Pointillist Journey Orchestration Discovers Customer Paths for Itself (Marketing Automation is Doomed, I Tell You)

This post will resume the tour I started in March of journey orchestration engines – our new friend JOE. But first I’ll interrupt myself to announce that I have officially decided to predict that JOEs will replace campaign management and marketing automation as the core system for marketing departments. I usually hedge my bets with this sort of prediction, but will abandon my typical caution because I’m convinced that campaign management and marketing automation are too deeply rooted in the old world of batch list generation to meet today’s need for continuous optimization of customer treatments. Their core architectures just aren’t up to it.

I’m not saying that JOEs will have no competition.  Plenty of other vendors have the potential to make the transition – in particular, products developed for real time interactions and Web personalization. I am saying the competition won’t come from today’s campaign management and marketing automation leaders.*

Now that I’ve shared this exciting bit of news with you, let’s get back to the topic at hand. That would be Pointillist, a just-released “customer intelligence platform” that has been incubating inside financial services technology vendor Altisource since 2014.

As Pointillist’s self-chosen label suggests, its own roots are in customer data analytics, not execution. Sure enough, the system is built around a custom data structure that (if my notes are accurate) they describe as a “combination graph relational time series”, which certainly sounds like something out of Dr. Who.

Put in terms simple enough for me to understand, Pointillist stores all data as events, which can have  attributes including customers, products and campaigns. Different event types contain different sets of attributes but there are no formal data tables or relationships among tables. This sounds broadly like Hadoop and other NoSQL data stores, although I’m sure there are Important Technical Differences that matter deeply to people care about such things. What matters from a marketer’s perspective is this approach makes it easy to add new types of information and to update information very quickly.

Also as with Hadoop and friends, the Pointillist data store needs some added structure to allow fast access and analysis, and that structure imposes some limitations. Pointillist has optimized for customer analysis, meaning that customer behaviors can be analyzed almost instantly but combining information about customers is harder. For example, it could be tough to find out which products two customers bought in common. All data is stored persistently on a disk somewhere in the Amazon cloud, but accessible data is loaded into memory.  This makes things really quick.

That’s probably more than you care or need to know about Pointillist’s technology. Let’s get back to the surface where things are bright and shiny. What makes Pointillist a journey orchestration engine is that it can describe and act against customer journeys. The acting part is especially important, because it makes Pointillist more than simply an analysis tool.

What Pointillist really does from a user point of view is let you pick sets of customers and events to analyze. Users drag the events onto a workspace and connect them with lines to indicate the sequence to analyze. The system then scans its data to find how many customers had an instance of each event and draws lines whose thickness indicates how many passed customers from one event to the next. In other words, it creates a journey map.

Or, and this is my favorite part, you can tell Pointillist to discover the most important paths on its own.  It does this using magic machine learning to determine which paths have the highest combination of frequency, exclusivity, and correlation to a goal (a user-specified event). Users can adjust the balance among those three factors and can further train the algorithm by telling it which connections they feel are important. Because Pointillist is doing the analysis in memory and considerately visualizes its results, you can  watch it test different connections until it settles on a final set. Hours of fun, for sure.

But there’s more. Pointillist can report on the disposition of people within each event, replacing its icon with a little circle graph showing how many people reached the final goal, moved to the next event (but never reached the goal), dropped out, or stayed behind. It can also display other statistics in graphs next to the flow diagram, as well as letting users analyze subsets of the audience or even a trace the path of a single individual. The analysis can run backwards or forwards, finding either where an initial set of customers ended up or where a final set of customers came from. Heck, that’s weeks of fun when you think about it.

Taking action within Pointillist works exactly as you’d think: for any event on the chart, the system can generate a list of customers that it will send to an external system. The list could include all people in the event or a subset with specified behaviors. When I spoke with Pointillist a few weeks ago, the list would be a file export, but API connections were close to being ready. They’ll probably be done by the time you read this.

Also under development when we spoke was an automated cluster builder that would find clusters most related to (or distant from) the target event. This is different, and often more useful, than traditional clusters that find groups that are similar or distant from each other. Pointillist was also working on letting users create calculated variables, such as a lifetime value or engagement score, that would be available for analysis or segmentation. And on automated tools to help load unstructured data and clean dirty data. And on connectors to push data out to other systems. And on fuzzy matching to supplement the existing, and quite powerful, tools to unify customer data from different sources. Because nobody ever had too much fun.

