Customer Data Analysis: How to Use Analytics to Unlock Growth
When Emily Weiss started her makeup and beauty blog, Into the Gloss, she wasn’t expecting to create a billion-dollar brand. But, less than a year after launching the blog, she was getting so much ad revenue from the blog that she was able to quit her day job. Three years later, her blog was so successful that she took a chance and launched the makeup brand, Glossier.
Glossier was an immediate success. It grew 600% between 2015 and 2016 and is now valued at over $1 billion. This incredible growth was great for the brand, but their analytics stack struggled to keep up. The company was struggling to build a customer experience that matched customers’ expectations.
Glossier knew that if they wanted to create an incredible customer experience, they needed to learn more about their customers. Over time, Glossier gained a good understanding of their customers, but they were missing pieces of the customer journey. That’s why the brand decided to become more data-driven and focus on customer analytics.
What is customer analytics?
Customer analytics, also called customer data analysis, is the process of collecting and analyzing customer data to gain insights on customer behavior. Customer analytics requires various tools for collecting and organizing different types of data, and a methodological framework for analyzing and understanding this data. Companies use analytics to make business decisions related to marketing, product development, sales, and more.
The business decisions that customer analytics allow you to make could be simple ones, such as figuring out which advertising platform is giving you the best ROI. They can also be complex business decisions like figuring out your entire customer journey and building personalized marketing campaigns to match that.
Customer analytics go beyond just making smart marketing decisions. They can also have a huge impact on your bottom line. A recent McKinsey survey found that companies that extensively use customer analytics are reporting 115% higher ROI and 93% higher profits.
How to collect and store customer analytics data
Collecting and storing customer data is an essential piece to using customer analytics, and there are a few main pieces you need to get a deep understanding of your customer analytics.
First, you need some way to collect data. Many of the marketing tools that your team uses collect data. Google Analytics, for example, collects data about the behavior of your website visitors.
Some of these data collection tools will give you certain insights into your customers, but it’s usually not enough. That’s why you need the next three pieces.
Next, you need a way to sort your data and direct it to the right place for analysis. A customer data platform (CDP), like Segment, will help you do this. Your CDP is like the traffic director of your data. It’s going to tell your data what to do and where to go.
CDPs are designed to connect multiple tools together and ensure the data those tools are collecting is standardized across your organization. Standardized data, controlled by a tracking plan, is much easier to sort and analyze than non-standard, unstructured data.
The third piece you’ll need is a place for your CDP to send data for storage. Data warehouses are built for this, and they’re essential to customer analytics.
Data warehouses collect and store data from a number of sources — website, app, email, other cloud-based tools. Redshift is the most popular data warehouse among our users, but there are plenty of others like BigQuery and Postgres.
The best part is that a data warehouse also keeps your data organized, which will come in handy for the last piece of customer analytics.
If you haven’t selected the data warehouse that you’re going to use to store your data, now’s the time to start that process. Read Selecting the Right Data Warehouse for Analytics to make the selection process easy for your company.
Lastly, you’ll need some way to analyze your data.
This is most often done with a business intelligence tool like Mode Analytics, Looker, or Tableau. The only downside to these tools is that they require some knowledge of SQL. If you don’t know SQL, you can also look at a tool like Chartio.
Why you need customer analytics
The questions that customer analytics will help you answer are nearly endless, and those questions will help every department within your company. With customer analytics, you’ll be able to build more personalized, timely marketing campaigns.
Customer journey analytics can also help your sales team understand your customers’ buying process. That knowledge can help them reduce your sales cycle.
Your product team can use customer analytics to build a better product by uncovering what features your customers love and what they don’t like.
The customer service and retention teams can use customer analytics to predict and reduce churn.
Those numbers are often going to be the basis for many of the customer analytics questions that you have. For example, say you want to know where you should be spending the bulk of your advertising dollars. Basing that decision just on the number of customers who came in from a specific ad platform isn’t good enough. Maybe Facebook ads have given you a lot of customers, but if your CAC is high and the LTV is low on that platform, it could be a waste of money.
If you have those numbers calculated, it’s time to go a step deeper. With customer analytics, you can answer:
What acquisition source results in the highest LTV?
Answering this question will help you figure out where your company’s money is best spent. With that knowledge, you’ll know to double down in some areas and cut back on others.
To get data for this one, you’ll need data from at least two sources — your website analytics tool and your payment processor.
Once you answer this question, you might find that even though you get more customers from a promotion you’re running, customers who come through Facebook provide you with a higher LTV. If that’s the case, you should make a bigger investment in Facebook.
What’s the most common customer journey?
If you can answer this question, you’ll have a better understanding of how visitors become customers — it might be more complex than you think. With the knowledge you gain from this, you’ll be able to make your marketing more timely and relevant.
To answer this question, you’re going to need data from a lot of sources. Any place where your potential customer interacts with your company will be part of the customer journey. You might need data from your website analytics tool, email provider, ad platforms, and sales tools, just to name a few.
You might find that a big segment of your customers contact support before becoming a customer. If that’s the case, you might consider making it easier to get in touch with support by offering that option earlier in the onboarding process. You could even be proactive and have your support team reach out to potential customers when they first start a trial.
Is there a time of year where our LTV:CAC ratio is higher?
This one can be a game changer for how your company spends on marketing and advertising. Why? When your LTV:CAC ratio is higher than 3:1, that means you’re under-investing in marketing and could grow faster. If you find a time of year when this happens, you should increase your marketing and advertising budgets during that time.
To get the answer to this question, you’ll need two things. First, you’ll need your customers categorized by the month they became customers. Then, you’ll need to know your total marketing and sales expenses for those months.
In the example above, this company found that their LTV:CAC ratio was higher in April and lower in January and February. With that knowledge, they’re able to spend their marketing and advertising dollars more efficiently.
What behaviors lead to higher retention?
The reason you’ll want to answer this one is simple. Find the answer and you’ll be able to encourage on those behaviors and reduce churn.
For this question, you’ll need data from a ton of sources. You might need data from your customer service platform, your email marketing platform, your website analytics platform, and your CRM.
Take this example from a music app. They found that when users favorited three or more songs, the user was much more likely to stick around for the long-term.
If you worked for that music app and uncovered that stat, you’d want to research and test ways to get more users to favorite three songs.
What product features do our top customers use?
It’s important to know which features all of your customers use, but looking at the features your top customers use can be even more helpful. The answer to this question will help you figure out which of your product features is most valuable. It can help you figure out what features you should develop next. It can also help you figure out how to structure your onboarding so that new customers find the most valuable tools immediately.
Answering it will require using customer behavior data from your own product.
If, for example, you find that your top customers use your data export tool every single day, that might cause you to dig deeper and try to understand why your customers are using that feature so often. You might be able to build a new feature to make this process easier for your customers.
The key to answering all of these questions is to have a complete, accessible, and accurate record of your customers. Data warehouses are built exactly for this. Then, use a business intelligence tool to analyze the data that is stored in your warehouse. Without a data warehouse, getting data from each of those sources and analyzing it would take weeks, maybe even months, just to answer one question.
Understand your customers with customer analytics
Today’s most well-known brands have a serious understanding of their customer analytics. If you want to take your company to the next level by creating a better experience for your customers, you need to take the time to build a customer analytics stack.
Customer analytics can make a huge difference to your company’s bottom line. Even though Glossier was already a successful company before implementing customer analytics, the insight that customer analytics provides allows the beauty brand to have a much closer connection with their customers.
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