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 scores how similar other individuals are to that target group. Model inputs include content consumption as sourced from Bombora, behaviors captured by a YesPath Javascript tag on the marketers’ own Web site and emails, and campaign responses imported from CRM (Salesforce.com only so far) and marketing automation (the first integration will be announced shortly). Selection criteria can also include data such as title imported from CRM. Accounts can be assigned to multiple programs but each individual is assigned to only one program at a time, based on whichever program’s target group they match most closely. This means that individuals from the same company with different personas will be in different programs and potentially receive different experiences.

- stage tracking within programs. In addition to assigning an account list and defining individual selection criteria, program set-up includes creating rules to classify accounts into buying stages. These rules draw on behaviors of all individuals within the account, looking primarily at direct interactions with the company Web site, CRM, and marketing automation. YesPath monitors behaviors as they occur and will reassign the account’s stage as appropriate. All individuals in the same account are considered to be in the same stage in a given program. Although stages could be used to describe a customer journey, the fact that each program has its own stage definitions means they can be used in other ways as well.

- stage-based actions. The final task in program set-up is defining actions to occur when an account reaches a new stage. Web site actions, including banner ads, modals, popups, and sliders, can be executed by YesPath itself, using its Javascript tag to identify visitors and display messages. Other actions would be sent by API to execute Madison Logic display advertising, Salesforce.com sales campaigns or other tasks, marketing automation campaigns, or acquire net new lead names from an external source. The system could apply machine learning to select content delivered by an action, but most YesPath clients have so far preferred to select the content in advance.  The system can run split tests to assess alternative actions..

- engagement scores and reporting. YesPath assigns points to interactions such as downloads and page visits. It sums these points for all individuals in an account to create an engagement score that is its primary measure of account activity. For example, program effectiveness is measured by showing the change in engagement after the program began. Other account and program reports show the number of accounts by stage within each program; account details such as stage, days in stage, engagement and visitor counts; distribution of visitors by department and level; interest in different topics; and drill-down to individual activity details. Like other ABM vendors, YesPath says its clients have been very eager to see account reporting on its own, even  before any programs were created.

This is an intriguing mix of features. Using intent data to identify active prospects early in the buying cycle makes sense but in practice will miss many potential buyers. This isn’t a fatal flaw, since marketers can  advertise to target accounts regardless of whether they show up on intent lists, and internal data from Web, CRM, and marketing automation will add precision once prospects start engaging with the company directly. But it does mean this feature is likely to be less powerful than users might expect.

My greater concern is the system’s approach to journey management. Automatically moving individuals among programs and moving accounts to new program stages sounds great: the system dynamically reacts to individual behaviors without defining every path in advance. But users must manually assign accounts to programs, select the individuals used to train the machine learning models, write stage definition rules, and assign actions to stages and messages to actions. It will take a very savvy user to design these elements so they interact in a way that delivers the desired customer experience. The challenge is even greater because actions can only be triggered by a stage change: this means that even a simple multi-step campaign would require multiple stages with tightly written rules to ensure the timing works as intended and that individuals are not reassigned to other programs midstream. And, since stages are assigned at the account level, additional cleverness would be needed to run people through the program at different times. YesPath managers argue their approach makes it easier to manage complex customer journeys than traditional campaign workflows, but I’m not so sure. Perhaps YesPath will find its niche as a way to manage relatively simple experiences, such as account-based advertising campaigns keyed to the buying cycle.

Pricing of YesPath is based on the number of accounts in the system and starts at $3,000 per month for 500 accounts. The system was launched in March 2016 and had ten clients as of June.
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* I’m talking here about ABM database and orchestration systems, not ABM data providers or advertising vendors.  Data inputs and message delivery are needed regardless of what core marketing systems a client uses.

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!

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 company had 18 clients when we spoke in late May.

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 Salesforce.com bought Jigsaw (now Data.com) 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 account.

This sequence sounds almost entirely automated, but ZenIQ recognizes that human input is needed (at least for now) to keep the machines from making foolish mistakes.  So classifiers will undergo a training period during which marketers review and correct their results.  Similarly, marketers will review the system's recommended actions and approve them before they are passed on for execution.  

Marketers will also design the actions themselves.  These will be processes that execute in ZenIQ and, mostly, in external systems.  For example, a typical action might be to buy new contact name and add it to a marketing automation database. Users can create such actions in ZenIQ today and embed them within "recipes" that also contain a rule for when they execute.  Recipes can be driven by real time events (“send a task to CRM if a decision-maker requests a meeting”) or by scheduled processes (“check daily for new accounts without a decision-maker and fill any gaps you find.”)  They can add contacts to campaigns in Salesforce, Marketo, Eloqua, Hubspot, Pardot, or other systems with a standard API connection. Those systems could in turn feed other channels such as display advertising. ZenIQ will also report on coverage, reach, engagement, pipeline movement, and results over time.