Speaking of data loading, Pointillist has its own Javascript tag to capture Web behaviors, uses third party connectors to import data from many common systems, and can import batch files from nearly any source. Mapping new event types requires some basic technical skills but Pointillist is working to make it simpler. While APIs to push data to other systems are under development, APIs that let external systems pull data from Pointillist are already available.  These can access customer data but not other data types (remember those special data structures?)

In short, Pointillist both builds a robust, unified customer database and presents exceptional tools to analyze and act on the customer journey. It is the very model of a modern journey orchestrator.

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*Nor, to be perfectly clear, am I saying that campaign management and marketing automation systems will go away. They will simply retreat to the execution layer where they will deliver emails and other outbound messages, playing an important role alongside other channel delivery systems like Web sites and call centers. But the fundamental decisions about which messages to send will be made by the orchestration engines.

Survey of Surveys: Budgets and Process are Main Barriers to Marketing Technology Success

I recently gave a Web presentation comprised almost entirely of slides from different surveys. This was a bit of an experiment and, sad to say, it didn’t seem terribly successful. I did weave the slides into a nice little story line – marketers know they need better technology, poor data is the root of their problem, and we know how to solve this – but even that wasn’t enough. Pity.

Still, preparing the slides gave me a chance to scan the surveys in my archives, which was entertaining in its own little way. Many surveys ask similar questions, which gave me some choices during my preparation. But I didn’t look carefully at how they compare.

Today I’ll do that. I’ve chosen one of the most popular questions: what are the barriers to marketing technology adoption? I have versions of this from seven different surveys within the past year.

Of course, each survey uses different terms. To make the comparison, I collapsed the various answers into a few reasonably-distinct categories, committing a certain amount of shoe-horning along the way. I then recorded where each answer ranked in each survey, compiled the results, and did a crude ranking with a combination of mathematical wizardly and body english.  (Multiple answers for the same survey indicate I placed several questions into the same category.)

Results are below.  I've shaded the first ranked answers in orange and the second and third ranked answers in yellow.

My first observation was the sheer inconsistency of the answers. Budget issues emerged as a clear number one, but they reached that rank on just four of the seven surveys and ranked quite low on the other two that included them. The second-ranked item (marketing process) was never listed first; it ended where it did because it had the most twos and threes. No other item was ranked first more than once or in the top three more than twice.

Things made a bit more sense when I looked at the survey audiences. Winterberry and Forrester were specifically about online marketing, Gleanster and Marketing Sherpa were B2B surveys, and IBM and the two CMO Council studies were of general marketers. Since most B2B marketing is also online, it makes sense to look at the first four as one group and the other three as another.

Now we see some interesting consistencies:

• Budget isn’t much of an issue for the online and B2B marketers, but dominant for the mixed marketers.

• Marketing process and marketing staff skills are major concerns for online and B2B but rarely mentioned by the mixed marketers.

• Senior management support, and to a lesser extent IT support and technology capabilities, are significant barriers for mixed marketers but don’t slow down the online and B2B groups.

• Metrics, organizational silos, and the economy are cited occasionally by both groups but don’t seem to be major issues for either.

So there’s a fairly coherent picture after all.

• Online and B2B marketers are struggling to keep up with a rapidly changing marketplace, meaning their biggest problems are people and process. The importance of their work is obvious enough that budgets and senior management support are generally available. They have the technical savvy and independence to avoid issues with IT support and organizational silos.

• Mixed marketers, working in traditional channels, still struggle with budgets, metrics, and senior management. They have mature marketing organizations, so process and skills are in place, at least for traditional programs. They do struggle more with IT, technology, and organizational silos, because they lack their own technical skills and have limited clout in the organization.

• Everybody says they care about metrics but it's rarely a top priority.


Or at least that’s my take. I’ve displayed the actual surveys below – if you reach other conclusions or spot any other patterns, let me know.

SDL Buys Marketing Automation Vendor Alterian for $107 Million

So, it turns out that while I’ve been obsessing over vendor selection workbooks, our friends at marketing automation vendor Alterian up and got bought last week by language technology vendor SDL for about $107 million. Why didn't somebody tell me?

I’m most familiar with SDL as a Web content management vendor, although their financial statements show that just over 75% of their revenue comes from manual and automated language translation. The company had more than $300 million revenue last year and is nicely profitable.

