Sunday, June 28, 2015

MarTech Stack Jenga: Official Rules

I spent some time in Atlanta last week, including a Friday afternoon visit with Sangram Vajre at Terminus to discuss his upcoming FlipMyFunnel conference. I’ll be keynoting the part of the conference devoted to technology stacks. Our conversation naturally turned to MarTech Jenga, my recent random thought of using the popular stacking game to illustrate how marketers assemble their technologies. Lacking adult supervision, Sangram and I spent too much time on the actual game design and came up with a seemingly workable idea. We're still debating the details – Sangram favors simplicity and my style is more complex* – but here are the official rules for MarTech Jenga** at the moment. Public comment is welcome:

Object of the game: assemble the most complete marketing stack before everything collapses.

Equipment: standard set of Jenga blocks, divided into nine groups of six. Groups are numbered 1-9 and (optionally) assigned a color and type of marketing system. Blocks are marked on each end with their group number and color. Individual blocks can also be marked with the logo of a specific vendor*** although this has no effect on the game play. Numbers 1-4 are marked with an asterisk to indicate that those groups are required for a complete stack.****

Setup: blocks are stacked three-across in alternating directions, as in standard Jenga. 

Game play: each player in turn may remove one block from the stack or pass. Players retain all blocks they remove in their own stack, whose contents must remain visible to other players. Play continues until the stack falls or all players have passed in sequence.

Scoring: the player who causes the stack to collapse loses. If at least one player has acquired all the required blocks, then players who have not acquired all the required blocks lose (if this rule is adopted). Remaining players are given one point for each group that is present in their stack. There are no points for additional blocks within the same group. Player with the most points wins.

Sangram promises me that he’ll have some version of the game available at FlipMyFunnel, but I’m not holding him to it. On the other hand, this seems the perfect thing to have at a vendor booth. The opportunities for customization are self-evident, as are things like a leaderboard for high scores through the conference.  I'll leave the drinking game versions up to the Internet.

*No surprise there: he's a practitioner and I'm a consultant.

**The name Jenga is actually trademarked so we’ll have to pick something else if this ever gets beyond the blogging stage.

***a sponsorship opportunity.

**** Whether to have required blocks is a particular point of debate. It originally reflected the reality that certain types of systems are essential in a marketing stack. But from a game design point of view, it adds strategy considerations such as picking the required blocks sooner and blocking other opponents from completing their stack. That makes the game considerably more interesting.  The question is whether that takes too much thinking for a casual game. The only real way to resolve this is through play testing, where we could fiddle with a number of variables such as number of groups in total, number of required groups if any, and whether to play topless (i.e., allowing players to remove blocks from the top of the stack).

Thursday, June 25, 2015

Campaign Management Is Dead. Here's What Next-Generation Marketing Automation Looks Like.

Scientists tell us that the attention span of the average human is now shorter than the attention span of a goldfish.(1)  In such a world, the chances of anyone reading this 2,000 word blog post are pretty much nil. But I think the topic is extraordinarily important, so here is a summary in sushi-sized bites:

- the conventional flow-chart model of marketing campaigns can’t capture the complexity of today’s  disjointed customer journeys.

- a new approach is emerging that identifies stages in the customer journey and picks “plays” (small, highly targeted sets of treatments) to execute in specific situations within each stage

- this approach is easier for marketers to manage because it lets them think in smaller, more comprehensible units

- it will eventually lend itself to greater automation as machines take over more of the marketer's job in a “madtech” world

You can stop here if you need to watch an important cat video.  But if you want to understand my thinking in more detail, please read on.

The first modern campaign manager, Third Wave Network’s MIND, was released in 1991. What made it modern was a standard relational database(2) and multi-step campaigns that that sent users down different paths depending on their actions during the campaign. The system displayed each campaign as a set of boxes connected with lines to represent movement of customers from one step to the next. This flow chart interface has been fundamentally unchanged ever since and remains the gold standard of marketing automation(3).

The significance of the flow chart is what it replaced. The previous standard was a list of segments, each described on one row of a (paper) spreadsheet, with characteristics including selection criteria, description, key code, promotion materials, and quantity. Marketers filled out these sheets and handed them to programmers to execute. This was how direct mail marketers worked for decades, and often still do: it’s an efficient way to manage dozens or hundreds of cells within a large outbound campaign. It supports multiple contacts, such as mailing a second catalog to high-performing segments several weeks after the initial drop. But the list for the second mailing is pulled at the same time as the first, so it doesn’t allow changes in treatment based on subsequent customer behavior. This adjustment is what branching flow charts provide.

