#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.


_________________________________________________________________________________________________
*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.

_____________________________________________________________________________

*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.

_______________________________________________________________________________
*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.

_______________________________________________________________________________________________
* 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.

______________________________________________________________________________

*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.

Hive9 Marketing Performance Management Includes Customer Journey Optimization

As I mentioned in last week's post on the MarTech Conference, there appears to be an emerging class of vendors doing what might be called “journey management” – although I think I’ll rename that “journey orchestration” since (a) orchestration is a trendier term right now and (b) orchestration more accurately reflects the key notion of a system that coordinates other systems.*  This coordination includes both gathering data from multiple sources and sending messages through other systems. Sending messages distinguishes journey orchestration engines from “pure” Customer Data Platforms, which assemble data but don’t make decisions about customer treatments. Some not-so-pure CDPs do combine the data assembly and decisioning, but they don’t use a system-assembled customer journey as the framework for message selection.

Whoa.  What the heck does that last sentence mean?  Let me unpack it a bit:

- By “system-assembled customer journey” I mean the systems automatically derive a customer journey from the customer data they’ve assembled. That’s quite different from pre-defining an ideal customer journey and trying to force customers to follow it. It’s even more different from taking conventional multi-step campaigns and calling them "journeys”.  A true "system-assembled journey" would be built by examining the sequence of events for each customer and finding the most common paths to purchase. This still isn't a purely objective process because some human or machine judgement is still needed to exclude irrelevant details, assign interactions to journey stages, and select the most important sequences.  But it's much more data-driven than starting with an marketer-created design.

- By “journey as the framework for message selection” I mean that marketing messages or campaigns are triggered when customers reach a particular journey step. Again, this is different from defining selection rules separately for each campaign, which is how conventional marketing automation and real-time interaction systems work. It’s also different from systems that draw journey maps but don’t connect them with campaigns for execution. Attaching all campaigns to a single journey map simplifies creation of selection rules and provides greater visibility into relationships among campaigns. In other words, journey orchestration makes it easier to coordinate customer treatments across multiple campaigns, which is one of the key problems with conventional marketing automation and interaction management approaches.**

Ok, let’s assume you’re now convinced that “journey orchestration engine” has a specific meaning that describes something useful. Your next question, presumably, is where can I buy one? (Oh, you’re not that easy to sell? Listen closely: It’s new. It’s bright. It’s shiny. New. Bright. Shiny. Newbrightshiny. Now are you ready to buy? I thought so.) My blog post listed three vendors from the MarTech show: Pointillist, Usermind, and Thunderhead. I promise I’ll review those soon. But I had already spoken with another relevant vendor before the show, Hive9. So let’s start with them.

If you look at Hive9’s Web site, you may wonder whether I’ve sent you to the right place.  They position themselves as “marketing performance management” with no mention of anything resembling journey orchestration. That’s because Hive9 actually has three connected modules: one for marketing planning, one for marketing measurement, and one for optimization (which is what I’m calling journey orchestration). These were all developed within B2B marketing agency Bulldog Solutions, which spun off Hive9 about a year ago.

The planning module was the original product. It lets marketers set up a hierarchy with plans at the top, going down to programs, campaigns, and tactics. Tactics have owners, budgets, start and end dates, revenue targets, types (usually a channel or asset) and other attributes such as journey stage, audience, business unit, geography, and language. These can be tailored to each client. Tactics can be tied to Workfront for project management, filtered on pretty much any attribute, and displayed on a Gantt chart-style calendar. Integration with Oracle Eloqua and Salesforce.com lets a new tactic automatically create a corresponding campaign in either system.  Each plan can have a marketing funnel with its own set of stages and targets for conversion rates, velocity, and deal size.

The measurement module reads information from plans and imports revenue, accounts, opportunities, and contacts from CRM, marketing automation, and other systems. Standard integrations are available for Salesforce.com, Oracle Eloqua, Marketo, Google Analytics, Adobe Marketing Analytics, and other systems. Other sources can be integrated through API connections or flat file imports. The system relies primarily on customer identifiers provided by source systems although it can stitch together identities when different systems share some IDs.

