4 Marketing Tech Trends To Watch in 2014

I'm not a big fan of year-end summaries and forecasts, mostly because I produce summaries and forecasts all year round.  But I pulled together a few thoughts last week in response to a request, only to discover I had misunderstood what was wanted.  Rather than let my precious wisdom go to waste*, I'll share below what I think will be most important marketing technology trends of 2014. 

Customer Data Platforms mature.  Marketers will have an increasing number of ways to build consolidated, multi-source customer databases without waiting for help from their IT departments. Systems that build such databases for specialized purposes such as lead enhancement, cross-channel campaign management, retention programs, and advertising audience management will increasingly provide more value to their clients by exposing the databases to other execution systems. As a result, the distinction between the customer database and execution systems will become more evident and companies will be able to succeed by offering either data or execution exclusively.

Digital advertising and customer marketing converge.  Data management platforms, which store semi-anonymous cookies for online ad networks, will converge with conventional customer databases, which store profiles tied to actual identities. The advantage will be marketing programs that span both channels, delivering personally targeted information via display advertising and simplifying personalized marketing on mobile platforms that don’t support conventional cookies. The unification of these previously separate data sets will allow more careful orchestration of customer treatments across all channels, increasing the effectiveness of all marketing contacts.

Predictive analytics finally take center stage.  More accessible customer data and broader opportunities to deliver personalized messages will support the long-expected mass deployment of automated predictive analytics tools. These will be part of a centralized customer management architecture that uses them to deliver the best message to each customer during each interaction in every channel where a customer can be recognized. Increasingly automated testing will allow incremental optimization despite constant changes in customer interests, product availability, creative executions, and offers.

The privacy dog won’t bark. Consumers will continue to allow marketers to track their behaviors, even if they become slightly more discreet about the personal information they post directly on social networks like Facebook, Instagram, and Twitter. Efforts to limit such tracking through government regulations will not result in significant limitations, at least in the United States.



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*Yes, that's a Dr. Strangelove reference.

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Webinar, December 18: How Marketers Can (Finally) Get Good Customer Data

Let’s face it: no real work will get done next week, what with all the holiday parties and caroling and so forth. So you might as well set aside 2:00 to 3:00 p.m. Eastern time on Wednesday, December 18 and register for the Webinar I’m co-presenting with RedPoint Global on Customer Data Platforms.

In addition to uncovering the secret relationship between cuneiform and Justin Bieber,

you’ll learn about our latest discovery: a new species of Customer Data Platform, bringing the known total to four. We’ll even provide a handy field guide to identifying which is which. Join us, and gain enough new information to fuel your party conversations for the rest of the holiday season!

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Woopra Grows from Web Analytics to Multi-Source Customer Data, Insights and Actions

I stumbled over Woopra in their tiny booth during last month’s Dreamforce conference, where I was intrigued enough to let them scan my badge and promptly forgot why.  Fortunately, a diligent sales rep followed up by email and I remembered it was worth a closer look. If you’ve been reading my recent posts, you won’t be surprised that I’ve decided they are yet another Customer Data Platform.

In fact, the people most surprised by this news will probably be the folks at Woopra itself, which positions itself as “an insight company” and has deep roots in traditional Web analytics. On the other hand, Woopra does distinguish itself from conventional Web analytics vendors by stressing the fact that it tracks individuals, not Web pages. In fact, one of its tag lines is “easily track, analyze, and take action on live customer data”, which is a pretty decent statement of the CDP value proposition.

Tag lines notwithstanding, Woopra wouldn’t qualify as a CDP if it only tracked Web behavior. But Woopra offers the core CDP function of building a multi-source database.  It does this by directly capturing behaviors from Web site visits and mobile app interactions (via Javascript tags and API calls from iOS or Android) and by loading operational data such as purchases and customer service interactions. As its Dreamforce presence suggests, Woopra can integrate with Salesforce.com to both import CRM data and to display the information it has consolidated from multiple sources.  The system can combine data with different identifiers, such as a cookie ID, mobile device ID, and Web session ID, although – like many CDPs – it relies on the client to figure out which identifiers belong to the same person.