A quarter century after its introduction, the flow chart is now ripe for replacement. Flow charts assume that customers will follow a small number of predefined paths. This was realistic when interactions were limited to a few company-controlled touchpoints.  But it doesn’t describe today’s self-directed, random-walk journeys through an ever-shifting media landscape. In this environment, the best a company can do is react intelligently wherever a customer appears, taking into account both the current situation and whatever it knows about the customer’s past. Even company-initiated messages, while not wholly reactive, must be consistent with other treatments.

The basic features of a better approach have been obvious for years. I’ve long described it as “do the right thing, wait, and do the right thing again.” What’s changed is visibility: marketers now can see vastly more data about their customers and can interact with them through vastly more channels. In the “madtech” vision I’ve been articulating recently, this translates to assuming that all data about each customer’s demographics, interests, behaviors, locations, intentions, and other attributes is available to everyone. This includes both data a company gathers through direct interactions and data aggregated by third parties and offered for sale. Indeed, third party data is essential for building a complete picture of each customer’s experience.(4)

The vision similarly assumes that messages can be delivered through channels the company does not own directly. These extend beyond conventional advertising to private channels that other companies have opened to external messages. A concrete example would be third party offers delivered through a company’s Web site. My shorthand for this is “everything is biddable”: meaning that marketers can pay to embed a message within every interaction the customer has with anyone. Since all marketers have the same opportunity to bid on all impressions, a corollary is that buying the right messages at the right price is the key to success – or, in another catchphrase, “the smartest bidder wins”.

Sadly, neither you nor I can make a living by repeating clever phrases. Someone has to do the hard work of figuring out what treatments to deliver in each situation. A flow chart can’t come close because the situations are far too varied for any one chart to capture all the alternatives. What’s needed is a system that will assess each situation as it arises and come up with a custom-tailored solution.

If the only goal were maximizing immediate response, this would be pretty simple. Existing recommendation engines and predictive models can easily tell you which content a person is most likely to pick or which product they are most likely to buy. Today's products often do this with limited information about the individual being targeted, but that’s just a reflection of what data is currently available: at least some current systems could incorporate individual details and history without a major revision. There are practical details of speed, scope, and accuracy but the broad design of such a system is straightforward.  It connects with every touchpoint (including exchanges that deliver external opportunities up for bidding), receives information about each interaction, and returns an optimal message along with the value to bid for the right to deliver it.

But immediate response isn’t the only goal. Each interaction is embedded in the context of a customer’s relationship with the company. The best message is the one that maximizes the long-term value of that relationship. This won’t necessarily be the message with the highest response rate or greatest immediate financial value. Automated systems can incorporate long-term value in their recommendations but only if they are tracking long-term outcomes and analyzing what changes them. I believe – and this is the main point of this entire post – that automated analysis can only optimize for long-term outcomes if customer data is classified by stages in the customer journey. In other words, you can’t just randomly test all possible treatments in all situations and let the best approaches bubble to the top. There are simply too many variables for that to work. Rather, marketers must assign customers to journey stages and use those stages as inputs when selecting treatments and evaluating results. The stages themselves can be adjusted over time in the light of experience. But without a journey map as a starting point, marketers and their machines will flounder endlessly in a sea of big data.

Maybe you're unimpressed.  Maybe that cat video beckons.  Maybe you're thinking, "All you've done is change the labels.  A map of journey stages looks a lot like a campaign flow, and for that matter an old-style campaign funnel."  I understand your doubts. But there are significant differences:

- journey stages are not associated with specific messages, while campaign steps are.  In fact, the whole purpose of campaign steps is to define which messages are delivered when. So a journey stage is a significantly higher level of abstraction. Put another way, journey stages are just one factor in deciding how to treat a customer, while campaign steps are the only factor.

- journey stages are inherently random, while campaign flows and funnel stages are relentlessly linear. The goal of a campaign or funnel is push customers from one stage to the next as quickly as possible. Journey stages do track a funnel-type motion but they’re more intended to capture a set of customer needs and interests. In conformance with the general notion that customers will control their own movement, journey stages are more descriptive than directional. It’s no problem for a journey map if customers move back to an earlier stage or if they stay in one stage indefinitely.  For stages such as “satisfied customer”, that’s actually a good thing.

