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Analytics aren’t universally sexy—but they’re the difference between guessing and growing. After reading this post, you might think “he put the cart before the horse on this one.”
You’re right, and it’s intentional. Understanding the how behind marketing analytics is a lot easier when the why behind it really clicks.
A Story to Set the Stage
At the beginning of my career, I was working with a client who had built a small chain of coffee shops over five or six years. They owned and operated a handful of locations with a business partner and a small team, and each location had attracted a loyal following of patrons.
As you might imagine, competition in this space is pretty intense. Here in Canada, there’s a mix of domestic/national chain, foreign/national chain, domestic/regional chain, foreign/regional chain, and domestic/independent coffee shop brands all vying to fuel us up with caffeine and pastries. At the time, it was also just becoming possible for pretty much anyone to become a decent at-home barista by purchasing a Keurig or De’Longhi (oh, the novelty!).
Differentiation under these market conditions is a challenge, to say the least.
But, that’s what we were out to do—carve out a differentiated space in a crowded market and stake an empty spot on the mountain to pan for gold. To have any clue about what the differentiated opportunities might be, we had to intimately understand our customer and their needs (preferably the unmet ones).
When your business has a well-thought-out website that integrates with your operations, the process of understanding customers and behavioural patterns is relatively straightforward. Install some analytics software, click some buttons, generate some reports. Rinse. Repeat.
But what about a business operating in a space that traditionally doesn’t have much need for that, one where customers walk through the door because they’re walking by… or they live or work nearby?
Tackling the problem was frustrating, to say the least. Particularly given that I was working in a marketing agency at the time, and my experience was almost exclusively with digital marketing for websites.
Why are Marketing Analytics Important?
What you’re building will be more successful if it stands out from the crowd. Standing out requires understanding the wants and needs of potential customers. Marketing analytics help paint a picture about who those potential customers are, and what their needs might be.
When combined with customer insights research, the picture you’re painting becomes even more compelling (I wrote about customer insights research recently, in this post).
At the end of the day, analytics exist to help you create insights—the nuggets of knowledge that you can use to grow and improve what you’re building. Analytics alone don’t provide the answers that you need, but they do provide the information needed to form the right questions.
Segmentation is a Building Block
Marketing to customers effectively involves segmentation (creating groupings of customers with like characteristics). This is where the do something useful part starts to take shape. When you have meaningful customer segments constructed, a plan of action can start to take shape (or start to really take off).
The first segments that I created with the coffee-chain client were the pre-work crowd (which I called grab-and-go) and the after-lunch crowd (which I called sip-and-sit). Basic, simple, and what was possible immediately with the limited information and understanding that we had access to at the very beginning of that engagement.
These two groups had different needs based on their behaviour. The grab-and-go segment prized convenience and speed (they were hustling to an office, mostly) and the sit-and-sip crowd prized creature comforts and community (they were mostly socializing or relaxing).
With these very basic, behaviour-based segments created, we could start to tailor offers and experiences to support those segments. First, by modifying operations accordingly during different times of the day (thus improving service and experience). Then, by shaping experiential conditions (adding table service between 1pm and 4pm, and including pastry sampling campaigns).
As a result, revenue increased during both the 7am to 10am and 1pm to 4pm day-parts. We built on it from there, eventually starting to conduct research which led to a very personalized loyalty program with experience-based perks (including a members-only lounge area at the flagship location—eventually it started to hold events after regular hours to bring in additional revenue).
Every business has its version of grab-and-go vs sip-and-sit. The key is spotting it early.
How to Create Meaningful Segments
There’s a difference between creating a customer segment and creating a meaningful customer segment.
Customer Segment : these people share those characteristics.
Meaningful Customer Segment : these people share those characteristics, and I can do something useful with that.
Take the customers that you have information about, aggregate the data, and look for patterns.
Group the like things together (demographics, known preferences, psychographics, interests, et cetera).
Look for behavioural patterns within your basic segments—are there combinations of basic classifications and behaviours which can be combined to create meaningfully-sized groups? For example :
Guests who purchased more than once from the “East” location and have an allergy to gluten
Guests who purchased from the “Financial District” location and prefer light-roast bean blends
Guests who purchased more than one hot beverage from the “North” location during a single visit
Think about the product-related opportunities and/or marketing activities that you could attach to each segment. If a segment doesn’t have a clear opportunity or activity that makes sense, then drop it.
Of course, creating segments requires that you have access to analytics data—and this is where we’ll unhitch the horse and move it up in front of the cart.
Where do I get the Data?
With a website or mobile application, it’s easy. Install some software or add a script and you’re pretty much set to go. Analytics packages like Matomo, Google Analytics, or any of the more use-specific tools will almost always include everything you need to run analysis on the data collected from the website that they’re installed on. You just log into your account, and everything is there. Click, click, click.
That’s not the entire picture though. The simplicity of tools like this makes it easy to forget that there’s more to marketing products and services than operating a website. And in cases where a website or app isn’t central to acquiring or serving customers, these tools aren’t that useful anyway.
For the coffee shop client I worked with all of those years ago, much of the data we used initially came from their point of sale system. It was heavily product and sales-based, but was detailed enough that we could aggregate it and start to identify patterns. None of the data was attached to customer identities (it was all anonymous), but that was OK. In that kind of business, you don’t need to have the personally-identifiable information of customers handy to get started with searching for trends.
Any system or tool that gives you a report is a potential source of data to use with marketing analytics, not just your marketing systems.
Integrating Multiple Streams of Data
As what you’re building grows and gets more complex—if it’s not complex from the very start—you’ll find yourself looking at multiple sources of data in different places and switching back and forth while assessing what’s going on (and thinking about what to do next).
That gets annoying quite quickly.
Plenty of enterprise software companies offer specialized business analytics packages that let you load in data from different sources to analyze in a single workspace (and automate the data loading tasks).
You can also build a simple system yourself to do the same thing, using spreadsheets or the cloud-based equivalents.
My quick-and-dirty toolstack in this scenario uses a Google Sheet with multiple tabs to aggregate raw data from different systems, and a Looker dashboard to combine and visualize it. A bit rudimentary, but sometimes that’s all that you need—especially if you’re not at the point in your growth journey where it makes sense to invest in enterprise-grade tools and specialists to leverage them. Don’t underestimate how far a humble spreadsheet can take you before you need to spend on expensive software.
Here’s a demo with some dummy data, to help contextualize how it works in practice.
Raw Data (Google Sheets) :
https://docs.google.com/spreadsheets/d/1UV9woF0i_Om51DSpjJ7ZY6nh9larUzDzNBqgcGO3V4g/edit?usp=sharing
Hosted Dashboard (Looker Studio) :
https://lookerstudio.google.com/reporting/6ff4b137-448b-458c-a3b1-da4eb9a7e4f3
Embedded Report Example :
https://6catalysts.ca/the-journey-blog/marketing-analytics-learning-demo/
Final Thoughts
Competition can be a pretty nasty brawl, but it’s also good for you (and your customers). Without competition, there isn’t a lot of motivation to improve what you offer or how you operate—so, it keeps us sharp and pushes us to be better.
In this backdrop, an important part of competing is building a sustained advantage. Strategic moat is another way of putting this concept, if you’re familiar with that term. Really, you’re just creating something for customers that’s difficult for competitors to counter or copy. That wins you loyalty, which wins you repeat business, which wins you the privilege to continue doing what you do.
Next week, we’ll carry on with this topic and dive more into the details of different types of metrics—and how to combine them in ways that make sense for what you’re building. We’ll also touch on how to develop your own custom metrics and benchmark your marketing activities over time to help improve the overall performance of your system.
Feedback
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