I collected 8+ months of emails from one of the largest sports retailers in the country to break down their lifecycle program.
Every year, I inevitably have to replace all my socks because they always get holes in them. As a result, I always end up buying new ones from the same place: Academy Sports & Outdoors. And this time, I decided to track what happens after the purchase, so for nearly a year, I’ve been storing every message they sent me.
While I expected a multi-billion-dollar brand to be running a super advanced lifecycle machine, what I actually found was a simple system built around consistency, volume, and discounts.
I just completed my largest journey breakdown ever: 218 emails. And I laid out all the key sends and learnings in Figma:
How they think about audience enrollment (e.g., segmentation and targeting)
How they leverage price to drive action and urgency
The entire CRM calendar is mapped out
The framework and structure behind every email (subject lines, preview text, CTAs, etc.)
And trends across the entire program
These bullets just scratch the surface (and having the actual visuals always makes things come to life), so I highly recommend checking out the full FigJam board to get all the takeaways.
🤔 Why is email so important?
Let’s do some quick math: In 2024, Academy earned roughly $5.9B in net sales. Since 20% of retail sales tend to come from ecommerce, that would mean they generate around $1.2B from digital channels. And if just 15% of those sales are attributed to email, and we assume the average order is likely somewhere in the range of $75 for Academy, that would mean their email program is driving around 2.4M orders per year. This is where things get interesting though, because you can see how small optimizations can drastically impact incremental revenue.
With roughly 11M loyalty members (assuming 2-3M actives), increasing purchase frequency by just +1 per year could add another $150M-$225M in annual revenue.
⚙️ The CRM system
Academy is obviously doing a lot right: Their stock has increased dramatically over the last five years. However, even a multi-billion-dollar brand like Academy still has room for more optimizations.
At a high-level every message is designed to drive purchases, but each one indexes toward slightly different behavior and outcomes. Once a user completes a purchase, that product is then mapped back to a category, and that category mapping then informs the audience selection, where users are enrolled into campaigns based on their purchase history.
It’s a very simple logic-based framework: purchase → category mapping → audience selection → campaign enrollment.
In my case, it seems like I was enrolled in a footwear and apparel audience because the overwhelming majority of sends across the tracking period never really deviated outside of that. The logic appears to be fixed: if you purchase socks, you must need shoes or apparel.
Ultimately, this program “reacted” to my initial purchase. The benefit of this model is the simplicity, repeatability across different initial purchase categories, and less manual work managing complex journeys and tons of segments.
However, this route has its drawbacks as well. One main drawback is that customers purchasing the same product are treated equally. This limits the conversion upside because there is little branching out to other categories. Some users, like me, may be buying socks to play tennis and would be inclined to purchase tennis balls, while others may be buying socks to go running and need new running shoes every 6 months.
Without AI or dynamic, behavior-based recommendations like I reviewed in the Netflix post, it is challenging to break out of the reactive send model. This is why retailers, like Academy, lean heavily into discounts and promotional sends to drive action because more often than not, it’s the easiest way to influence behavior since 80% of consumers are influenced by price.
💡Key takeaways and insights
Creative choices
Nearly every email begins with a discount banner at the top, followed by a product module. They’re leaning on discounts as the clearest way to drive clicks.
The creative barely changed in 8+ months, which makes it easier to scale, but it can signal to customers that every message is the same.
Message choices
Most of the copy is almost entirely price-driven “Save,” “Deal,” “% Off,” which is great for urgency but not great for building brand and loyalty.
CTAs almost never deviate from “Shop” [insert], which keeps things simple, but doesn’t leave much room to test for other emotional drivers.
Even the biggest companies make mistakes. On July 25th, the team sent an email with no subject line.
Interesting trends
Discounts/promotions and product spotlights overwhelmingly dominated the majority of sends, which would suggest that the company views this mainly as a sales channel.
On average, the CRM team is sending close to six emails every week. This is an extremely aggressive cadence designed to maximize revenue, but it also risks fatigue.
The majority of sends tended to be apparel and footwear focused. There was very little exploration into other categories, which is especially interesting, given my low engagement in the emails.
Calendar insights
Across all 218 emails, there were five message categories. The tight framework makes production easier, but it lacks variety. Even the different categories feel the same when you look at the creative and the copy.
May had the highest number of email sends out of any of the eight months, most likely tied to the seasonal summer kickoff. There were also more transactional emails this month.
76% of all emails are sent between 6 AM and 12 PM (likely tied to my opening habits or other data they have across their customer base).
94.5% of all emails were evergreen, and 5.5% were seasonal or holiday-focused. Holding to such a rigid structure makes it difficult to leverage the urgency that seasons and holidays create.
🔎 The outlier insight
The most interesting factor that stood out to me in this entire breakdown actually happened in the transactional emails because I purchased the exact same product in 2024 and 2025. In both instances, the recommendation section was the exact same, which means that this flow likely hasn’t been updated in over a year. Even more confusingly, product recommendations were for socks, which is the exact same product SKU I had just purchased.
Swapping this module for something more relevant recommendations like “customers also purchase” would likely drive a substantial lift in cross-sells. Multiply this by the fact that Academy likely has hundreds if not thousands of these flows set up for other products and you quickly realize just how much revenue they might be leaving on the table.
This very much aligns with the trend I saw across 8 months of sends because there was very little deviation or exploration across the campaigns or products. When you zoom out at a macro level, basically every email looks and feels the same: Discounts/sales + product categories. The program has a lot of untapped opportunities, especially when it comes to updating some of the outdated flows and testing some new campaigns like “trending”, “best sellers”, “back-in-stock”, etc.