During normal operation, ZenIQ will receive regular updates from CRM, marketing automation and Web tags, and react appropriately. Think of it as a smart little robot supervising every account relationship and suggesting the right thing to do in each situation. In short, it pretty much matches my original expectation of what ABM would be.  Score one for reality (and ZenIQ's creators).

ZenIQ was founded in 2015 and released its product in early 2016. It is currently out of beta but not quite officially launched. Pricing starts at $36,000 per year for 6,000 accounts, plus $50 per user per month. There are additional fees for more accounts and for downloaded contact names. The company reports 11 current clients.

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..

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 Journey

I'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 Rule

And 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 this year for it to launch the actual product. (Whether they waited patiently is a question I didn’t ask.) As of March, the company reported 15 live customers and was actively looking for more.

You may be wondering whether Usermind can truly be called a JOE since I've defined the essence of JOE-ness as a system that discovers the customer journey for itself rather than relying on the user to define it. Usermind doesn’t pass that test. In fact, Usermind journeys are individual processes rather than an overview of the customer’s lifetime experience. But Usermind still looks JOE-ish because it’s capturing events that occur naturally, not creating its own events like messages in a nurture flow. And its ability to use the journey as a framework for managing customer treatments is exactly what JOEs are all about. So marketers looking for a JOE should put Usermind on their list.

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.

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 immediate transaction.  Also remember that a personalized experience is relatively easy to implement in the digital world, but much harder to achieve with physical products or services.  Those often involve situations where the user’s identity is unknown or where everyone is treated pretty much the same. So personalization may create diverse brand promises that the company ultimately cannot deliver.

• Society has an interest in building a community with shared experiences and understandings. While opinions about social health differ, I think most people would agree that fragmented communities are problematic.  To take one common concern, it’s hard to build political consensus when different parties get wildly different versions of the news from different outlets. PML presents a very literal illustration of this problem: different people view the same painting but actually see totally different things. It would be very hard for them to have a meaningful discussion after their visit.

I'm not saying that personalization is always bad. There are many times when the consumer has a specific purpose and is best served when personalization helps her accomplish it quickly and easily. The trick is knowing when that’s the best approach and when it’s better to let the consumer can see a bigger picture and maybe explore a bit before getting down to business. Personalization isn’t always bad but it isn’t always good either. Marketers need to make considered decisions about when and how to apply it.

Taking my own advice, I’ll now return to take a broader view of PML herself. After all, there's more to life than marketing.

• PML’s division of Mona Lisa into pieces could be a reference to objectification of women: seeing them as objects that exist for the use of others, and in particular as collections of (mostly sexual) body parts. In Mona’s case, this is doubly ironic because she is, in fact, a painting – an actual object, not a person. Even more ironically, cutting the physical Mona into pieces would destroy her value – the exact reversal of the usual relationship where focusing on female body parts creates more value. Digging still deeper into the irony pit, Mona was the wife of a merchant who paid Leonard to create her portrait in good part to show he could afford it: so the world’s most famous example of “high art” was a thoroughly commercial object from the beginning. I'll let you follow this trail to questions about the intrinsic value and purpose of art.

• Or let’s back up and take a different path. PML’s treatment of the painting as an object can remind us that real-life Mona was herself treated as an object, sitting with her mouth shut while the artist and her husband made every the important decision. Capturing her personal identity was so unimportant that there is still some doubt about whom the picture actually portrays. Yet this person who was effectively anonymous in life has now become literally the best known face in the world. Would you like a little more irony in your tea?

• Breaking Mona into pieces also raises the question of what makes her so special.  It clearly isn’t any individual piece, so it must be something about the whole. But when you look at the entire painting to find what's unique, nothing really jumps out. In fact, Mona is rather plain and so are her clothes, setting, and background. So we've reached the question of celebrity: why is this painting so famous when it’s not really that different from many other paintings? In Mona's case there's a specific historical answer which is actually quite interesting.  But that's less important than the broader question is, How does celebrity happen and why? Clearly the answer lies more in the viewer than the viewed.  This in turn raises another question: by fracturing the mass audience into specialized sub-audiences, does personalization make celebrity different or even impossible?

That’s more than enough irony to meet your minimum daily requirement. So let’s take a more light-hearted look at how we could extend PML. Here are some possibilities:

• an extract of Mona’s hands targeted at a segment of “just got engaged”. This is intended as gentle teasing – it suggests that someone who just got engaged is so obsessed with her engagement ring that she wants to compare it with Mona’s. Beyond the teasing, it carries a deeper warning about the risks of simplistic stereotypes.  There's also a reminder that people must look beyond themselves to appreciate what’s around them.

