Can’t See the Forest for the Trees: When Data Becomes the Trees.
November 2017

If you are in marketing, or business for that matter, you can’t escape the conversation around data. Key performance indicators. Measurement. ROI. Conversions. 

You’ve heard all of the terms and use many of them. According to research released by Economist Intelligence Unit in 2016, approximately 58 percent of executives surveyed felt they were generating value from data. In that same report, 48 percent felt that their organizations have, in the past, failed to take advantage of opportunities to capitalize on their data. 

Why is it so challenging to effectively utilize data when we know it is vital to our success? 

Are we getting lost in data?  We often don’t see the correlations because we can’t see past the data points. 

One of the first rules in data analytics is to begin with business questions in mind.  

+ Why are sales down in X market?  

+ Who is our best target for our new product?  

+ How many widgets can we sell next year? 

For the data to provide relevant and meaningful insights, it comes down to asking the right questions. Frequently, internal departments or executives want to review specific datasets looking for patterns that may or may not exist. 

As written in the article The Difference Between Data Science and Data Analytics on, “Essentially, analytics sorts data into things that organizations know they know or know they don’t know and can be used to measure events in the past, present or future.”  

Before you head down the path of analysis, here are a few tips to ensure you start your data journey with the right road map. 

(1) You want your questions to be extremely specific. The more specific the question, the more valuable (and actionable) the answer is going to be. So instead of asking, “How can I increase revenue?” you might ask: “What are the channels we should focus more on in order lead to bigger profit margins?”. Or even better: “Which marketing campaign did we run this quarter that got the best ROI, and how can we replicate its success?”

(2) Be sure to identify the KPIs you want to measure (and get consensus company wide).   

(3) Determine where you will get your data. For now, pull in all of the data sources available, as long as the data sources are relevant to the agreed upon KPIs. 

(4) Apply the correct statistical analysis technique. How do you know which is correct? There are dozens of statistical analysis techniques that you can use. 

However, there are 3 statistical techniques that are most widely used for business analysis:  

Regression Analysis: a statistical process for estimating the  relationships and correlations among variables. More specifically,  regression analysis helps one understand how the typical value of the  dependent variable changes when any one of the independent variables is  varied, while the other independent variables are held fixed.  Usually, regression analysis is based on past data, allowing you to learn  from the past for better decisions about the future  

Cohort Analysis: enables you to easily compare how different groups,  or cohorts, of customers behave over time. This can provide you with quick and clear insight into customer retention trends.  

Predictive & Prescriptive Analysis: in short, it is based on analyzing current and historical datasets to predict future possibilities, including alternative scenarios and risk assessment. 

Keep in mind that if you don’t have the capabilities of doing this initially or if you don’t have the data set up to do it, you don’t have to do nothing. You can certainly use data that already exists or is automatically generated to do some descriptive analytics. This would include things like website or social data. Because looking at the things you do have is better than looking at nothing. 

Your choice of method should depend on the type of data you’ve collected, your team’s skills and your resources.

(5) Consider your final audience or the recipients of the analysis. Who are they? How will they apply your reports? What will they expect to learn or understand? Knowing the answers will help you to decide how detailed your report will be and what data you should focus on when reporting. 

It’s important to consider how much understanding of data your audience has before you present. For example, executives normally want to see a 
less detailed set of analysis, as well as audiences who aren’t as versed in data and statistical analysis.

(6) Select the correct data visualization to tell your story. This step, more often than not, gets mistaken for the first step.   

As with most marketing efforts, even the analytic reporting should tell a story. You should consider how to put together the findings of your analysis in a way that will tell a story and conclude by answering the “so what?”. 

You can have the most valuable insights in the world, but if they’re presented poorly, your target audience won’t receive the impact from them that you hope. 

We live in a visual world where everyone wants information delivered in brief answers that can be told using key visuals. The trick here is that you have to use these brief visual representations to convince others of major business decisions. 

Keeping these six tips in mind may help you and your team stay focused on the actual business objectives at hand and help you avoid getting caught up in the actual data. 


About Littlefield
Founded in 1980, our Top 25 North American B-to-B agency specializes in companies who sell through a strong dealer/distributor network.