I do recognize that “journey stage” implies motion, which means it's probably not the best term for what I have in mind.  A more neutral term like "state" would be better.  But I’ll stick with journey for now because it will intuitively make more sense to most people.

So let’s assume, at least for sake of argument, that you’re convinced marketing systems should consider  journey stage when they’re picking the best message for a specific customer in a specific situation. Does that mean campaign flows can be replaced by any recommendation engine that adds journey stage to its list of customer attributes?

I think not. The system needs an intermediate level between broad journey stages such as “interested prospect”, “active buyer”, “satisfied customer”, and “advocate”, and actual treatments during single interactions. That level is needed because marketers have to create the content that the machines will choose from during those treatments. Marketers will decide what content to create by envisioning experiences that span multiple interactions and then creating content for a complete experience. The best analogy I’ve found is “plays” in a sport like football: tightly choreographed sets of actions that serve a narrow purpose. Teams prepare plays in advance and pick the right play for the moment but they don’t try to set the sequence of plays before the game begins. They know that games, like customers, follow their own unpredictable course and all the team can do is react appropriately in each situation. It’s true that each play is directed towards a long-term goal, but execution of the play is really a self-contained project. What’s critical is that the marketing play can incorporate multiple interactions over time; this allows a more coherent, richer customer experience than treating each interaction as wholly independent.

Good marketers and sales people already think this way, although they usually describe it in terms of tactics to handle different types of buyers, personas, or situations. I’ve also recently heard several innovative marketing system vendors describe approaches that I believe are fundamentally similar to this concept, again in different terminology. I’ll tentatively adopt the term “plays” precisely because it’s vendor-neutral and because I think most people are familiar enough with sports plays for the analogy to be helpful.

To clarify, then, I believe that marketers of the future will think in terms of broad customer journey stages (i.e., states), such as “active buyer” or “satisfied customer”. Each stage has strategic objectives, which might be to move an active buyer closer to purchase or to strengthen the relationship with a satisfied customer. Marketers will pursue those objectives through “plays” that are appropriate in specific situations, such as “active buyer requests a demonstration” or “satisfied customer has a service problem” and take into account other context such as location, device, and recent behaviors. They will create content and process flows to execute those plays.  These plays will resemble current multi-step campaigns but on a smaller scale and with narrower goals. This limited scale is exactly what makes plays so useful, because marketers can easily visualize each play as a whole. This lets them construct coherent sets of messages, rules and metrics to execute each play from start to finish, measure its impact, and make changes to optimize its effectiveness. Today’s massive nurture campaigns are too large and complicated to do the same.

Marketing systems of the future will also be designed to support this model. It may even happen that systems designed on this model come first and marketers adopt the model as they come to appreciate the systems. Or, because the stage/play model is especially well suited to automated campaign design and the general “madtech” world, perhaps it will be embedded in automated systems and grow as such systems are adopted.

Whatever the mechanism, the traditional campaign flow model has reached the end of its useful life.  I believe the stage/play model will be its successor.


(1) The study actually measured the attention span of average Canadians.  It didn’t specify the nationality of the goldfish.

(2) The preceding generation of campaign managers, called MCIF systems, used proprietary columnar databases to wring adequate performance from the PC hardware available at the time. The first of these, MPI Max$ell, was introduced in 1984; other major products included OKRA Marketing (1986), Customer Insight (1987), and Harte-Hanks P/CIS (1988). Interestingly, the height of sophistication among these products was a feature called “matrix marketing”, which identified the best offer to make each customer after each (monthly) update. Sound familiar?

(3) click here for my 1994 review of MIND and a couple significant contemporaries. If you add a reference to digital media, I could be describing any of today’s cutting-edge marketing automation products:

“At the core is a very powerful campaign management function that allows a marketer to define sequences of marketing events–each including a mix of direct mail, telephone contacts and personal sales efforts–to be followed in different circumstances, and then to automatically execute these sequences.

“The system uses an efficient graphical interface to lay out the alternate sequences that can be followed within each campaign, the tasks associated with each step in each sequence, and even the specific promotional materials used with each task. As a result, the marketer gains extremely precise control over the marketing approach used with each customer–including the ability to switch the customer to a different sequence depending on actions during the campaign.”

(4) I'm perfectly aware that reality will be messier than the vision implies.  Coverage will be incomplete, people won’t always be recognized across devices, and some predictions will be wrong. But perfection isn’t necessary for success: systems using this data only have to be more effective on average than systems that don’t.