• an extract of Mona’s breast targeted at a segment of “pornographers”. What’s amusing here is that Mona’s breast is shown very modestly, so we’re lampooning the pornographer’s presumed obsession, the tendency to relate everything to sex, and the still broader idea that some people see everything in terms of business.

• a blank space targeted at a “jeweler”. This is a brain teaser: the viewer asks herself why and then tries to remember what jewelry Mona is wearing. She either remembers or looks up that Mona has no jewelry. That’s actually perplexing, since most portraits of the era were intended to advertise the owners wealth and included ostentatious jewelry as part of the display. Perhaps the viewer is even inspired to do a little research into why Mona is different. (And perhaps you will be too…so I won’t share what my own research uncovered.)

• a thumbnail of the full picture, targeted at “anonymous”. Hopefully you can figure this one out by yourself: since we can’t personalize for anonymous viewers, they get shown the whole picture. The irony here (one last spoonful before you go) is that the people who get the best Mona experience are those who give us the least data, and thus are impossible to personalize.Which pretty much summarizes my concerns about personalization.

You may be wondering why I haven’t offered an image of Mona’s smile: after all, it’s her most famous feature. There’s a bit of serendipity in that – I couldn’t think of a segment that would find the smile most interesting (it might be dentists but she doesn’t show any teeth). But, on reflection, not showing the smile is a powerful engagement device in itself, leading viewers to wonder, Where is it? and Why isn't it here?  Perhaps, if they really get into the spirit of things, they'll even ask themselves, What segment would it fit?   Most interestingly, leaving out the smile illustrates what I think I'll christen the Mona Lisa Paradox: finding the best image for each segment could leave no one seeing the most important image of all.  That’s yet another reason to be cautious about over personalization.

So, right now I'm sure you're thinking, “Wow this is fun.  Can I play this game at home?”

You sure can. Take a copy of Mona Lisat (NOT the original, please) and cut it into pieces, each showing a recognizable image – her hand, the bridge, a sleeve, etc. Distribute the pieces among your uber-ironic friends and have them write a related customer segment on the back of each piece. Then show the rest of the group the front of each piece and have them guess the matching segment. Whoever gets the most right answers wins the game and becomes CMO for a day. Once you’re done with Mona, you can do this with other famous paintings too. Hours of fun!

Sincerely,
David

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.


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*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 with an “influencer panel” observation that mobile devices are now the standard tool for doing everything.  That doesn’t sound too controversial until you realize it means that mobile is no longer a “channel” or a “trend”.  Instead, mobile is simply how things work whether the interaction is on the Web, in email, in social media, by text message, or, for the Luddites among us, by voice.

This matters because mobile interactions are inherently different.  The small screen means they must be simple and on-the-go use means they must be quick. Further reinforcing these trends is the customers’ increasing expectation for personalization.  This expectation also means that mobile (and, implicitly, all other) interactions must exactly match the customer’s needs of the moment.

Put these together and you come with a goal that might be called “precision”: interaction designs and interfaces that give the customer what they want and nothing else. Imagine a painting that’s covered with a sheet of paper with one tiny hole cut out.  The paper hides most of big picture but lets the user see the one detail she cares about at the moment.  She can also move the paper to see different details at different times. The customer’s experience is made simple and direct – at its best, she sees one choice (the thing she really wants or needs) and a button to accept it. Yet the picture behind the paper can be immensely complex.

This vision has (at least) two implications. The more obvious is that it takes incredibly powerful technology to anticipate the customer’s needs.  This is where things like context and machine intelligence will come into play. The location- and situation-aware nature of mobile technology, especially when it’s connected with other devices through Internet of Things, will provide the information needed to understand the customer’s precise situation. Machine intelligence will provide the processing power to interpret this data correctly, continuously, and for millions of customers at a time.

As I said, that implication is important but it's not exactly news. The second implication has been less discussed.  It's that when you strip away everything except what meets the customer’s present need, you lose the opportunity to communicate other messages that might serve your long term purposes. Metaphorically, the hole in that piece of paper is so small that the customer sees only one button, and not whatever advertising might have previously surrounded it. So there’s no opportunity for branding or nurturing or educating the customer – and, perhaps most frightening for a marketer – no way at all to reach potential new customers.

It's as if the Mona Lisa were presented in personalized parts – with geologists shown only the mountains, hairdressers shown only her hair, ophthalmologists shown only her eyes, and plastic surgeons shown only her nose.  Each might come away satisfied with their Mona Lisa Experience, and perhaps even delighted. But I think we'd all agree that something would still be lost.*


Conversely – and this is a third implication – those narrow interactions provide less information about the customer. (The marketer is looking back at the customer through that same small hole in the paper.)  This makes personalization even harder. 

