Wednesday, December 17, 2014

Do Self-Driving Cars Pick Their Own Gas Stations?

I had a delightful and well-lubricated dinner this week with Scott Brinker of @chiefmartec fame, ostensibly to discuss the next edition of the MarTech Conference but mostly just to chat about the industry and what comes next.

Scott wasn’t too impressed by my notion of an advertising-supported toaster (see my last blog post), even though I pointed out you could segment the messages based on the type of bread the person was eating. On the other hand, I was very intrigued by his notion of marketing through services such as alerting a driver when they need gas and where to find the most suitable gas station.

Where that example got interesting was when we added self-driving cars to the mix: why couldn’t the car take itself out for gas when the driver isn’t using it, or indeed, take itself on other chores like state inspections, oil changes, and scheduled maintenance? And if it does that, how will it pick the supplier?   Sure, the owner could specify in advance, but won’t at least some owners want the car to find the best price on gasoline or respond to special offers such as coupons?

If you do give the car some discretion, how do you know it won’t make choices based on its own preferences?  Perhaps it will favor the gas station that wipes its windshield or gives it a free tire rotation, which you have to suspect feels mighty good to an auto. Indeed, how do you know your car isn't taking Uber jobs on the side, or drag racing with its car friends from the other side of town who you never really liked?  Could the manufacturer have some involvement in this, pocketing that Uber revenue or biasing those purchase decisions in return for payment from suppliers? More generally, when devices become autonomous, do marketers still address their owners or are there ways to sell to things themselves? 

There’s at least a bad science fiction story in all this (“Do self-driving cars pick their own gas stations?” with apologies to Philip K. Dick)., which I naturally proposed to Scott as a short video for the next MarTech conference.  He didn't exactly leap at the chance.

But there are also more serious issues and opportunities to consider. Perhaps interruptive marketing really will be replaced by embedded services and subscriptions which will make product selection and purchase timing decisions without the owners being involved. In some ways, it already happens: think about the choices that a doctor makes when selecting your treatments or building contractor makes when constructing your house. We already know there is plenty of trade advertising to affect those choices. As more decisions get delegated to automated agents, this may be an area we can learn from. But of course, it won’t be exactly the same, so there will be plenty of new approaches to pioneer as well.

This is definitely the kind of discussion to have over drinks. I can’t go into details but rest assured that Scott’s plans for the next MarTech conference do take this into account.

Saturday, December 13, 2014

BlueConic User-Driven Marketing Maturity Model: Surprises on the Road to Customer-Centric Marketing

I’m as fond of hearing my voice as most consultants, which is very fond indeed. But the best part of my recent presentation with BlueConic was listening to the voice of someone else’s experience: in this case, the experience of more than 60 BlueConic clients, distilled into a maturity model that traced the stages they passed through on their way to full customer-centric marketing. (Click here to see the Webinar and download the related paper.)