Woopra stores its data as a combination of customer attributes and time-stamped individual events. The event data lets it report on individual movement through funnel stages, performance of start-date cohorts, and paths through a Web site or app, in addition to the usual profile reports. It stores the information using proprietary technology that allows continuous real-time updates and ad hoc segmentations, so users can run any report against a subset of the customer universe. Users can also design their own custom reports.

Woopra provides an API that lets external systems access its database, although it places some limits on volume to avoid performance issues. This access meets the minimum requirement for a CDP.  The system can also continuously scan for user-specified events or conditions and execute user-specified actions when these occur. The actions can run Javascript on the client Web site, add tags to a customer record, send an email or push notification, or a call an external system via a Webhook. This allows marketers to manage some basic customer treatments within Woopra itself. But the system doesn’t have any predictive modeling or recommendation engines, so more advanced approaches would require external assistance.

Woopra was founded six years ago and relaunched in 2012. It has more than 3,000 paying customers in a wide range of industries, including many small businesses and several large ones. The system is offered in three editions with different sets of features, including a free version for simple visitor tracking. Pricing is based on activity volume and starts at $80 per month for the mid-level edition.

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Optimove Helps Optimize Customer Retention (And, Yes, It's a Customer Data Platform)

As I wrote last week, it sometimes seems that every system I look at these days is a Customer Data Platform. Of course, this is partly because I’m choosing to look at that type of system, and partly because CDP vendors are reaching out to me. But I do believe another reason is that CDPs are an idea whose time has come: I’ve recently seen at least three CDPs that are just emerging from stealth or beta mode. All were developed because someone else recognized the huge unmet need for getting  better customer data to marketers.

One of the vendors that contacted me was Optimove, a Tel Aviv-based firm that calls itself a “retention automation platform” but definitely fits the CDP criteria. This means that Optimove is a marketer-controlled system that loads data from multiple source systems, puts it in a marketing-friendly format, and makes it available to external marketing execution systems.

Like many CPDs, Optimove also includes a campaign engine that pushes specific marketing actions to the external systems. Optimove’s approach is unusual in basing its campaign interface on a calendar that lays out the campaign schedule for each user-defined customer segment. This makes it easier for marketers to build a comprehensive contact strategy from multiple campaigns.

The campaigns themselves each have their own schedule, allowing them to run once, daily, weekly, or monthly. Users can also limit the number of messages sent to each customer by assigning an exclusion period to each campaign. Other campaigns can be instructed to respect or ignore these exclusion periods, ensuring that high priority messages are delivered in all circumstances. Each campaign triggers a single action, which can be directed to email, banner ads, direct mail, Facebook custom audiences, in-app pop-ups, SMS, app message boards, call centers, or other channels. The connections may be through file transfers or APIs.

Optimove's campaign interface is unusual, but the system is even more unusual in taking performance measurement very seriously. Its standard campaign setup requires users to assign a success measure and to either set aside a control group or set up a multi-way split of alternative treatments. This enables standard reports, including the campaign calendar itself, to show the incremental value provided by each campaign – the critical information needed for long-term optimization. By contrast, most marketing systems make success targets and testing optional if they support them at all. Users can also see a history of all campaign results for a given segment, making it even easier to identify the most productive programs.

The campaign segments themselves, which Optimove calls target groups, are built by accessing data that Optimove has loaded from the client’s data warehouse and operational systems. Optimove has standard data models for different industries, reflecting its current customer base: online gaming (bingo, casinos, poker, sports betting, etc.), foreign exchange trading, and ecommerce. The system assumes the data has already been coded with customer IDs, which something that makes reasonable sense given the focus on retention rather than acquisition. 

Data is typically loaded daily or weekly. After each load, customers are assigned to life stages (typically, new customers, active customers, and churned customers) and to multiple segments based on behaviors and attributes, such as location, product preferences, and spending levels. The system then uses the life stages and segment attributes to assign customers to "microsegments" that cluster analysis has found will behave similarly. It’s important to understand microsegments represent a current customer state that will change over time: that is, each customer belongs to different microsegments at different stages in her life cycle.

Optimove calculates the probability of moving from one microsegment to the next and uses this to predict how a given group of customers will behave in the future.  This is the basis for its lifetime value and churn predictions – key metrics in system reports. This type of forecasting is something else that really should be done by every marketing system, but rarely is. Optimove also provides cohort analysis reports, comparing performance of customers who joined during different time periods. This is yet another important type of information that is not always available.