Monday, June 22, 2015

Tealium Grows from Tag Manager to Customer Data Platform

It took me an embarrassingly long time to recognize why Tealium’s AudienceStream is just a bit odd. The oddity itself I saw immediately: while most marketing systems focus on sending messages to customers, AudienceStream is organized around overwriting data and sending it to other systems. The reason took longer to grasp: eventually I recalled that Tealium started as a tag management system and passing updated attributes to other systems is what tag managers do. So it’s perfectly natural for AudienceStream to offer precise control over which attributes are sent to which systems in which situations.

To some degree, the difference is just a matter of presentation. The data that Tealium sends to other systems can trigger customer messages from those systems.  So Tealium does support customer messaging.  But AudienceStream's heritage does give useful insights into what might otherwise seem a random set of strengths and weaknesses.

Let’s step back a bit. Like other tag management vendors, Tealium recognized several years ago that its core competency at capturing customer behavior could be applied to build unified customer profiles. This meant it could serve as a Customer Data Platform to support many other marketing applications. In fact, Tealium explicitly calls its product a CDP, using the tagline “build your own marketing cloud” to stress that it can connect systems from any vendor, not just components within a single vendor’s suite. That is indeed the essence of the CDP value proposition.

That doesn’t mean the Tealium is merely offering a relabeled tag manager. The company’s own diagram illustrates this clearly: the tag manager, Tealium iQ TMS (circled in red) is just one component of its CDP.  Here are key points to understand:

- scope is beyond Web channels. Conventional tags go on Web pages.  Tealium now offers connectors to gather data in other ways including APIs, batch file transfers, and system development kits (SDKs) for mobile apps.

- speed is real-time when possible. This is one benefit of the company’s tag management legacy, where real-time execution is required. It’s an important differentiator compared with other Customer Data Platforms, which often don’t support true real-time interactions.

- data storage is extensive. As the Tealium diagram implicitly indicates, a pure tag manager doesn’t need to permanently store much data. But Tealium incorporates several layers of persistent data, including semi-structured raw data in AudienceStore and EventStore (using Amazon Redshift), structured data in EventDB and AudienceDB, and an access layer in the AudienceStream iDMP (Data Management Platform).

- the system tracks individuals. The “i” in “iDMP” stands for individual, which pretty much says it all. Traditional DMPs are based on cookies, which are theoretically anonymous although they can often traced to specific individuals in practice. Tealium’s iDMP stores both cookies and personal identifiers such email addresses. It builds profiles by linking people to devices they have used for identity-revealing tasks such as opening an email or logging into an account. Devices used by the same person are then linked to each other. The system does not apply “probabilistic” or “fuzzy” matching to infer linkages using other data, such as simultaneous activities, shared locations, or similar names.  Users who want such links can import them from external services such as LiveRamp.  Tealium does link its cookies with major ad networks through other DMPs, although it won’t pass on individual identifiers.

- sophisticated rules support enhancement, segmentation, and triggers. This is another outgrowth of tag management, which sends different information to different partners. Derived attributes can be calculated with complex formulas including current data, time periods, ratios, rolling averages, random splits, and other functions. Similarly complex rules can assign people to audiences or segments and can set up triggers that change data values and send messages to external systems. Users can give higher priority to more important actions and can limit how many times an action is executed for the same person (for example, to avoid sending too many messages or repeating the same message). Again, this all happens in real time as data flows into the system.

- lots of connectors. Tealium cites integration with nearly 1,000 other systems. Most of these are for tag management but about 20 are to message delivery systems including ecommerce, email, marketing automation, CRM, and advertising vendors. Users can connect with additional systems through Webhooks, SDKs, or APIs. Reporting and analytical systems can query the underlying data directly, receive file extracts, or subscribe to a live stream from the collection server.

- it doesn’t do everything. Tealium offers a remarkably powerful data-layer CDP.  It extends a bit into decisions by applying segments, creating derived variables, setting up triggers, and sending audiences to execution systems. But it doesn’t do multi-step campaigns, predictive analytics, offer tracking, and supporting functions like content management. So, although AudienceStream can connect directly with message delivery systems to manage some types of campaigns, most users will combine it with a more robust decision system to properly manage relationships. It probably makes the most sense to view system’s segmentation, enhancement and (to a lesser degree) trigger capabilities as part of creating a rich customer database, not as directly managing customer relationships. This is perfectly consistent with Tealium’s own “do it yourself marketing cloud” goal of letting its clients work with whatever decision and delivery tools they wish.