Alterian hasn’t been doing so well lately, with about $55 million revenue for the past year and cash-basis loss around $6 million. Management has also been in flux: CEO David Eldridge resigned in April, a new CEO Heath Davies was named in July, and president and co-founder Michael Talbot resigned in October. The company was nearing the end of a 100 day restructuring plan that dropped its headcount from 440 to 260.  It had also taken several red-flag accounting actions including restating revenue, changing its revenue recognition policy, and taking large asset write-downs.

You math whizzes out there will have already noted that the purchase price is just under 2x revenue, compared with the 5x-ish prices paid a year ago for Unica and Aprimo. Whether this puts a damper on the prospective valuations of other marketing automation vendors is hard to say: Alterian was obviously struggling, and its main business model was to license its software to marketing service providers rather than selling it directly or via Software as a Service. On the other hand, Alterian did have some SaaS components to its business, notably SM2 social media monitoring (formerly Techrigy).

Alterian also had a bold vision of extending beyond traditional campaign management and analytics to include marketing resource management and web content management as well as social media. I’d still argue the strategy was correct, but that Alterian didn’t have the financial resources or market clout to execute it. Certainly its costs got ahead of its revenue: at 440 employees on $55 million revenue, it had just $125,000 revenue per employee, compared with the $200,000 I consider standard (see my post from last January on revenue ratios -- even at that time, when Alterian had just 370 employees, it was already below par.)

SDL’s chairman is quoted as saying that “The marketing analytics, campaign management and social media were the big attractions” of Alterian, so presumably the company will keep those businesses. The content management piece, about 27% of Alterian sales, will presumably be merged with SDL’s much larger Web content management business.

The big question for the marketing services providers who are Alterian’s primary customer base is how SDL will treat them, since they are not SDL’s current core clients. That’s more than a little scary, especially given the dearth of alternative mid-priced marketing automation systems for consumer marketers. (See my list of B2C vendors from September and my discussion of the differences between B2B and B2C marketing automation from October.)

On the brighter side, I can argue that the Alterian acquisition supports my long-standing contention that marketing automation and Web content management will eventually coalesce into a single system. Any gloating is restrained by the fact that Alterian had already combined the two and didn’t succeed. But this probably just shows that deep pockets will be needed to pull off the combination in a world where the competitors are heavyweights like IBM, Oracle, Adobe, SAS and Teradata.

ExactTarget Survey: Lack of Skills Slows Growth of Digital Marketing

Summary: a new survey from ExactTarget shows that digital marketing is growing faster than database marketing or mass media, and that agencies have a harder time adding digital capabilities than their clients. It also suggests that marketers are moving into digital channels even when they can’t measure their value very well. No surprises in any of this, but good to see confirmation of previous research.

I really and truly was going to drop the topic of moving from database to digital marketing, but then I saw a survey last week from email vendor ExactTarget which reinforced several of my key points. (You can buy the complete survey from Econsultancy. A detailed slide show is available here for free, at least as I write this.) Key findings include:

- digital marketing budgets are growing faster than marketing in general (66% plan to increase their digital budget in 2010, vs 46% planning to increase their total marketing budget). Database marketing channels (email, direct mail and telephone) are growing at lower rates (54%, 27% and 26% plan to increase, respectively), while mass media (television, newspapers/magazines and radio) are lagging the most (20%, 17% and 15%).

Note that these are just the percentage of companies planning a budget increase; the actual average increase in digital budget was 17%. The average proportion of budget spent on digital was 24%, which is higher than other figures I’ve seen, suggesting the respondents were more digitally oriented than the industry as a whole.

- lack of skills is the key impediment to digital growth: lack of staff, company culture and lack of digital understanding were three of top four problems (after lack of budget, which was number 1). Inability to measure ROI and lack of business case ranked only ahead of “other”.

What is preventing your company from investing more money in digital marketing?

40% restricted budget for all types of marketing
35% lack of staff to make most of any digital investment
32% company culture
25% lack of understanding about digital
20% reliance on traditional marketing
16% inability to measure return on investment
9% lack of business case / case studies around digital
7% other

- agencies are more constrained than marketers by lack of skills. “Lack of understanding about digital” was cited by 45% of agency respondents, compared with about 13% of client-side marketers.* My interpretation is that clients can always go and hire a digital agency if they need to add the expertise, while the agencies themselves find it much harder to expand their offerings.


In fact, although 35% of both groups apparently cited “lack of staff” as a problem, they may mean different things. Agencies are probably referring to lack of staff with digital marketing skills. Client-side marketers probably mean lack of staff to oversee digital programs executed by an outside agency.