Maybe you think I'm overreacting.  After all, operational interactions where the customer has specific goal within an established relationship are not the only thing people do. But think how time-starved most people are today and how little attention they have for anything beyond their immediate agenda. “Interruptive” messages like display advertising and most marketing emails are already easy to ignore.  They'll be even easier to avoid as screens get smaller and automated assistants get better at screening out things their masters don’t want to see. And even when people are purposely searching for new information, they will rely on ever-smarter systems to many of the preliminary choices.  The days of buyers leisurely gathering a wide variety of information, slowly forming opinions about their options, and interacting with your marketing materials and sales people along the way are already gone. The buyer’s journey isn’t a stroll through the garden smelling the flowers and picking whatever fruit looks ripe: it’s a dash to the store pick-up counter where she grabs a package that someone else has already assembled. If there’s any good news here at all, it’s that journey mapping just got very, very easy.

I’ve covered some of this territory before in my discussions of trust-based relationships and marketing to machines. But even before we get to the point where humans are completely cut out of the buying process,  we'll have the problem of how to optimize the customer journey. The trick will be to deliver value during every interaction – to optimize the journey from the customer’s perspective, not the company’s.

Customers who haven’t bought yet (okay, they’re really prospects) still have a specific intention when interacting with us – presumably to learn something about our company or products so they can decide whether they want to do business.  They’ll presumably be a little more understanding than current customers if we can’t guess exactly what they want, but they’ll still have high expectations and little patience. So marketers and their systems will need to gather as much information as possible, both directly and from external sources such as intent data.**  And we’ll need to use every scrap of information as fully as possible to deliver as much value as we can.

Specifically, we want to keep prospects engaged so that each offer they accept leads to another offer they also accept.  Beyond building our own relationship, this will consume their limited time so they can't use it to research competitors. This leads towards materials like interactive content (which both is engaging and gathers information to tailor the next offer) and metrics like engagement.

In this world, the traditional view of the buyer’s journey as a sequence of steps is almost wholly irrelevant.  Our job as marketers is to meet the customer’s needs in whatever sequence she presents them, not to push her down a predetermined path. The measure of success is the ability to keep someone engaged – following the simplistic but (I think) irrefutable logic that prospects who stop being engaged never become customers.  It would be easy to base an optimization methodology on this approach.

Of course, a goal beyond avoiding disengagement would be encouraging purchase.  In addition to meeting the customer's needs with each interaction, we want to shape the evolution of those needs in the customer’s mind, so at some point her "need" will be buy our product. This provides another, more conventional metric to guide optimization.  But even the purchase need, and the resulting interaction, is just one step among many: marketing in this world is seamlessly integrated with the rest of the customer experience – and all customer experiences are part of marketing.

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*Since marketing technologists have their own tunnel vision, my next thought is finding a technical solution, perhaps presenting a thumbnail of the full image linked to underlying videos.  What's scary is this approach, which I intended as joke, has probably been applied by museum curators as a serious solution.

**Which happened to be my topic at CRM Evolution. You can download my "Understanding Intent Data" slides here.)

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 extremely happy about her new bucket.  Those were simpler times indeed.
 

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 an instance of each event and draws lines whose thickness indicates how many passed customers from one event to the next. In other words, it creates a journey map.

Or, and this is my favorite part, you can tell Pointillist to discover the most important paths on its own.  It does this using magic machine learning to determine which paths have the highest combination of frequency, exclusivity, and correlation to a goal (a user-specified event). Users can adjust the balance among those three factors and can further train the algorithm by telling it which connections they feel are important. Because Pointillist is doing the analysis in memory and considerately visualizes its results, you can  watch it test different connections until it settles on a final set. Hours of fun, for sure.

But there’s more. Pointillist can report on the disposition of people within each event, replacing its icon with a little circle graph showing how many people reached the final goal, moved to the next event (but never reached the goal), dropped out, or stayed behind. It can also display other statistics in graphs next to the flow diagram, as well as letting users analyze subsets of the audience or even a trace the path of a single individual. The analysis can run backwards or forwards, finding either where an initial set of customers ended up or where a final set of customers came from. Heck, that’s weeks of fun when you think about it.

Taking action within Pointillist works exactly as you’d think: for any event on the chart, the system can generate a list of customers that it will send to an external system. The list could include all people in the event or a subset with specified behaviors. When I spoke with Pointillist a few weeks ago, the list would be a file export, but API connections were close to being ready. They’ll probably be done by the time you read this.

