OneSpot Offers Automated Content Selection Targeted at Long Term Results

As you know from previous blog posts, I’ve been borderline obsessed recently with systems that automatically create multi-step campaign flows. So when I saw that OneSpot calls its product a “content sequencing engine” you can bet they had my attention. When I read that “OneSpot’s machine learning technology serially delivers multiple pieces of content to your users based on their interests and digital journey stage,” I thought I might have found the Holy Grail itself.

OneSpot was already on my list of interesting companies because they automatically reformat content to use in different channels. This is a good example of applying artificial intelligence to reduce the workload on marketing departments so they can deliver more targeted messages at lower cost. The company had been doing this and programmatic ad buying since its start in 2012.

The content sequencing engine is a more recent addition. According to OneSpot Chief Marketing Officer Adam Weinroth, one of the things that make the engine special is that it finds the best content to generate repeat engagement rather than immediate response. Another is that it delivers the content through advertising on external Web sites as well as in email, a company’s own Web site, mobile and social. In other words, OneSpot's “sequencing” is about coordinating messages in different channels, not delivering groups of messages in a fixed order. Since my own quest has been automated creation of ordered messages, OneSpot isn't the Grail I seek.  But it's still quite special: as Weinroth points out, there are many systems to select offers for ecommerce but few for content marketing. Even fewer support advertising along with other channels.

OneSpot deploys several major pieces of technology to make this happen. A content analytics engine automatically classifies existing content without manual tagging. The classification categories (a.k.a. taxonomy) are themselves created automatically. This automation removes one of the largest bottlenecks in deploying high volumes of content. The reformatting engine then prepares the content to be distributed across multiple channels, again without manual labor. The automated recommendations are based on a profile that includes results from all the channels supported by OneSpot. This cross-channel perspective is what lets OneSpot base recommendations on repeat engagement rather than immediate response. It also lets the system bid on advertising to non-customers as well as messages to current customers. OneSpot also has its own real-time bidding engine. It is integrated with major Web ad exchanges, which Weinroth said allows it to potentially bid on 36 million impressions per minute.

Other aspects of OneSpot are more conventional. The system uses a Javascript tag to integrate with company Web sites and third party ad sites. Email is personalized through API connections with email service providers. Advertising audiences are selected by defining targeting criteria against standard Web audiences. Customers and prospects are identified through Web cookies, hashed (anonymized) email addresses linked to cookies, and third party services for device targeting.

OneSpot continues to extend its technology. Weinroth showed me a beta version of a report that compares demand for each topic with the number of existing content pieces for that topic and the impact of content with that topic on reengagement. While OneSpot won’t actually create the additional content, this is still another step towards replacing manual tasks with automation.

Pricing for OneSpot starts north of $100,000 for an annual contract. The actual fees are based on traffic volumes and channels supported. Weinroth said most clients use the system in at least two channels, typically Web site messages and Web ad retargeting.

Adobe Marketing Cloud Marches Towards Martech and Adtech Integration

At pretty much the same moment I was publishing my post on the merger of martech and adtech into madtech, Adobe was announcing its latest marketing products, including a press release on uniting “Data-driven Marketing and Ad Tech” . Naturally, this caught my attention.

As you might expect, Adobe’s reality is considerably more complicated than the simplicity of the “madtech” vision. Like the other enterprise software vendors who offer broad martech and adtech solutions, Adobe has built its marketing cloud by buying specialist systems. And, again like its competitors, it has only integrated them to a limited degree.

In Adobe’s case, the various products remain as distinct “solutions” served by a common set of “core services”. The current set of eight solutions includes Analytics (Web, video and mobile analytics, née Omniture Site Catalyst), Social (social publishing, based on Context Optional), Target (Web optimization and personalization, derived from Omniture Test & Target/Offermatica), Experience Manager (Web content management , originally Day Software), Media Optimizer (based on Efficient Frontier and Demdex), Campaign (formerly Neolane); Primetime (addressable TV) and Audience Manager (data management platform, formerly Demdex). Of course, the products have all been modified to some degree since their acquisitions.  But each still has its own data store, business logic and execution components.

