Today’s marketers are increasingly – perhaps excessively – focused on exploring social media. Efforts to date have largely aimed at attracting attention, both through viral promotions (think: YouTube videos) and longer-term programs to build relationships (friends, fans and followers). But more sophisticated marketers are already looking at applications beyond message delivery. Here are some challenges they’ll face.

- response tracking. Marketers want to know how many people have seen their messages, how many have reacted, and what those reactions were. At the simplest level, this means tracking actions such as page views, recommendations such as “likes” and “plus signs”, registrations as friends or followers, and content sharing by emailing a link or posting it on their home page. Services like bitly and ShareThis make it easy to track sharing behavior, including re-sharing by people who received the original share. Some services can trace the re-shares back to the original sharer, providing a measure of individual influence.

- audience profiles. Traffic counts are interesting, but marketers care even more about whom they are reaching. Direct audiences include visitors to your Web site, readers of the company blog, and social media connections. Some direct audiences come with an ID that can link to an existing profile, either within the same system (a Facebook friend) or by matching to external data (a LinkedIn profile). In other cases, audience members remain anonymous but it’s still possible to gather data such as location or company (based on IP address), interest (based on search terms), or approximate demographics (based on the referring Web site). Indirect audiences include people discussing or reading about you in forums beyond your control, including blogs, news sites, and interest groups. Vendors like Quantcast and Compete.com build profiles of Web site audiences; in other cases, a forum host may make this data available.

- monitoring. Marketers may give a higher priority to talking than listening, but the good ones do both. In the social media world, monitoring involves scanning blogs, public forums, and discussion groups for mentions of the company, its products, and competitors. The simplest forms of monitoring count these mentions, which can be useful to measure mindshare vs. competitors, track public attention during a crisis, and read the awareness generated by an outbound campaign.

- content analysis. More advanced monitoring goes beyond counting to evaluate the content of social messages. This can identify topics and report on positive or negative attitudes. Content analysis is sometimes done manually, but this gets expensive when message volumes are large. Automated content analysis relies on semantic techniques to make sense of natural language. These systems already do a good job at some tasks, such as extracting keywords to identify topics and products mentioned. More subtle interpretations, such as understanding positive or negative sentiments, are still problematic.

- connections. Social networks often expose formal relationships among individuals, such as whether they are friends or follow each other. But this data can be difficult to process effectively using standard relational databases. Alternative database engines have been designed for social analysis, including Cassandra, FluidDB, and Neo4j. Their key capability is to navigate a network of social connections, making it (relatively) easy to identify friends of friends or friends with shared attributes. This supports analyses and selections that are very difficult or resource-intensive using standard database technologies.

- traffic analysis. Formal relationships are just the start of social analysis. It’s often more interesting to understand the interactions among related individuals: how often they message each other, the size or duration of those messages, whether messages are broadcast to a group or directed at individuals, whether there are patterns and how these change, and so on. Traffic analysis is often used by military, security and law enforcement agencies to infer organizational relationships. But it can also be used by marketers to identify influencers and to track dissemination of marketing messages.

- influence. The number of connections someone has, the number of messages they transmit, and the number of visitors they receive are crude measures of influence. More subtle metrics include how often someone’s messages are retransmitted, linked to, or commented upon, as well as who is doing the retransmitting, linking, and commenting, and how those patterns are changing over time. Vendors including Klout, Social Report, and PeerIndex provide various influence measures.

- case management. Social media are increasingly used for customer service. In fact, since “service” interactions on social media are often visible to the public, the distinction between marketing and service has almost vanished. Social media systems therefore increasingly include a case management component, allowing company staff to identify, track and interact with individuals over time. These interactions could also be managed through a conventional customer service system, but the public nature of social interactions means additional supervision is needed to protect the company’s image. Social media cases also extend beyond service interactions to include sales conversations and conversations with influencers, such as press, bloggers, and expert users.

- cross platform identities. Most individuals maintain separate identities for different social media systems, as well as other channels such as email, telephone, and postal address. Even though major vendors including Facebook and Google offer unified sign-in services, substantial fragmentation is likely to continue. This means that marketers who want to build complete relationship profiles will still need to capture and cross-reference individual identities across platforms. Systems are already in place to handle this sort of identity resolution outside of social media, so it’s most likely that social identities will be managed within the same framework.

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David M. Raab is a consultant specializing in marketing technology and analytics and author of the B2B Marketing Automation Vendor Selection Tool (www.raabguide.com). He can be reached at draab@raabassociates.com.

Data quality has never been easy, but it used to be simple: marketers defined quality as the completeness and accuracy of information in their systems. This reflected the simple goal of the marketing database itself, which was to create a central location holding all information about each customer. This unified view provided the foundation for all customer treatments, ensuring a consistent, coordinated experience across all channels throughout the customer life cycle.

But marketing is no longer so simple. Companies now interact with customers and prospects in many situations where the individual is not tied to an existing marketing database record. These include anonymous Web site visits, social content consumption or creation, appearance at known geographic locations, and membership in behaviorally-targeted Web audience segments. Although individuals can sometimes be identified in those situations, the programs are valuable even when they cannot. Pragmatic marketers will, and should, continue run them regardless of whether they fit the standard database marketing paradigm.

For the marketing database, the result is something like the transition from academic portraits, which stressed hyper-accurate portrayal of the details of each individual, to cubism, which presented a simultaneous view of loosely-connected fragments. Accuracy and completeness are reasonable criteria for evaluating a classical portrait by Ingres, but make little sense for judging a Picasso. As with modern art, modern marketing databases require a revolution in terminology to discuss them productively.

The revolution in marketing databases comes down to one word: effectiveness. An effective marketing program is worth doing even if you can’t link the resulting revenue to a customer profile in the marketing database. Of course, the desire for marketing effectiveness is no newer than the search for Truth in art. Both have been with us forever and both have seemed equally elusive. But while art remains a matter of taste, it’s now possible to assess the value of many marketing programs with enough precision to be useful.

The new, quasi-anonymous marketing programs are still data-driven. You may not know precisely who is reading your Tweets, passing by your store, visiting your Web site, in a cookie-based audience segment, or abandoning your shopping carts. But you know enough to send them a targeted message and to track the results. This knowledge is embodied in data: and where there’s data, there’s data quality.