Also under development when we spoke was an automated cluster builder that would find clusters most related to (or distant from) the target event. This is different, and often more useful, than traditional clusters that find groups that are similar or distant from each other. Pointillist was also working on letting users create calculated variables, such as a lifetime value or engagement score, that would be available for analysis or segmentation. And on automated tools to help load unstructured data and clean dirty data. And on connectors to push data out to other systems. And on fuzzy matching to supplement the existing, and quite powerful, tools to unify customer data from different sources. Because nobody ever had too much fun.

Speaking of data loading, Pointillist has its own Javascript tag to capture Web behaviors, uses third party connectors to import data from many common systems, and can import batch files from nearly any source. Mapping new event types requires some basic technical skills but Pointillist is working to make it simpler. While APIs to push data to other systems are under development, APIs that let external systems pull data from Pointillist are already available.  These can access customer data but not other data types (remember those special data structures?)

In short, Pointillist both builds a robust, unified customer database and presents exceptional tools to analyze and act on the customer journey. It is the very model of a modern journey orchestrator.

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*Nor, to be perfectly clear, am I saying that campaign management and marketing automation systems will go away. They will simply retreat to the execution layer where they will deliver emails and other outbound messages, playing an important role alongside other channel delivery systems like Web sites and call centers. But the fundamental decisions about which messages to send will be made by the orchestration engines.

Engagio Goes Beyond Account Based Marketing to Unify Marketing, Sales, and Service

Maybe you think Account Based Marketing is utterly revolutionary or maybe you think it’s nothing new at all. Or maybe, like me, you’ve decided that getting marketers to think in terms of accounts is a modest but real change whose true significance is that it finally aligns marketers with how the way salespeople have worked all along. None of that matters, according to Engagio’s Jon Miller, because ABM is dead. The new boss is Account Based Everything. Done truthfully, that could be called Honest ABE, which would be sort of fun.  But I digress.

Of course, Miller is a master marketer, so we could just chalk up his claim to skill at attracting attention. But Engagio's latest product, Engagio PlayMaker, actually does occupy a space in between marketing, sales development, field sales and customer success by connecting them without replacing existing marketing automation, CRM, or customer success systems. So – like all good marketers – Miller has found a distinctive message that accurately distills what’s unique about his product.

Let’s look a little closer. Like Engagio’s earlier analytics products, PlayMaker starts by pulling data from Salesforce.com CRM and attaching leads to accounts. (Remember, or be shocked to learn, that leads in Salesforce are independent of accounts; when they are connected to accounts they become contacts. It almost makes sense if you think about it a little. But don't think about too much or your head might explode.) This matching is a major challenge that has itself been the basis of entire products such as LeanData.  Its primary use has been accurate lead-to-revenue reporting but it supports Account Based Marketing as well. Engagio tries to match on information within the lead record itself and, if that fails, appends data from LeadSpace to help.

Once the lead-to-account relationships are established, Engagio feeds them back to CRM and to marketing automation. It then uses information from those systems, plus Engagio’s own Web behavior tracking tag, to prepare account-level reports on reach (percentage of leads at target vs non-target accounts), coverage (number and positions of leads at target accounts), awareness, engagement (time spent with company materials), and impact. Target account lists can be built within Engagio or imported. This reporting was part of Engagio’s original products.

What’s new in PlayMaker is, well, plays. These are account-specific sequences of messages which can be executed in multiple channels (email, phone, social, direct mail) in multiple systems (marketing automation, CRM, customer success) and targeted at different people within the account. Some messages can be automated but most are assigned to human sales or service agents. Plays can also include internal communications, tasks, and project checklists that don’t involve an external message. The system can store templates for standard messages such as emails or call scripts, although users are expected to customize these before delivery. Naturally, the system offers a range of reports on play status, execution, and results. It can also execute batch plays that apply to multiple accounts.

The actual functionality here is pretty modest – a play is just a set of tasks assigned to different people.  But coordination across marketing, sales, and support departments is actually a pretty big deal. Combining it with an account-centric perspective is even more important because neither marketing automation nor CRM are inherently account-based.  None of this exactly replaces Account Based Marketing itself, but it does highlight the marketing-to-sales alignment that I feel makes ABM important.

PlayMaker is due for release at the end of this month.  (Pre-announcement is another habit of master marketers.)  Pricing will be based on the number of unique "account owners" (as shown in the CRM system) and users.   It will start as low as $1,500 per month.

Teradata Sells Its Marketing Applications Business for $90 Million

Teradata announced on Friday that it had signed a deal to sell its marketing applications business for $90 million to private investment firm Marlin Equity Partners. The company had announced plans to sell the business last November. The sale involves the former Aprimo, eCircle, FLXone data management platform, real-time interaction manager, and other cloud-based marketing products. Teradata will retain its on-premise Customer Interaction Manager (CIM) and an on-premise version of Real Time Interaction Manager (RTIM).