Once the data is loaded, the measurement module provides dashboards and other reports to show marketing results including revenue impact; counts, conversion rates and velocity by funnel stage; and whatever other data the client has integrated, such as social sentiment or customer satisfaction. Revenue impact can be measured with first-touch, last-touch, evenly-weighted, position-based, and several other algorithms.  The vendor plans to add statistically inferred weights in April. Results can be filtered by plan, audience, tactic type, assets, or other attributes; compared across time periods; and examined for trends. Dashboards are customized by the vendor for each client, although Hive9 plans to add self-service capabilities in the future.

The optimization module is where journey orchestration happens. Journey stages are defined within the optimization module.  Tactics can be tagged directly with stages, or stages can be assigned to assets which are themselves assigned to tactics.  Events or assets managed in other systems can also be tagged with a journey stage and channel.  However the connection is made, campaign responses are tagged with channel and journey stage and then assembled into a journey map. The map shows the number of interactions by channel and stage and highlights the most common path taken by buyers. This isn’t fully automated journey mapping because the stages are preassigned by the marketer. But the system does discover the most popular paths and most effective marketing assets on its own. So that’s pretty close.

Even more important, the optimization module can contain rules that trigger external marketing campaigns when a customer enters a given journey stage. This is what really qualifies Hive9 as a journey optimization engine. Here's how it works: users can set up a rule tied to journey stage, product type, or customer attribute such as industry or persona. Customers who qualify for a rule can be sent to specified marketing automation campaign. Rules can avoid repeating messages to the same person and can select a “next best message” for the external system to deliver. This definitely qualifies as journey orchestration.

Hive9 pricing starts around $25,000 per year for mid-market clients.  Modules are priced separately. Fees are based on the size of the company marketing budget for the planning module, on the number of records, dashboards, and data sources for the the measurement module, and on the number of touchpoints for the optimization module.

________________________________________________________________________________
* Also, this lets me call systems that do this “journey optimization engines”, giving a three letter acronym of JOE, which is so darn cute.

** You may notice that “journey optimization” sounds a lot like what I’ve previously called “state-based marketing”. Both do select marketing treatments based on a customer’s location within a state/stage framework. If I had to draw distinction, I’d say that journeys suggest forward progression from one stage to the next, while movement among states is not necessarily linear. Similarly, journey orchestration engines send marketing messages through other systems, while state-based systems could use internal or external delivery functions. In other words, journey orchestration is a special type of state-based system.

Open Letter to Scott Brinker: Suggestions for Next MarTech Conference

Dear Scott:

Congratulations to you and Third Door Media on another great MarTech conference. (And, as an aside, I’m astounded that you found time to write a blog post on the Stackies within a day of the conference ending. Do you NEVER sleep?)

I think I’m still technically on the advisory board for this conference, so I thought I’d share some thoughts. To encourage input from the larger community, I’m posting it publicly on this blog. You’re welcome.

Over-all, the conference went tremendously well. Attendees I spoke with were uniformly pleased with the quality of the presentations. The closest thing I heard to a criticism was that some sessions were more theoretical than action-oriented, but that is really a matter of taste. One person told me he liked the case studies best; no surprise there. Someone else said they were surprised at how many had a B2C focus, although that was more an observation than a complaint. The only frustration I heard consistently was having to choose between two interesting sessions when they were on at the same time.

From my own perspective, the worst thing was that no one laughed at the cave man joke.  Unga bunga bink! I did feel the exhibit hall was more crowded than optimal, although some vendors seemed to like being able to grab attendees as they walked by.  I’ll also complain that many vendors lacked signage that explained what they did – not your fault, of course, and maybe a conscious strategy to force people to engage?   Perhaps we could have one of the automated content generation vendors read all the vendor materials and write optimal signs for them!

On to suggestions for next year. I expect you’ll go to even more tracks, which will make it still harder to choose which to attend. Video recordings would let people catch up on sessions they missed; if that’s too expensive, voice recordings to accompany the slides would be a big help.

Maybe a better delineation among the tracks themselves would help too. I’d love to see a track of sessions analyzing product groups within the big landscape: i.e., one for marketing automation, one for content marketing, one for marketing analytics, etc. That would also be a good way to segregate analysts like me from people who want to avoid them. You might also have separate technology and organization tracks and maybe even have an ad tech track (a very underrepresented topic this year). If things get really big, you could also split B2B vs B2C or enterprise vs. SMB. The idea would be to find categories that are more or less mutually exclusive in terms of their audience, although of course some cross-over would be expected and a Good Thing.