Optimove does have some limitations. I was surprised there are no standard reports to highlight attributes that separate responders from non-responders within a promotion audience: this is pretty common information that helps marketers to refine their segmentations and better understand what is driving results. Nor does the system current recommend the best action to take with individual or a group. Both features are being worked on for future release.

Optimove was founded in 2009 and currently has about 70 clients, mostly in Europe. It has a few U.S. customers and is looking to expand in this market. Pricing is usually based on the number of customers and begins around $2,500 per month.

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Aginity Puts a Customer Data Platform on an Analytical Appliance

When your only tool is a hammer, everything looks like a nail.  I’ve been illustrating the point recently by asking whether every system I see is really a Customer Data Platform (CDP). The question comes up because nearly every customer management system builds its own customer database, which is one core function of a CDP.  What distinguishes CDPs is that they make their database accessible to other execution systems and add some type of customer management intelligence. This intelligence ranges from behavior flags, segment codes, or predictive model scores to treatment recommendations to full-blown campaign management. Sometimes the enriched data is all that’s exposed to the execution systems, although usually the underlying customer profiles are available as well. Often the CDPs support just one stage of the customer life cycle, such as acquisition or retention: this in itself doesn’t disqualify a system, since I expect that they’ll expand in the future. The other key feature is that CDPs are designed to be run by marketers, not IT staff, even though IT will usually play a role in connecting to company-managed data sources.

I bring all this up partly to clarify that I'm actually being more selective than you might think in deciding what to call a CDP and partly because I’m writing today about Aginity, which refers to itself as a “customer insight appliance” but I think can rightly be classified as a CDP.  This in turn matters because CDPs solve a critical problem – marketers’ need for better customer databases – so identifying the widest possible range of CDP vendors increases the chances of each marketer finding a solution that fits her requirements.

On to Aginity itself. Functionally, the system is organized into layers for data loading, database management and analytics, and data consumption, which is exactly the model you’d expect from a CDP.  Where it differs from most CDPs is the underlying technology.  Aginity runs on a Netezza or similar "massively parallel processing" (MPP) data appliance that would typically run on-premise at the client, rather than being accessed remotely in a “Software as a Service” (SaaS) model.

Of course, most marketers couldn’t care less about this difference. They might care more if Aginity was a tool for IT departments, but in fact marketers can control most Aginity functions beyond the initial connections with source systems, and those connections require IT help even for SaaS systems.

Digging a bit deeper into the technical details (and feel free to skip the rest of this paragraph; it will not be on the final exam), Aginity uses a combination of relational and Hadoop data stores, which lets it add new data sources without formal data modeling.  It uses a simple wizard that lets non-technical users add new data elements and expose them on a metadata layer. The system automatically generates scripts to load new data and distribute it appropriately on the data appliance.   The system doesn't do the type of "fuzzy matching" needed to associate customer identities across different platforms when no direct link is available; it relies on the client or external partners to make those connections.

Once loaded, the data can be queried directly via SQL, typically using Aginity’s free Query Workbench, which is widely used for MPP databases throughout the industry.  Or the data can be published using other Aginity tools that create data marts for external analysis and execution systems. The publishing tools can be run by a marketing analyst, although Aginity says most clients let IT staff use them so IT can enforce quality standards, governance rules, backup management, and similar best practices.

The net result is that Aginity can have a new customer database available to marketing in 90 days or less (often much less), compared with the six to twelve months this typically requires. It’s this speed and flexibility that make me consider Aginity a tool for marketers – and thus a CDP – rather than a tool for IT departments.

Aginity also provides some analytical and customer management features of its own. These include ability to add derived attributes such as lifetime value calculations and segment codes to customer records. These attributes can call on any data gathered by the system, a critical advantage of a CDP. Customer lists can be fed to external systems for direct execution, such as sending an email, or can be loaded into data marts that external systems access with their own segmentation and campaign management tools. Aginity currently provides a range of analysis features including dashboards, profile reports, and segment migration over time. It relies on external systems for advanced analytics such as predictive modeling and plans tighter integration with such systems to allow more precise control over customer treatments.

Aginity was founded in 2006 as a service firm to assemble data for analytics and marketing execution. Its current product, first released in January 2012, is based on tools it developed as a service agency. The company’s clients are concentrated among large retailers but include some ecommerce, manufacturing, and other industries that handle large amounts of customer data.