The price is much lower than usual for cloud-based marketing systems. Teradata reported just under $200 million revenue for marketing applications last year. This includes about $40 million for the pieces that Teradata is keeping. So the $90 million price is about 0.56x revenue ($90/$160). This compares with Marketo stock selling at roughly 5x revenue ($1 billion market cap on $200 million revenue). It’s true that Teradata lost $45 million on the marketing applications business last year, but that’s still less on a percentage basis than Marketo’s loss of $71 million. The differential suggests that buyers saw little potential for growth in the businesses that Teradata is selling. The low price may also reflect a departure of many human assets from the Teradata business in recent years.  Teradata itself paid $540 million for Aprimo back in 2011, again roughly 5x revenue. 

It’s not surprising that the buyer is a private equity firm. That was what had been rumored. Marlin hasn’t had much previous involvement in marketing applications but it did buy email provider Blue Hornet in December. Presumably it will combine the two businesses, reduce the losses, and try to sell the result either to other businesses or on the stock market. I don’t understand why Marlin thinks the combined firms would be much more attractive than the separate businesses but presumably they feel there is greater growth potential for a better-managed business. Or, Marlin may plan to split up its acquisitions and sell individual components such as marketing resource management and data management platform separately.

In a move that borders on surreal, Teradata's Marketing Application division itself today announced the latest release of its integrated marketing cloud.  I suppose this signals the hopes within the marketing applications team to remain intact.  Whether that's more than wishful thinking, only time will tell.

I’d like to say I was clever in predicting that Teradata will hold onto CIM and RTIM, but this was something the company announced just after it said it was selling the marketing applications group. CIM and RTIM both started as separate products from Aprimo and, for the most part, remained technically distinct.  My understanding is they held onto the on-premise pieces because they were important to major Teradata clients, whereas the businesses being sold were used by smaller companies who were not buying much else from Teradata.

The very low price certainly isn’t good news for other SaaS marketing vendors, but I think it’s more about the unique situation of the Teradata products than the industry in general. So I’d expect valuations of other cloud-based marketing firms to be largely unaffected.

SAS by the Sip: SAS Viya Offers Open APIs to Individual Services in the Cloud

SAS held its annual Global Forum conference this week, which marked the company’s 40th anniversary. One key to its long-lived success was an early decision to sell software by annual subscription, rather than the one-time perpetual license standard in the industry when SAS started. This provided a steady income stream and focused attention on customer satisfaction to ensure renewals.

In recent years, much of the software industry has adopted a subscription model under the label of “Software as a Service” (SaaS).  But the triumph of SAS’s pricing approach has been accompanied by new challenges to SAS’s business. Subscription pricing notwithstanding, SAS has largely sold its software for on-premise operation by its clients and required them to purchase a large stack of core technologies. This demanded a high initial investment but made expansion relatively easy – an approach that made sense when SAS's core analytical applications were pretty much essential to many clients. By contrast, the new SaaS vendors run software on their own servers and allow clients to access it remotely. This greatly reduces implementation effort and allows volume-based pricing, both of which lower entry costs to the client. The new SaaS software has also been relatively easy to integrate with other systems through open APIs and standard scripting languages such as Python. This also makes it easier to sell SaaS applications for narrow tasks rather than as part of a massive suite.

SAS’s growth and financial performance have been just fine despite the new competition, thanks to technical leadership in its core analytical products and pry-it-from-my-cold-dead-hands loyalty of its core customers. But the benefits of the new SaaS systems have made new sales harder, especially in peripheral markets such as marketing applications.

I’ve subjected you to this long-winded exposition because it provides context for SAS’s major announcement at its conference: a true SaaS version called SAS Viya.* This is a cloud-native system** that will reproduce existing SAS functionality and be compatible with the existing SAS 9 products. More exciting than the cloud deployment (which SAS had previously offered for SAS 9), Viya will be accessible through open APIs and scripting languages including Python, Java, and Lua, and – gasp – some components will be offered as on-demand services. In the SAS universe, this is truly revolutionary. It should open the door to new clients who were not likely to invest in a conventional SAS implementation.  Initial Viya apps will be available in third quarter 2016.

