Once you're collecting customer data, you can do way more than build charts and funnels (don't get us wrong—those are pretty great). Now there are so many tools out there that use customer data to help businesses do other important things—sending personalized emails, running A/B tests, retargeting specific segments of your audience, and more.
But with these new tools, it can be difficult to maintain clean and consistent data across all of them. This is important, because, data discrepancies can lead to a waste in time and resources. Often times it's like not even having any data at all.
In this article, we’ll outline what exactly customer data is and what you can do with it. We’ll discuss the common challenges companies face, as well as how to prepare for and overcome them. Let’s get started!
What is customer data?
Customer data is any piece of data that indicates who your customers are and how they are using your product or service. Businesses collect customer data to better understand their customer base and the customer experience with their company, with the goal of improving marketing, product, or support efforts.
Some people call this "customer lifecycle data," "analytics data," "behavioral data," "digital marketing data," or "event data." As you can see, this is a new field, and no one has settled on a name yet!
When we talk about customer data we mean information like: Jim viewed a pair of polka-dotted boxers on your ecommerce site, added them to his cart, and completed a purchase. Sarah clicked through your ad on Facebook, viewed your landing page, entered her info, and had a sales call with your team.
This is what customer data might look like in tracking code:
analytics.identify ('1231', {
name: 'Jim Bean',
email: 'jim@bean.com',
});
analytics.track ('Song Played', {
song_name: 'Hello',
artist: 'Adele'
});This kind of data is super helpful for understanding your customers and their experience with your product.
Why it’s important to collect customer data
Customer data is all the rage because there are so many things you can do with it and so many actionable insights you can gain (if you know how to use it). Here are just a few popular use cases:
1. Measure product-market fit
Use engagement retention cohorts to measure whether users are coming back to your product week over week. If your engagement retention trends toward zero, users are abandoning your product over time and you do not have product-market fit. If your retention trends plateaus toward a positive value many weeks after signup, then you likely have product-market fit.
2. Understand product funnel
Understand the customer journey by monitoring your funnel metrics, specifically acquisition (e.g., new signups week over week), engagement rates (e.g., video watchers week over week), and monetization (e.g., new subscription week over week). You can now focus on the weakest part of the funnel, whether it’s acquisition, engagement, or monetization.
3. Grow your audience with personalized campaigns
The best kind of marketing feels like it’s 1:1. By collecting customer data and creating campaigns to match specific customer profiles, marketers can send super personalized emails and messages to your customers at scale. Emails based on the context of what a customer has done and hasn’t done in your product feel better and convert better than a batch-and-blast marketing strategy.
4. Gather targeted user feedback
To make sure you’re building something people want, you have to talk to your customers! The best product companies have tight communication loops with their customers. Customer data makes this easier, since you can easily target surveys and chats to particular groups of users. You’d probably have different questions for power, casual, and dormant user segments.
5. Provide amazing customer service
This type of data is also super useful for sales and success teams during 1:1 conversations. If your team knows what a user is already doing, what she is missing, and what she is having trouble with, they can provide better support and cut down on the back and forth about a customer’s situation.
Where can you use customer data?
Over the last few years, we’ve seen a proliferation of tools designed to help you do one of these jobs (and others) extremely well.
For example, Customer.io and Vero give you a delightful experience building automated emails based on in-app behavior. Optimizely is perfect for quickly putting together variate tests with little technical skill pre-requisites. Intercom takes minutes to get setup and provides you instant ability to communicate with your customers.
Rather than using monstrous suites like SAP and Oracle, many nimble companies are moving to a stack of these kinds of niche tools to make the most of their customer data. Fast growing startups like Mention, PagerDuty, and One Month use more than 5 tools for optimization, analytics, marketing, and more. (You’ve all seen that marketing cloud diagram.)
Challenges with using customer data
However, with easier access to customer information and corresponding tools, you’ll likely also run into some challenges wrangling them.
1. Too many tools to choose from
With so many helpful services on the market, it can be difficult to figure out which one is best for your particular needs or even what tools you should start with! With a Customer relationship platform (CRM) and a Customer Data Platform (CDP) you can access and organize huge amounts of data, but how do you use it? Accel made an entire website to map this space, and Stacklist has emerged just to help startups find the right tools.
2. Data inconsistencies
When companies start using lots of tools at different times and drawing from different data sources, often they become cluttered with too much data and duplicate events with different names. Commonly people track too many events, name them all differently, and aren’t strict about where they fire off events. This makes using the data in end tools difficult.
3. Knowing when to level up
Most out-of-the-box analytics tools will help you answer important questions about your product and marketing performance. At the early stages, setting up a relational database might be overkill. However, it’s tough to know when do you need to have a more flexible, custom setup and how to set yourself up for future growth. Often when you need to answer those tough questions, you don’t have the data.
3 ways to make the most of your customer data
But don’t fret, my little analytics apprentice; we’re here to help! Throughout Analytics Academy we will be diving into how to overcome these challenges and make the most of your customer data. Here’s how to start.
1. Pick your north star metric
In the last article, we discussed good vs. bad metrics. As a business, you need to know what is the MOST important metric you should focus on. That will guide what you track and what tools you’re going to use to measure and improve that number.
For example, if activation rate is the metric you’re focused on improving, don’t waste your time with advertising, retargeting, and referral tools that are focused on acquisition. Marketing automation and A/B testing tools are likely your best bet.
2. Be thoughtful about what you’re tracking
So many companies track everything under the sun. Then they go to do some analysis and don’t know where to start with tons of events to choose from. Instead, start with the questions you need answered. Then figure out what you need to track to answer them. Be meticulous about how you name and track events to keep consistency, and only add new events and properties when you have new questions.
3. Store your data now, so you can answer questions later
For the early part of a company’s life, tools like Google Analytics, Kissmetrics, and Mixpanel will do the analytics trick. However, in the future, you might need to answer a really specific question that doesn’t fit their drop downs or reporting options. To prepare for this time, you should start storing your own data now.
New technology has made it easier than ever before to access and make use of your customer data. But to get to the ideal setup and avoid getting overwhelmed, you need do some planning upfront.
In the next few articles, we’re going to get into the nitty gritty details of how to follow the best practices outlined here. We’ll go over how to create a tracking plan, how to navigate the sea of tools, why it’s important to own your data, and how to setup a storage system to leverage later. In no time, you’ll be an analytics expert.
Intro to analytics