Another way to help people with narrow interests would be to have special interest groups, such as ‘birds of feather’ tables at lunch or open-mike roundtables during sessions. I could see tables for CMOs, CMTOs, CTOs, marketing ops, newbies, and other peer groups with their own sets of challenges.  Or maybe separate bar set-ups during the receptions.

Speaking of social interaction, I still think the conference could do more to help people have fun. I’ve suggested this before but would still like to see 30 second videos submitted by attendees on topics like “tall tales I've heard from vendors”, “organizational horror stories”, “if people said what they really thought” (video of a typical meeting with thought bubbles showing people's real thoughts), or pitches for an imaginary, absurd martech product. People could do these in advance or have an opportunity to record them during the show. You’d show the best ones in between speeches or on a loop in the exhibit hall. I suspect there are some really hilarious MarTech folks out there. (Heck, I know there are – I’ve seen some of their project plans.)

I could also see some more conventional competitions in the exhibit hall. These would be team sports, so you could have marketing vs. IT, big companies vs. little companies, vendors vs buyers, etc. Or maybe mixed teams to practice their alignment skills. Picking a sport is tough: foosball is obvious but might give an unfair advantage to the IT folks. We could balance that with something that marketers are especially good at – maybe darts, which I believe are still the standard tool for setting media budgets. Just a thought.

There’s always Powerpoint karoke, although that’s best for after hours.

Or let’s get really high tech. We could have a speech recognition vendor listen to the presentations and score them for buzzword bingo. Or, at least, do a word cloud of what people are saying, either in speeches or in Twitter comments. We could have a competition among speakers for the most Tweets (although I guess that already happens).

Back to team sports. How about teams of attendees competing to create the most successful marketing campaign during the conference? We’d have vendors create preintegrated stacks with tools for research, content creation, campaign execution, optimization, and analytics. The vendors and team members would design, execute and optimize their campaigns, which shouldn’t take much time if the systems are really efficient. Two days is enough time to get some initial results. The campaigns could be for imaginary products or, better still, for worthy charities. Attendees could form their own teams or we could let something like CrystalKnows profile them and then assign attendees to teams with a good balance of skills and personalities.

If that’s too complicated, we could do a standard bake-off competition where vendors and novice users are given a simple task (e.g., create an survey or dynamic email) and do it while everybody else watches and then votes on the results.

Looking forward to next year!

Best,

David

MarTech Madness: Marketing Technology Managers Come Out to Play in San Francisco

MarTech Madness hit this week in San Francisco as 1,200 delighted marketing technologists escaped from their cubicles to cavort under the genial gaze of @chiefmartec Scott Brinker at the latest MarTech Conference. My own experience was a bit skewed because I was pulled into many side meetings, but the attendees seemed pleased and the sessions I did attend were excellent as always. MarTech is unique in its focus on the process of marketing technology management, with sessions covering organizational, staffing, and training issues even more than the technology itself. It’s also unusual in attracting people from both B2B with B2C companies, two groups that rarely mix.

As usual, I spent much of my conference time prowling the exhibition booths. I was struck by the number of content-related exhibitors – nearly half, based on my analysis of the conference program. Many of those were doing standard content management tasks such as workflow, approvals, and repository management.  But several offered some kind of interactive content, which I’d define as content that captures information about the user either through recording behaviors or explicitly asking for input (HapYak, ion interactive, LookBook HQ, SnapApp, and arguably Uberflipand Vidyard). This is a category that also caught my eye at the Content2Conversion conference last month.  Two mentions officially makes it a trend.

There were also several vendors doing what I’ll tentatively call “journey management” (Pointillist, Usermind, Thunderhead [represented at the conference by its partner Arke], and arguably IBM Journey Designer). These resemble conventional campaign managers but execute campaigns based on movement through a comprehensive journey map. These products differ from the earlier generation of journey managers in being tied to live customer data and execution systems, rather than simply drawing a map. There’s considerable variety among these vendors so I need to analyze them more closely before I can confidently call them a meaningful category. But seeing several products emerge simultaneously with similar features is usually a sign that something interesting is afoot.

