably more than you care or need to know about Pointillist’s technology. Let’s get back to the surface where things are bright and shiny. What makes Pointillist a journey orchestration engine is that it can describe and act against customer journeys. The acting part is especially important, because it makes Pointillist more than simply an analysis tool.What Pointillist really does from a user point of view is let you pick sets of customers and events to analyze. Users drag the events onto a workspace and connect them with lines to indicate the sequence to analyze. The system then scans its data to find how many customers had [...]