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Marketing Automation News from Dreamforce: B2B More Integrated, B2C Stays Separate

I spent the early part of this week at Salesforce.com’s annual Dreamforce conference. Here are my observations.

The big news was for geeks. The main theme of the conference was Salesforce1, a new set of technologies that make it vastly easier to deliver and integrate mobile versions of Salesforce-based applications. It is apparently a major technical accomplishment and at least one of my technical friends was hugely impressed. But I can’t say I personally found it all that exciting. Perhaps we’ve reached the point where we expect technology to do pretty much everything, so the line between what's already available and what's new is only visible to experts.  Any way you slice it, focusing on platform technology is much less exciting than last year's vision of "social enterprise".

The bad news was for B2B marketing automation. Conference presentations confirmed that Pardot, the B2B marketing automation system that Salesforce acquired as part of its ExactTarget acquisition, has been separated from the rest of ExactTarget and made part of the Sales cloud. There, Pardot is described only as providing lead scoring and nurture programs, which ignores landing pages, behavior tracking, and other features that B2B marketing automation usually provides (and Pardot includes). In terms of infrastructure, Pardot will eventually work directly from the CRM data objects, rather than maintaining its own synchronized database. (Data outside the CRM structure, such as detailed Web behaviors, will remain separate.)

What this means is that Salesforce sees B2B marketing automation as just an appendage of sales automation.  This is pretty much the same constricted view of marketing automation that Salesforce management has held all along.  The logical consequence is to make lead scoring and nurture campaigns standard features within the Sales offering and discard Pardot as a separate product.  I should stress that no one at Salesforce said this was their plan, but it seems inevitable. If and when that does happen, only the most demanding companies will purchase a separate B2B marketing automation product.

To put a more optimistic spin on the same news: Salesforce will continue to let independent B2B marketing automation apps synch with Sales.  If Salesforce does merge Pardot features into its core Sales product, then marketers who have a more expansive view of B2B marketing automation functions (or who simply want a system of their own) will be forced to buy from someone else.

The interesting news was that B2C marketing automation remains separate. Salesforce’s list of business groups includes the Sales Cloud, Service Cloud, and ExactTarget Marketing Cloud. Did you notice that just one of these has its own brand? As this suggests, and conference presentations confirm, Salesforce has kept B2C marketing distinct from its Sales and Service businesses, most importantly at the data and platform levels. The ExactTarget Marketing Cloud does now include Salesforce’s previously-purchased social marketing components, Radian6 social monitoring and Social.com social advertising. It also includes the iGoDigital predictive personalization technology that came along with the ExactTarget acquisition.

Salesforce did announce some plans to integrate the Marketing cloud with Sales and Service, but they are pretty much arm’s length: Marketing can receive alerts about changes in Sales (and I assume Service) data, even though that data remains separate; Sales and Service can send emails through the ExactTarget engine; Sales and Service can receive content recommendations from the Marketing predictive modeling tool. As near as I can tell, this is the same type of API-level integration available with any third-party system. For what it’s worth, the ExactTarget Marketing Cloud APIs are also part of Salesforce1, but don’t confuse that with sharing the same underlying platform.They don't.

The good news is the B2C marketing vision. It’s not really surprising that Salesforce kept its B2C platform separate, since Salesforce's core technology isn’t engineered for the massive data volumes and analytical processing needed for B2C in general and consumer Web marketing in particular. Happily, this technical necessity is accompanied by what strikes me as a sound vision for customer management.  ExactTarget framed this around three goals: single view of the customer; managing the customer journey; and personalized content across all channels and devices. It described major features for each of these: a unified metadata layer to access (and optionally import) data from all sources; a “customer journey” engine to manage multi-step, branching flows; and predictive modeling to select the best offers and contents across email and Web messages.

This felt like a more coherent approach than Salesforce described for the Sales cloud, where external data and predictive modeling in particular were barely mentioned (or, more precisely, are still being left to App Exchange partners). The ExactTarget cloud still lacks tools to associate customer identities across email, phone, postal, social, and other systems, although there are plenty of partners to provide them. I didn’t get a close look at the details of the ExactTarget functions, which will really determine how well it competes with other customer management platforms. But the general approach makes sense.