The good thing about hearing from someone else is you find out things you didn’t already know. In this case, I was certainly familiar with the general notion of a maturity model, as a sequence of increasingly-sophisticated stages that companies pass through on their way to the highest level. And, for what BlueConic calls “user-driven marketing”, I already knew that the final stage would be a central database and decision engine that gather data from all channels and select the treatments that each channel delivers. So it wasn’t too hard to imagine that the preceding stages would start with totally disconnected channels and move slowly to complete integration. But there were still some new insights from BlueConic’s hands-on experience. Some that particularly struck me are:
  • Listening first. The very first stage of the model, Level 0, involves no differentiation at all: every customer is treated the same; in fact, customers may not even be identified. BlueConic gets involved at Level 1, where treatments are tailored to the individual but each interaction managed independently within each channel. At that stage, all the central marketing system can do is “listen” to customer activities and make the data it assembles available to the channel systems to help guide their own decisions. I would have expected the central system to actually drive decisions at that stage, but BlueConic's experience is different.
  • Coordination later. Level 2 of BlueConic’s model still has each channel running separately, which again is a bit surprising. What changes at this level is that  interactions within each channel are now coordinated by the central engine. It’s only at Level 3 that interactions are coordinated across channels, and even then the scope is limited to online channels. On reflection, an intra-channel-only Level 2 makes sense: marketers need several new skills to design and measure multi-interaction programs, and mastering those is a big enough challenge without also adding the complexity of managing across channels.
  • Segmentation. The growing importance of segmentation at successive model stages was perhaps my biggest surprise. When I think of tailoring interactions to individuals, I think of working with each individual’s data directly. Segments don’t enter into it. But, as BlueConic’s experience reminds us, practical marketing tasks like content creation, program flows, and result analysis are organized around groups of similar customers. This ensures resources are spent effectively and you have enough volume to measure results meaningfully. In fact, the segments get increasingly refined with each maturity level as behavioral data is added (Level 2), segments are adjusted in real time (Level 3), and segments include predictions and events (Level 4). Thus, the process does move closer to treating each individual differently, but always in a segment-based framework.
  • Complexity of data. This was less a surprise than an observation. Part of the presentation was a set of examples presented by BlueConic CMO Dan Gilmartin. By the time we got to Level 4, where interactions are being coordinated across all brands as well as all interactions in all online and offline channels, the example was offering a soccer jersey as a holiday gift idea to a mom reading a lifestyle Web site. Superficially, this seems like a simple, obvious thing to do.  But, on reflection, it’s amazingly complex. It requires not just knowing who the viewer is, but who she’s related to (child or spouse), the interests of that related person (soccer), and the temporal context (holiday gift buying season). That is some pretty fancy data management.

Not everything in the model surprised me. In particular, BlueConic’s experience confirmed the importance of process and organizational change to support the new technologies. BlueConic reported a steady expansion of the scope of measurements from tracking response to independent interactions (Level 1) to tracking movement through the customer journey (Levels 2 and 3) to measuring the incremental impact of each interaction on customer lifetime value (Level 4). Similarly, it showed a shift in management perspective from optimizing results for individual interactions (Level 1) to each channel (Level 2) to maximizing value for the organization as a whole (Levels 3 and 4). And, finally, it reflected a shift in control from channel managers operating more or less independently to central managers who focus on customers and segments. This all ties back to the central notion of the maturity model: that technology, process, and organization must all be aligned at each stage for the business to execute effectively.

By all means, download the Webinar and white paper, which contain plenty of insights beyond those I've just described.  Incidentally, if you're wondering about that interactive toaster, I was already aware that you could get static custom images on bread and have since discovered that there are some higher tech options.  I see no technical reason one of these couldn't be connected to the Internet to deliver dynamic messages sent by an advertiser, significant others, or favorite government agency. 

Monday, December 01, 2014

Radius Provides High Quality Data on Small Businesses

When I first spoke with Radius just over one year ago, the company had already pivoted from its initial concept as a mobile app to connect consumers with local business events, to building a comprehensive list of small businesses and their attributes. Fast-forward twelve months and the company has again adjusted its offering, now presenting itself as a “marketing intelligence platform” that helps business marketers find prospects who are similar to their current buyers. This latest vision was appealing enough to attract $54.7 million in funding in September, bringing the announced total to over $80 million. So I’m guessing Radius will stick with this approach for a while.

What Radius does will sound broadly familiar to loyal readers of this blog: it scans social media, Web pages, government records, and other online sources to build a list of more than 20 million U.S. businesses and their attributes. It supplements these with conventional data sources to capture businesses with a limited digital profile. In its current incarnation, Radius also imports a list of won and lost deals from each client’s CRM system (direct connection to, batch imports from others) and shows how well each attribute correlates with success.

Users can review the attribute list, create segments based on attributes, and analyze the attributes of each segment as a group. They can also flag existing segment members within the client’s current CRM database and import segment members who are not already in the client’s CRM (a.k.a. “net new prospects”). The imported records include basic company information and other attributes the client has preselected, but the system will not correct or enhance existing CRM records. Segment membership is adjusted automatically as Radius updates its data, which happens weekly. The system does not store fixed lists of segment members at a point in time, although users could achieve this by tagging records in CRM as they are imported. 