What has changed is that the relevant data quality metrics are tied to the specific programs, while metrics for customer profiles are not. For example, the effectiveness of a marketing program based on sending messages to people passing by your store or looking for a new car depends greatly on how quickly you can react. If they’re already walked past your door (a matter of minutes) or bought their car (could be hours, days, or weeks), then it’s largely too late, although you might influence their next purchase of the same item. For those programs, speed is a critical data quality metric. In fact, several types of speed may matter: how quickly the data is gathered, how quickly it reaches the marketing system, how quickly a message can be transmitted, and how quickly you can see any response. The unit of measure may also vary: it could be milliseconds if you’re trying to snare a pedestrian passing by your storefront, while minutes or hours may suffice to influence a considered purchase like an automobile.

By contrast, the traditional measure of completeness will matter much less for many situation-based programs. You may reach only 2% of the people passing your store, but so long as you can profitably contact those you reach, the program is worthwhile. Things are different in a database-driven program where missing data could result in expensive messages to inappropriate recipients.

Other program-specific quality metrics may include:

- reliability: much of the new data will reside outside of your company, either because suppliers want it that way (for example, social networks may be unwilling to share member profiles) or because it changes too quickly to import (e.g., current location data). Reliability measures, such as how often the data is unavailable, how consistent are connection speeds, and how often there are changes in access methods or data formats, are critical in assessing an external source’s value.

- consistency: external providers often consolidate information from multiple sources. These inputs may themselves change, impacting accuracy, coverage, currency, or other aspects that affect the utility of the data for a marketing program.

- specificity: many of the new marketing programs gain their effectiveness from the precision of one piece of data, such as current location, product interest, recent purchases, or attitudes. The level of precision may vary as topics change or as sources evolve.

- clarity: categories such as product interest or content topic may be hard to interpret because different terms can describe the same concept. Marketing programs that rely on matching such inputs to offers need quality measures that track the ability map inputs to a known, stable taxonomy, or to quantify potentially vague measures such as “social influence”.

Part of the challenge in this new style of data quality is to understand which metrics apply to which programs. This requires investigatory analytics that quantify each attribute and then assess its impact on results. These analytics must also separate the impact of quality-related factors from other factors, such as offers and creative execution. Once the key quality-related metrics are identified, data quality programs can use them to monitor existing sources for changes, to assess potential new sources, and to prioritize the search for improvements.

This change in the mechanics of data quality implies a deeper change in the relationship of quality efforts to the marketing database and to marketers themselves. A central marketing database supporting many different programs encourages generic quality measures such as accuracy and completeness. But as marketers increasingly use specific data sets for specific programs, quality measures can be tied directly to each program’s unique requirements. This means that quality managers must work more closely with marketers to understand those requirements, to develop appropriate measures, and to correlate results with quality changes. This closer relationship is more demanding but will ultimately provide greater insights into marketing results and new opportunities for improvement.

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David M. Raab is a consultant specializing in marketing technology and analytics and author of the B2B Marketing Automation Vendor Selection Tool (www.raabguide.com). He can be reached at draab@raabassociates.com.

As online advertising consumes a larger share of marketing budgets, measuring its impact correctly becomes more important. Information management professionals will inevitably become involved because they control so much of the data needed to construct sound answers. So, whether you work in IT or marketing, it’s worth taking some time to understand the issues.

- fractional attribution. If attribution experts agree on anything, it’s their disdain for the most common approach to online marketing measurement. This method, called “last click attribution”, assigns all credit for each sale to the last message received. The obvious problem is that this ignores the impact of all previous messages. One common alternative, assigning all credit to the first message a customer received, has the same flaw. Most discussion among online attribution experts is aimed at finding logical ways to split the credit among all messages.

This often involves assigning weights to messages based on their type (a display ad is assumed to have less impact than an email), when they were received relative to a purchase (later messages get higher weight than earlier messages), or simply by assigning an equal weight to each message. Weights can also be based on more elaborate statistical methods, but this requires structured tests that few marketers actually conduct. Regardless of how the revenue is allocated, the portion attributed to each message can be compared with the cost of the message to calculate a Return on Investment.

- incremental attribution. While fractional attribution methods often feel arbitrary, most marketers can find a rule they feel gives reasonable results. But the technique has a more fundamental flaw: it assumes that all revenue is the result of marketing messages, and that the sum of revenues created by each message equals total revenue. Neither assumption is correct. Most businesses have a base volume of revenue they would earn even if they did no advertising, at least in the short term. And sales that are driven by advertising are often the result of several messages working together; taking away one message might have no impact or a very high impact depending on the circumstances.

Marketers who recognize these issues are increasingly turning from fractional attribution to incremental attribution, which attempts to calculate the change in revenues resulting from a particular message. This can be measured directly through structured tests, although these are often hard to execute. A more common approach is based on stages in the sales process. A typical set include includes the initial contact; learning more about the product; and making the purchase. Messages are classified by the stage they support and buyers are tracked as they move through the stages. Marketers use this structure to compare the effectiveness of different messages in moving buyers from one stage to the next. This lets them estimate the incremental impact on cost and revenue of sending on different messages, allowing a meaningful ROI calculation.

- cross channel attribution. Fractional and incremental attribution are both designed to measure messages across multiple marketing channels. But most implementations consider only the several digital channels, including paid search, display ads, email, mobile and social media. This is better than measuring one channel but still ignores offline activity. That’s a big problem because online marketing often influences offline sales, just as offline activities influence online sales. Combining online and offline measurement is more challenging than merging online channels because many offline channels don’t lend themselves to tracking individuals. You probably don’t know whether a particular person saw last night’s TV ad or what they bought in the grocery store this morning. And even when you can track an offline individual, perhaps through a loyalty program or credit card, you often can’t link their offline data to online identities such as cookies and email addresses.

These tracking problems are not insurmountable. Many vendors offer databases that merge online and offline identities with varying degrees of success. Surveys and research panels can provide detailed information on a sample of individuals, even though their actual identities are unknown. But most offline attribution programs still feed aggregate data for marketing spend and sales into marketing mix models, without attempting to track specific individuals. Although less precise, these can still show major correlations between online and offline activities. This is often enough to substantially change the value attributed to different marketing programs.

