Military writers love to emphasize that it’s not the glamorous stuff that wins wars. It’s the dull game of getting the most men and firepower to the battle. Many a brilliant strategy has miscarried due to poor planning.
We’re not quite down to logistics yet but we aren’t far away either. We are at the point where the resources necessary to execute our digital measurement strategy need to be fleshed out. With the model of the business and the Data Science roadmap, we’ve identified what we need to accomplish. It’s time to figure out what’s needed to get the job done.
We do this in two steps. The first step is to map out the data and technology necessary to tackle each project in the Data Science roadmap.
When doing this, we group these projects back into systems (and their stages and components) to tie them directly to the models we’ve built. For each system, we map out the data science projects and the resulting data/technology requirements:
The Campaign Targeting Analysis highlighted above is designed to separate out campaign performance into its constituent components: targeting effectiveness, creative effectiveness and offer effectiveness. To accomplish this, we need campaign buy data, intercept survey data on campaign responders and converters, plus Web analytics data on site behavior by campaign. With this combined data, we can measure targeting precision and targeting effectiveness (both of which are critical and nearly always entirely unmeasured in digital) and we can separate out the impact of targeting from creative and offer. Separating out creative and offer efficiency is more challenging, but we have some behavioral analytics techniques for proxying each fairly effectively.
The beauty of this type of analysis is that it creates optimization strategies that are better than “drop this campaign” or “spend more on this campaign.” The analysis provides deeper insight into why a campaign is working or failing and, with that insight, often comes direction for better optimization regardless of how good the initial performance is.
For a strategic perspective, what’s compelling about the diagram above is that we create a clear tie between the business model, the data science projects, and the technology and integrations that are needed. If you want to optimize the targeting of your digital campaigns, this is what you MUST do. If this what you MUST do, these are the technologies you MUST have.
By aggregating up the requirements across every model, we can create a full technology ecosystem. The diagram below is for the Web system only (there would be separate diagrams for customer analytics, call-center, etc.):
We provide high-level detail on each element of the ecosystem and we use the Data Science Roadmap to create an explicit prioritization of the full technology stack.
Obviously, these last two slides are the sort that might be created in any strategy. Anyone can lay out a Web analytics ecosystem, assess which tools are currently installed, and recommend some prioritization of the rest.
It’s the first slide tying the data science project to the required technology that makes this approach powerful. Remove the Assessment, the Model and the Data Science Roadmap, and you may have exactly the same set of technology stack recommendations. What you don’t have is any real justification/explanation for what you’re asking for. Even if (and I think it’s unlikely) nobody in your organization cares why you’re spending all this money, the rest of the plan isn’t just window-dressing. It’s true that one of the main functions of a Strategic Plan is to justify a budget request (indeed, building a high-level budget plan is the next step in our process). But just as the Grinch discovers that Christmas is more than just presents and toys, a Strategic Plan really should be more than just a way of putting presents under the tree.
The Data Science Roadmap that lives at the hub of the plan (the Assessment and the Model drive TO the Roadmap; the Data, Technology and Budget steps drive FROM the Roadmap) is the guide-map for the entire digital measurement effort. You can have all the technology and people in the world and still get nothing useful done. Without leadership, all the firepower in the world will generally accomplish little. And leadership, more than anything else, is about knowing which direction to go!