Rather than replacing these components with common systems, Adobe has enabled a certain amount of sharing through its core services. In the case of customer data, the “profiles and audiences” core service maintains a common ID that is mapped to identities in the different solutions. This means that even though most customer data stays in the solutions’ own databases, the core service can use that data to build audience segments. There's also an option to load some attributes into the core services profiles themselves.   Audiences, which are lists of IDs, can either be defined in solutions and sent to the core service or built within the core service itself.  Either way, they can then be shared with other solutions. Data from external systems can also be imported to the core service in batch processes and used in segmentation.

Adobe says that data stored in the solutions can be accessed in real time.  I'm skeptical about performance of such queries, but the ability to store key attributes within the core service profiles should give marketers direct access when necessary.  There’s certainly a case to be made that digital volumes are so huge and change so quickly that it would be impractical to copy data from the solutions to a central database. Where external data is concerned, marketers will increasingly have no choice but to rely on distributed data access.

But here’s the catch: Adobe's approach only works if all your systems are actually tied into the central system. Adobe recognizes this and is working on it, but so far has only integrated five of its solutions with the profiles and audiences core service. These are Analytics, Target, Campaign, Audience Manager, and Media Optimizer. The rest will be added over time.

The second big limit to Adobe’s current approach is sharing with external systems. Only Adobe solutions can access other solutions’ data through core services. This makes it difficult to substitute an external product if you already have one in place for a particular function or don’t like Adobe’s solution.

Adobe does connect with non-Adobe systems through Audience Manager, its data management platform, which can exchange data with a company’s own CRM or operational databases, business partners, and external data pools and ad networks. Audience Manager can hold vast amounts of detailed data, but does not store personally identifiable information such as names or email addresses. Audience Manager can also copy Web behavior information directly from Analytics, the one instance (so far as I know) where detailed data is shared between Adobe solutions.


So far, I’ve only been discussing data integration. The various Adobe components also have their own tools for segmentation, decision logic, content creation, and other functions. These are also slowly converging across products: for example, there is an “assets” core service that provides a central asset library whose components can be uploaded to at least some of the individual solutions. The segmentation interface is also being standardized product-by-product. There’s no point in trying to list exactly what is and isn't standard today, since this will only change over time.

The lesson here is that suites are not simple. Marketers considering Adobe or any other Marketing Cloud need to examine the details of the architectures, integration, and consistency among the components they plan on using. The differences can be subtle and the vendors often don’t explain them very clearly. But it pays to dig in: the answers have a big impact on whether the system you choose will deliver the results you expect.

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

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

The good thing about hearing from someone else is you find out things you didn’t already know. In this case, I was certainly familiar with the general notion of a maturity model, as a sequence of increasingly-sophisticated stages that companies pass through on their way to the highest level. And, for what BlueConic calls “user-driven marketing”, I already knew that the final stage would be a central database and decision engine that gather data from all channels and select the treatments that each channel delivers. So it wasn’t too hard to imagine that the preceding stages would start with totally disconnected channels and move slowly to complete integration. But there were still some new insights from BlueConic’s hands-on experience. Some that particularly struck me are:

• Listening first. The very first stage of the model, Level 0, involves no differentiation at all: every customer is treated the same; in fact, customers may not even be identified. BlueConic gets involved at Level 1, where treatments are tailored to the individual but each interaction managed independently within each channel. At that stage, all the central marketing system can do is “listen” to customer activities and make the data it assembles available to the channel systems to help guide their own decisions. I would have expected the central system to actually drive decisions at that stage, but BlueConic's experience is different.

• Coordination later. Level 2 of BlueConic’s model still has each channel running separately, which again is a bit surprising. What changes at this level is that  interactions within each channel are now coordinated by the central engine. It’s only at Level 3 that interactions are coordinated across channels, and even then the scope is limited to online channels. On reflection, an intra-channel-only Level 2 makes sense: marketers need several new skills to design and measure multi-interaction programs, and mastering those is a big enough challenge without also adding the complexity of managing across channels.

• Segmentation. The growing importance of segmentation at successive model stages was perhaps my biggest surprise. When I think of tailoring interactions to individuals, I think of working with each individual’s data directly. Segments don’t enter into it. But, as BlueConic’s experience reminds us, practical marketing tasks like content creation, program flows, and result analysis are organized around groups of similar customers. This ensures resources are spent effectively and you have enough volume to measure results meaningfully. In fact, the segments get increasingly refined with each maturity level as behavioral data is added (Level 2), segments are adjusted in real time (Level 3), and segments include predictions and events (Level 4). Thus, the process does move closer to treating each individual differently, but always in a segment-based framework.