- long term impact. Attribution discussions are generally framed in terms of assigning credit for a single sale. But one message can actually affect multiple purchases and may also impact behaviors such as payments, service requests, and recommendations. In other words, serious marketing measurement must include multiple time periods and multiple transaction types as well as multiple channels. This usually requires tracking individuals over time, or at least tracking groups that can somehow be separated – for example, by making different offers in different cities and seeing how future behaviors in those cities diverge. This sort of analysis is much easier in industries where companies have direct customer relationships, such as bank accounts or service contracts. Even in those situations, it can be difficult to isolate the impact of a single marketing treatment. Many analyses therefore look at short-term results and use simulation models to project their long-term consequences.

Final thought: The common thread linking all attribution issues is the importance of data capture. Although online activities generate enormous volumes of data, much of it can only be used for attribution if tied to identifiers that persist over time and are recognized across channels. This doesn’t happen by accident: systems must be designed to capture identifying data in ways that are both useful to marketers and respectful of their customers’ privacy. Industry vendors are constantly offering new ways to achieve this, but the true strengths and weaknesses are rarely apparent – and may not even be understood by the vendors themselves. It will increasingly be the job of information management professionals to assess these methods and understand how they fit into the broader picture of marketing measurement.

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David M. Raab is a consultant specializing in marketing technology and analytics and author of the B2B Marketing Automation Vendor Selection Tool (www.raabguide.com). He can be reached at draab@raabassociates.com.

One way you know that a technology is poised for takeoff is when your boss tells you buy it but can’t explain why. Marketing automation systems have now reached that stage in their lifecycle. This is great news for vendors and consultants but poses a challenge to workers assigned to select a system. With limited guidance from above, they must make a choice that can bring great benefit to their organization or bring great harm. And there are no do-overs.

One key to successful selection is understanding the systems themselves. Marketing automation systems can be broadly defined as tools to help marketers do their jobs. A more concrete description might be systems to manage non-operational contacts with customers and prospects. This distinguishes marketing automation from customer relationship management (CRM), which is primarily concerned with operational contacts.

Still more specifically, marketing automation systems typically provide: marketing planning and budgeting; a database with customer and prospects profiles, promotions sent, and responses; campaign management including media buying, list selection, and message delivery; and reporting including campaign measurement, customer analysis, and revenue projections. Systems for business marketers add features to support sales people, including lead scoring and integration with sales automation or CRM systems,

Systems that meet the general definition of marketing automation have been available for at least two decades. The current set of products can be divided into four categories based on their target users:

- micro-business: these are very small businesses, typically under 20 employees and $5 million revenue. Marketing automation at these firms is driven by the fact that all marketing is done by one person, whether it’s the owner, sales director, or a full-time marketer. That person will not want to switch among separate systems to do different tasks, so systems for this market strive to include the broadest possible functionality. This also reduces the number of systems the company needs and the amount of cross-system integration, which are both important because small firms have very limited technology staffs and budgets. In fact, micro-business marketing automation products from companies including Infusionsoft and OfficeAutoPilot even provide built-in CRM. These vendors provide intensive user support, including both technical assistance and marketing advice. This also addresses the reality of their clients’ limited internal resources, helping to ensure their clients actually benefit from the product (and renew their subscriptions).

- small to mid-size business: this covers the broad swath of the companies from $5 million to $500 million in revenue. Although the group could certainly be subdivided, companies of all sizes within this range have remarkably similar needs and purchase pretty much the same systems: Pardot, Genius, Marketo, Manticore, Eloqua, and a dozen or so smaller competitors. These companies have small marketing departments that are separate from sales and run a broad range of programs. Thus, key features of these products include support for multiple channels (email, Webinar, landing pages, search marketing, social media, trade shows), Web behavior tracking (at the visitor level, which differs from conventional, page-oriented Web analytics), and sales integration (lead scoring and sales automation synchronization). Vendors serving this category have applied a spectrum of business strategies, ranging from self-service sales and deployment (keeping down costs and speeding up implementation) to extensive personal selling and support (requiring higher fees but offering assistance throughout the process) to reliance on third-party agencies and consultants (to provide marketing advice and change management). Although the self-service vendors tend to have smaller clients and the high-touch vendors sell to larger companies, the majority compete vigorously and successfully across the size spectrum.

- enterprise business marketers: this covers firms larger than $500 million revenue that sell to other businesses. These companies use all the techniques of their smaller brethren, but apply them across multiple product lines in multiple countries. This adds a layer of complexity that requires more formal planning and budgeting as well as precise control over the rights assigned to each user. These systems are still closely tied to sales automation data, but they also support custom data tables originating within the marketing system itself or tied to other sources. This segment is served by vendors from the upper end of the mid-size market (Eloqua, Marketo) and others from consumer marketing (Aprimo, Neolane, Unica). Buyers in this segment have complex deployment needs that almost always require assistance from third-party consultants, system integrators, or marketing services agencies.

- enterprise consumer marketers: this the core market for the previous generation of marketing automation systems, from Unica, Aprimo, Alterian, Teradata, and SAS. Clients are concentrated in the traditional database marketing industries of financial services, retail, travel, and communications. They demand sophisticated segmentation and marketing administration (planning, budgeting, project management, content management). (If you’re playing buzzword bingo, the combination of marketing execution and marketing administration is what’s now called “Integrated Marketing Management”). These systems run on the company’s primary marketing database. This is nearly always a custom data model that’s much more complicated than the designs baked into business marketing systems and much less reliant on sales automation data. Even more than enterprise business systems, the consumer products rely on third party integrators, consultants, or marketing services agencies to help with database development and system deployment.

Most people tasked with selecting a marketing automation system will know which of these categories best describes their company. And it’s true that the features of vendors serving each category can seem indistinguishable to a non-expert. But that doesn’t mean any vendor in your category will serve your firm equally well. You’ll eventually need to understand how they do differ. But first you have to figure out what you need – otherwise, you could get distracted by differences that don’t matter in your situation.

In other words, understanding your needs is the second key to successful selection.

