Academy Sports & Outdoors

  • Completed: 2022

  • Technology stack:

    • IBM WebSphere

    • Node.js and React Native

    • Algolia AI search

    • Contentful CMS

  • Objective: Increase conversion and revenue

  • Industry: Omni-channel retail e-commerce


Brought in following an initial rollout developed by an external consultancy BCG (Boston Consulting Group), our team quickly identified critical gaps—namely, the absence of usability testing and minimal alignment with technical or product constraints. To address these shortcomings, we launched an in-depth user research initiative, conducting interviews to surface pain points and clarify user goals. We evaluated the current journey, tracked task performance, and established meaningful KPIs to guide our process.

Academy’s objectives were clear: increase conversions & revenue, streamline the overall experience, and introduce key features such as “buy online, pick up in-store” and a fully personalized shopping journey. Drawing inspiration from leading social media platforms, we set out to deliver a modern, intuitive shopping experience that mimics familiar, engaging interactions—seamlessly blending personalized design with user-centric functionality.

Quick wins

Faced with the aftermath of a failed transformation effort by an external consulting agency, our team conducted a comprehensive evaluation and quickly concluded that the entire front-end would need to be rebuilt from the ground up.

However, before committing to a year-long redesign, we shifted focus to identifying low-hanging fruit within the existing platform. At that point, the site was converting at just 1.2%, with a year-over-year decline of 35%—a clear sign that immediate improvements were necessary to drive momentum and build a case for full-scale redevelopment.

We began with a thorough website audit and quickly uncovered a major performance bottleneck: slow page load times, averaging 15 seconds per page. This latency was driving user frustration and high abandonment rates.

To address this, we implemented several performance enhancements:

  • Responsively resizing images based on device screen size

  • Applying best-practice image compression

  • Optimizing CSS and JavaScript

  • Tackling critical UX issues within the cart flow

This optimization effort, completed over three months, significantly improved performance. Meanwhile, Tony and the team laid the groundwork for a complete rebuild—internally dubbed Project NextGen.

The initial improvements led to a 20% reduction in abandonment and an increase in conversion rate to 2.8%, proving that even incremental changes could yield meaningful results ahead of a full-scale transformation.


Gathering Insights

Following the user interviews, we synthesized insights through affinity mapping—grouping pain points into meaningful themes and platform features. This allowed us to uncover patterns and prioritize user needs more effectively.

To guide our evaluation, we applied a data-informed severity framework, ranking usability issues based on the formula:

Severity = Task Criticality × Impact × Frequency

Tasks grouped in common topics

This approach ensured we focused on high-priority issues that would make the greatest difference in user experience.

In addition to qualitative feedback, we drew from multiple data sources—including Customer Care logs, Google Analytics, Quantum Metric, and Hotjar—to gain a holistic view of pain points across both the app and our broader digital ecosystem.

Heat map of the existing website


Insight Synthesis

To turn research into actionable strategy, our team took the additional step of grouping identified usability issues into broader Epics. This approach gave product managers and engineers clear visibility into the most critical problem areas across the platform. It also helped streamline prioritization and directly shaped the roadmap for the initial sprints.

Drawing from over 200 beta user interviews, we uncovered key insights that highlighted both friction and opportunity within the user experience:

  • 57% found the curbside pickup process to be overly complex and confusing

  • 52% expressed a desire for a more personalized homepage experience

  • 45% were frustrated by a homepage cluttered with promotions, lacking relevance to their interests

  • 32% had difficulty completing forms due to poor visual hierarchy and easily overlooked input fields

These insights not only clarified where users were struggling but also provided a solid foundation for prioritizing UX enhancements in the early stages of development.


Wireframes

With the key user pain points clearly identified, our team began exploring design solutions to address them effectively. Our proposed improvements included:

  • Implementing a clear and streamlined in-store pickup experience

  • Introducing a new personalization feature to tailor the shopping journey

  • Redesigning the homepage by separating promotional content into a dedicated "Deals" tab

  • Establishing a unified visual system by creating a standardized component library to ensure design consistency throughout the app

We rapidly created wireframes to visualize these ideas and gather feedback from key stakeholders—including Product, Engineering, Customer Care, and Store Operations—as well as from users. This early collaboration helped align cross-functional teams around the new layout and user experience direction.