- The fastest-growing digital channels (social media and mobile) are the least measurable. In fact, there’s an almost inverted relationship between growth rates and measurability. This probably reflects that fact the fastest-growing channels are the newest, with least-established measurement methods, rather than a perverse hostility to measurability.


In this context, it’s also worth noting that agencies felt much more hobbled by lack of ROI and business cases than client-side marketers, and that a very-hard-to-believe 65% said their company measures marketing effectiveness based on ROI. These further reinforce the view that marketing measurement isn’t a top priority when moving into new digital channels.


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* The published materials show total and agency figures. I've estimated values for client-side marketers based on the numbers of respondents reported for the two groups: 648 client-side, 385 agency/supplier-side. This won’t be precisely correct, since everybody didn’t answer every question. Hence that -3% response to "lack of business case" for client-side.

Coremetrics Survey: Online Marketers Eager to Consolidate Data Across Channels

Summary: a survey sponsored by Coremetrics shows that online marketers are eager to merge data from multiple sources. This is the long-term solution to closing the gap between database and digital marketers.

I was debating yet another post on database vs digital marketing when I saw a Direct Newsline headline that said “Online Marketers Talk The Talk, But Don't Walk The Walk”. The accompanying article suggested the online marketers don’t give personalization a high priority, which supports the theme of my last few posts. Sweet.

But reality proves a bit more complex.

The article referred to a survey of online marketers sponsored by Web analytics vendor Coremetrics. As the headline suggests, about three-quarters of the marketers listed personalized email, display advertising and onsite pages as a high priority, but just under half are actually using them. So, yes, there’s more talking than walking.


But a closer look* shows that the “future priority” numbers are also related to current deployment: items like basic email marketing have low future priority scores because they’re already in widespread use. So the apparent discrepancy in the personalization rankings is less because online marketers don’t really care about it, than because they’ve had other, more fundamental things to do first.

If I were feeling particularly tendentious, I could argue other data in survey supports my claim that digital marketers are relatively disinterested in personalization. For example, “manual onsite cross-selling promotions and product recommendations” has a higher deployment rate (63%) than “manual onsite personalized content and recommendations” (49%). But a simpler explanation is that personalized recommendations are just technically harder. Indeed, the two “technology-driven” options, recommendations based on individual behavior and on “wisdom of the clouds”, have the lowest of all current deployment rates.

That said, it’s still interesting that the survey shows personalized email (52% deployed) as not significantly more common than personalized advertising (50%) or personalized site content (49%). This seems to contradict my position: if email is run by personalization-oriented database marketers, while Web advertising and (perhaps) site content are run by behavioral-targeting-oriented digital marketers, then email personalization should be more common.

But the actual question asks about email, display advertising and onsite content which are personalized "based on individual online behavior”. This adds the additional constraint of whether marketers have been able to tie (mostly anonymous) online behavior to other channels. That constraint applies across all the delivery channels, and is likely why the deployment rates are so similar. Surely the vast majority marketers are personalizing their email using information in their databases, particularly if you extend the definition of "personalization" to include segmentation that determines which messages are sent to whom.

A separate question asked marketers to rate the importance of automating different marketing tools.


What's interesting about those answers is that five of the top six didn't involve individual-level data: three are about campaign, channel and vendor performance, and the other two are about search keywords in aggregate. The only exception, "personalized content or product recommendations based on online behavior" is based on reusing data within a single channel, which means that individuals need not be personally identified. (The survey makes clear that its definition of "personalization" includes treatments based on anonymous behavior tracking.) Actually, the two applications that do rely on consolidating personal data across channels are the lowest ranked of all the options presented. I'd say this supports my fundamental contention that digital marketers are mostly concerned about non-personal, channel-specific applications.

On the other hand, respondents did rate “obtaining an integrated view of customers across online marketing touch points” as their highest challenge, or at least as a tie with measuring marketing impact. Since it was only listed by 45% of the respondents, I could speculate that those might have been the database (email) marketers in the group, while the digital (Web) marketers could have all ignored it.

But I’m not inclined to bother: I have no problem believing that digital marketers are perfectly willing, even eager, to consolidate data across channels when it’s possible. My main point is consolidation is generally not possible because most digital touchpoints do not collect identifiable, addressable information. (See yesterdays’ post for my definitions of those terms.) And, because consolidated data is often not available, the digital marketers have learned to work without it.