As with any selection project, the place to start is by defining your business goals and then developing a list of capabilities needed to meet those goals. For marketing automation, those goals will probably be defined in terms of the types of marketing programs you want to do. These might be based on program objective (acquisition, nurturing, or retention), on channel (email, search marketing, TV, social media, etc.), or on marketing operations (lower execution cost, higher staff productivity, better visibility). Or some combination of the three.

Once you identify the programs that marketing automation is expected to support, you can define your requirements by listing the tasks for step of each program, and specifying the system features, business processes and staff skills necessary to execute them.

It’s important to include process and staff skills in this analysis, because often those will be larger obstacles than system capabilities. After identifying your process and staffing needs, you can compare them to existing resources to identify gaps. This will indicate the scope of change needed deploy marketing automation and clarify the timeframe and external assistance you might need for success. These should be captured in your project plan. If substantial external assistance looks important, potential vendors’ ability to provide it will weigh heavily in your vendor evaluation. In fact, those abilities may be more important than feature differences.

Your list of marketing programs is also used to estimate the value expected from marketing automation. Doing this properly requires a financial model that lets you calculate the profit impact of changes in the number of new customers (acquisition programs), customer value (nurture programs), customer longevity (retention programs), and marketing costs. Although you won’t have exact information, enough benchmark data is available build realistic expectations.

But let’s face it: that list of target programs often won’t exist. And even if it did, it won’t cover the full range of uses the system will see over its lifetime. Marketing today is simply too dynamic to predict tomorrow’s programs with any certainty. You certainly don’t want to get stuck with a system that won’t grow with you.

This ambiguity adds other considerations to your vendor selection. These include:

- scope of features and ease of learning, to make it easy to do things you don’t currently anticipate
- marketing services and technical support, to help you move quickly when you take a new direction
- technical flexibility, to incorporate new data sources and to integrate with external systems
- vendor resources, to ensure the system will grow as the industry evolves

Evaluating vendors along those lines is harder than checking for features. But it’s not impossible either. Talk to references, look at the product roadmap, interview the professional services staff and partners, review training materials and documentation, see what management presents as thought leadership, query your own social networks, and get your in-house technical and marketing specialists involved.

Of course, the effort you put into your selection will depend in part on the size of your investment. It’s hard to justify spending $50,000 in time or consulting fees on a system that costs $30,000 per year. But bear in mind that the real stakes are much higher than software fee. They include the time that all your users will spend with the system, the marketing expenses that they’ll manage through it, and the revenue that those expenses will generate. These can be changed substantially by making a wise or poor choice. So take your time and research carefully. A new marketing automation system is a tremendous opportunity. It would be a pity to waste it.

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David M. Raab is a consultant specializing in marketing technology and analytics and author of the B2B Marketing Automation Vendor Selection Tool (www.raabguide.com). He can be reached at draab@raabassociates.com.

Unlike their hyperactive younger brothers in business-to-business marketing automation, the business-to- consumer marketing automation vendors have been a quiet group in recent years. Most of the companies spawned during First Internet Bubble of the late 1990’s had vanished altogether or been assimilated into enterprise software suites. The handful of remaining independents – primarily Unica, Aprimo, and Alterian – made few changes to their core products, expanding instead through acquisitions and limited organic growth.

This all changed in late 2010 when IBM acquired Unica for $450 million and Teradata purchased Aprimo for $525 million. The prices were eye-popping: four to six times revenue and 50 or more times earnings. Either the buyers were insane, saw values that others had missed, or hungered to join the Second Internet Bubble. Or perhaps a bit of all three.

For sake of argument and charity, let’s focus on possibility number two. What value do consumer marketing automation systems bring to IBM and Teradata? And what do the acquisitions mean about the future of marketing technology in general?

There are two fundamental scenarios that justify the Unica and Aprimo transactions.

- Fragmented marketing systems will to be replaced by integrated marketing suites. This is the vision that Unica, Aprimo and Alterian were already chasing in recent years. So were the two other big players in the market, Teradata and SAS. It’s why they have all steadily expanded their products’ scope. The argument for consolidation is primarily that marketing itself has become more integrated, as interactions across all channels are increasingly coordinated to provide a unified customer experience. Oddly enough, this coordination doesn’t necessarily require you to link actual customer identities across those channels: so long as you can target messages by segment, people will be treated consistently across all channels. (Of course, this assumes that the segment messages themselves are coordinated across channels.)

Cross-channel coordination is still more dream than reality at most companies. But IBM in particular seems willing to invest in the missionary work needed to make marketers more aware that it’s proven and practical. This won’t be easy: until now, most big marketing automation systems were sold to database marketing specialists within marketing. Features with marketing-wide application, such as centralized planning and budgeting, shared content libraries, and cross-channel performance measurement, have gained little traction although interest has picked up a bit in the last year or two.

The fundamental problem is that database marketing remains peripheral to many marketing departments. Attention and budgets are still dominated by mass media advertising, which is created, purchased and measured outside of the marketing automation system. Even the Internet hasn’t necessarily changed things. Much Web advertising is purchased like traditional media, based on audience reach and frequency and with just minimum attention paid to building database-marketing-style relationships. Until relationships replace mass media as the focus of the Chief Marketing Officer’s attention, demand for integrated marketing suites will remain limited.

On the other hand, imagine a world where that demand does exist. Marketing automation systems will be the center of large integration projects, generating revenues for hardware, software and services that dwarf the revenue from the marketing automation software itself. Corporate IT departments will be deeply involved in the purchase, implementation and operation of the marketing automation systems. Big projects run by their friends in IT? You can see why IBM and Teradata would be very, very interested.

- Marketing becomes a branch of analytics. There’s a plausible argument that all types of marketing, including mass media, will soon be devoted to targeting messages at individuals. This includes the anonymous, segment-based “individuals” described earlier. In this world, marketing victory goes to the best data and the best analytical tools. Marketing automation systems provide those tools. Or, more precisely, marketing automation systems orchestrate database, analytical, and customer-facing systems to select and deliver appropriate messages across all channels. In this scenario, marketing automation systems drive related systems, both in terms of encouraging their development (bigger data warehouses) and pulling through related products (systems in same software suite as the marketing system).

These scenarios overlap substantially, but the second doesn’t rely on marketers moving their entire organization onto a single integrated platform. Indeed, functions that don’t relate to managing specific interactions could remain completely separate, such as planning and media buying. Even cross-channel integration is less central to the scenario, since analytics can be applied effectively to one channel alone.