And that’s pretty much it. No list of the most important attributes, no predictive modeling, one contact name per company, no alerts based on buying signals, no campaign analysis: just company information compared to your own customers, an way to build segments, and an option to purchase new prospects. The company plans to address some of these gaps but has not released the details.

Whatever its limits, Radius has attracted some big-name customers, most notably American Express, as well as all that funding. The primary reason seems to be data quality: the company says it can usually match 80% to 90% of the businesses in a well-maintained CRM system and that client tests have shown it is more accurate than competitors. This is both impressive and important, especially where small businesses are concerned. Available data includes basics (address, phone, industry, company size, revenue, contact name), Web activity (presence of a Web site, Facebook and Twitter accounts, use of daily deals and check ins, and average review ratings), and technologies used.

The system has some other advantages.  New clients are deployed in 24 hours, including the time to import CRM data and calculate success rates by attribute. The user interface is attractive and intuitive.  Pricing starts at $15,000 per year for small enterprises.  It is based on the number of company records in the client database, so it doesn’t increase based on how heavily the system is used.  It also includes use of the system software and credits for some number of new prospects imported from the Radius database.

In short, Radius strikes me as a solid solution for what it does, which is provide targeted company-level prospect lists and profiles of your current customer base. If that’s what you want, take a closer look. If you want to know more about trigger events or individual contacts or want lead scoring or other types of predictive modeling, you’ll probably be happier with something else.

Friday, November 21, 2014

Sailthru Offers End-to-End Omnichannel Personalization for B2C Marketers

I know this is blasphemy, but I’m beginning to have doubts about solution selling – the idea that marketers should describe the customer problems they solve, not the features of their products. The issue, at least in marketing technology, is that all systems address pretty much the same general problem of sending the right messages to the right customers (in the right time, place, medium, device, language, tone, etc.). This means that solution statements sound pretty much alike, even when the actual products are different. It’s left up to the poor buyer to figure out what each product does and whether that is something she truly needs.

Sailthru is a good example. The corporate home page says “Sailthru makes it easy to personalize every channel for every customer,” which is accurate enough.  But plenty of other companies also help with omni-channel personalization.   A marketer looking for personalization solutions might add Sailthru to her list of options, but wouldn’t know whether it's a good or poor fit without digging much deeper..

On the other hand, resolving that sort of ambiguity is what keeps me in business.   Perhaps I shouldn't complain.  In any event, there are several differentiators that determine whether a product like Sailthru is suitable for a particular situation.

  • customer profiles. Sailthru builds a history of information about individual customers.  You  might think that would be done by all personalization systems but it's possible to do something that can reasonably be called “personalization” using only anonymous information such as traffic source, search terms, location, or Web pages viewed during a visit. Sailthru goes beyond this to store behaviors over time.  These are linked across channels to a customer identity that is usually known at the start of an interaction. The identity might be available because the customer is interacting with a mobile app for which she has registered, is responding to an email or text message that was already tied to her identity, has logged into an ecommerce Web site, or is known through a cookie that was previously linked to her identity. Sailthru generally does not deal with anonymous customers. It can store several identifiers for the same customer, which is how it coordinates interactions in different channels. The identifiers would be linked through “hard” matches such as an email address provided when registering a mobile app or an ID number embedded in a Web link in an email.  "Fuzzy" matching, which attempts to link identifiers that have not been directly connected, is generally avoided by Sailthru.

  • data store. Sailthru stores data in MongoDB, a “No SQL” database that can handle nearly any data type and can easily add new “fields” (not really the correct term) without formally defining them in advance. This makes it extremely flexible, which is very important in the fluid world of marketing information. Mongo is also fast and scalable and good for analytical processing in general. A fair number of multi-channel personalization systems use Mongo or something similar, but many others use conventional relational databases (which are less flexible) or other data stores.