Tealium introduced AudienceStream in late 2013.  It is currently used by about 100 of its 550 clients.  Pricing is based on events processed and starts around $12,000 for a small implementation. Mid-size and bigger customers can expect to pay more..

Friday, June 19, 2015

Mautic Offers Free, Open Source Marketing Automation

The only real question about free, open source marketing automation from Mautic is what took so long. The core features of B2B marketing automation have been well understood for nearly ten years and prices have been dropping steadily for about the same time. Open source has been successful in related applications including analytics (R, Jaspersoft, Pentaho), CRM (SugarCRM, vTiger) and Web content management (WordPress, Joomla, Drupal). Small businesses constantly cite cost as a major roadblock to adoption. So the opportunity seems obvious.

Mautic campaign builder

The reason for the delay may be as simple as the generous funding available to marketing automation start-ups.  This made commercial products more financially attractive to potential developers and probably scared off others who couldn’t compete for attention on an open-source shoestring. Or perhaps people felt that the real barriers to adoption were lack of time and skills, so even a free product would not unlock a large new segment of customers. The failure of freemium (though not open source) offerings from LoopFuse and Genius are strong evidence that being free is not enough. (See this post for more on that.)

Or maybe it’s just a matter of timing. David Hurley, the open source industry veteran behind Mautic, argues that marketing automation has just now become widely enough understood for many marketers to purchase it without a lengthy sales cycle.  I suspect that’s true but still wonder whether those buyers will know how to use a system effectively once they get it. Hurley's hope is that the user community will largely support itself through public forums and that service professionals such as consultants, agencies, and Web developers will fill the rest of the gap. Mautic certainly has an appeal for service vendors, since it removes the cost of payments to a HubSpot or Infusionsoft. Mautic will encourage such support by building a marketplace for users to sell or share resources such as workflows and templates. It is also putting together its own set of templates and workflows to help users get started.

What about the product itself? I promise I’ll get to that in a minute. But first let me cover one more business issue, which is that there are two ways to get Mautic. You can download the source code for free at, install it on your own server, and modify it as you please. Hurley said this has appealed to some large firms and government agencies who want to modify source code for themselves and to run an on-premise deployment. Or, you can sign up at, which will host a system with up to 2,500 contact names, one user, and three integrations for free. runs an enhanced version of the system called AllydeMautic, provided by Allyde, a for-profit business also run by Hurley. Starting this week, users can also buy a Pro version of AllydeMautic for $12 per month. This gets them further enhancements including unlimited database size, custom domains, additional integrations, and a second user. Allyde will eventually add other packages with more features. But pricing will remain well below standard marketing automation products.

None of this would matter if the actual Mautic product were no good. After all, the real cost of marketing automation is the time spent running the system and creating content and the real value comes from improved business results. In other words, a free system that wasted users’ time or produced substandard results would be a very costly investment. To judge whether Mautic is really a good deal, I set up a free account at and tested it for a bit.

My general impression was positive.  The interface is straightforward and intuitive, using tabs to make advanced features available without intimidating clutter. I do have some quibbles – most annoyingly, objects like emails and contact records open in a “view” rather than “edit” mode, so an extra mouse click is almost always needed to get real work done. But, for the most part, things worked efficiently and about as I expected.

The functions cover all the marketing automation bases: you can import contacts or enter them manually; assign them to lists; create emails, Web pages, and Web forms; upload other assets; assign points for lead scoring; build multi-step, branching campaign flows; and integrate with CRM systems.  I started with the lead import feature, which was rather barebones.  You can import a CSV file but not an Excel spreadsheet or other format; map the input file to database fields but not see a sample of the results; create custom fields but not custom tables; apply tags during the import but not link people within the same organization using a company field.  On the other hand, the system automatically imported pictures of people on my test list, presumably from public social media profiles. It apparently could have imported more social data if I had connected with my own Facebook, Twitter, or LinkedIn accounts.