As these observations suggest, many of my conversations during the conference – especially after the bars were open – related to the ever-popular game of What’s The Next Big Thing? The spotlight has clearly moved from predictive analytics.  ABM is still the current focus but it’s starting to feel dangerously familiar. Interactive content and journey management are definitely candidates, but the consensus at the deepest (and best lubricated) discussion seemed to be what you might call user-tuned content* – that is, content that adjusts to the viewer’s behavior, presumably using artificial intelligence to choose what to do next. That’s different from capturing behaviors with interactive content, as described above, although the same system could do both: in fact, I'd put LookBook HQ in both categories.

"User-tuned content" appeals to me in part because my own MarTech presentation was on machine intelligence, and one strand of development I foresee is combining systems that recognize an individual customer and select the best product or offer (e.g. Evergage or BlueConic); identify the customer's persona (e.g. CrystalKnows, Mariana, CaliberUX); and automatically write data-driven content tuned to that persona (Automated Insights, Narrative Science, Data2Content, and Arria). Concretely, imagine an ecommerce Web site that gives each customer different product descriptions based on her personality. That’s a step beyond current personalization or recommendation engines, which select the best message or product but don’t change how it's presented or use manually-created variations.  It’s also a step closer to what a good human salesperson does, tailoring their presentation to whatever they feel will best resonate with the person they’re talking to.

The pieces needed to deliver user-tuned content are all available, although I haven’t seen any one vendor combine them into a single product. Maybe that won’t happen soon enough to the very next Big Thing, but I’m pretty sure it will be a Big Thing in the not too distant future.

_______________________________________________________________________
*I really wanted to call this "smart content", but that's used by HubSpot for something else.  "Intelligent content", "responsive content", "contextual content", "dynamic content", "personalized content", "tailored content" and many others are also taken.  "User-tuned content" has been used in pretty much the sense I have in mind, although specifically relating to content tuned to the needs of autistic children.

Mariana Uses Artificial Intelligence to Build Personas and Find Target Audiences

Can computers understand an individual human’s personality (and then, presumably, use that understanding to better target marketing messages)? It turns out that’s no longer even a question: if you haven’t yet played with CrystalKnows, be prepared for some weirdly accurate insights into yourself and those you know well, based on public Internet information. And, yes, Crystal advises you how to interact with others based on those insights, going so far as to suggest changes to your emails to better fit the style of the recipient. If there’s a gap between this and letting a computer just manage the whole relationship without any human involvement, it’s almost too small to worry about.

So the real challenge isn’t having the computer analyze your data; it’s having enough data for the computer to analyze. People like me, who blog and Tweet incessantly, are easy enough to understand (or, at least, it’s easy to describe our carefully curated public personas). But many of your marketing targets are less visible. Finding enough of them to be useful is a major limitation for systems that rely on machine intelligence to help target marketing messages.

This is where Mariana comes in. The whiz-bang part of its pitch is using artificial intelligence (“deep learning” as in the Mariana Trench – get it?) to build personas by analyzing a sample of your existing customers. Users see attributes for each persona such as interests, titles, functions, tenure, and average deal size. Pretty cool, I must say.

But the really special part is what comes next.  Mariana finds other people who match the personas, using its deep understanding of social data to accurately identify and find contact information on larger numbers of relevant people than other vendors.

How many more? Mariana told me that while other vendors might find social profiles on less than 30% of the records on a B2B email list, it regularly finds data on 50% to 80%. The difference is that Mariana uses its artificial intelligence to analyze connections between people – including unique access to the Twitter social graph, as well as where people work or have worked, groups they belong to, and the types of work they do. This helps it pick the right person when several people share the same name and lets it build detailed, accurate profiles ncluding Twitter and Facebook handles, employer, job function, and interests.  Of course, it can also assign each person to the correct persona for each client.

What does Mariana do with this information?  It currently offers two products: Prospect IQ to find new prospects and Conversion IQ to market to names already in the client's database.  Both products start by uploading a training set of target accounts or closed and won opportunities and contacts from CRM. The system then does its magic to look up the companies and individuals in that set, find data about them, and built personas based on common clusters of attributes.  Clients can build one set of personas for their entire customer base or create separate personas within segments based on company size or industry.  You can call this Account Based Marketing if you like; Mariana does.