Precisely because the second scenario doesn’t require so much change, it seems more plausible than the previous one. Major departments within marketing, such as a Web group, can buy and run analytical systems without support from a visionary CMO. The might even be able to use them through outsourcers or Software as a Service vendors, further reducing the need to involve corporate IT. This scenario ultimately yields more total business for the vendors, even if the individual projects are smaller.

Of course, we still don’t know whether either approach will really create enough business to justify the acquisition prices. But unless you’re a stakeholder at IBM, Teradata, or a competitor, why would you care? Here are a few implications:

- if the major vendors do focus on integrated marketing suites, they’ll educate IT and senior management about the value and methods of marketing integration. This will be good for marketers who want to do such projects, although they may be less pleased about losing some of their autonomy.

- a focus on integrated suites could also lead major vendors to ignore marketers who don’t want to or can’t afford to buy them. This is mostly a good thing: it will open opportunities for other marketing automation vendors, resulting in greater innovation across the industry and, eventually, more products for marketers to choose from.

- if the major vendors focus on analytics, they’ll chase a broader range of clients and explore options such as cloud-based solutions more aggressively. This will make it easier for marketers to find customer targeting solutions but creates a tougher environment for competitors. It does leave a relatively open field for other types of marketing systems, such as planning, media buying, project management, and content libraries.

- smaller competitors will emerge to serve clients who are too small to interest an IBM or Teradata or who simply want something those vendors don’t offer. Even though Unica and Aprimo may themselves fall into the black holes of their new corporate parents, a spray of small new stars will emerge to meet marketers’ needs. With the original marketing automation vendors now almost completely gone, the ground is now clear for a new crop.

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics. He can be reached at draab@raabassociates.com.

It doesn’t rank with Good vs. Evil or even Dogs vs. Cats, but the relationship between marketing and IT has its own history of conflict. The dynamic is simple: marketers feel neglected by corporate IT, so they hire outside vendors to run their systems. This makes it still harder for corporate IT to help marketers, who then rely on IT even less.

Such mutual isolation is increasingly untenable. As all parts of the customer experience are increasingly driven by technology, marketing systems must integrate more closely with the rest of the corporate infrastructure to be effective.

The CMO Council explored this issue in parallel surveys of marketing and IT leaders, published as The CMO-CIO Alignment Imperative: Driving Revenue Through Customer Relevance. The broad findings are no surprise – CMOs and CIOs know they need to work together but don’t do it very well. But it’s worth digging into the details to understand the dynamics of this dysfunctional relationship, in the hope of helping them to improve it. (Survey results are reproduced here with permission, although the interpretations are my own.)

First things first: Yes, both groups agree they must cooperate. They even cite the same reasons: need for customer insight, the central role of technology in the customer experience, and digitally-driven marketing.

They also agree that current capabilities are inadequate. In the critical realm of analytics, nearly three-quarters of each group felt that neither their online or offline functions were well integrated, while just six to eight percent felt integration across all functions was complete.

The agreement extends one step further, to a recognition that marketing projects often run into problems. But we already see some divergence: 64% of CMOs reported problems compared with just 48% of CIOs, suggesting the CIOs aren’t fully aware of CMO unhappiness with IT services.* And when asked about the causes of problems, each group placed the blame squarely on the other: Marketing cited lack of IT priority, resources and expertise, while CIOs complained that marketing had by-passed and isolated IT.

In this case, at least, it seems IT has a point. Just 25% of CMOs said they consulted with enterprise IT or other back office groups when selecting marketing systems. The good news is that 56% of CIOs said they consulted with marketing – although this still means the other 44% did not.

Why would marketing and IT not consult each other about marketing systems? Apparently one reason is that they don’t believe their counterpart understands what’s needed. Only 76% of CIOs believe their CMO understands marketing requirements, while just 54% of CMOs think their CIO does. That’s a pretty deep level of mutual mistrust.

This brings us closer to the crux of the matter: Who’s in charge here? By now you won’t be surprised to find that just half of CIOs (51%) think their CMO is playing a major role in their digital marketing strategies, while a mere 19% of CMOs think their CIO is significantly engaged.

There’s a similar misalignment in what CMOs and CIOs think the CIO should be doing. CMOs see CIOs as plumbers whose job is to install systems and keep the data flowing. CIOs set themselves a loftier agenda that includes furthering the use of social media, mobile and other new technologies.

If there’s any good news here, it’s that both CMOs and CIOs are eager to help their companies improve their marketing technologies – indeed, they agree success is vital. But a deep chasm of distrust and misunderstanding prevents them from working together effectively. Both groups must recognize the depth of this problem and work to overcome it. They can’t succeed on their own.

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* An alternative explanation is that CMOs have more problems on projects where IT isn’t involved. But another question that directly asked how well marketing and IT worked together also found that IT felt the two groups had cooperated better than marketing did.

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics. He can be reached at draab@raabassociates.com.

It’s fitting that the most famous quote about marketing measurement – “Half the money I spend on advertising is wasted. The trouble is, I don’t know which half.” — is itself unreliable: although it’s typically attributed to John Wanamaker, there is no definitive citation.(1) Nor has much changed since Wanamaker’s time: to paraphrase what Mark Twain (2) didn’t say about the weather, everybody still talks about marketing measurement but few do anything about it.

The problem isn’t lack of data. Marketers today can track the messages received and actions taken by their customers in more detail than Wanamaker could imagine. It’s true that the information is still not complete or fully accurate, but even perfect information would not yield the answer. The problem is fundamental: even if we knew everything that happened to a customer and everything that the customer did, we couldn’t calculate the how individual events affected the final result. All we see is what a particular set of inputs will produce in aggregate. It’s like following a recipe in a cook book: you know what the combination of ingredients will produce, but not how each one contributes.

Measuring the impact of individual marketing programs requires breaking apart the combination of inputs and testing the effect of changing them individually. This is why even complete data cannot solve the measurement problem. Only tests allow marketers to measure the incremental impact of specific activities.