  • data sources. Sailthru gathers most of its data from its own tags placed within emails, Web pages, and mobile apps. This distinguishes it from systems that rely primarily on feeds from external systems via API connectors or batch files. Technical users can still set up a feed using API calls or JSON posts when necessary. Prebuilt integrations are available for Magento ecommerce and WordPress, with others on the way. There are no standard integrations for marketing automation or CRM. The system usually stores emails sent, Web pages visited, purchases, content read, and mobile interactions. The system can scan, classify and tag company’s marketing contents and then use the tags to build a customer’s content consumption profile. It can do similar tracking based on merchandise category tags from ecommerce systems. Users can also set up custom variables derived from original inputs.

  • predictions. Sailthru has a recommendation engine that uses customer history to suggest the product or content they are most likely to select next. It has recently released the beta version of a predictive platform that automatically generates probability scores, rankings, and estimated values for nine actions such as making a purchase within the next 24 hours, opting out of future contacts within the next week, and expected revenue within the next thirty days. These can be used in segmentation and message selection. General release of the prediction tool is planned for early 2015. Predictive models are rebuilt and records are rescored nightly, with no changes during the day in response to new customer activity. Recommendations do adjust in real time to customer behaviors. The system can create control groups to measure the impact of recommendations on long-term customer behavior. These predictive capabilities are among the biggest differentiators for Sailthru: predictions and recommendations are oriented to consumer marketing, not B2B lead scoring, and Sailthru doesn’t (yet) allow clients to choose what they wish to predict. On the other hand, the modeling is fully automated, while many other systems require at least some manual set-up for each new model.

  • message selection. Users can define lists based on any data in the system and then send a specified email or mobile message to each list. They can also export lists for Facebook promotions or to other channels.  Messages and Web pages can contain real-time recommendations and can also adjust their contents based on data and scripts written in Sailthru’s own Zephyr language.  Marketers should look closely at this aspect of Sailthru: while powerful, it's not creating rules to send different content to different segments, doesn’t send sequences of messages over time, and doesn’t support real-time interaction flows.*  Be sure you're getting the personalization features you need.
  • message delivery. Sailthru builds and delivers email, mobile, and Web messages directly, rather than sending lists or recommendations to other systems. Many marketers will like this,  since it avoids the need to integrate with another product. But marketers who have want to use other delivery platforms may not be happy.  This is one reason I haven’t classified Sailthru as a customer data platform: although Sailthru does a great job of building a unified customer database, most CDPs are specifically designed to work with other systems when sending messages.

  • data access. Sailthru lets clients export lists based on profile data, can display individual customer profiles, and provides some limited API access to the profiles. But it doesn’t support mass exports of the profiles or allow external queries of the profile database. This is the other and more important reason I don’t consider Sailthru a CDP: making the database available to external systems is the very core of the CDP concept.

  • pricing and company background. Sailthru was founded in 2008. It currently has about 400 clients, mostly in ecommerce and media. Pricing is based on the number of active profiles and (unlike many personalization products) does not increase as clients support more channels. Prices begin around $30,000 per year. 
* More precisely, it can't do those things within a single campaign flow, or what Sailthru calls a "smart strategy".  Each strategy identifies a single event which triggers a single action, such as sending an email.  Sending different emails to different customer segments would require setting up a separate strategy for each segment, each linked to its own message.**  Similarly, a sequence of messages would require setting up a separate strategy for each step.  In both cases, it would be up to the user to write event conditions that ensure the right people are selected each time.  Real time interactions would also need to be created by defining criteria that link a series of unconnected events.

**Although users could use the Zephr scripting language to build a single message that delivers different versions to different people.  This is functionally similar to delivering different messages but is harder to manage since the variations are buried within the script.

Sunday, November 16, 2014

Hushly Helps Marketers Connect With Anonymous Web Site Visitors

When this blog last left Geoff Rego in 2010, he had just sold the assets of pioneering B2B marketing automation vendor Market2Lead to Oracle. Since then, he’s been gnawing at the bone of anonymous business leads, suspecting that there’s some way to gain value from people who are interested in a product but haven’t identified themselves to vendors. Rego has shown me a couple of approaches over the past few years, none of which quite worked out. But when we saw each other at Dreamforce last month, it seemed he had settled on a keeper.