• Complexity of data. This was less a surprise than an observation. Part of the presentation was a set of examples presented by BlueConic CMO Dan Gilmartin. By the time we got to Level 4, where interactions are being coordinated across all brands as well as all interactions in all online and offline channels, the example was offering a soccer jersey as a holiday gift idea to a mom reading a lifestyle Web site. Superficially, this seems like a simple, obvious thing to do.  But, on reflection, it’s amazingly complex. It requires not just knowing who the viewer is, but who she’s related to (child or spouse), the interests of that related person (soccer), and the temporal context (holiday gift buying season). That is some pretty fancy data management.

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

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

NextPrinciples Offers Integrated Social Marketing Automation

Social marketing is growing up.

We’re seen this movie before, folks. It starts when a new medium is created – email, Web, now social. Pioneering marketers create custom tools to exploit it. These are commercialized into “point solutions” that perform a single task such as social listening, posting, and measurement. Point solutions are later combined into integrated products that manage all tasks associated with the medium. Eventually, those medium-specific products themselves become part of larger, multi-medium suites (for which the current buzzword is “omni-channel”).

But knowing the plot doesn’t make a story any less interesting: what matters is how well it’s told. In the case of social marketing, we've reached the chapter where point solutions are combined into integrated products. The challenge has shifted from finding new ideas to meshing existing features into a single efficient machine. More Henry Ford than Thomas Edison, if you will.

NextPrinciples, launched earlier this month, illustrates the transition nicely. Originally envisioned as a platform for social listening and engagement, it evolved before launch into a broader solution that addresses every step in the process of integrating social media with marketing automation. Functionally, this means it provides social listening for lead identification, social data enhancement to build expanded lead profiles, social lead scoring, social nurture campaigns, integration with marketing automation and CRM systems, and reporting to measure results.

It’s important to clarify that NextPrinciples isn’t simply a collection of point solutions. Rather, it is a truly integrated system with its own profile database that is used by all functions. It could operate without any marketing automation or CRM connection if a company wanted to, although that doesn’t sound like a good idea.  Its target users are social media marketers who want to work in a single system of their own, rather than relying on point solutions and social marketing features scattered through existing marketing automation and CRM platforms.

The specific functions provided by NextPrinciples are well implemented. Users set up “trackers” to listen to social conversations on Twitter (today) and other public channels (soon), based on inclusion and exclusion keywords, date ranges, location, and language. Users review the tracker results to decide which leads are of interest, and can then pull demographic information from the leads’ public social profiles. Leads can also be imported from marketing automation or CRM systems to be tracked and enhanced. Trackers can be connected with lead scoring rules that rate leads based on demographics and social behaviors, including sentiment analysis of their social content. Qualified leads can be pushed to marketing automation or CRM, as well as entered into NextPrinciples’ own social marketing campaigns to receive targeted social messages. Campaigns can include multiple waves of templated content. The system can track results at the wave and campaign levels. It can also poll CRM systems for revenue data linked to leads acquired through NextPrinciples, thus measuring financial results. Salespeople and other users can view individual lead profiles, including a “heatmap” of topics they are discussing in social channels.

If describing these features as “well implemented” struck you as faint praise, you are correct: as near as I can tell, there’s nothing especially innovative going on here. But that’s really okay. NextPrinciples is more about integration than innovation, and its integration seems just fine. I do wonder a bit about scope, though: if this is to be a social marketer’s primary tool, I’d want more connectors for profile data such as company information and influencer scores. I’d also want lead scoring based on predictive models rather than rules. And I want more help with creating social content, such as Facebook forms, sharing buttons to embed in emails and landing pages, multi-variate testing and optimization, and semantic analysis of content “meaning”.

NextPrinciples is working on at least some of these and they’ve probably considered them all. As a practical matter, the question marketers should ask is whether NextPrinciples’ current features add enough value to justify trying the system. In this context, pricing matters: and at $99 per month for up to 100 actively managed leads, the risk is quite low. For many firms, the lead identification or publishing features alone would be worth the investment. Remember that NextPrinciples is only the next chapter in an evolving story.  It doesn’t have to be the last social marketing system you buy, so long as it moves you a bit further ahead.