Platform Foundations

Selecting the right platform components was critical to the project’s success. Research had revealed that search and personalization were major friction points in the user experience—issues that could only be resolved by first improving the structure and quality of the product data.

To lay this foundation, we partnered with the catalog team to implement a new catalog management system. In tandem, the product team began developing a refined product attribution model and an improved category hierarchy. This collaborative effort was essential, especially given the scale of our catalog—over 3 million products spanning hundreds of brands.

It was a massive undertaking, but one that set the stage for smarter search functionality and a more personalized, intuitive shopping experience.

Search & Personalization

With the catalog data now structured and a clear hierarchy in place, we turned our focus to implementing a robust search and personalization solution. After evaluating several providers, we selected Algolia for its ability to deliver fast, scalable, and highly customizable search experiences.

To power meaningful personalization, we began capturing user events to generate behavioral signals unique to each individual. These signals allowed Algolia to build dynamic personalization profiles, enabling users to more easily discover products across a massive catalog of over 3 million items—ranging from apparel and fishing gear to RV equipment and firearms.

One of our biggest challenges was creating a homepage and search experience that could adapt to a wide variety of user interests. For example, an outdoor enthusiast interested in fishing would see curated content and product recommendations related to that category, while someone browsing for apparel would have an entirely different experience. The goal was to make all categories discoverable, while still surfacing the most relevant content for each user based on their preferences and browsing behavior.

Algolia also introduced advanced semantic search capabilities, allowing users to enter natural-language queries like “men’s sports shorts in blue.” Previously, such queries would result in zero results. With Algolia, the system intelligently parsed the query into meaningful attributes, applied them to the appropriate category, and returned highly relevant products—solving a key pain point in the old platform.

Additionally, because we now had insight into each user’s brand affinities, we could fine-tune search rankings accordingly. For instance, if a user frequently engaged with Nike products, search results would prioritize Nike items first. Meanwhile, another user might see Adidas products featured more prominently based on their preferences.

The result was a more intuitive, responsive, and personalized shopping experience—one that significantly enhanced discoverability, reduced friction, and aligned closely with individual user needs.

Product Details Page

With search, discovery, and product data successfully addressed, we turned our attention to optimizing the Product Details Page (PDP). Early research revealed two consistent frustrations: slow page load times and the need to scroll excessively to access key information.

To resolve this, we focused on enhancing above-the-fold content, ensuring that the most relevant details were visible immediately—without requiring users to scroll. While not everything could be shown upfront, we made efficient use of available space to surface critical information first.

Our research also indicated a strong user demand for streamlined purchase options, particularly in-store pickup and home delivery. To support this, we anchored both CTAs persistently at the bottom of the screen, accompanied by a dual-column layout highlighting the most important info related to each option.

We also reimagined the top of the screen: instead of a standard header, we used a content overlay approach, allowing the product information to extend across the full viewport. This layout maximized screen real estate while reinforcing a seamless, content-first experience


Cart & Checkout Optimization

To complete our funnel optimization efforts, ensuring a fast and frictionless checkout experience became a top priority. The legacy checkout process was riddled with usability blockers that made completing a purchase slow and frustrating.

We addressed these pain points through a series of targeted enhancements:

  • Save for Later:
    A long-requested feature, this allowed users to save items directly from the cart for future purchase—helping reduce decision fatigue without losing intent.

  • Power Guest Checkout:
    Registered users could now complete purchases without logging in, significantly reducing drop-off caused by authentication barriers.

  • Google Address Autocomplete:
    To streamline address entry, we integrated Google’s address suggestion feature. As users typed, they received real-time suggestions. Selecting one instantly populated all necessary fields—eliminating manual entry and reducing the risk of delivery errors.

  • Smarter Address Flow:
    In the old system, users were required to enter shipping information first, even for in-store pickup. We reversed this logic. Billing address entry came first, and only if the user selected “shipping address is different” did additional fields appear. For in-store pickup, the shipping address step was removed entirely.

  • One-Tap Payment:
    By adding support for Apple Pay and Google Pay, we gave users a fast, secure way to complete purchases—cutting down the number of steps and boosting mobile conversions.

These changes collectively transformed the checkout experience. They not only reduced friction but also reflected a deep understanding of real user behaviors, resulting in higher conversion rates and fewer abandoned carts.

Cart Flow


Checkout Flow