There’s nothing new about this insight. But, with the glaring exception of direct marketing, testing has traditionally been expensive and inexact. As a result, marketers limited testing to a handful of major issues and sought additional insights from statistical correlations such as marketing mix models. But mix models are effective only in industries with very large customer bases and marketing budgets, and even there they can only measure broad effects.

In today’s data-rich environment, testing is much easier. Marketers can more often build databases of their customers and track many of the messages those customers see and respond to. Even formerly anonymous media such as broadcast and print advertising often drive consumers to trackable behaviors on the Web.

But tests face another obstacle: the time lag between many marketing contacts and a customer’s eventual purchase. Although revenue is the ultimate measure of marketing success, many marketing efforts occur early in the purchase process. Waiting until a purchase is completed before measuring their results introduces a lengthy delay. It also allows time for intervening events that could affect the final outcome. For both these reasons, marketers need a different measure they can read more quickly.

Much of the current innovation in marketing measurement is directed as finding such measures. The general approach is to divide the buying process into stages and to measure the impact of marketing efforts on moving prospects from one stage to the next. This is similar to the sales funnel or pipeline traditionally monitored by sales automation systems, except that that it begins earlier in the purchase process. It’s also similar to psychological models such as AIDA (awareness, interest, desire, action), which are often used to guide brand advertising.

The difference from past psychological models is that marketers are now tracking the status of individual consumers. This is what today’s new data sources make possible. In the past, companies rarely knew more about non-customers than basic demographics (age, income, gender, location). But these can’t track movement through purchase stages. Movement must be inferred from fresh information, whether provided directly through survey responses or inferred from behaviors such as Web site visits. Behavior is a much richer source because there’s more of it and it’s not limited by what prospects choose to provide.

In sum, then, marketers are finding they can greatly improve marketing measurement by using behavior to track consumers through stages in the purchase process, and using tests to correlate changes in this movement with changes in marketing programs. This creates several technical requirements, including:

- identifying individuals consistently over time and across channels, so you can build a database of their behaviors and marketing contacts. This database provides the input for tracking their movement through the purchase stages.

- data mining and analytical tools that uncover patterns, such as x Web visits within the past Y days, which indicate a prospect’s location in the purchase process.

- scoring systems that can apply complex patterns and other information to identify the current stage of thousands or millions of prospects on a continuous basis and to identify movement from one stage to the next. (In practice, movement does not always follow a fixed sequence: some prospects will stagnate, move backwards or skip ahead several stages at once.)

- tagging of content by stage, to help the scoring system. While the volume of behavior is important, the specific information consumed gives much more insight into a prospect’s current state of mind. Three visits to the white paper library means they’re just doing some research, while three visits to the contract terms means they’re seriously considering a purchase. Although content tagging can be major task, the tags are also needed to help select the right message for each individual. So the measurement system should be able to use tags that are already in place.

- maintaining a history of previous statuses, since they cannot be reconstructed using only current information. This is a classic “slowly changing dimension” in data warehouse terms.

Converting this data into actual measurements adds still more requirements:

- testing features including random selection, tagging of test group members, and test/control reports.

- stage movement measures such as average time per stage and continuation rates from one stage to the next.

- correlation of marketing contacts with prospect stages. This is used to calculate the marketing cost per stage and to show the impact of different contacts on stage progression

- integration of purchase history with prospect profiles, needed for revenue-related calculations such as return on investment.

- projections of future revenues from the current prospect pool. This uses the number of prospects in each stage, the expected continuation rates from one stage to the next, and the expected value of ultimate purchases. It is used to forecast business results to estimate the financial impact of differences revealed by tests.

Few of these capabilities are wholly new to marketing systems. Most are required for other purposes such as offer selection and segmentation. But making them available for measurement requires exposing them in different ways, so marketers and IT staff still need to ensure they are available for measurement projects.

The advantages of stage-based measurement make it worth the trouble. Breaking the buying process into stages lets marketers understand what’s driving results, identify problem areas and find opportunities for improvement. It lets them measure the true incremental value of individual marketing contacts, replacing arbitrary “revenue attribution” on the first or last touch or simplistic fractional weighting. Perhaps most important, stage-based measurement provides near-immediate feedback, allowing marketers to quickly reallocate resources to the most productive programs.

Stage-based measurement isn’t a complete solution to all marketing measurement problems. It has a short-term, incremental focus that may not capture the deeper value of branding programs. It measures each marketing program in isolation, making it difficult to assess interactions among several programs. And it often relies on the assumption that changes at one stage in the buying process ripple through to the end more or less undiminished. This last assumption is particularly dangerous, because the opposite is often true: positive changes at one stage often have negative consequence later on. (For example, a free introductory offer may attract more orders but fewer conversions to paid customers.) Sophisticated marketers will be aware of these issues and compensate for them. They are a reasonable price to pay for finally knowing which half of your marketing budget is wasted.

1. The Quote Verifier: Who said what, where, and when, Ralph Keyes, New York, NY: St. Martin’s Press, 2006, pg 2
2. It was more likely Charles Dudley Warner. See http://quoteinvestigator.com/2010/04/23/everybody-talks-about-the-weather/

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics. He can be reached at draab@raabassociates.com.

Here’s a metaphysical puzzle: can you send a marketing message before it’s created?  The answer used to be no: messages remained unchanged after they were sent, either because they were broadcast and vanished immediately or because they were physically persistent but inert, like a printed catalog or recorded TV ad.  Even messages that were dynamically tailored to a specific individual and situation were rendered and then frozen before they were sent.  A Web site might adjust its offers over time, but each offer was itself fixed.

Because marketers knew the contents of each message when they sent it, the only subsequent information they needed was who received it and how they reacted.  Indeed, most marketing measurement boils down to answering those two exceedingly difficult questions.

But marketers today face an added challenge: capturing the message itself.  Paradox notwithstanding, an increasing number of messages can now change after they’re created.  Consider:

- Adobe’s latest design software, CS5, can create ads that send different messages to different individuals and record the results.  Specifically, Web designers can embed the testing, segmentation and automated optimization of Adobe’s recently-acquired Omniture Web analytics system.  The solution relies on the Omniture server to execute tests and store results.  But the next logical step is to embed test logic, tracking and automated self-optimization within ad itself so it can function when a server connection is unavailable.  This would result in a truly autonomous marketing message.

