BlueConic Launches Marketing Technology Self-Assessment Tool

It's a little more than a year since I collaborated with BlueConic on a marketing technology maturity model.  They've been busy improving their product, in particular by adding a set of templates for prebuilt marketing programs, which they call "blueprints".  Users first select a goal, such as "decrease bounce rates".  They are then led through a sequence of tasks to collect the necessary customer data; assemble the data into profiles; segment customers using the profiles; and deliver the messages to external systems.  The goal is to make it easier for marketers to create useful programs with the system.

But BlueConic was also working on a little side project: an online self-assessment tool based on the maturity model.  This asks users about a dozen questions about their marketing methods, business processes, and organizational resources.  It then provides an assessment of how they compare with other companies and makes recommendations for how to make improvements.  I provided much of the content, so obviously I'm biased, but I do think the results are pretty interesting and useful.  And it's free...did I mention free?  You can read more about the tool in this BlueConic blog post and access it here.  Enjoy!

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. 

BlueConic Selects Targeted Messages Using a Cross-Channel Marketing Database

This blog has mentioned BlueConic in passing a couple of times but never quite gotten around to reviewing it in detail. Until now.

The delay may seem surprising, since BlueConic qualifies as a Customer Data Platform, a type of system I’ve been arguing will play an increasingly central role in marketers’ futures. For those of you who haven’t been paying close attention, a CDP is defined as a

• marketer-controlled system that
• supports external marketing execution based on
• persistent, cross-channel customer data.

This definition distinguishes CDPs from traditional marketing automation products, which do their own execution, and from real-time interaction managers, which lack persistent data stores. CDPs are important because few marketers have been able to build adequate cross-channel databases and because connecting those databases with execution systems has been difficult. The databases and connections are needed because today's customers expect personalized, coordinated treatments across all channels.  Call it the “Amazon fallacy”: customers believe that since Amazon.com can give them highly personalized treatments, so can everyone else.

Anyway, back to BlueConic. The system has two main capabilities, which are to maintain customer profiles and to deliver targeted messages. The profiles can be based on data imported from other systems via batch processes or APIs or captured by BlueConic itself.  It does this with “listeners” that can read data from forms or monitor behaviors via Javascript tags on Web pages, emails, and other media. “Listeners” can also create interest rankings and scores based on user behavior. All of these become available as attributes on the customer profile, which in turn can create customer segments and drive targeted messages.

The messages are delivered by what BlueConic calls “dialogues”, each of which sends a single message to a single location (email, text message, section on a Web page, etc.) to a specified customer segment.  Messages tailored to customer interests would require creating a separate segment for each message.  Similarly, presenting a sequence of messages would require a separate dialogue for each step in the sequence.  If a customer is eligible for several dialogues at once, the system currently relies on an optimizer to pick best-responding option and will soon let users create rules to further guide the results. There is no built-in predictive modeling but the optimizer can continuously test alternative messages within a dialogue and automatically deploy the winner. Users can also apply a frequency cap to dialogues to limit the number of times any customer sees the same message.

BlueConic’s integration features are more extensive than its decision management. The system can capture data entered into forms even if the form ise not submitted. Users can insert message contains into an existing Web page without writing HTML code. Profiles capture  customer identifiers provided by other systems and are automatically merged when two profiles are linked to the same external ID.  Data is exchanged with other sources and execution systems via REST APIs. There are standard integrations with Twitter, Facebook, and Salesforce.com, as well as a system development kit for integration with mobile apps. The underlying data store is Apache Cassandra running on Amazon Web Services, which is highly flexible and scalable at moderate cost.

Integration and data management are what make BlueConic most interesting from a CDP perspective, since those are the core CDP functions. A “pure" CDP would provide only those services while leaving decision management and message delivery to other systems. I expect “pure” CDPs to appear, but most marketers prefer a broader solution, like BlueConic, to assembling the components for themselves. Pure CDPs will become more attractive as integration becomes easier through more standard APIs and connectors, a promise that cloud-based systems often make but are just starting to deliver.

BlueConic’s pricing is already data-centric: fees are based on numbers of profile and channels, not interactions or messages. Prices start around $1,000 per month although most clients pay more. Current implementations are mid-size and enterprise firms in B2C industries including retail, publishing, financial services, utilities, telecommunications, sports and travel. The system has about 70 current customers and is sold both directly and to partners such as ad agencies, other software vendors, and marketing service providers.