Content building was more impressive. Users are presented with a free-form canvas to build emails or Web pages, with the usual controls for fonts, colors, inserted images, URL links, etc. Emails can include personalization tokens such as {first name}. Predesigned email templates give more structure in the form of blocks for headlines, body text, footers, unsubscribe messages, and viewing the email as a Web page. Template-based emails can also be linked to a landing page. Advanced tabs for emails let users specify the sender name, sender address, reply address, BCC address, and attachments. Emails and other content can be assigned to categories and given dates when they are published and unpublished. Forms can be attached to campaigns and users can specify where to send the visitor after a form is submitted. Forms support a variety of input types including fancier options such as radio buttons , checkboxes, and Captcha validation. There are some other features too: the set is pretty complete.

The campaign builder was better still. It offers a real drag-and-drop interface to build a flow chart with a modest list of actions (send email, update lead, push lead to integration, add or remove lead from a list, change campaigns, and adjust lead points).  The flow can branch on a few lead behaviors (downloads asset, opens email, submits form, visits page). Movement can be triggered by user behavior, happen after a specified number of days, or be scheduled for a specific date and time. The real power will come from pushing leads to external integrations with CRM, email, social media, and cloud storage. There are about twenty of these, including major vendors in each category. More will be added over time.

Lead scoring is also fairly powerful.  Scores can be adjusted by actions or triggers. The actions can be generic or specific: that is, being sent any email or being sent a specific email.  That’s better than some commercial systems, but doesn’t include advanced lead scoring features like limiting the number of points that generated by repeating an action or reducing points for past behaviors over time.  Triggers can be linked to reaching a specified point total.

All told, these features are enough to run a reasonable marketing automation program.  Unfortunately, my experience was marred by considerable bugginess: features to select a list and upload an image weren’t working when I tried them, although a note to tech support received prompt human response – under 15 minutes – and the issues were resolved within an hour. I might have been testing on a particularly bad day, but it still seemed odd that such basic features could be broken without anyone else noticing. Hurley said that Mautic has accrued 9,000 users since its first stable release in January 2015, but apparently few of them were active that evening.

Despite my positive impression, I have mixed feelings about Mautic.  I love the idea of open source marketing automation and think its time may finally have come. I generally liked the product, which combines simplicity with considerable power under the hood. The bugginess worries me a little but I assume it will be straightened out over time – and know that commercial products have bugs too.

My problem is two-fold. I missed the richness of commercial marketing automation products – even though I can’t identify a particular missing feature missing that Mautic needed to be effective. The glaring omissions such as CRM, ecommerce, and social are provided through integrations. But, even though I know that, the basic nature of Mautic leaves me uncomfortable.

The other issue is more concrete. I think new marketing automation users will need more hand holding than Mautic or Allyde can afford to provide or that the community will offer for free. The product should definitely help service vendors by reducing costs for their clients, assuming the service vendors decide it’s powerful enough for their purposes.  But I worry that unassisted small business users won’t know what to do with Mautic and won’t take the time to figure it out.  Remember that screening out uncommitted users is exactly why firms like Infusionsoft charge a substantial implementation fee – although the economics of Mautic are different because Allyed will invest almost nothing in acquiring or supporting new customers.

Then again, there are an awful lot of small businesses out there. Even a small fraction could be enough to support Mautic until it adds the features needed to serve a broader audience. So while I won’t necessarily recommend Mautic to many of my own friends and clients, I’m glad to have it as an option and will root for its success.

Thursday, June 11, 2015

Highspot Sales Enablement Helps Sales People Find Content and Marketers Measure What Works

“Sales enablement” is something of a catch-all term for a wide range of solutions that help sales people do their jobs better. Highspot has staked out the corner of this world occupied by systems that help sales people find the right marketing materials. It grew out of the pain that co-founder Robert Wahbe felt which running marketing for Microsoft’s Server and Tools Division, where he found no good tools to help sales people and channel partners find the right materials when they needed them.

When Highspot was founded in 2012, it focused on better content discovery for sales people. But the firm soon learned that this wasn’t enough. It has now redefined its core mission as improving results by showing which content is working.  This is currently measured by tracking how often each item is used by sales people and read by recipients. The July release will supplement this with opportunity information from CRM, allowing correlation of content usage with funnel stage conversions and revenue.

Highspot mostly does what you’d expect from this sort of system: it lets users load content and sales people, tracks who sends which content to which prospects, and reports on results. Users can set up collections (called “spots”) of materials for a particular product, sales team, funnel stage, region, or any other purpose. They can find content by looking in a spot, by filtering on sales stage, industry, product, and other attributes, or by doing an “intelligent semantic search” that recommends content based on past choices by the user and others. Users can view, download, bookmark or email the selected content or do a live pitch to a prospect. The system automatically adds pitches and emails to the prospect history in Salesforce.  It can also track when a piece of emailed content is opened by the prospect, how long they kept it open, and which pages they viewed. A dashboard can highlight new and featured content. The system will also analyze the inventory of available contents to find gaps or redundancies by sales stage, product, region, etc.