- Vendors including smartFocus, Genius.com and Genoo have extended social media sharing to tag each item with the ID of the individual who shared it, so they can be credited as the source of later visits by recipients.  In other words, if Jane posts a link to this article on Twitter, marketers will later know not just which visitors came from Twitter, but also which came from Jane’s Twitter post.  This lets them measure how much traffic Jane generates and identify the members of Jane’s social network.  In effect, the original message is being modified by adding the identity of each sharer, which must then be captured with responses.

- Barcodes on products and advertisements are being linked via mobile phone applications to Web sites that vary their content based on location.  Here, the original message is being enhanced with the viewer’s location and, perhaps, actual identity.  One obvious use is to deliver different offers based on local weather and competitive promotions.  Vendor StickyBits make a buzzword triple play by adding social media to mobile and geo-targeting, with separate social media sites for different locations of the same UPC code.  Since the social site also evolves, marketers can only know the message received by each consumer if they capture a snapshot of the site as it appeared to each visitor.  They must also capture whatever contextual variables (time of day, weather, competition, current promotions, etc.) play into offers and results.

These examples point to the emergence of autonomous marketing messages: communications that are launched into the world to operate more or less independently, occasionally phoning home like a dutiful college student to report results and perhaps get some advice.

The concept poses challenges for everyone involved.  For marketers already struggling with the transition from one-way broadcasts to peer-based communities, it’s a further loss of control.  For technologists serving those marketers, it’s another set of delivery systems and reporting systems to manage.  For marketing analysts, it’s a new type of data to incorporate.

But the concept also creates new opportunities for success.  Messages that can track their own movement from consumer to consumer can provide important insights into the always-mysterious connection between messages sent and resulting customer behavior.  Autonomous viewer logs, testing and optimization can enhance media where continuous real-time connections to central servers are unavailable.  Periodic contacts with central servers let the applications download their information and update their libraries of offers, models and business rules.  Combining mobile, location and social media provides rich information about consumer behavior, along with direct opportunities to deliver highly targeted messages.

Autonomous messages add to the flood of data already generated by digital marketing.  This increases the need for ways to load, store, access and analyze tremendous volumes at reasonable cost and speed.  Similarly, the complex and variable structure of the new data reinforces the existing demand for technologies that can easily incorporate new data types and models.  Autonomous messages also require improvements in techniques to automatically uncover significant patterns within the data and infer appropriate marketing treatments.

The major new challenge posed by autonomous messaging is portability.  Autonomous systems must somehow incorporate decision rules, self-adjusting analytics, alternative treatments and data capture mechanisms while making minimal demands on host resources.  This is a particular issue in mobile environments, where bandwidth, storage and processing power are scarce.   The messages must also find efficient ways to exchange data with central servers.

Customer identification is another issue.  Autonomous messaging could ease some privacy concerns by tracking and responding to behaviors without sharing them externally.  But it also extends to platforms where customer identification is more difficult than usual, making it still harder to gain the most value from data that marketers have permission to use.

Progress will be incremental.  The immediate future will see hybrids that combine different aspects of autonomy with centralized techniques.  Marketers and technologists will need to assess the strengths and weaknesses of each approach and look for opportunities to combine them to deliver solutions more powerful than any one method provides by itself.

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics.  He can be reached at draab@raabassociates.com.

Database marketing is based on sending messages to known individuals.  This has always been in sharp contrast to conventional mass media, such as television and print advertising, where the marketer did not know exactly whom they were reaching.

But the introduction of online marketing has blurred this distinction.  Online marketers often know a great deal about the people they interact with, even when they don’t know their actual name and address.  This means that they can gather, analyze and react to data in ways similar to database marketers.  But without personal identifiers, online marketers cannot integrate this data into the individual profiles that are the heart of a conventional marketing database.

One result has been a surprising separation between online and database marketers.  Even though both rely heavily on technology and apply disciplined analytical approaches, each has developed its own universe of service and software vendors.  Database marketers rely on marketing services agencies and marketing automation systems whose core competency is building and managing customer databases.  Online marketers rely on search marketing, Web analytics and Web site development vendors who are skilled at attracting traffic and tailoring Web treatments to visitor behaviors.

Email and ecommerce are exceptions: online activities dominated by database marketing vendors and techniques.  But they merely prove the rule, since both face situations where personal identities are known and it’s possible to build a conventional marketing database.

Another result has been continued fragmentation among online marketing subspecialties.  Search engine optimization, paid search marketing, site personalization, Web display ads, mobile marketing, downloadable applications and social media are usually managed separately, even though they rely in part on traffic statistics from same Web analytics systems.

Some fragmentation is inevitable.  New varieties of online marketing appear so quickly that marketers must rely on internal or external specialists for quick deployment.  But this fragmentation, as well as the separation from conventional database marketing, imposes extra costs and prevents consistent treatments for individual customers.

Of course, if it were easy to integrate online with offline data, the database marketers would have been doing it all along.  Social media can help by providing an additional source of personal identifiers that can link individuals across sources.  But what’s really needed is a change in attitude: one that recognizes it’s worth centralizing information even when it cannot be tied to a specific individual.

This is a radical switch for database marketers who have spent their careers looking for better ways to identify individuals.  But they (and the rest of us) need to adjust to a concept of “semi-anonymous” marketing, which means being able to reach individuals who share certain characteristics even if you don’t know who they are.

To bring home the importance of this concept, let’s look at the types of information available in online marketing channels.

- cookies are the primary means of tagging individuals who visit a Web site.  By itself, a cookie only identifies a computer, but it can be linked to additional information that’s either observed (e.g. pages visited) or provided by the visitor (e.g. registration).  This data can be stored within the cookie or, preferably, in a database linked to the cookie ID.  This means you can use a cookie to, say, show an ad with a discount coupon to someone who previously discarded a shopping cart, even if you don’t know who that person is.  In other words, even anonymous cookies let you put people into identifiable marketing segments and send them appropriate messages.