The new product is Hushly, which addresses the reluctance of prospects to provide their email address even in return for valuable content. This is the gating dilemma: more people will read your content if it’s not gated, but you don’t capture their email address unless you gate. In its current form, Hushly waits until visitors have abandoned a content form and then pops up an offer for them to download anonymously via Hushly. Visitors determined to remain unknown can view the content online, while those willing give Hushly their email address can actually download and save it.  Making the offer after the form is abandoned ensures that Hushly only captures people who would not otherwise have connected with the company directly.

Once a Hushly member has downloaded vendor content, vendors can send emails to the member via Hushly.  This allows communication without the vendor actually receiving the member's email address.  Members can block messages from a vendor if they wish.

Hushly also lets members send questions to vendors and receive answers through the system, still without revealing their identity. They can even contact competitive vendors with the same protection. The system lets members send a list of questions to multiple vendors and tabulates the results, allowing more detailed anonymous research. 

Each member gets a Hushly library to store their downloaded documents and communications. Members can grant other people access to their library for collaboration. On the vendor side, Hushly creates anonymous lead records in the client’s instance, so companies can track their interactions with anonymous prospects and keep the history once the prospect identifies herself. The system can integrate with other CRM vendors through batch file transfers.

All told, I think Hushly is a pretty clever idea. The concept takes a bit of explaining to potential members, which could be a barrier to success. There’s also a chance that people simply won’t believe Hushly’s promises to protect members’ privacy – not because of anything about Hushly but simply because they distrust of Web services in general.  Happily, both obstacles can be overcome through good marketing.  Rego reports that initial results show Hushly can confidently guarantee a 200% increase in distribution of gated content and 20% increase in identified leads. So it looks like enough potential users are receptive to make things interesting.

Hushly has been deployed in various forms by more than 200 companies since early 2014. Set-up is quite simple: users associate content with Hushly widget, which they embed in a landing page. Pricing is based on the number of form abandons, starting at $200 per month for 100 abandoners and falling on a per-abandoner basis as volumes increase.

Tuesday, November 04, 2014

Lytics Adds Marketing Recommendations to a Customer Data Platform

It’s just over one year since I first spoke with Lytics*, which at that time was (accurately) calling itself a Customer Data Platform but had not yet released a beta version of its product. The company has been busy since then, raising $7 million to supplement its initial $2.2 million funding, enrolling about 30 beta clients, releasing its initial system and a new self-service option, developing an automated process to recommend marketing programs to its clients, and abandoning the CDP label to call itself a “marketing activation platform”. CEO James McDermot said the label was changed because big companies thought a CDP sounded like an IT project, not something run by marketers. Fair enough, but Lytics still perfectly fits my definition of a CDP: a marketer-controlled system that supports external marketing execution based on persistent, cross-channel customer data.

In fact, Lytics could pretty much the poster child for the CDP concept. While many CDPs also provide some execution services, Lytics draws a sharp distinction between its core data layer, supporting analytics, and message delivery.  Data and analytics are included in the system; execution is not.  Also in the CDP spirit, Lytics makes extensive use of external products within its data and analytics layers, relying on third party systems to connect social media, email and postal identities; to import social and Web site data; for reporting; and to do natural language processing. All told, the company has prebuilt connectors with more than 80 software-as-a-service products. Execution systems on the list include, Marketo, Eloqua, Act-On, Facebook, Twitter, Youtube, Demandware, Optimizely, Adobe Target, and most major email providers.

But perhaps I’m getting ahead of myself.  I should really start with what Lytics does.  Basically, it imports data from multiple sources, builds a consolidated profile for each customer, tracks individual behavior over time, builds segments of customers with similar behaviors, and makes those segments available to external systems for marketing messaging. It uses several data storage technologies, including Cassandra, Elasticsearch, and Titan Graph DB, to handle large amounts of structured and unstructured data. It combines its own identity matching techniques with third party resources to consolidate the profiles across channels, add more data, and extract meaning from text. It lets users define and extract audience segments and can push alerts to execution systems as customers change audience segments in real time.