For marketers in particular, SAS also announced Customer Intelligence 360, a SaaS version of its primary marketing suite. Like Viya, this is a separate product from the existing Customer Intelligence 6 suite, which will continue to be offered. The initial release is not a function-for-function duplicate of CI 6 but a “digital marketing hub” that delivers real-time messages in digital channels (email, Web, and mobile apps). Key features include customer-level data collection via on-page scripts, and applications for marketing tasks such as sending an email, delivering in-app messages, or building Web a/b tests. These applications combine previously separate SAS functions such as model building, visual analytics, segmentation, and content creation. They include some nifty advanced features such as recommending when to run tests and automatically discovering which customer segments are most responsive to each test version. The initial CI 360 release includes two modules, Discover (mobile and Web reporting) and Engage (digital interactions including testings). They will eventually be followed by marketing resource management. CI 360 works on a very flexible customer data hub, although that’s a separate product owned by SAS’s Master Data Management group.

CI 360 uses much of the same technology as Viya, including REST APIs and HTML5 interface. It will officially run on Viya once Viya is released. Like Viya, it does not require clients to purchase the full SAS stack and will be priced on volume rather than a simple subscription. In the case of CI 360, fees will be based on the number of “customer equivalent records” and marketing messages. A minimum installation might start around $10,000 per month, considerably less than the current CI 6 product and competitive with other mid-market digital marketing solutions. The initial CI 360 modules are available now.

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* The name is a little odd but it could have been SASaaS, so I guess we can be thankful for small mercies.

** Viya can run on the Amazon Web Services public cloud, SAS’s own cloud, or a company’s own private cloud.

Thunderhead ONE Provides Powerful Journey Orchestration

As I wrote a couple of posts back, I’ve recently noticed a new set of vendors offering “journey optimization engines”*. The key feature of these systems is they select customer treatments based on movement through a journey map. The treatments are usually executed through external systems such as email service providers, CRM, or Web content management. The systems also assemble the unified customer database needed to track customer journeys. This, of course, is a function they share with Customer Data Platforms. But CDPs don’t necessarily have journey mapping or treatment selection functions. On the other hand, journey optimization engines don’t always expose their data to external systems, which is a core requirement for CDPs. Journey optimization engines also provide at least some tools to analyze customer journeys and choose the best customer treatments. These may include predictive models, machine learning, and automatic creation of journey maps, but don’t have to.

Thunderhead ONE Engagement Hub is a charter member of this new little club. UK-headquartered Thunderhead itself was founded way back in 2001 and launched its original customer engagement product (highly personalized customer communications such as account statements) in 2004. ONE was developed by a U.S.-based engineering team.  It was released in Europe in 2015 and in the U.S. earlier this year.

Let’s look at how ONE handles the three core journey optimization functions:

- Data assembly. ONE provides its own Javascript tag to capture Web and email interactions and a SDK to connect with mobile apps. Other systems can feed data into ONE using a REST API or batch file imports. There are prebuilt API connectors for Salesforce.com CRM, Microsoft Dynamics CRM, and SAP Cloud for Customer. The system will automatically replicate the structure of imported data, maintaining relationships between different data elements. This allows ONE to store nearly any kind of data including not just customer attributes and identifiers, but also interaction and purchase details, touchpoint configurations, and product information.

Data is time-stamped to allow trending and give access to previous values of individual elements. Users can define calculations to create derived values such as engagement score, customer type, preferences, or interests. In addition to storing the imported information in a persistent database, ONE can lets users define in-memory profiles available for real-time access during interactions. These are updated immediately as new data is gathered, so the system is always working with the most current information.

ONE can link data using customer identifiers from different sources so long as there is a common element somewhere in the chain, such as an email address that is attached to a Web browser cookie through a form fill and to a mobile device through app registration. This allows the system to start tracking anonymous users when they first appear and later connect them to a personal profile when they identify themselves. But ONE does not standardize customer attributes, such as name or address, or use “fuzzy” matching to infer likely relationships.

All told, this is an exceptionally broad set of data management features. Many systems that build profiles – both CDPs and journey optimization engines – lack ONE's ability to store information about entities such as touchpoints or products. Nor do they always provide both a persistent data store and in-memory access. And while most can stitch together identities using shared identifiers, some rely on external systems to provide a common ID.

- Journey mapping. ONE lets users assign journey stages to activities and then classify interactions by activity type.  Interactions can also be tagged with other attributes such as channel, product, and marketing asset. The system uses this information to create many varieties of journey maps, including one that shows movement between stages broken out by channels, which is delightfully similar to the Customer Experience Matrix** I’ve been working with since 2006.*** Other versions filter the inputs to show maps for specific products, customer segments, or touchpoints within a channel (such as specific Web sites, retail stores, or phone agents). Maps can also compare attributes of different groups, such as customers who advanced towards purchase vs those who dropped out. Slicing the data in yet another way, maps can show the impact on engagement score of specific actions.  Hours of fun, eh?