- IP address (showing where a user has connected to the Internet) and other information provided with each Web visit (browser type, operating system, etc.) can sometimes act as a proxy identifier for individuals, since many systems keep their IP address over time.  However, this is controversial in privacy circles and not wholly reliable.  Yet even discarding this approach, IP address can often be traced to a corporate account owner (this works for business computers, not for home computers which typically connect through an IP address registered to a phone company or other Internet service provider).  And nearly all IP addresses can be mapped to a geographic location, which in turn can be linked to geo-demographic databases such as Nielsen PRIZM clusters.  Again, this information can be used to target messages to unidentified individuals.

- mobile phone location is known to the phone network operator, although how much they share with marketers depends on privacy and commercial considerations.  It’s certainly possible to target messages to people within a certain geographic area, either in network-based advertising or through user-downloaded apps.  More advanced but still semi-anonymous applications, such as targeting based on whether someone is outside of their usual territory, are possible but demand more data retention.

- social media support many kinds of marketing, including advertising based on member profiles and groups, direct messages where a prior relationship exists, monitoring public activities by usernames, and linking usernames to email and other personal identity information.  The opportunities depend on the particular medium and the operator’s terms of service, but the general point is it’s worth building a history that may later become useful even if you can’t market to it directly today.

As marketers centralize their online information, technical demands will increase.  Marketing databases not only become much larger, but they will hold more kinds of information, much of it less structured than the traditional customer and transaction records.  There will be increased opportunities to use sophisticated matching techniques to associate specific individuals with semi-anonymous information, although this can raise privacy concerns.

But even if an addressable individual is never identified, marketers will gain by integrating information across channels at the level of the semi-anonymous segments themselves.  This will allow them to identify similarities in interests and behaviors, which in turn will lead to coordinated messages and clearer understanding of results.   The ultimate impact will be to help unify the marketing departments themselves, ending the fragmentation that detracts from business performance.

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics.  He can be reached at draab@raabassociates.com.

Online media account for about one-third of consumers’ time (1) but receive less than one-sixth of all advertising expenditures (2).  So it’s a safe bet that marketing systems will increasingly need to support online advertising.

This is a challenge for current products, which were developed for outbound campaigns via mail, email and telephone.  Even real-time interactions have been treated as extensions of traditional campaign flows rather than versions of online advertising.

The distinction is significant.  Outbound marketing assumes that marketers know who will receive their messages, can control the delivery of those messages, and can directly capture responses.  It further assumes that the promotions are the primary influence on consumer behavior.  This lets marketers execute carefully structured champion-challenger tests to measure the impact of alternative customer treatments.

Advertising results have never been anywhere near as measurable.  Advertisers in traditional mass media had only the vaguest notions of who was seeing their messages.  Most measurements relied on consumer samples such as panels or surveys or on statistical correlations captured in marketing mix models.  Despite formidably complex statistical techniques, these are inherently imprecise measures of marketing impact.

The flood of data provided by online advertising seems to offer an escape from the uncertainly of traditional media measurements.  Unfortunately, it won’t work.  The key assumptions of outbound campaigns – that marketers control who receives their messages, what those messages contains and how the customer can reply – are not true in the online advertising world.

- In online advertising, consumers decide where to direct their attention.  Marketers can access an audience with presumed interests and attributes, but cannot target specific individuals in advance.

- Online advertisements are delivered in the context of some other consumer activity, such as visiting a Web site or conducting a social interaction.  This greatly reduces the amount and type of content that can be delivered.  More important, the online environment contains information from competitors, neutral experts, and other consumers.  Even if the consumer is lured to the marketer’s own Web site, she can exit at any time.

- Although many interactions are captured, few are directly linked to an actual purchase.  This invalidates the central simplifying assumption that a single promotion can be credited as “causing” a specific purchase.  With this cornerstone removed, champion-challenger testing requires elaborate analytical scaffolding for support.  The challenge is magnified because interactions may be recorded but not linked to the same identity, making it difficult to understand the full set of contacts that influenced each consumer.

If a common problem threatens the outbound campaign systems, it’s the need to handle unstructured data.  Online advertising can capture huge amounts of detailed information about each consumer and each interaction, including Web pages the consumer has visited, competitive ads the consumer has seen, comments she has posted, profiles gathered by third parties, geographic location, and even the interaction device (computer, smartphone, kiosk, etc.).  But the structure of this data and the significance of individual attributes is not always known in advance.  Thus, systems must analyze it in many different ways before they can identify features that are relevant for marketing.  From a technology perspective, this implies specific capabilities including storage and easy access to unstructured data; text and semantic analysis to extract and classify contents; audio and video analysis to deal with non-textual content; time-series and pattern analysis to identify significant behavior patterns; and network analysis to understand social media influences.  None of these are handled easily by traditional outbound campaign systems, which assume a highly structured environment.

The second new core requirement is better prediction of relations between content and subsequent behavior.  Traditional champion-challenger tests can’t isolate the impact of the large number of contextual variables that apply in online marketing interactions.  Even if they could, consumer behavior is too volatile to assume relationships remain valid weeks or months after they are collected.  Rather, marketers must deploy self-adjusting models that can observe consumer behavior and predict which content (both tested and untested) is likely to be effective under different circumstances.  Beyond predicting immediate response, marketers need new ways to track long-term results.  This requires linking fractured consumer identities from first- and third-party cookies, member profiles, social media personas, email addresses, IP addresses, location, customer accounts, and other sources with varying degrees of anonymity, all while respecting legal and ethical privacy constraints.  As with traditional advertising, marketers will often find themselves relying on consumer panels and surveys to gather data that cannot be assembled without individual cooperation.

A third key requirement is data-gathering, analysis and execution across complex networks in real-time and near-real-time.  These capabilities will evolve over time: it will be easier to execute dynamic models on your company’s own Web site than through a third-party ad network.  But as the advantages of superior content selection become apparent, marketers will increasingly press for the ability to deploy them more broadly.

All of these capabilities – unstructured data management, self-adjusting analytics and real-time execution – are outside the scope of traditional outbound marketing systems.  Whether existing vendors can adapt to support them or whether new systems emerge will determine who dominates the growing market for online marketing execution.

(1) Forrester Research, “Consumer Behavior Online: A 2009 Deep Dive”, July 27, 2009
(2) PricewaterhouseCoopers and Wilkofsky Gruen Associates, “Global Entertainment and Media Outlook: 2009-2013”, June 16, 2009

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics.  He can be reached at draab@raabassociates.com.