Author: Michael Engberding, Senior Strategic Data Consultant at Mapp
It’s a truism that collecting and analyzing data is business critical. Hence, data has a strategic impact on your company, your business objectives, your department and, ultimately, on your personal goals as well. By organizational nature, many departments and individuals are involved when you talk about data and how to collect it. This encompasses prioritizing the data, methods of acquiring data, processing of data, and analyzing and reporting insights. Whenever you start collecting data, different rivaling perspectives come into play. Such perspectives can be technical vs. analytical; detailed data vs. big data aggregates; business objectives vs. feature performance; or poor metrics vs. KPIs; and many more.
It is crucial to balance these rivaling data perspectives. In the case of digital analytics, this means a cross-device and user-centric approach to track and analyze streams of visitors, their acquisition, their behavior and the goal achievement on your website or app.
Therefore, I take up the well-known idea of using a measurement model to come to a valid data strategy for all involved stakeholders. Then, supplementing this measurement model with the decoded analytical content for a purposive and efficient tracking concept. As a result, with the help of a measurement model you can close one gap between competing perspectives on data and another one is closed by developing a technical tracking concept which contains only relevant business objectives and data goals. Having a well-worked measurement model containing the “what” to measure will tell you “how” to measure it by translating the model into a technical tracking concept, thereby closing the gap between analytical requirements and technical implementation procedures.
Throughout the complete revolving process of measurement modelling, implementation, reporting and measurement improvement and optimization, you can count on professional consultation from Mapp’s data strategy team and data analysts.
The kick-off for the measurement model is a well-planned workshop, which marks an entry point into the measurement lifecycle. The number of participants ranges from 4 to 9 people and includes experienced people from all departments who work with digital data, like management, marketing, sales, finance, IT, etc. Here you are already at the point of creating awareness for the importance of data and the measurement model. To underpin such an awareness, each department’s data requirement must be treated respectful as it’s a relevant part of your data strategy. This workshop results in a measurement plan for further implementation. Gathering all people for such a workshop is extensive, but it is worthwhile. High data awareness and a complete and satisfying data strategy are your benefits.
With the measurement model, you want to reach a set of goals, KPIs, metrics and user segments which should cover the main scenarios acquisition, behavior and goal achievement. In the evolution of the measurement model you can follow roughly 3 top-down steps.
Following the presented steps, rivaling data perspectives are avoided as every idea and KPI from all relevant stakeholders will be part of the data strategy. You arrive at this point with the above-mentioned data awareness and by replacing every “but” with an “and“. This means the measurement model allows every idea and KPI from all stakeholders to be part of the data strategy, as long all results stick to the overall business objectives and website goals. Implementing such a broad approach does call for prioritization. To get everything done, you have to isolate the business-driving goals, KPIs and use-cases to start with in an iterating process. Prioritization models like MoSCoW or Eisenhower are helpful heuristics for the planning.
With the refined measurement model plan in hands you can go straight to the technical conceptualization. Our data consultants will help close this gap. The challenge is to transfer the model into an actionable use-case driven implementation utilizing all technical standards and hacks to meet your business and measurement goals. The approach is use-case driven, because it follows, in an illustrative way, the goals, KPIs and metrics to track in making them concrete. This helps to prevent getting lost in time-consuming issues and tracking data no one might be interested in. All technical instructions follow the requirements of the measurement model. Accordingly, all these instructions are as customized as necessary and as constructive as needed. Such a concept represents state of the art tracking.