News of the revolution may be exaggerated. Salesforce argued during the AppExchange Partner keynote that the AppExchange and Salesforce platform have created a “golden age of enterprise apps” by enabling small software developers to sell to big enterprises. One part of the argument is that the platform itself lets small vendors break through the credibility and scalability barriers that have historically protected large enterprise software vendors. The other is that end-users can purchase and deploy apps without involving the traditional gatekeepers in enterprise IT departments. A corollary to this is that end-users have different priorities than IT buyers – in particular, end users care more about ease of use – so successful software will be different.

Of course, this is exactly what the AppExchange partners wanted to hear and exactly the strategy behind Salesforce’s platform approach in the first place. But that doesn’t necessarily make it untrue: and, if correct, it would indeed be a revolution in the enterprise software industry.

But some revolutions are bigger than others.  Even in an app-based world, individual users won't be making personal decisions about how to run core business processes.  Rather, systems will be chosen at the department level because companies can more or less safely assume that whatever the department chooses will integrate smoothly with the corporate backbone. That's certainly a change but bear in mind that departmental buyers will have the same preference as corporate IT groups for working with the smallest possible number of vendors. This means there will still be the familiar tendency for individual vendors to add more functions over time. So industry dynamics may change less than you’d expect.

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ReachLocal Provides Turn-Key Lead Management for Small Business

There are about 3 million companies with revenue between $1 million and $5 million in the U.S., according to Manta. This is an enticingly huge market for marketing automation vendors, and one that seems largely untapped. The largest marketing automation vendor in the segment, Infusionsoft, has under 20,000 clients. This is barely scratching the surface.

But this perspective is misleading. Many small businesses do their marketing through CRM, email, and search advertising. Search marketing is particularly important as online searches replace newspapers and telephone directories. Companies that provide small businesses with online directories and ratings, search engine optimization, Web sites, and paid search marketing all have client bases that dwarf the small business marketing automation industry.

Those other vendors could easily see marketing automation as a natural line extension, since it would help their clients make better use of the traffic those vendors generate. Last month ReachLocal – a $450 million public company that purchases online ads for more than 23,000 local businesses -- moved in exactly this direction.

ReachLocal’s new service, called ReachEdge, provides clients with a custom Web site, contact database, automated email streams to leads and customers, and automated alerts to company staff.  All the Web and advertising design is done for the client. There’s no automated lead scoring or branching campaign flows: when a new lead enters the system via a Web form or phone call, the user receives an alert, reviews whatever information was provided on the form or voice mail message, and manually classifies the lead as active, long term, new customer, or existing customer. Each category kicks off its own stream of messages (to the leads) and alerts (to company users), which can be spaced over time. Messages are sent by email; alerts can be sent by text, email, or a mobile app. Users can enter notes, add tags, and record revenue on contact records, providing a very light CRM option, or they can manually export the contact list to an external CRM system. Revenue can be used in campaign Return on Investment reports.

And that’s it, features-wise. If you’re used to looking at all-in-one small business marketing automation systems like Infusionsoft, Ontraport, or Venntive, the list may seem laughably primitive. But it’s a safe bet that many ReachLocal advertising clients have no interest in anything more complicated. The stumbling block facing all of marketing automation – that it takes more training, skills, and effort than most potential users can invest – is higher for very small businesses than anyone else. ReachLocal has reduced its clients' preparation to a minimum, and then left it up to them to pursue each new lead individually.

When a vendor does this much of the work, the key questions are less about the system than quality of the marketing.  ReachLocal said that each Web site is custom designed, based on interviews with each client by U.S.-based industry specialists. I looked at a samples for three different plumbers (here, here, and here) and found they were indeed different and detailed enough to be effective. I’ll assume that advertising and email are similar. ReachLocal’s service includes one hour of customization per month and a completely new Web site every two years. The price is $299 per month, which is comparable to low-end marketing automation systems although higher than simple auto-responders.

Let me be clear: ReachEdge doesn’t provide the process automation or even email segmentation of a conventional marketing automation system, let alone serious CRM, ecommerce, or external integration. So small businesses that want to market aggressively will probably find it insufficient. But small businesses that just want to generate a stream of new leads while they focus their energies elsewhere may well find ReachEdge an appealing alternative.

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