Big Data propaganda is rampant these days. One analytics vendor after another is selling you on their ability to analyze and make sense of “any” data.
Demand generation leaders know it's not true, though. Garbage in; garbage out. And most marketing and sales data is just that — garbage.
So what is the key to better demand generation analytics? How can we better tie buying and content/channel interactions to opportunities and closed revenue in a closed-loop fashion — to fuel our demand process optimization?
We must take a different approach — a structured approach — to demand generation analytics.
A few thoughts on where to start:
Analyze critical path, not attribution: Marketing analytics is mired in poorly considered "attribution" models. Really we want to know the critical path of content offer and engagement channel dialogue that led to someone becoming a qualified lead, opportunity and/or closed revenue? Analyzing this means building a new class of fields that track time-based engagement with every content offer and engagement channel in your perpetual demand generation engine. And this allows us to assess two new KPI categories — elasticity and velocity.
Elasticity looks at the probability that interacting with a given content offer or engagement channel will lead to a given outcome (e.g., closed revenue).
Don't analyze silos: As a marketer, you should focus on the alignment of people, process, content, technology and data in demand process outcomes. This is the starting point in truly closing the loop. This means evolving your KPIs beyond clicks, page traffic and cost per contact. Your KPIs should be focused on outcomes and the alignment of key demand process elements, such as how your content marketing model is interacting with your lead management framework.
An example of an outcome-driven KPI is one that examines the elasticity of engagement channels and of content offers from the perspective of lead stage — i.e., connecting the dots between channel and content activity and how it is driving buyers through lead qualification stages.
Connect account-level outcomes to contact-level activity: CRM platforms such as Salesforce.com are largely organized around account and opportunity objects. The problem is that most marketing automation platforms focus at the lead/contact level, collecting every action and every step taken by an individual buyer. Successful demand generation analytics must bridge the two.
Thus it's critical that you configure integration between your marketing automation and CRM systems to populate account-level data back to the contacts that originated this data — allowing you to see true lead to revenue and analyze down to the individual content offer and engagement channel interaction level.
Build structured, exportable demand data from the start: Know what you want to measure — i.e., what your demand process KPIs are going to be — and envision the structure of the data required to populate these KPIs in advance. Then make sure you build a progressive profile model that supports this and that collects the information in a structured way (e.g., pick lists and hidden fields). This means avoiding free-text fields and structuring your persona options versus capturing open-ended titles for prospects.
This also will involve adding dozens or even hundreds of fields into CRM and marketing automation that do not exist today — fields that capture movement through the demand process and that enable the data to be exported to an external analytics tool such as Tableau.
Success in anything starts with first building a strong foundation. If you want to close the loop on your demand generation programs, you've got to first build a structured system of data collection that mirrors this process and that populates the insights you need to optimize your demand process on an ongoing basis.
Adam B. Needles, Chief Strategy Officer at ANNUITAS, and author of Balancing the Demand Equation.