In our "marketing analytics agency" model, as distinguished from a more traditional consulting one, we measure success not just by the quality of the insights and opportunities we can help clients to find, but on their ability to act on the ideas and get value for their investments. Sometimes this means we simultaneously work both ends to an acceptable middle: even as we torture data and research for bright ideas, we help to define and influence the evolution of a marketing platform to be more capable.
This raises the question, "What's a marketing platform, and a good roadmap for making it more capable?" Lots of vendors, including big ones like IBM, are now investing in answering these questions, especially as they try to reach beyond IT to sell directly to the CMO. These vendors provide myriad marketing materials to describe both the landscape and their products, which variously are described as "campaign management systems" or even more gloriously as "marketing automation solutions". The proliferation of solutions is so mind-blowing that analyst firms build whole practices making sense of the category. Here's a recent chart from Terence Kawaja at LUMA Partners (via Scott Brinker's blog) that illustrates the point beautifully:
Yet even with this guidance, organizations struggle to get relevant stakeholders on the same page about what's needed and how to proceed. My own experience has been that this is because they're missing a simple "Common Requirements Framework" that everyone can share as a point of departure for the conversation. Here's one I've found useful.
Basically marketing is about targeting the right customers and getting them the right content (product information, pricing, and all the before-during-and-after trimmings) through the right channels at the right time. So, a marketing automation solution, well, automates this. More specifically, since there are lots of homegrown hacks and point solutions for different pieces of this, what's really getting automated is the manual conversion and shuffling of files from one system to the next, aka the integration of it all. Some of these solutions also let you run analysis and tests out of the same platform (or partnered components).
Each of these functions has increasing levels of sophistication I've characterized, as of this writing, into "basic", "threshold", and "advanced". For simple roadmapping / prioritization purposes, you might also call these "now", "next", and "later".
Targeting
The simplest form of targeting uses a single data source, past experience at the cash register, to decide whom to go back to, on the idea that you build a business inside out from your best, most loyal customers. Cataloguers have a fancy term for this, "RFM", which stands for "Recency, Frequency, and Monetary Value", which grades customers, typically into deciles, according to... how recently, how frequenty, and how much they've bought from you. Folks who score high get solicited more intensively (for example, more catalog drops). By looking back at a customer's past RFM-defined marginal value to you (e.g., gross margin you earned from stuff you sold her), you can make a decision about how much to spend marketing to her.
One step up, you add demographic and behavioral information about customers and prospects to refine and expand your lists of folks to target. Demographically, for example, you might say, "Hey, my best customers all seem to come from Greenwich, CT. Maybe I should target other folks who live there." You might add a few other dimensions to that, like age and gender. Or you might buy synthetic, "psychographic" definitions from data vendors who roll a variety of demographic markers into inferred attitudes. Behaviorally, you might say "Let's retarget folks who walk into our store, or who put stuff into our online shopping cart but don't check out." These are conceptually straightforward things to do, but are logistically harder, because now you have to integrate external and internal data sources, comply with privacy policies, etc.
In the third level, you begin to formalize the models implicit in these prior two steps, and build lists of folks to target based on their predicted propensity to buy (lots) from you. So for example, you might say, "Folks who bought this much of this product this frequently, this recently who live in Greenwich and who visited our web site last week have this probability of buying this much from me, so therefore I can afford to target them with a marketing program that costs $x per person." That's "predictive modelling".
Some folks evaluate the sophistication of a targeting capability by how fine-grained the target segments get, or by how close to 1-1 personalization you can get. In my experience, there's often diminishing returns to this, often because the firm can't always practically execute differentiated experiences even if the marginal value of a personalized experience warrants it. This isn't universally the case of course: promotional offers and similar experience variables (e.g., credit limits) are easier to vary than, say, a hotel lobby.
Content
Again, a simple progression here, for me defined by the complexity of the content you can provide ("plain", "rich", "interactive") and by the flexibility and precision ("none", "pre-defined options", "custom options") with which you can target it through any given channel or combination of channels.
Another dimension to consider here is the complexity of the organizations and processes necessary to produce this content. For example, in highly regulated environments like health care or financial services, you may need multiple approvals before you can publish something. And the more folks involved, the more sophisticated and valuable the coordination tools, ranging from central repositories for templates, version control systems, alerts, and even joint editing. Beware though simply paving cowpaths -- be sure you need all that content variety and process complexity before enabling it technologically, or it will simply expand to fit what the technology permits (the same way computer operating systems bloat as processors get more powerful).
Channels
The big dimension here is the number of channels you can string together for an integrated experience. So for example, in a simple case you've got one channel, say email, to work with. In a more sophisticated system, you can say, "When people who look like this come to our website, retarget them with ads in the display ad network we use." (Google just integrated Google Analytics with Google Display Network to do just this, for example, an ingenious move that further illustrates why they lead the pack in the display ad world.) Pushing it even further, you could also say, "In addition to re-targeting web site visitors who do X, out in our display network, let's also send them an email / postcard combination, with connections to a landing page or phone center."
Analysis and Testing
In addition to execution of campaigns and programs, a marketing solution might also suport exploration of what campaigns and programs, or components thereof, might work best. This happens in a couple of ways. You can examine past behavior of customers and prospects to look for trends and build models that explain how changes and saliencies along one or more dimensions might have been associated with buying. Also, you can define and execute A/B and multi-variate tests (with control groups) for targeting, content, and channel choices.
Again, the question here is not just about how much data flexibility and algorithmic power you have to work with within the system, but how many integration hoops you have to go through to move from exploration to execution. Obviously you won't want to run exploration and execution off the same physical data store, or even the same logical model, but it shouldn't take a major IT initiative to flip the right operational switches when you have an insight you'd like to try, or scale.
Concretely, the requirement you're evaluating here is best summarized by a couple of questions. First, "Show me how I can track and evaluate differential response in the marketing campaigns and programs I execute through your proposed solution," and then, "Show me how I can define and test targeting, content, and channel variants of the base campaigns or programs, and then work the winners into a dominant share of our mix."
A Summary Picture
Here's a simple table that tries to bundle all of this up. Notice that it focuses more on function than features and capabilities instead of components.
What's Right For You?
The important thing to remember is that these functions and capabilities are means, not ends. To figure out what you need, you should reflect first on how any particular combination of capabilities would fit into your marketing organization's "vector and momentum". How is your marketing performance trending? How does it compare with competitors'? In what parts -- targets, content, channels -- is it better or worse? What have you deployed recently and learned through its operation? What kind of track record have you established in terms of successful deployment and leverage from your efforts?
If your answers are more like "I don't know" and "Um, not a great one" then you might be better off signing onto a mostly-integrated, cloud-based (so you don't compound business value uncertainty with IT risk), good-enough-across-most-things solution for a few years until you sort out -- affordably (read, rent, don't buy) -- what works for you, and what capability you need to go deep on. If, on the other hand, you're confident you have a good grip on where your opportunities are and you've got momentum with and confidence in your team, you might add best of breed capabilities at the margins of a more general "logical model" this proposed framework provides. What's generally risky is to start with an under-performing operation built on spaghetti and plan for a smooth multi-year transition to a fully-integrated on-premise option. That just puts too many moving parts into play, with too high an up-front, bet-on-the-come investment.
Again, remember that the point of a "Common Requirements Framework" isn't to serve as an exhaustive checklist for evaluating vendors. It's best used as a simple model you can carry around in your head and share with others, so that when you do dive deep into requirements, you don't lose the forest for the trees, in a category that's become quite a jungle. Got a better model, or suggestions for this one? Let me know!
Comments