The operational details are all nicely executed, which is probably the most important consideration for a sales enablement system: if it's not easy, sales people won’t use it. But from a technology standpoint, what’s most interesting about Highspot is what the vendor calls “content genomics”. This uses machine learning to examine each piece of content – such as each slide in a Powerpoint deck – and identify properties including text, color, graphs, and images. Different pieces are then compared to find similarities and grouped into “content families”. This approach lets Highspot recognize when a piece has been modified and reused, for example by taking a slide from one deck and adding it to another with some reformatting along the way. Identifying these relationships gives a much more accurate understanding of how often each item is used and how well it is performing. Without this grouping, results from the system could be highly misleading.

Highspot now has more than 100 paying customers. The system is now sold primarily to marketing departments as the system of record for marketing content. There’s a limited function Business Edition and a full function Enterprise Edition, which includes Salesforce integration. Pricing for the Enterprise Edition isn’t published but the vendor says that, once volume discounts are included, it is usually less than the $30 per month per user charged for the Business Edition.

Tuesday, June 02, 2015

Blueshift Offers a Simple B2C Customer Data Platform

[Note: this post is from 2015.  Click here for a newer post about BlueShift.]

It’s just over two years since I started writing about Customer Data Platforms. One thing that’s become clear since then is that only big companies will purchase a marketing database by itself. Everyone else wants to combine the database with a practical application. B2B CDPs have favored analytical applications like lead scores and churn predictions. B2C CDPs have often included campaign engines that manage triggers, query-based segmentations, and multi-step program flows in addition to predictive models. But even the B2C CDPs rely on external systems such as email agents and Web content managers to deliver the campaign messages.

Blueshift fits nicely into the B2C CDP mold: it builds a multisource database, incorporates machine learning-based predictive models, uses filters to create segments, and runs multi-step campaigns that are executed by external systems in email, SMS, mobile apps, and display and Facebook retargeting. What sets Blueshift apart – and this is typical of later entrants to a new market – are a lower price point and simpler operation than early B2C CDPs like RedPoint  and AgilOne.

How low? Pricing for the most basic version of Blueshift starts at $999 per month.  The most advanced version starts at $3,999 per month for all features and up to 1 million “active users” across all channels.  (The company says that most clients are in fact larger than one million users, with the largest at 100 million.)  The fact that prices are published is itself a mark of a later entrant.

How simple? Well, one measure is implementation time.  Blueshift says can be operational in as one day (if data is loaded through an existing Web page tag or push-button integration with Segment) or under two weeks if some work is required.  Technically, this is plausible: the system has JSON API that can accept pretty much anything and will put it into MongoDB and/or Postgres with minimal data modeling.

Another measure of simplicity is the campaign building interface.  Blueshift lets users specify a sequence of steps by filling out forms to define the segment, channel, and content template for each step and time between steps. This is nowhere near as pretty or flexible as graphical flow charts, but does qualify as simple.

Segments are also built using forms to define one or more filters.  Again, nothing fancy but it gets the job done. What’s more important is that the segments can use a wide range of data including online behaviors, attributes from CRM and other systems, predictive model scores, and product information from catalogs.  This is what gives the system its power.  Content templates do incorporate some visualization, as well as tokens for personalization and machine learning-based product recommendations. Split testing, ecommerce integration, and predictive models for activation, churn and repeat purchase are available in advanced versions of the system. Reports show model performance and attributes, segment counts, and campaign results using several basic attribution methods.

So, apart from some missing bells and whistles, what doesn’t Blueshift do? The main limit is that it works only with known individuals (i.e., those reachable through an email or SMS address, app registration, or similar identifier) and primarily in outbound channels. This means that Web display ads, site personalization, and anonymous visitor targeting aren’t part of the mix, aside from retargeting. And, while data and models are updated continuously, the system isn’t designed to manage real-time interactions.

Blueshift was launched earlier this year.  It has more than ten clients, who are mostly multi-channel marketers with a majority of revenue from mobile payments.

In sum, Blueshift isn’t the fanciest marketing system available but it provides a solid mix of highly usable features at a reasonable price. B2C marketers will find it worth a look.