By contrast, Coremetrics is focused on a future (or, perhaps, imaginary) world where data-gathering techniques have improved. Coremetrics is arguing, and I fully agree, that consolidating data across channels does add value and that marketers should be willing to invest in making it happen.

In fact, if I hadn’t seen the survey this morning, my intent was to write about the convergence of database and digital marketing, precisely because digital marketers are increasingly aware of the value and possibilities of working from a consolidated database. So even though I’ve been arguing that database and digital marketing today are quite different, I do think they’ll become more similar over time as each group learns from the other. The marketers themselves are already leading in that direction, and vendors who want to survive will surely follow.

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* very close indeed. Sorry for the small print in the charts. It's the best I could do. The actual data is available in the surveys.

Clarifying the Differences Between Database and Digital Marketing

Summary: Database and digital marketing are both data-driven. But they differ in plenty of other ways that make it hard for specialists in one to adapt smoothly to the other. Here's a detailed look at the differences.

Yesterday’s long (or merely long-winded?) post described the different mindsets of database and digital marketers but it was pretty short on differences between the two marketing methods themselves. Today I’ll try to be more concrete.

DB or Not DB

Database marketing is built around a marketing database that contains addressable, identifiable individuals. By “addressable”, I mean there is information such as a mailing address or phone number that lets the marketer contact the individual. By “identifiable”, I mean information is available to link data about the same individual from multiple sources. Addresses are the most common identifiable information, although there are also non-address identifiers such as Social Security Number. Addresses and identifiers are both required: a database without addresses couldn’t be used for most marketing, and a set of records that can’t be linked to other sources is just a list.

The consolidated database is the heart of the database marketing concept. Data from multiple sources lets database marketers make more effective predictions about the best treatments for each individual, and treatments across multiple channels are more effective when they are coordinated centrally. The marketing database contains attributes (age, income, location, etc.) and behaviors (promotion responses, purchases, customer service interactions, etc.). It can certainly include digital activities such as Web page views and social media comments, so long as these can be linked back to a known individual.

Digital marketing does not use a database of addressable, identifiable individuals. It may gather information from one source and even track it over time for the same entity. (Example: Web site behavior tied to a browser cookie.) But unless the entity can be linked to other sources through an identifier, the digital marketer can only make treatment decisions based on information captured in the source channel itself. This is far from useless – behavioral and contextual targeting can be quite powerful. But from a database marketing perspective, the data is frustratingly incomplete.

Addressable Media

Database marketing only works in addressable media: that is, where a message can sent to a specific individual. Addressable media include direct mail, email, outbound telemarketing, and customer service interactions. They can also include digital channels such as Web pages, mobile messages, kiosks and ATM machines, but ONLY where the recipient is known before a message is sent. Thus, a Web page that has identified me because I’ve registered and logged in (manually or via a cookie) is addressable; a Web page that I visit anonymously, even if it recognizes me as a previous visitor from a cookie, is not addressable.

Digital marketing includes many non-addressable media, including paid and organic search, Web banner advertising, social media, and anonymous forms of Web sites, kiosks, mobile (e.g., location-based messages), and the rest. These generate plenty of useful data, such as click through rates, search rankings, sentiment analysis, and page views. But this data and related analysis are quite different from what database marketers are used to.

Prediction vs Reaction

Database marketers have the rich information needed to accurately predict which offers are most appropriate for each customer. Combined with their access to customer addresses, this allows them to initiate effective outbound marketing campaigns and to define static rules for interactive dialogs. Note that in most addressable media (mail, email, outbound telemarketing), the offer must be selected before the customer is actually contacted, and making multiple offers often reduces response. So database marketers have strong reasons to work on making highly accurate predictions.

By definition, digital marketers cannot target outbound campaigns at individuals. They do have opportunities to manage interactions, but often know only what has happened during the current interaction itself. This greatly reduces their ability to make predictions. Instead, they present multiple options and react as people respond. Happily, most digital media are inherently interactive, so this is a practical approach. Since rule-based decision flows are less viable as the number of options increases, digital marketers lean more heavily on self-adjusting automated decision engines.

Message Control

Database marketers directly control the messages they send to each customer. This is yet another factor that helps to justify the costs of building a comprehensive database, running sophisticated predictive models and precisely customizing each message.

