Building a Marketing Measurement & Reporting Strategy

A few years back I wrote a master’s thesis titled Service Systems and the Social Enterprise, which, if you have a strong stomach and a lot of patience, you can read here. The goal of the research was to contribute to the ongoing development and discourse of a multi-disciplinary field, known as service science, through a case study of a particular business setting: the social enterprise (as in social good, not social media).

It's been a while now since I've done any reading on service science, but just recently, I found myself returning to the discipline’s foundational works for some help in terms of understanding how businesses, small and large, develop sustainable and effective analytics practices. 

First, some context and definitions are in order. Service science is a multidisciplinary field concerned with the study of service systems and value co-creation. The goal is to develop a philosophical basis for which to apply scientific methods and understanding to the study of service exchange. The service system is considered the most fundamental abstraction for analyzing service exchange, and can be defined as a “value co-creation configuration of people, technology, value propositions connecting internal and external service systems, and shared information.” You can read more about the discipline at the Journal of Service Science, or by following people like Jim Spohrer and Kelly Lyons, two of the foremost thinkers in this field.

Service Science & Marketing Analytics

So, what do service science and marketing have in common? The distinct roles of people, technology, information and knowledge were primary to understanding and modelling the process of service exchange, and I find that the relationship between these four concepts is critical to defining what makes a robust measurement and reporting (M&R) strategy. As such, I like to define an M&R strategy as the configuration of people (e.g. analysts), knowledge (e.g. policies, protocols), information (e.g. data) and technology  (e.g. tools) that enable an organization to measure the outcomes of their marketing activities effectively. This is done so the organization can optimize their marketing efforts for greater effectiveness and efficiency using a data-driven approach. Let’s break this down a little further.

People

Avinash Kaushik, one of the pioneers in the marketing analytics industry, has an interesting tip for balancing your investment in the people and tools that drive your measurement capabilities.  He proposed the 10/90 rule, which effectively states that for every $10 you invest in an analytics tool or vendor, you should be spending at least $90 on human resources (e.g. analysts). This can be a difficult pill to swallow for many marketing managers or c-suite execs, mainly because they may already be investing heavily, and somewhat disproportionally, in technological solutions. The simple fact is if your investment in technology is exceeding the cost of your existing human resources, you’re probably doing something wrong. Analytics software, no matter how fancy the dashboards, visualization features or deep-dive analysis tools, are simply aggregators of data. They are critical to the processes involved with collecting data and creating efficiencies related to mining and analyzing data, but in the end, the tool is only as good as the person using it. That is, actionable insights can only be achieved through the "intelligent resources" (i.e. humans) who can interpret the data and provide meaning. Karla Wachter captured this point well in the below tweet late last year:

Tools need to be a productivity enhancer for experts, not designed to bypass the expert -- @big_analytics data science panel #BARS12!

— karlawachter (@karlawachter) December 12, 2012

Between Kaushik and Wachter, the point is plain and simple; focus on people first and technology solutions second.​

Knowledge

The application of knowledge related to marketing analytics within your organization is critical to the effectiveness of your strategy. This is a fairly expansive domain, and can include issues such as the policies and protocols you have in place that determine access rights and use of your data, the measurement frameworks you apply for analysis, or the operational resources you have developed that dictate how and when you collect, measure and report (e.g. reporting schedule). Whether your measurement capabilities are in house or you outsource this to an agency your knowledge-base is absolutely critical to measuring the performance of your marketing activities effectively, efficiently and consistently.

Information

I define information as assets or resources that the people and technology within your measurement practice utilize to co-create value. The most basic example of this is simply the raw data that your marketing activities produce, such as clickstream or spend data. If people represent the brains of your operations, information (i.e. data) is the heart. As a consideration within your M&R strategy, it's important that you know exactly what types of data you are working with, how it is collected, extracted and compiled, and finally, what data will be analyzed together (e.g. clickstream, research data, spend or revenue data, etc).

Technology

Finally, technology is the last piece of the puzzle. Technology can include hardware (e.g. IT infrastructure) and software (e.g. analytics tools) that drive your measurement capabilities, but here I’ll focus on software. I won’t go into this in-depth, but suffice it to say there's no shortage of software vendors that provide measurement and reporting capabilities. Whether you're a non-profit, SME, or large-scale enterprise, the cost and resource investment in these tools can range considerably. If you're on the brink of a software procurement or just shopping around, I would recommend taking the time to clearly understand your needs before signing on the dotted line. Some software can become so deeply embedded within your organization that it can be costly, if not catastrophic to uproot. Kaushik wrote a great chapter about this in his book, Web Analytics: An Hour A Day. I highly recommend reading it if you're currently (or could be) involved in software procurement for an analytics tool. But above all, and at the risk of sounding like a broken record, remember to always focus on people first and foremost.​

Overall, my goal here was to apply the foundational works of service science as a lens for understanding how businesses and organizations can and should build analytics practices that consider the complex relationships between their resources. 

Simplicity is your goal

In closing, I leave you with the following words of wisdom from renowned business magnate Richard Branson. In a recent tweet, he stated the following:

Complexity is your enemy. Any fool can make something complicated. It is hard to make something simple virg.in/cye

— richardbranson (@richardbranson) September 3, 2012

Branson's point is especially germane to the field of performance marketing and analytics. The outcome of your measurement and reporting activities should always be actionable insights that are easy to put into perspective. This isn't to say that deriving these insights will be simple, but rather, the perspective your data gives you should be. This can be a difficult task indeed, but if you fail at this you can be assured that the wonderful dashboards, tables and charts you send to your executives will be appreciated more for their aesthetic value than their practical use. If you’re stuck with understanding how your investment in digital is paying off, start with building your M&R strategy, and remember that you should always strive to make the complex simple.

​Thumbnail Source: HeyThereSpaceman, Flickr