- Execution. Users can create “conversations” that send messages to customers who match a specified combination of journey stage, customer attributes, and channels.  Eligibility and relevance rules can ensure the chosen messages are truly appropriate. One conversation can include several  messages in different channels.  Message contents can be drawn from a repository within ONE or from an external asset library.

The system uses machine learning to estimate how each customers will respond to each conversation and to calculate the value of the conversation. An arbitration function can then find the highest value conversation in each situation. The system can deploy conversations in real time, presenting CRM agents with recommended actions (along with a detailed customer profile and history) or Web pages with personalized contents (deployed in user-specified locations on the page). Personalized content and data can also be pushed to other execution systems such as email through API connections, either in batch or real time. External systems can access individual customer records through the ONE API.  Data can also be extracted from ONE to standard SQL databases, which external systems could then query.

Pricing for ONE starts at $30,000 per year and is based on the volume of interactions and personalization recommendations, with unit costs varying by channel.  The system has 38 clients in Europe and about a dozen in North America.

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*I would love to call these JOEs but don’t have the heart to inflict another obscure acronym on the industry. You’re welcome.

** Originally developed by my colleague Michael Hoffman. Click here for his take on it.

*** I’m not suggesting that Thunderhead based their map on the Customer Experience Matrix. Many people have come up with similar ideas. I do like to think that Hoffman and I were ahead of our time.

Magisto Uses AI To Create Emotion-Inducing Videos. How Do You Feel About That?

My on-going research into marketing applications of artificial intelligence led me today to Magisto, which promises not merely to automatically create videos, but to do in a way that elicits a user-specified emotion.  That was enough to pique my curiosity,especially combined with the claim that all you had to do was upload some video and photo clips and Magisto would do the rest.  I mean, this is something for people who are both cool and lazy – sign me up!

Which is exactly what I did.  I took a few video selfies (velfies?) around the office with some simple narration, uploaded them to Magisto, made the necessary choices, and waited a few minutes to see what came back.  To see the available range, I made two different versions, one with a storyteller theme and the other – why not? – for a fiesta. The music choices were for songs I didn't recognize, but that only confirms how long ago I stopped listening to current music. You can see the results here: storyteller and fiesta.

Obviously Magisto wasn't smart enough to recognize that one video was recorded sideways or that one clip was a retake of another clip.  But for something that took almost zero effort, it's not bad.  If I'd wanted to pay $9.99 per month for a business subscription, I could have made a longer video, reordered the clips (within some limits), and even added captions.  For more on creating a business video, see Magisto's own video on the subject.

What about that promise of making videos that elicit emotions?  Well, I'm not so sure.  The music sets a mood but that doesn't seem like enough to drive anyone to tears or cheers.  On the other hand, the results were much better than plenty of home-grown videos I've seen, and Magisto certainly knows a cute pet when it sees one.  So don't fire your agency quite yet.  But for your own amusement, Magisto is worth a try.

Salesforce Purchases Deep Learning Artificial Intelligence Vendor MetaMind: Yeah, That's a MarTech Trend

It’s worth a brief note to record that Salesforce.com purchased artificial intelligence vendor MetaMind on Monday. There aren’t many details available: in the announcement, MetaMind founder Richard Socher said Salesforce will use its technology to "automate and personalize customer support, marketing automation, and many other business processes." In a way, that vagueness is exactly what’s most interesting about the deal: it supports the notion that AI will be embedded in many system features rather than limited to a handful of specific tasks.

This vision of pervasive AI is how I personally expect the industry to develop. The cumulative impact will be to make all aspects of marketing more effective as treatments are tailored more precisely to individual customers and contexts. You can also see this as making marketers more productive in the sense of letting them generate more individualized customer treatments per work hour. Those benefits are two sides of the same coin.

Regarding MetaMind itself: I never explored the system in detail but the Web site shows it could do both visual and text analysis. That’s intriguing because those tasks were traditionally handled by highly specialized systems using very different techniques.  But general purpose "deep learning" systems that can be tuned for multiple uses are becoming more common, so MetaMind serves as an example of industry trends rather than a fabulous exception. This flexibility makes it a good choice for Salesforce to use as a foundation for all sorts of AI-based enhancements to its products. It’s safe to assume that other major platform vendors will follow a similar path.

One possible implication to consider is whether pervasive AI could serve as a catalyst for the long-expected martech industry consolidation. The argument would be that a general purpose AI engine allows enhancements across many different marketing functions, so there is scale economy for vendors who can use one AI tool. Presumably there would also be some marketing effectiveness/productivity benefit from having a single AI engine that could share its intelligence across different applications, rather than having each function develop insights independently. I’m by no means convinced this is truly what will happen but it’s something to think about.