Digital marketers have vastly less control over who sees what. Much of their messaging is blind to the audience who will see it, or can only be targeted on limited information about behavior or context. Indeed, some of the most effective and intriguing digital marketing techniques, such as viral campaigns and shareable widgets, rely on distribution that's totally beyond the marketer's control. Social media provide even less control, since the messages themselves are composed outside the company. The net result of all this is to reduce the degree of individual targeting that digital marketers can execute.

Response Measurement

Database marketers can typically capture response to a promotion directly, with a coupon, telephone call or Web click. Even when they can’t, their database still ultimately tells them who bought what, so they can correlate the promotions they’ve addressed to an individual with that individual’s subsequent behavior. The ability to do precise response measurement is yet another factor that lets database marketers fine-tune their programs.

Digital marketers can also measure who clicks on a Web ad, and sometimes can track that person further into the buying cycle. But they don’t know what other promotions or social media that person saw, what else they purchased, who else saw the same promotion but didn’t respond, or who responded through some other channel. All these uncertainties leave digital marketers reliant on indirect measures, such as consumer panels and surveys, which are more typical of conventional mass media. These are approaches that most database marketers would find almost laughably imprecise.

What’s It All Mean?

Database marketers and digital marketers both have plenty of data and the good ones are highly analytical. Both can apply advanced statistical techniques and rigorous testing methods. Both can work to integrate their data and their customer strategies across channels. To some extent, they even work with the same media: in particular, a Web site can support both digital (anonymous) and database-driven (addressable) marketing programs.

Yet despite these similarities and interactions, the two groups work in largely different media, use different techniques and have different priorities. Database marketing is inherently more controlled and precise; digital marketing is more fluid. Good marketers will learn to apply both. But individuals who have specialized in any one area will find it hard to adjust to the other. At a minimum, they’ll need to be conscious that the old rules don’t apply.

Adjustment is even harder for organizations, who will have invested in specialized systems, processes and people to support one technique or the other. This, in my opinion, is why the leading database marketing vendors have not been the leading digital marketing vendors. Which, if you’ll recall, was where I started this discussion.

One final point: there's no reason the same organization or individual can't master both database and digital marketing. That is, although there are major differences between the two, there is no fundamental conflict. My point in these articles is simply that it will take conscious effort to address the differences and fill the gaps that they imply.

Can Database Marketers Learn Digital Tricks?

Summary: Database marketing and digital marketing are more different than it seems. It's hard for experts in one to adjust to the other.

Yesterday’s post touched briefly on what I see as a fundamental transition between database marketing and digital marketing, and in particular on the changes that marketers and their supporting vendors must make to navigate the change successfully. This is an important topic, so I thought I’d return for a closer look.

It’s self-evident that digital marketing (mostly on the Internet, but also mobile, in-game, and eventually interactive TV) is a major change from both traditional mass media and more recent database marketing (mail, email, telemarketing, CRM). What’s less obvious is that the skills and attitudes that have served database marketers well for the past twenty or more years – an entire career for many – don’t transfer to the digital world. It’s true that database and digital marketing are both technology-enabled and thus seem as if they should draw on similar talents. But the similarities are superficial while the differences are profound.

Let’s cut to the core of the matter: the first rule of database marketing is that whoever has the biggest database, wins. Database marketers strive to gather ever-more information about their customers and (to a lesser extent, because less data is available) about their prospects. Their Holy Grail is the ever-receding “360 degree view of the customer,” a phrase I’ve always disliked because (a) it treats the customer as an object and (b) no one can possibly know everything about their customers. Today, at least to my mind, it also conjures up a full-body scan X-ray, an image I hope enough people find so offensive that it will finally put the phrase to rest.

Sorry for the rant. My point is that database marketers’ ideal is a perfectly detailed customer database, which would allow them to target precisely the “right offer to the right customer at the right time.” This attitude leads to highly structured, finely segmented campaigns and carefully-plotted, rules-driven interaction flows which make the best possible use of whatever data is actually available.

Digital marketers have no such illusions about the completeness of the data they could ever hope to assemble. I’m not saying many of them wouldn’t like to identify each person they interact with, just that this is obviously impossible in most situations. Thus, digital marketers start from a premise that they’ll be interacting with people cloaked by varying degrees of anonymity, and look for ways to make the best use of the limited information available. In one case this might a search term they used to reach a Web site; in another it might be a history of movies they and others have rented; in yet another it might be their current physical location. Most innovations in digital marketing involve improving the value extracted from such limited data, rather than attempting to link the data to an identity that can then be enhanced with large volumes of personal information from other sources.