For Prospect IQ campaigns, users can define a target audience based industry, company size, location, and persona.  The system then returns contact counts for Facebook, Twitter, and email. Users create the marketing messages, helped by Mariana’s suggestions for which topics would interest each persona. They then choose which personas get which messages, set a budget and timeframe for each channel, and let Mariana execute the campaign.  The system will send message to the same person in multiple channels when possible and can run automated a/b creative tests to optimize results. Responses go to a user-specified landing page; Mariana does not reveal prospects’ identities unless they respond.

Conversion IQ follows a similar process except that Mariana works from a set of known contacts uploaded by the client. These are cleaned, enhanced with company and individual data, and assigned personas. Users then define campaigns and have Mariana execute them. Mariana has prebuilt integrations with Twitter, Facebook, Salesforce.com CRM, Pardot, Eloqua, and Marketo.

Pricing for Mariana is based on the number of campaign responses. It starts at $3,000 per month for a three month pilot, which includes the cost of advertising media at specified CPMs. The company was started in 2013 and officially released its product on March 1 of this year. It reports that pilot projects during the test stage yielded ten times the conversion rate of conventionally targeted ads.

ICON16: How Infusionsoft Plans To Dominate Small Business Marketing (and Make Life Better For Small Businesses Everywhere)

I spent part of last week at ICON16, Infusionsoft’s annual combination of customer conference, revival meeting, and group therapy session for small business owners. The company made a few announcements, most notably a vastly improved email builder and tighter integration with online accounting software from Quickbooks Online and Xero. These changes were evolutionary at best, but company leaders made clear this was on purpose: their current strategy is to make small improvements to ease of use, not add major new or deeper features. This is based on Infusionsoft’s learning over the past ten years that the main barriers to success with its software are small businesses not knowing what to do with it and not seeing immediate value from the efforts. The real focus of the conference was introducing the latest version of Infusionsoft’s Small Business Success Method, an approach to helping small businesses plan their marketing programs. Infusionsoft’s goal is to bake as much as possible of the methodology into the software, its partner ecosystem, and prebuilt assets such as campaign plans.

This all makes perfect sense and is wholly consistent with Infusionsoft’s historical focus on helping small business grow. Although the company’s growth slowed a bit in 2015 (customers up 30% to 35,000; revenue up 25% to $100 million) CEO Clate Mask said the management has recently committed to a goal of five million customers by 2030. This is certainly audacious – it implies growing 40% per year for 15 years in a business that is highly prone to disruption – but big long-term goals are part of Infusionsoft’s culture. Whether it’s realistic is another question.  Some companies serving small business have indeed reached multi-million client counts (see table), but their products cost much less and are essential for basic operations.** Infusionsoft’s challenge is to convince a large fraction* small business owners that their product is also essential.

company	services	customers (2015)	revenue (2015)	revenue / customer
GoDaddy	domain registration, Web site hosting	13.8 million	$1.6 billion	$116
Endurance International (without Constant Contact)	domain registration, Web site hosting	4.7 million	$747 million	$159
Constant Contact	small business email	650,000	$367 million	$564
Intuit Quickbooks	small business accounting	4-5 million	$2.1 billion	$420 - $520
Infusionsoft	small business integrated sales and marketing software	35,000	$100 million	$2,857
HubSpot	integrated marketing and CRM software	18,100	$181 million	$10,000

Infusionsoft managers recognize that expanding the customer base requires making marketing easier. This is a major reason for developing the Small Business Success Method and baking it into the software. Product managers described a plausible glide path from having the system identify opportunities to having it suggest marketing programs to having it execute those suggestions autonomously. Machine intelligence would play a key role at every step of this evolution. Although such work is in early stages at Infusionsoft, the product is being engineered to allow insertion of automated marketing features as these become available. The company seems to be giving lower priority to a more flexible database, which will also be essential to handling future needs such as unstructured data. It is already behind small business oriented competitors that permit custom data tables, including HubSpot and Ontraport.  (Infusionsoft does allow custom data fields in its standard tables.)

