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Analysis and Data Interpretation

Analysis and Data Interpretation

data Jul 14, 2021

By Andrea Tartaglia – Co-Founder/Director @ The Marketing Leaders Ltd.

 

There are some parts of my job that enjoy more than others.

Unsurprisingly, as a marketer, I love everything that has to do with generating ideas and finding creative solutions to difficult challenges.

But I also love the analytical side of marketing, which, thanks to the huge amount of data available today, is becoming more and more business critical.

Why do I love it?

Simple: because looking at data, analyzing and interpreting it allows me to take better decisions. And it allows me to inform the idea generation, ground it on solid insights and ultimately deliver better results.

This is why I do not shy away from tasks like gathering data, managing the process of turning data into insights and then using them to define or refine marketing strategies and activities.

This process not only gives me vital information on the status of the business and its strengths and weaknesses; it also gives a solid foundation to my hypothesis or challenges them, showing me that what I was thinking was not actually accurate. More on this a little later.

Here are a few considerations I make while managing this process.

 

What am I trying to achieve?

The whole process has a very clear and simple intent: I use, analyze and interpret data to generate insights. And, as mentioned already, I use insights to inform strategies and activities; to validate or disprove working hypothesis; to refine my thinking and provide evidence that the course of action I recommend is solid and will yield results.

And then I use data again to monitor progress and track that every activity is delivering the expected outcome, within the required timeframe and at the cost I budgeted for.

Clearly, it is not a process that has a beginning and an end. It is ongoing and never ending. However, there are certain milestones that make it easier to manage and provide some structure and sense of closure (I think of it as cyclical closure of parallel data analysis projects that are always on-going).  Think about the annual planning and budgeting process as one of these milestones. Or the end of a campaign and the post evaluation that follows.

 

Sources of data

I use a variety of data points. Anything that allows me to really understand what is going on and what the cause-and-effect relationship between marketing activities and results could be.

To keep things simple, I like to think about 2 macro sets of data:

  • Business data: these are data points that help me understand the market situation, any business trends that might affect the outcome of my marketing strategies, how the industry is performing, what my competitors are doing and anything that can help establishing the role my brand has in its addressable market
  • Consumer/customer data: these are data points around the brand and how it is perceived by its consumers/customers; they help answering questions like what the relationship consumers have with the brand, how are they behaving in each phase of the marketing funnel and the buyer’s journey; how they are interacting with the brand and what sources of information are they looking for, and so on.

I use a combination of quantitative and qualitative data (please read Nick Bottai’s article on this subject).

Accordingly, not all data is numeric. In particular, I spend a considerable amount of time talking to key internal and external stakeholders, gathering their views, sharing my thoughts and collecting feedback.

This is a part of the process that is quite ‘manual’ but in invaluable in generating rich data points and insights.

The numeric side of things is more automated, provided you have the resources to build a data warehouse and a dashboard summarizing the most critical data points to be used. Alternatively, you can use off-the-shelf services that provide a more standardized and limited set of data but are very cost effective.

 

How I interpret the data

I like to immerse myself into the data. Get lost for a short time and then emerge having understood connections, causalities, the big picture, and some or most of the details, as needed.

Having worked for large, resource rich organizations, I have been lucky enough to collaborate with data analysists and insights teams that take care of a lot of this work and give me everything I need so I can spend my time on strategy and implementation.

However, I still like to be part of the process and contribute to the analysis and interpretation of the data, keeping an inquisitive attitude and asking lots and lots of questions to the data experts.

This is the part of the process where I generate the insights that will inform my strategic thinking.

There are 3 outcomes from this phase:

  1. Goals setting: with a more informed understanding of the addressable market, the key consumers/customers and the performance of my brand, I can set goals for the overall marketing strategy and its constituent parts
  2. Formulating hypothesis: I normally start the process with some ideas in mind and use the data to prove or disproof my theories, which leads to…
  3. Reaching conclusions: anchoring goals, strategy and implementation on data supported thinking.

 

Traps to avoid

When working on gathering, analyzing and interpreting data, there are 2 big traps I make every effort to avoid for me and my teams; both of which are easy for me to fall into. So I need to diligent and pay attention…

  • I mentioned I normally start with a working hypothesis, which then I confirm or disproof with the interpretation of the data gathered. I think this is fine, provided you do not insist on your original ideas and are not open for the ideas to be proven wrong. If you are married to your own hypothesis, you might be tempted to interpret the data in a way that confirms your thinking. It is extremely easy to be influenced, tweak the collection and interpretation of data (for example asking leading questions), and only look at connections and causalities that confirm what we are thinking. This might lead to wrong assumptions, and it is far from being an objective process. I let the data ‘speak’ and tell me what the situation is regardless of my hypothesis. I am happy if my thinking is proven right, but I am equally happy to find out that my assumptions were incorrect and follow the new direction the data is suggesting.  
  • I also mentioned I like to get ‘my hands dirty’ and look at the data but I avoid getting lost in it. The abundance of data available nowadays can cause paralysis. There is always more data available, and perspectives might change once new data is available, analyzed and interpreted. Where to stop? The answer is simple: apply common sense and keep focusing on your objectives. Taking the right decision is very important, but the problem might remain unresolved if that decision comes too late. Speed of action is critical and I push forward, provided I have enough data and insights to have a good understanding of the situation, without waiting for more data and insights to have the richest and most detailed information, unless it is absolutely indispensable and I do not compromise the reaching of my goals by delaying a decision.

 

If there is only one thing I hope you take away from my experience in gathering, analyzing and interpreting data is this: it is an exciting process, a process of discovery and its output can lead to the best strategies and marketing campaigns.

It is something to embrace  while applying a pragmatic approach, keeping the goals and the timelines always in check.