(Caveat: yes, there are some major efforts aimed precisely at providing digital marketers with individual identities. But these run up against both the fundamental difficulty of identifying people in most digital media. Even more important, their value is limited because immediate past data about behavior and context is usually more powerful at predicting immediate future behavior than static personal information from external sources.)

A corollary to the limited and contextual nature of most digital customer data is that marketing programs don’t have enough information to make reliable predictions about the most appropriate treatments. Thus, multi-step marketing campaigns or highly structured interaction dialogs are less useful than simply giving people a variety of choices and letting them guide the process for themselves. Again, this is a matter of degree: deciding which choices to present itself requires predictions about which items the customers will prefer. But presenting multiple choices is quite different from trying to guess in advance which one is best.

In other words, we’re talking about a loss of control over the marketing process. This is still more obvious at the start of the marketing cycle, when companies are first attracting customers into a relationship. Database marketers spend lots of effort acquiring and enhancing prospect lists so they can decide whom to approach and which offers to send them. By contrast, most digital marketing contacts are initiated by the prospects themselves in response to an advertisement or social media message. Certainly digital marketers can select their advertising audiences, but this resembles traditional media buying more than an outbound direct marketing campaign. Even (or, perhaps, especially) with social media interactions, the marketer has very little control over what is communicated to whom.

Indeed, even though database marketers do plenty of acquisition, I think it’s fair to say that they find it relatively frustrating because the available data is generally so limited. Most would probably prefer to work on customer management – cross sell, upsell and retention – where richer data is available. By contrast, digital marketers have happily embraced the notion of “inbound marketing”, which is precisely the art of attracting new people to their products. To speculate still further, the reason that business marketers are adopting marketing automation much more enthusiastically than they ever adopted traditional database marketing may be that business marketing automation is largely being used in acquisition-friendly digital media, and business marketers are more acquisition-oriented (i.e., focused on lead generation) than their consumer marketing brethren.

Control is also a major differentiator when it comes to marketing measurement. Perhaps the proudest claim of database marketers is that all their efforts are highly and precisely measurable. Reality is a bit more messy, but it’s true that database marketing does support proper champion/challenger testing for companies willing to make the investment. Digital marketing also supports such testing. But many digital efforts involve display advertising where at least some of the value comes from exposures that do not prompt immediate, measurable activity. This is another area where digital marketing more closely resembles traditional mass media advertising than anything else. In fact, digital marketers increasingly base their measurements on consumer panels and surveys, almost precisely duplicating the conventional mass media approach. Again, the fundamental point is a difference in attitude: database marketers treat precise measurement as their ideal, even though they realize it isn’t fully attainable. Digital marketing doesn’t permit that illusion, so its practitioners can more easily accept less exact approaches.

By now I’ve probably annoyed many of my friends in both the database and digital marketing industries. Let me make clear that I’m not arguing that database marketing is obsolete or somehow inferior to digital marketing. They do different things and will coexist, just as mass media survived when database marketing appeared. In fact, good marketers will learn to integrate them effectively, letting each do what it does best. Actually, I’d argue that rule- and data-driven Website personalization has more in common with classic database marketing than with most digital marketing methods. In that case, integration between the two types of marketing happens within the Web site itself.

Nor am I arguing that database and digital marketing have nothing in common. Both are, obviously, dependent on technology and both are measurable in their own ways. Both work with customer databases – in fact, as digital marketers get better at capturing and integrating customer data, they will find themselves increasingly reliant on database marketing techniques. And, of course, both ultimately perform the basic marketing tasks of understanding their customers and using that knowledge effectively.

Rather, I’m trying to show that different skills and assumptions are needed for success in the two areas, and to suggest that this makes it difficult for people and organizations to transition from one to the other. This, in my opinion, is why the direct marketing agencies, marketing service providers and marketing software vendors who dominate the database marketing industry have not transferred their leadership to the digital marketing channels. The only new medium they easily adopted was email, but that was essentially database marketing to begin with.

This doesn’t mean that database marketing vendors are inevitably doomed or trapped in a shrinking specialty. But it does mean that those firms must recognize the fundamental differences between their old industry and the new one. They cannot make the easy but false assumption that digital marketing is a natural extension of database marketing techniques. Only the marketers and vendors who aggressively embrace digital marketing in its own terms will be able to lead the new industry.