“What did they buy next?” is one of the most asked questions we’ve gotten from our customers in the last 2 years. Brands care about increasing repeat sales, and often the best way to do this is by analyzing what repeat customers have bought after their first purchase, so they can personalize offers for every customer based on what has worked in the past.
We’re extremely excited to introduce Product Journey, an interactive discovery tool for understanding what your customers buy in their 1st, 2nd, and 3rd orders in sequence:
iOS 14.5 is upon us, and opt-in rates for app tracking have dipped below 10% globally. IDFA. ATT. FLoC... the Great Cookie Apocalypse is upon us. It’s more important now than ever to leverage your most valuable marketing resource: 1st-party customer and order data, completely owned by you.
Starting now, you will be able to take advantage of the highly converting intelligent segments with our Facebook integration for directly syncing custom audiences:
We owe everything to our customers, who’ve taught us so much and gives us the drive to continue pushing the envelope to make data more intuitive and actionable, with more powerful segmentation and insights. On that note, we are extremely excited to share with you the launch of our new experience, Segments 2.0.
What’s new in Segments 2.0:
We’re all busy folks. Sometimes we can’t get to checking our analytics, and that’s completely understandable! This week we’re prototyping a weekly report delivered straight to your email inbox — it combines your latest calendar week (Sunday to Saturday) performance across your key metrics and products, including a week-over-week and year-over-year comparison to see how you’re trending.
The goal is to get a high-level executive summary in a painless way, with the option to dig into areas where needed.
We added an “order distribution” chart to the "All customers" page. Now, you can quickly see the breakdown of signups (0 orders), one-timers (1), repeats (2), and loyals (3+ orders) to any segment or filters combination.
Amazon has famously “razor-thin” margins on their ecommerce business. As business operators ourselves however, we know we can do a lot better! This week we started rolling out Cost of Goods Sold (CoGS) to our analytics. In the Executive summary, you’ll find two new metrics Gross profit and Gross margin:
Gross profit and margin gives us a better idea of how much money we actually keep, and how healthy our business actually is. If you already have the “Cost per item” values set for your products, then Segments should already be incorporating them. If you don’t have them set yet, then Gross profit will equal Net revenue with Gross margin showing 100%.
One feedback we received was the relevancy of comparing metrics to the same period a year earlier, or a “year-over-year” (YoY) comparison. YoY comparison does a better job of accounting for seasonality (Q1 is different from Q4), and so we’ve updated the Executive summary accordingly. We are continuing to use period-over-period or YoY (or both) in other modules.
We’re happy to recommend a partner app called Klickly, an advertising platform for acquiring impulse-buy customers. We previewed a demo and were impressed by their technology and advertising reach of 25M partner sites. They’re currently invite-only and limited to US-based brands — Segments Analytics users get access and a guaranteed 30K free impressions.
This page! We've been mostly sending email updates about new features and changes, but figured it was high time we had a central location for people to see our changelog. Of course, we'll still push updates via email and other channels. Come back soon and often.
We moved the lifecycle "playbooks" into the grid directly for ease-of-use and access. Additionally, the lifecycle grid is driven by segments now for faster loading — if you don't see any of your lifecycle grid segments, let us know aswe may need to re-create the segments driving them.
We created a new customer segment filter for "discount code" include and exclude usage. Now you can filter for users that have used a particular (or many) discount codes, as well as those who have not. We also automated two new segments based off of this filter:
For Growth+ stores, we extended customer cohorts to up to 2 years for a deeper look at how monthly cohorts have been performing. We’ve also added a “churn” table view that complements the “repeat” customers table.
How do we define churn? We use each store’s historical data to calculate a churn days threshold for 0x, 1x, 2x, and 3x+ order customers.
We added the ability to filter customer segments by product variants purchased. Previously, you could only filter by the higher-level product or product type. A variant filter gives more control of the exact product that a customer purchased, whether it’s color, flavor, size, or any other variation that exists. Unlocking purchases at the variant level leads to interesting segment possibilities.
