How to Grow Your Shopify Store with Clean Customer Data
Every business using Shopify for its e-commerce store has access to all sorts of data. Your brand probably relies on Shopify’s analytics (or a third-party app) to understand things like average order value, repeat purchase rate or customer acquisition cost. If you’re a Shopify Plus or Shopify Advanced member, you’re already getting a lot of really valuable analytics that can lead to better strategic moves and better daily decisions.
But there’s still a lot of room to squeeze more growth out of your first-party data. There’s a major untapped opportunity here for most DTC brands, which doesn’t require a data team or even a dedicated resource.
From more accurate metrics and forecasting to more effective strategic decisions, this guide will help DTC brands better understand how to use cleaned, validated and unified first-party data to scale their business.
In this guide:
What is customer data?
For the sake of this guide, when we use the term “customer data” we’re referring to two specific types of data available to Shopify, Advanced Shopify and Shopify Plus users. This data is collected every time a customer enters the required information to make a purchase, and is your brand’s first-party data.
Order: a customer's request to purchase one or more products from a shop (with or without discounts).
Customer: information about a shop's customers, such as their contact details, their order history, whether they've agreed to receive email marketing and if they have an account.
This order and customer data is what feeds your Shopify Customer Reports (a downloadable version of the analytic dashboard), including: customers over time, first-time vs returning customer sales, customers by location, returning customers, one-time customers, at-risk customers and loyal customers.
These reports are used by brands in a variety of ways, from segmenting marketing audiences, to store location planning and even product decisions.
Is first-party data enough?
When it comes to strategic decision-making, first-party data really is enough. And we would argue that it can take brands really, really far in their marketing and customer experience efforts as well.
Of course, there’s a lot that can be learned from zero-party data like a post-purchase survey and matching your first-party data with responsibly collected third-party data can absolutely enhance personalization abilities. But these are like the jelly in the donut. Or the caramel syrup on your latte. You can still have a really good donut and a fantastic latte, even without the jelly or the syrup. And we believe if your brand only has first-party customer data, you can make decisions today that will move the needle toward your goals.
Brands that operate on Shopify have a major leg up here. The ability to collect and store enough data has been a major barrier to brands’ ability to rely heavily on first-party data, and that barrier is removed when selling DTC.
Why data needs to be cleaned
Every paying customer in Shopify has many different pieces of data associated with their account. Or - on the flip side - many different, disconnected accounts associated with their shopping history. With this much data in any scenario, the chances are high that some of it is inaccurate or invalid. In fact, more than half of the Shopify data that comes through our system typically needs to be cleaned.
By far, the highest impact data point for DTC brands is the email address. Within Shopify, email is assumed as a unique identifier. This means that any time a shopper uses a different email address for any reason, they’re assigned a new customer id. Even if their first name, last name and phone number are exactly the same. This can cause the paying customer count to be much higher than the actual number of paying customers (we’ve seen up to 10% duplicates!).
Many brands will try to resolve duplicate accounts by hand. But when you consider typos, formatting and intentional misinformation…it can be really hard to write programming language that accounts for every possible circumstance. Looking through .csv files by hand to see if a customer accidentally added an additional letter to their email address or used an invalid phone number so they don’t get robo calls leaves a lot of room for error.
All of these examples plus so many more can make it very difficult to unify data around IRL customers.When flawed data means a flawed view of the customer, the importance of accurate customer data is abundantly clear. Imagine running a local advertising campaign or choosing a store location because it appears a large percentage of sales are coming from that zip-code, but then finding out the sales are actually coming from a reseller. Or going all-in on an expensive marketing campaign that wins first-time buyers who never buy your brand again or pay back the cost of acquiring their purchase.
Cleaning, validating and unifying customer and order data can seem hard and expensive. Most growing DTC brands don’t have an internal tech resource dedicated to this task, and even those that do find themselves constantly reinventing the wheel. Using a trusted third-party to seamlessly clean and unify Shopify data before it’s used for analytics can inject the confidence your brand needs to fully trust its data with important decisions.
9 ways you can use clean customer data to grow your Shopify store right now
Three of the primary areas DTC brands differentiate themselves in a crowded market are through customer experience, marketing and operations. These areas allow them to stand out and compete against traditional retail brands, putting the response to the customer journey directly in the hands of the brand. Each of these functions rely (or should rely) heavily on customer data.
In practice, most DTC brand leaders are so busy just making the next decision to serve customers today, they don’t have time to build out a full data function. And the task seems herculean, so a lot of valuable data gets left on the table. Here are nine ways your brand can rely on clean customer data to make better decisions this month (without hiring any staff, learning any coding language or retooling any of your business practices).
Customer Experience
Often, the most important consideration of all is customer experience. A lot of effort, energy and expense is put toward creating the best experience - optimizing every touchpoint a customer might have with your brand. It makes sense, since seventy-three percent of consumers point to customer experience as an important factor in their purchasing decisions.
