Wednesday, July 20, 2016

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

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

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

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

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

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

Friday, July 01, 2016

YesPath Takes Its Own Route to Managing ABM Journeys

Account based marketing is clearly an important technique for B2B marketers, but I don’t see it displacing all other approaches. For exactly that reason, I also don’t see specialized ABM systems replacing the core marketing databases and decision engines that coordinate all marketing efforts. Today, the core roles are most often filled by marketing automation, although there are emerging alternatives such as Customer Data Platforms and Journey Orchestration Engines. Most of these tools will eventually add ABM features if they don’t have them already.

But marketers whose current tools don’t support ABM will need to something new if they want to participate in the ABM gold rush. This gives ABM database and orchestration specialists an opportunity to sell to clients who would otherwise be uninterested in new core systems. The long term, though often unstated, goal of most ABM specialists is to replace the incumbents as their clients’ primary marketing platforms.* But before they can do that, they need to get their foot in the door by making ABM easier than it would be with clients’ existing tools.

The main function provided by ABM specialists is account-level data aggregation.  This in turn makes possible account-level analytics and orchestration. But data and analytics don’t create revenue by themselves, so vendors naturally stress their orchestration features. Hence “plays” from Engagio (discussed here ) and “recipes” in ZenIQ (discussed here).  It’s important to recognize that both those systems also build an account-oriented database and provide ABM analytics. They are also similar in relying primarily on external systems to deliver the messages they select.  This is a primary difference from conventional marketing automation products, which deliver email and often other types of messages directly. (Special bonus: by relying on marketing automation to deliver their messages, the ABM orchestrators also show that they don’t intend to replace existing marketing automation systems, removing one objection to their purchase. What they don’t say is they are diminishing the role of marketing automation from the central marketing platform to a simple delivery system, clearing the way for the ABM vendors to eventually take over the central role.  But don’t tell anyone I told you.)

YesPath is another ABM orchestrator (ABMO?). It too builds an account-oriented database, provides ABM analytics, and selects messages to be delivered by other systems. Of course, every system is unique.  Here are some important details that distinguish YesPath:

- focus on unknown prospects. YesPath relies heavily on Bombora intent data to find companies and individuals (or, more precisely, anonymous cookies) that are interested in topics relevant to a marketer’s products. This lets YesPath programs (which are rather unimaginatively called “programs”) determine which companies on the client’s target list are in active buying cycles, even before they have visited the company Web site or responded to an outbound promotion. YesPath can then reach those companies and individuals through a just-announced integration with the Madison Logic display ad network.

- persona-based programs. Marketers set up YesPath programs by uploading a list of target accounts and then defining the selection criteria for individuals to enter the program. These criteria can be considered a persona definition because they identify a set of similar individuals: in this sense, each program relates to a single persona. To create the selection criteria, users select a target audience of people who have shown interest in one or more topics and YesPath machine learning builds a model that scores how similar other individuals are to that target group. Model inputs include content consumption as sourced from Bombora, behaviors captured by a YesPath Javascript tag on the marketers’ own Web site and emails, and campaign responses imported from CRM (Salesforce.com only so far) and marketing automation (the first integration will be announced shortly). Selection criteria can also include data such as title imported from CRM. Accounts can be assigned to multiple programs but each individual is assigned to only one program at a time, based on whichever program’s target group they match most closely. This means that individuals from the same company with different personas will be in different programs and potentially receive different experiences.

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

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

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

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

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

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

Wednesday, June 22, 2016

Future-Proof Your Marketing Technology Stack: Whitepaper and Webinar

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


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




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



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

Friday, June 17, 2016

Strikedeck Adds Automation to Customer Success Management

I first started paying attention to “customer success management” systems when I realized they were assembling data from multiple sources to build a consolidated customer view – something that could potentially serve other departments throughout the organization. This made them a fifth subtype of Customer Data Platforms (CDPs), along with systems based on marketing, lead scoring, sales advisory, and tag management. In practice, this classification is more potential than real because few if any customer success systems actually expose their data to other systems in true CDP fashion. On the other hand, several do use rules and/or predictive analytics to help manage the post-purchase portion of the customer relationship – making them possible Journey Orchestration Engines (JOEs). Again, though, they fall short on other parts of the definition, in this case the one related to journey mapping.  If you're wondering why I'm going through this, my point is that customer success systems share functions with other kinds of customer management systems and should be evaluated in that larger context.