I still think Infusionsoft could be displaced by an aggressive, well-funded competitor, especially as marketing technologies and methods continue to change.. Infusionsoft argues that their partner network is a competitive barrier that would be hard for another software vendor to overcome. This is true to some extent but Infusionsoft doesn’t dominate among marketing agencies in the way that Intuit Quickbooks dominates among accountants. Nor are many of the tiniest businesses ever going to use a marketing agency.  Similarly, Infusionsoft's existing customer base provides invaluable data to help machine intelligence systems make recommendations, but other firms could use data from fewer customers in similar ways.  Infusionsoft also argues, I think correctly, that very small businesses are harder to serve than even slightly larger ones, because the smallest business owners have so many other priorities and often so little interest in marketing. This makes channel partners, methodologies, and client support even more important – making Infusionsoft’s head start in experience, methodology, and partner network harder to overcome.

Of course, the only people who really need to worry about Infusionsoft’s business prospects are its investors and employees, although channel partners and clients do have some stake in the results. So let’s move on to the fun stuff, which is technology.

As usual at these conferences, I spent a good chunk of my time cruising the exhibition floor for interesting new vendors. One booth was staffed by attractive young ladies in dark glasses and tight-fitting police uniforms, which literally gave me nightmares. Adding insult to injury, their company provided small business financing, which isn’t even of interest to me.

Among the more relevant firms, I noted a high concentration of reporting tools, including freshlime, hipdash, cloudlink, graphly, and Wicked Reports. These address a recognized gap in Infusionsoft’s own reporting, particularly regarding visualization, custom reports, and integration of external data. Of these, I found WickedReports the most interesting: they use customer tracking to do multi-channel lead attribution and customer value analysis. According to the person I spoke with – who was fortunately not wearing a police uniform – they actually track all contacts with each customer, meaning they have the data to assign fractional credit to each touch using advanced statistical methods.  But they don’t expose that data, having judged that Infusionsoft customers aren’t ready for anything so sophisticated.  I’m pretty sure they’re right.

Two other vendors also caught my eye. ThinkingChat promised "artificial intelligence lead capture agents"  engage site visitors and capture contact information. I was a bit disappointed to find that what they really do is scan for keywords in the chat inquiries, and then provide fixed responses. That's clever but a very low grade of AI at best.  Keywords alone have major limits – “price is too high” “price is a bargain” or “what’s your price” would all trigger the same reply if the system simply looks for the word “price”. ThinkingChat adds a bit more flexibility by letting users use a keyword to direct the dialog to different collections of keywords and answers, for example to give prices for different products if a product name is mentioned first.It will also recognize when a visitor is having a problem and admit it can’t answer a question, although the user must manually review the failed dialogs to make refinements. On the other hand, ThinkingChat reports that it more than doubles the rate of capturing contact information on their clients’ Web sites, which is a pretty good deal for as little as $149 per month. So it's worth a look.

My favorite product by far was DilogR, which provides interactive content including assessments, quizzes, and surveys; dynamic videos that let users choose which sections to view; and interactive images.  This puts them in competition with firms like SnapApp, ion interactive, Survey Monkey, and Brightcove but at a fraction of the cost: DilogR plans start as low as $97 per month.  I think interactive content is an extremely important tool for engaging prospects, but have been frustrated at the time and cost it has taken my own clients to deploy it. DialogR could open this up to many more marketers, providing value for their businesses and customers alike.  I'm glad I found it.
____________________________________________________________________________
* There are just over 5 million small businesses in the U.S. with employees plus about 15 million companies with no employees and at least $10,000 revenue.  The total market is larger because Infusionsoft can also sell to companies outside the U.S..

** To put this in perspective: five million clients at $3,000 per client would give Infusionsoft $15 billion revenue, ranking it just behind SAP as the worlds fifth-largest software company and nearly three times Salesforce.com's 2016 revenue of $6.67 billion.***  One possible inference is that Infusionsoft would need a much lower-priced offering to reach that many customers.  Company managers did seem to be giving this some thought but it would conflict with their strong position that only business owners willing to invest serious time and effort in marketing would succeed with their product.

***Note also that Salesforce.com is just two years older than Infusionsoft; by the time it had reached Infusionsoft's current age, its revenue was already $4 billion.  Even if you count from Infusionsoft's first venture capital funding in 2007, Salesforce.com had $500 million revenue at the same age.  In other words, Infusionsoft is growing much more slowly than Salesforce.com.  That's not surprising: small businesses are slow adopters and Infusionsoft faces more competition than Salesforce ever did.  But it's hard to imagine Infusionsoft ever matching Salesforce.com's growth rate.