In the e-commerce world, the customer experience revolves heavily around what happens on your website. From finding it, to each product page, to the checkout process all the way to confirmation email. While we could write a whole book about how to use customer data to create the optimal customer experience (maybe someday!), we’ll just focus on one example each for the pre-purchase, purchase and post-purchase steps of the process.
Pre-Purchase: Use clean and unified customer data to choose website product leaders. When a semi-interested party first visits your website, it’s important to immediately make your value clear. How are you going to make their life better? Maybe your homepage isn’t the right place to put a product that has a loyal, cult following…but rather to showcase a product/product-line that is effective at winning first-time buyers. Of course, this is only possible to do when you actually know who your first time buyers are. (Which requires you to know that Sharon.shultz@gmail.com is the same customer as Sharon@shultz.com.)
Purchase: Use clean customer data to upsell or cross-sell. Now that you’ve got the customer hooked with your basic offering, it’s critical to show them what they’re missing. Maybe they’re missing out on a much better product fit (because they live in a zip code with different weather) or maybe there’s a subscription plan that will get them free shipping and a discount. Customer data will tell you which upsell is attractive to first-time buyers, loyals, etc. And there’s nothing a customer loves more than a brand that knows what they want.
Post-Purchase: Use customer data to give them a reason to return. With clean data unified around IRL customers, you can treat your customers like you know them. Because you do. It becomes possible to prioritize loyal customers in the chat queue, deliver offers that reward repeat purchasing and create an overall personalized engagement. But of course, this is only possible if the customer data actually shows the right information about real people.
Marketing
Of course, every brand relies on marketing to grow. DTC brands, especially, have seen major success from social marketing campaigns. Raising capital to saturate the market with their message via social media advertising has often resulted in scale. And since no one needs a primer on the importance of personalization in 2022, we’ll immediately jump into how clean customer data can result in better campaigns (read: more products sold and loyalty driven for fewer marketing dollars).
Make sure your marketing segments are made up of IRL customers. Shopify offers 10,000 free marketing emails every month and charges $1 for every 1,000 emails beyond that number. Their automations are relatively easy to set up. Whether your brand uses Shopify, Klaviyo or another third-party for its email marketing, it’s important that marketing segments are made up of [all] your real life customers. Clean, unified data is the only way to ensure your well-planned messages get in front of the intended shopper cohorts. (And you can stop wasting money marketing to the same person more than once.)
Reward repeat purchasing. It’s a well known fact that keeping existing customers costs less than winning new ones. But it’s only possible to reward loyalty if data is unified around real customers and real customer activity. This means that every email address, social media profile, physical address and phone number Sharon Shultz has ever used when interacting with your brand is boiled down to the truth and combined under one customer id.
Make major spending decisions that result in more sales. Sometimes, the next marketing campaign is really high stakes. It’s the billboard in Times Square. The one that’s going to help your brand scale. The results are being scrutinized heavily and you need to be sure the cost won’t outweigh the benefit. Clean customer data is the only way to make the right decisions about which products to feature and which audiences to reach, with the confidence that the cost of acquisition will be paid back.
Operations
Forecast inventory more effectively. Knowing the customer journey is the best way to forecast inventory needs, and proper ordering is an issue clean data can certainly help solve. Do customers buy your products in a certain order? Is there a large portion of customers poised to hit step number four on their journey? You can be prepared when they do, because no one wants to see the dreaded “out of stock” message.
Evaluate major policies like lifetime guarantees, free shipping or flexible returns. Are these potentially operationally intensive or expensive practices worth the cost? Thinking about these policies as late-stage customer acquisition costs, does CAC then become higher than customer lifetime value? Mattress companies with a no questions return policy, for example, have to pay for mattress removal and donation. With clean data, it’s possible to reevaluate these policies and correctly calculate their value to the business. Who knows, maybe they’re holding back growth? Or maybe a new policy would catapult growth to the next level.
Link purchase and return data to get a real picture of average order value. We’ve seen it over and over again. When using different systems for online purchases and returns and in-store POS, it’s hard to link the data. Sometimes a data warehouse is used to put all of the data in one place, but it’s still not unified. For example, an online purchase followed by an in-store return will very likely not be connected to the same customer id, which throws off average order value and every other important metric. This could be an immediate way to increase LTV, simply by getting the numbers right.
How to get clean Shopify data in 3 simple steps
You might be reading this thinking: all of those sound great, but we don’t have a data team or a budget or the expertise. In the past, all of those might have been required to clean, validate and unify your customer dataset. Third-party identity resolution is typically expensive. Cleaning and validating data by hand can take weeks of time from a dedicated resource who has more immediate priorities. Enter Orita. With our data confidence platform, you can start acting on clean data next week for less than the cost of a MacBook Pro. There are three simple steps even our Grandma can do (ask us how we know this).
Download your order and customer CSVs from Shopify
2. Upload the files to Orita.ai
3. Receive clean data and a usage guide
Upload the clean CSVs into Shopify and start acting on better data today.
If you’re still not convinced that your data is messy enough to require cleaning, check out this tool that will help you see the impact clean data can have for your brand.