This brings us to Strikedeck, which emerged from stealth in April after about a year of development. Strikedeck is aiming squarely at the same market as customer success leaders Gainsight and Totango but includes more automated execution of recommended actions. In fact, the execution is fully automated: users define rules, called “recipes”, that listen for triggers such as support issues, new contracts, or late invoices and specify the action to take when the trigger occurs. Actions can include emails, surveys, and updating customer data or assigning a task in an external systems.  Actions can also initiate a "playbook", which is a sequence of recipes (some serious mixed metaphors there, alas).  This allows for standard treatments to be fully automated.


Users can see the tasks they’ve been assigned in a list or on a calendar, as well as looking at account details and an overview of key metrics for all accounts. They can also define account segments to select accounts for playbooks or reporting. The system includes features to create and send emails, surveys, and in-app mobile messages.

If this sounds like “marketing automation for customer success managers”, that’s not a bad way to think about it. In fact, Strikedeck referred to itself as “customer success automation” in an early discussion I had with them, although they don’t seem to be using that positioning at present.

But Strikedeck goes beyond standard marketing automation in a couple of key ways. Most notably, it takes data from many sources: not just CRM and its own tracking codes, but also customer support, marketing automation, analytics tools, and any other system with a suitable API connector. It also stores this data in what it called a “polyglot” data model of several technologies (Solr, Redis, Mongo, and Cassandra, fronted by Kafka data collection) that allows vastly more flexibility than a conventional marketing automation product. And it embeds Spark machine learning to build churn and upsell predictions, soon to be extended to other predictions such as willingness to give references or participate in case studies. On the other hand, the playbook sequences lack the event-based branching available in most marketing automation nurture flows. Strikedeck says it takes one to two weeks to deploy at most firms, compared with months for a typical customer success management system.

Strikedeck pricing is based primarily on the number of accounts, with some adjustments based on deal size. Pricing starts at $30,000 per year for 500 accounts. The company had 18 clients when we spoke in late May.

Monday, June 13, 2016

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

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

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

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

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

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


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

When I first starting thinking about Account Based Marketing, I assumed that an ABM system would let marketers replicate at scale how sales teams manage key accounts: that is, to analyze each account in depth, set goals specific to that account, and then execute against those goals. But most vendors serving the ABM space have taken a much narrower approach, either in providing data about accounts, managing campaigns against externally-built account lists, or providing account-level metrics such as coverage, engagement, and funnel velocity. Vendors who offered these things told me that the account-specific planning I imagined wasn’t practical and, in fact, was rarely done even by account teams in sales.

I was disappointed but figured it was just another case of expectations outpacing reality.

Then I saw ZenIQ.

ZenIQ assembles account data from a company’s CRM, marketing automation, and Web systems; supplements this with account and contact information from external sources; assesses the current state of each account; and takes actions to improve that state. At present, the actions are chosen by rules set up manually by marketers – although even this is a step ahead of having marketers directly assign accounts to specific campaigns.  Later this year, ZenIQ plans to release machine learning-based recommendations that will, in effect, generate the rules themselves.  Even automated recommendations in place, ZenIQ won’t select your target accounts or execute the recommended actions. But tools for both of those tasks are widely available and, when it comes to execution, most companies don’t really want to replace their existing email, Web, CRM, and other execution systems. So ZenIQ comes about as close as anyone could want to to providing a complete ABM system.

Let’s take a closer look at how all this works:

ZenIQ starts by importing accounts and contacts from a company’s marketing automation and CRM systems, including static attributes and behaviors. It also places a tag on the company Web page to capture visitor behavior directly. The system applies sophisticated matching to unify contact data and to link contacts to accounts. It then enhances the contact and account data with attributes, events, and intent from the usual ABM data vendors.

Now things start get interesting.  Contacts in each account are assigned to one or more “buying centers” and then classified by their role and importance within each center. This classification relies on machine learning to map titles and interests to standard buying roles such as influencer and decision-maker. ZenIQ next examines each buying center to find coverage gaps – that is, standard roles for which no contact has been identified. The system then fills those gaps with contact records from external sources. This is the sort of work you’d previously have needed a pretty smart sales rep to handle properly.

Once the machine learning pieces are fully operational, ZenIQ will look for accounts with unusually low message volume and engagement, relative to all accounts for that client.  It will also infer contacts’ personal interests, channel preferences, and optimal message frequency from their behaviors in marketing automation, CRM, and the Web site. Automated classifiers will tag CRM, Web, and marketing automation activities across multiple dimensions (channel, engagement level, initial vs later contact, etc.), assign stages to opportunities (early, middle, and late) and find correlations between activities, gaps, and outcomes. These correlations will be the basis for recommending the next best action for each account.

