Friday, May 27, 2016

#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


Wednesday, May 25, 2016

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.)


Tuesday, May 17, 2016

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.
 

Wednesday, May 11, 2016

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.

Thursday, May 05, 2016

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 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.

Tuesday, April 26, 2016

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.

Thursday, April 21, 2016

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.