Lytics would be a perfectly fine CDP if it did nothing beyond what I’ve just listed. But the system actually takes two additional steps – and is tip-toeing towards a third – that make it quite exceptional.

The first step is to summarize customer behavior with scores for interaction momentum, quantity, frequency, responsiveness, and intensity. These are combined to create about thirty segments, such as “burn out” customers, defined as people with high intensity and low momentum. The segments can be further qualified based on what addresses are available (email, postal, phone, Facebook, etc.) and on other profile data specified by the user. The resulting audiences give marketers a structured way to manage customer treatments.

The second step is to actually recommend those customer treatments. Lytics has a database of marketing tactics, such as reengagement programs for dormant users or upsell programs for active users. It looks at existing audience segments and the execution tools the client has in place, and calculates which tactics to which audiences in which channels would yield the highest results. It then recommends the most promising options to the client, who can activate the suggested program with the push of a button. This isn't actual program execution: Lytics only sends the audience to the selected tool, where the client must still set up the program and its message. But it's still a big stride towards helping marketers make choices that otherwise depend entirely on their own expertise. This is important because shortage of marketers with adequate skills has been a major stumbling block for many advanced marketing technologies.

The third step, which Lytics hasn’t yet taken, is to select the content itself.  McDermot was quite adamant that the company is not in the content recommendation business, leaving that marketers’ creativity. But he did say Lytics is experimenting with a “content graph” that classifies content and shows how it is related to individuals, which suggests the system will eventually be able to make some suggestions. There are other capabilities Lytics would need to make optimal content recommendations, notably decision rules to address business goals such as selling excess inventory or satisfying unhappy customers. These don’t seem to be on the company’s radar. But they could appear as it moves ahead.

So, about that self-service option. This might Lytics’ most impressive news of all. The initial release of the system was targeted at large enterprises and relied on traditional programing to connect with external systems using APIs. McDermot and I didn't discuss pricing but you can be sure it was in the five or six figures.  The self-service version enables automatic connections to the 80+ partners already in place. Pricing is based on the number of customer profiles and channels managed and includes all the existing connectors. It starts at a shockingly affordable $1,000 per month, making Lytics an option for just about any business. Combined with the product’s predictive scoring and tactic recommendations, this could empower a huge number of marketers whose firms couldn't previously afford a powerful marketing database and the integrations needed to make it useful.  We'll see how this plays out, but Lytics could be revolutionary indeed.

* not to be confused with Lityx, which offers LityxIQ cloud-based predictive modeling and data management and is worth a look in its own right.

Saturday, November 01, 2014

Seven Marketing Automation Myths to Ignore - Illustrated Edition

I’m sad.

I’ll be giving a speech in Milwaukee next week on marketing automation myths, and early in the preparation process had the idea of illustrating it with mythical creatures from the films of Ray Harryhausen, the stop-action animation genius whose best known images are probably the skeleton warriors in Jason and the Argonauts (1963).

This led to many pleasant hours scrolling through galleries of Harryhausen images. I even found an illustration that vaguely matched the theme of each myth.

But there’s a problem. Every bit of presentation-giving advice, training, and experience I’ve ever had tells me that these illustrations will distract attention from my points rather than reinforcing them. The responsible adult inside of me knows I have to get rid of them while the fun-loving child says, Yeah, but they're just so cool. 

This blog post is my compromise: I’ll publish them here, which will make dropping them from the actual presentation much less painful. **sigh**

So, the illustrated version of my talk goes like this:

Marketing Automation Myth Busting: we start with Mighty Joe Young, Harryhausen’s 1949 tribute to King Kong. I could tell you he’s about to smash some myths, but who are we kidding? It’s just a great image. 

Trouble in Paradise: marketing automation is growing quickly but users are dissatisfied. Maybe that’s not as bad as being attacked by a giant crab, but it’s still problematic. Image from Mysterious Island (1961).

