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I have a confession to make: Marketing analytics isn't my strong suit. I've always been a marketer driven by creativity, not data, but after working in this field, I realized it's not that difficult. You don't need a data science background in order to effective analyze marketing data.
Nowadays we have several marketing analytics tools and guides that make the task so intuitive that even a data-phobe like me can become a marketing analyst. Here’s a guide on how to analyze marketing data for those who don't have a background in statistics.
A target audience analysis is a process that involves collecting and analyzing user data in order to nail down who is most likely to buy your products. The key features to look out for in your data are: profession, interests, and demographic information (age, gender, location etc.).
Nailing down who the target audience is will allow marketers to optimize their advertising campaigns, especially when it comes to PPC. With so much data available, it might seem difficult to identify that your ideal customer is a male aged 34-42, living in New York, married with one kid in a middle-class household.
After all, there's so many variables to analyze, but I'll show you the quickest way to do this later.
A revenue analysis is one of the key ways to gauge a business' health. It shows the history of sales over a certain time period (usually yearly or quarterly) and allows the business to make forecasts on how it will perform in the future.
When performing a revenue analysis, you want to look at trends: Does revenue dip or peak during weekends or certain holiday periods? Do certain products sell better during certain seasons?
Analyzing the fluctuations in sales will allow for more profitable decisions in regards to marketing budget allocation.
Rarely do marketing professionals do a proper competitor analysis, but it's a crucial part of the process.
A competitor analysis involves researching your main competitors in order to gain insights into what they're doing from a product and marketing perspective. It allows your business to capture more market share by staying ahead of the curve.
If your competitor implements a product feature or marketing strategy that isn't working, you'll also know not to do the same thing. After all, learning from other's mistakes is the best way to learn.
A big part of marketing analytics is A/B testing. And part of determining how effective a marketing strategy is, is by looking at the conversion rates.
Say you're running Facebook ads. Performing a conversion rate analysis is key to nailing down your ad creative, copy, landing page, and ideal customer profile.
Through conducting and analyzing surveys, a marketing analyst can gain key insights into the current state of the market by identifying:
Market research involves taking a small sample out of the population and extrapolating the data to discover insights that'll allow you to build a better product, and craft smarter marketing strategies.
Usually, in marketing data, we have a very clear goal of what we want to achieve. Ultimately, it's to increase ROI (return on investment), but there are many smaller steps in the equation such as achieving higher click-through rates, newsletter sign-ups, followers, Google rankings, product conversion rates etc.
We need to define which variables we want to maximize.
We also need to define which variables we need to minimize. Almost always this will be cost.
We’ll be using these variables later on - so keep note of them!
Look through your spreadsheet.
Look at each column.
Think: Which variables will have an influence on our KPIs? Which ones can we control?
If we're running Facebook Ads, those are:
Take a mental note of these.
Since these are variables we control, we're going to analyze how these can influence our overall ROI.
Our ultimate goal is to determine which combination of factors yield the highest ROI.
This applies to most kinds of marketing data, not just Facebook ads data. Some examples are:
Since there are so many variables (age, gender, location, targeting type, bidding type etc.), it might seem daunting to find the best combination of factors that yield the highest ROI.
However, using Polymer’s Auto Insights tool, you can get this data within seconds. Here’s how to do that:
Polymer’s Auto Insights tool is designed specifically for marketers to be able to find the best combination of factors that influence their ROI.
The beautiful thing is, it doesn’t require any programming knowledge or advanced knowledge of stats. Using the drag and drop interface, anyone can become a data pro in minutes. Here’s how:
1. Firstly, head over to the Auto-Explainer tool within Polymer.
2. Input your “goal” into the “Metric to Maximize” field. This should be metrics like ROI, conversions, sign-ups etc.
3. Change the operation to “average.” (Most of the time, you want to use average. Sometimes, you want the SUM. The best way to learn which one to use is through experimenting). Luckily in this case, it doesn't matter whether we use SUM or AVERAGE.
Now, here’s where the magic happens:
Polymer will spit out a summary of the data for you - showing all the variables that influence your goal and order them from most important to least important.
The variables at the top have the biggest influence on your goal and demand special attention.
The ones at the bottom have the least influence on your goal metric, but that doesn’t mean you should ignore it. Least influence could still mean some influence, just not as much as the top variables.
Using the sample Google Ads data within Polymer Search, we want to see which demographics, landing pages and ad-spend strategies are responsible for the highest ROI.
There is no “ROI” column in the data, so in order to do that, we’ll need to put in “conversion value” into the “metric to maximize” field, and “cost” into the “metric to minimize” field. This will divide conversion value by cost, giving us ROI:
Here’s the report that Polymer auto-generates:
Notice how “country user” is at the top, followed by “date” and “search keyword?”
This means “country user” has the highest impact on ROI, followed by “date” and “search keyword.”
This is where you need to look through each one individually by clicking “See Details” for each one. This will give you a full list of countries, dates, search keywords etc. to analyze.
Let’s see which countries are producing the highest ROI and which ones are performing poorly.
The things you should pay attention to are the ROI column and the # of Results.
I would sort the ROI column from largest to smallest.
At the top of the list, we can see that India is doing very well - with an ROI of 46! The number below (+587%) gives us context on where this country stands. It means India is performing 587% above average.
Now looking at the number of results, we only see 1 result for this country. This could mean that it’s an outlier. In this example, it’s not actually an outlier because the data is divided into ad groups rather than individual users. The ad group targeting India has over $10,000 ad spend, making it a decent sample size.
Next: we can look at the worst performing countries which are: Mexico, Spain and Costa Rica. These countries are performing over 50% worse than average!
This data provides us guidance on the next steps. We either remove these countries from the ad spend, or we try to diagnose the problem and figure out “why.”
Now that we know how to analyze the effect of individual variables on ROI, it’s time to find the best combinations of variables.
For instance, it’s useful to know that a certain age group prefers our product, but it’s better to narrow down our target audience into something like this:
But how do you find the top performing combinations? The answer is exactly the same as above, but by also utilizing the ‘breakdown by segments’ feature.
If I wanted demographic information, I would input “gender, age, country and device” into the “breakdown by segments” field. Like this:
This will give us the top performing combinations of whatever factors you want to see!
Again, I’d sort the information by ROI from largest to smallest.
Which variables should you include in the “breakdown by segments” field? It depends on the dataset and you’ll have to use your “marketer’s intuition” for this part. There’s no right or wrong answer.
It’s often a good idea to start with the demographic information like I did. Start simple, then build upon that knowledge.
The next question I would have about the data is: What’s the relationship between “search keyword” and “gender?” Do certain genders convert better with certain keywords?
With marketing data, there are many instances where correlation =/= causation.
The only way to see if there's a causal relationship between variables is to do further testing by running more ads. Make sure to use a control group!
Not all correlations are worth testing, so it'll be up to you to put on your thinking caps and decide which correlations are worth testing.
The biggest mistake people do when testing is not controlling all other factors. Example:
They find targeting the US results in higher ROI than targeting the UK, but in reality, the ads targeting the UK are only targeting the poorer cities where people have less to spend, whereas the US ads are targeting rich cities.
Be sure to control for other variables when testing and avoid these mistakes.
If you have any other questions on how to maximize your results with marketing, feel free to email us at: firstname.lastname@example.org
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