We tucked away all of the customer segment filters behind a modal to make the filtering experience easier. Applied filters will still show directly on the page. Look out in the coming weeks for better search and typeahead for navigating within the filters (we know this is cumbersome!).
We completely revamped the "Product analytics" page to give better insight into how well your products are performing. Insights include overall winners, hot & cold products, most discounted, and trending tags. Use these insights to spot trends to take action on.
We expanded coverage of email campaigns into an "All campaigns" page. Now you can easily compare different email campaigns across the basic metrics like opens, receives, and clicks, as well as 7-day and 30-day conversions and revenue.
Does a customer’s first purchase affect their future behavior? We think it does. Holding the products from that first order is the first tangible, physical impression your customer has with your brand — if they liked what they bought, you can bet they’ll be back for more.
The first purchase effect is what we set out to illuminate in the product cohort analysis. Like in customer cohort analytics, we look at different cohorts but base them on which products your customers bought first, and follow their journeys through subsequent months with metrics like repeat orders, revenue, order value, CLV, and more.
We launched email campaign analysis this week to provide a more detailed look at how your email campaigns are doing, including open & click rates, 7D & 30D orders and revenue, as well as purchased products. Knowing email campaign ROI is a crucial piece to help close the loop starting from your insights → segments → campaigns → back to insights. Keep the data flywheel going by creating better, more targeted campaigns using our playbooks as a starting template.
We updated our logo! It is still based off of an “S”, and now also reflects the never-ending pursuit of answering all the questions we have about our businesses.
One of the greatest benefits of being on Segments is the community network — hungry, data-driven merchants constantly trying to improve their stores. When we see something come up again and again, that’s usually a good sign that everyone could benefit from it. Most companies call this “feature requests”. We call this the only way to keep our stores competitive with BigCo.
This week, we launched a series of playbooks to give you a starting point for targeting every stage in the lifecycle grid. The overall strategy is simple:
Each stage has a different strategy for signups (0 orders), one timers (1), repeats (2), and loyals (3+), for a total of 12 playbooks. We provide high-level suggested actions and discounts, so you can design specific strategies suited to your store.
We hope these playbooks will drastically reduce the friction to begin experimenting with segmented marketing. One day soon, we want to give you even more personalized playbooks — think product recommendations, specific discount amounts, and more niche segmentation.
We’ve optimized the way our customer lifecycle filters work. Now, instead of a single tag for each lifecycle + order combo (eg. “Active one timers”), different lifecycle + order combos can be filtered with a “Lifecycle” tag that uses dynamic timings based on the customer orders number. For example, to filter for “Active one timers”, you would filter “Active” lifecycle with “1” order. As another example, using “Active” lifecycle with “1+” orders would show all customers in the “Active” lifecycle for one timers (1), repeats (2), and loyals (3+), using each of their respective timings.
You can now filter customers by "Order tag" for any orders they’ve purchased in the past. One use case is for those using ReCharge subscriptions, with their orders being tagged with “Subscription”, “Subscription First Order”, and “Subscription Recurring Order”. For example, you could create a segment of all customers that have ever ordered a subscription order.
🎉Happy new year!
We launched Segments last year to give Shopify stores, no matter how big or small, access to the same advanced data analysis and segmentation used by the tech giants. Since then, we’ve helped stores:
We’ve got huge plans for Segments this year, including better analyses, better segmentation, and better guided use cases. On that last point: we know the real value is in translating the massive amounts of data into an actionable playbook, and we’re going to make this a focus going forward.
When a new customer segment is created, we begin calculating and storing the size, spend, AOV, and CLV of that segment every day. Now you can see historical metrics per segment in the segment report page.
In addition to analyzing monthly cohorts by spend and ARPU, we've added a new cohort table showing the % of customers from each cohort who have come back and purchased again. Repeat % are shown as a cumulative % for the proceeding months.
Segments Analytics officially lists on the Shopify App Store 🎉
Our journey of a thousand miles begins.