Myth: All systems are the same.  This is an easy mistake because systems all look and sound alike during the buying process. But in fact they differ greatly. The myth leads buyers to think it doesn’t matter which system they purchase, and therefore that they can buy without first defining their requirements. In fact, our research shows that unsatisfied marketers often have purchased a system that didn’t meet their needs. Conversely, the most satisfied users did select based on specific features. The image here is Cyclops from The Seventh Voyage of Sinbad (1958). He has vision problems; it’s hard to see the differences between marketing automation systems. Get it?

Myth: Integration is easy. This echoes the first: all marketing automation products integrate with CRM, so people assume they don’t have to look into the details. But products differ hugely in which systems they connect with, what data they import and export, and how much control uses have over the details. Integration is the single most commonly cited obstacle to success and is linked to the most dissatisfied users. So people really need to ensure that the system they’re buying meets their integration needs. Kali from The Golden Voyage of Sinbad (1974) coordinates fighting with six arms, so she is the goddess of successful integration.
Myth: Failure is the user's fault, not the system's. This myth follows from the first two: if all systems are the same, then failure must be fault of the user. But, as we’ve seen, systems aren’t the same and many failures result from a system that doesn’t meet the user’s needs. Other research shows that users generally overcome obstacles they can control, like organization, training, and staffing levels. Of course, system selection is itself done by users, so they do have some responsibility for any problems. Talos, an animated statue from Jason and the Argonauts, ultimately fails to protect the tomb he is built to guard, so he represents a system that doesn’t work.

Myth: New users should crawl, walk, run.  Many experts – myself included – have suggested that new marketing automation users can safely start without planning by just duplicating their existing programs like email blasts, and then add more sophisticated uses over time.  But our research found that marketers who used more features from the start were happier. My interpretation is that successful marketers took the time to plan and train before deployment, while marketers who didn’t prepare in advance never found the time to learn what they needed. It’s possible to overstate this position – even successful users will add some new features over time. But the point about preparation is important. Kraken, from Clash of the Titans (1981), is a sea monster with no legs, so he never had a chance to move beyond crawling.

Myth: Bigger companies do better.  You might expect that bigger companies would do a better job with marketing automation because they have larger and more sophisticated staffs. They do in fact select more wisely, paying more attention to features and integration than marketers from smaller companies, and less to cost and apparent ease of learning. But they also face more non-technical obstacles such as training, staffing, and organizational barriers. So their over-all satisfaction level is no higher than smaller firms. I chose the giant octopus from It Came from Beneath the Sea (1955) because it’s big – no deeper meaning is intended.

Myth: Marketing automation creates prospects and saves money.  Marketers who expect their system to generate more prospects with less effort are usually disappointed. Marketing automation is basically about nurturing existing leads, not finding new ones, and most companies add staff and budget. Medusa, from Clash of the Titans, is the boss you don’t want to give bad news about system results: her dirty look will turn you to stone.

Myth: Marketing automation has stopped evolving.  Commoditization and consolidation may make marketing automation look like a mature industry.   But there's still plenty of change: new vendors entering the space, existing vendors being bought and repositioning themselves, and expanding scope to include consumer marketing, display ads, external data, better databases, identity resolution across channels, mobile apps and formats, advanced attribution, social promotions and new types of content.  The Beast from 20,000 Fathoms (1953) is a dinosaur who hasn’t evolved one bit.
So what? That’s the end of the myths, but we need to leave on a positive note.  So I end the presentation with some sound, if predictable, advice to prepare carefully, define and select against actual requirements, test integration in advance, deploy quickly, and expect the unexpected. The puzzled look on Troglodyte’s face, from Sinbad and the Eye of the Tiger (1977), represents the confusion marketers feel when wondering what to do next..
Marketers who want help selecting a system could try blowing on a ram's horn like Calibos from Clash of the Titans. Or they can just send me an email at

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Speaking of art that's amusing if irrelevant, here's a link to a Twilight Zone-themed introduction to a football recruiting show produced by my son Brian.  The apple doesn't fall far from the tree.