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

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

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

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


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

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

Thursday, June 02, 2016

Usermind Makes Journey Orchestration Simple

Maybe you’ve been waiting with increasing impatience for me to finish reviewing the set of Journey Orchestration Engines (JOEs) I first mentioned in March.  More likely, it slipped your mind entirely. But I do worry about such things so I’m especially pleased for finish out the set by telling you about Usermind.

Usermind Journey

I'm not saying that Usermind calls itself a JOE.  Its self-description is “the first unified platform for orchestrating business operations”.  But the company uses the language of journeys and customer data stores. So although they see themselves as enabling all kinds of business processes, I think it’s fair to view them largely in the context of customer management.

Usermind is all about simplicity.  Its main screen sets the tone by offering just three tabs: Analytics, Journeys, and Integration. Deploying the system actually starts with the last of these, Integration, which is where the user connects to external systems that are both data sources and execution engines. The company lists about a dozen standard integrations including major marketing automation, CRM, email, customer service, collaboration, and analytics systems. Another half-dozen are “coming soon.”

A key feature of Usermind is it makes integration easy by reading the contents of the source systems automatically, so any custom data elements or objects are incorporated without user effort.  This also means it adjusts to changes in those systems automatically. Users do build maps that show which fields to use to link customers (or other entities) across systems: for example, a map might use email address to link marketing automation to CRM, and customer ID to link CRM to customer service. The system can also map on combinations of fields and do fuzzy matching on inconsistent data. There can be separate maps for individuals, companies, products, customers, partners, or whatever other entities the user wants to work with. Usermind figures out relationships among tables or objects within each source system, so users simply see a list of available fields without having worry about the underlying data structures.

Once the maps are in place, Usermind copies selected data elements into its own database, where they are available to use in journeys. Each journey is a sequence of milestones, which can each contain one or more rules. Each rule has selection conditions and one or more actions to take if the conditions are met. Actions can push data or tasks to back to the source systems.  Rules can be triggered by events or executed on schedule.

Usermind Rule

And that’s pretty much it. The Analytics tab reports on movement of customers through journeys, providing counts, conversion rates and drop-out rates for each milestone. It also analyzes the impact of actions on results.  The system can be connected to business intelligence tools for more advanced reporting. But there’s no predictive analytics, content creation, or message execution. True to its description, Usermind is designed to orchestrate actions in other systems, not take actions itself.

Don’t let that simplicity fool you. Usermind (and other JOEs) address the critical challenge of unifying customer data from different sources and coordinating customer treatments. Tools to make this easy are rare; tools to send emails and deliver other messages are not. So Usermind fills an important gap – which is why the company has attracted $22 million in venture funding since it was founded in 2013, and why its investors waited until this year for it to launch the actual product. (Whether they waited patiently is a question I didn’t ask.) As of March, the company reported 15 live customers and was actively looking for more.

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

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

I got all excited when I saw this description from messaging vendor Intercom about new "smart campaigns" in marketing automation that automatically send the best message at the best time in the best channel to each person without pre-designed campaign flows.  Their critique of the current process -- essentially that fixed flows are too complicated -- is spot on.

Alas, a deeper look left me a little disappointed.   Here's how Intercom describes the smart campaign process:

  1. First, choose the people you want to message and the goal you want to achieve, e.g. send a series of messages to people who start a trial to get them to become paying customers.
  2. Then decide how often you would like them to receive messages, e.g. you may want to send, at most, a message every two days.
  3. Choose triggers for your messages, based on time, behavior or interaction with other messages.
  4. Then simply rank them by priority, with the most important message listed first.
  5. When people are eligible to receive a new message, Intercom looks at all the messages in the campaign, identifies the ones the customer matches the rules for and sends them the highest priority message.
 My problem is step 4: messages are ranked by priority.  This means that everyone receives basically the same sequence unless there are triggers that interpose specific messages first.  So, the smart campaigns aren't really figuring out the best message to send; they are applying static rules to pick the messages.

This is still pretty impressive but it puts most of the work back on the user to figure out those triggers.  It doesn't automatically adjust the core priority ranking (which drives the default message sequence) based on user attributes or behaviors.  I'm sure that clever trigger design could achieve pretty much any use case I could imagine, but it means all the thought I previously put into building clever campaign flows now goes into building clever triggers (and to predicting the customer experience resulting from interactions among those triggers).  So the Promised Land of fully automated, optimized campaign design still hasn't been reached. 

Note: I haven't spoken with Intercom.  I'll try to find time for that and to write a real review.  But I did want to put this out because it's a good example of people thinking about alternatives to the current marketing automation campaign flows, even if they haven't found a perfect replacement.

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