Dick’s Sporting Goods Adopts In-House Software Development to Accelerate Innovation

Recommendation: Create an internal platform team that owns end-to-end e-commerce enhancements, focusing on search, merchsearch, and buy-online experiences, with quarterly milestones during springone. The goal is to reduce handoffs and shorten cycle times so associates in stores and online can respond to customer needs without downtime.

Key data points: Williams said the program goes beyond legacy routines. It focuses on revamping core services with $150 million in investments, reduces downtime by 25%, and extends the merchsearch footprint to stores and buy-online channels; associates across teams contributed to the impressive success during the first year, using black-box tests and streamlined efforts to speed delivery.

To scale, adopt a modular stack built around search and merchandising flows, with clear ownership for each capability. Enable باستخدام microservices, automated testing, and CI/CD to cut release cycles; maintain a foot in both stores and online to synchronize priorities, so downtime is minimized and teams can respond during peak periods, with only sanctioned changes moving forward.

For peers aiming to replicate these gains, align investments with measurable outcomes across channels: optimize search relevance, boost ordering flows, and maintain a steady cadence of experiments during springone and beyond. Williams said their ongoing efforts demonstrate progress, with the team reporting continued success and a path to scalable impact for customers buying online.

Implementing In-House Software Development to Accelerate Product Data Decisions

Recommendation: Create a centralized, cross-functional data squad that owns the product data lifecycle, with a formal charter tied to revenue outcomes and a clear budget for tooling. This structure drives data-backed choices for sales, consumers, and athletes who rely on accurate products information, reducing marketing and product development efforts. This supports sporting category teams according to category priorities.

Define a repeatable level of governance and a lightweight process that blends business context with technical rigor, enabling decisions in a matter of months rather than long cycles behind requests. The team grew to 30 specialists, underscoring the need for scalable practices.

Invest in a documentary-style data catalog with clear lineage, metadata, and ownership; expose clean interfaces through reusable applications that support what teams need to answer, from dashboards to ad-hoc analyses.

During the revamping phase, align product managers, engineers, and analysts around a single data model; publish dashboards for consumers and executives at the conference to validate the approach. As jason notes in the fiscal review, this alignment reduces cost and drives value faster through a consistent process, avoiding decline in decision quality, according to priorities.

Roll out in three phases over six to nine months: first, a data-clearinghouse layer; second, a set of reusable processes and applications for product data decisions; third, an enablement program to upskill teams. This approach reduces risk and delivers impact across the world of products, while reading external case studies beyond the company helps refine the process. Learnings from springone and other industry gatherings inform the approach and support a huge opportunity to influence margins through a continuous improvement loop through the fiscal year.

Organizational model: cross-functional squads, roles, and governance

Recommendation: establish cross-functional squads of 6–9 members, each led by a product owner and a squad lead, aligned to major product families across channels. Through this model, they deliver end-to-end solutions that solve frontline problems while meeting business goals. Each squad owns 2–4 product streams, with a plan to release a new capability every 4–8 weeks. These squads reuse a shared component library and standardized interfaces to speed time-to-value, while preserving quality. A centralized backlog and regular coordination ensure search and discovery work aligns with retailer needs across store and digital experiences. These outcomes extend beyond the store floor into planning with suppliers and merchandising.

Governance and roles: A steering committee chaired by president gaffney, with representation from merchandising, e-commerce, store operations, and finance, meets monthly to set the quarterly roadmap and review progress. The squad lead handles prioritization and backlog grooming, while the product owner represents business outcomes and customer value. Behind the scenes, a platform owner maintains reusable assets and enforces governance rules to prevent scope creep. They require a documented process for reviews, with milestones linked to measurable outcomes. The structure ensures alignment at every level of leadership.

Delivery process and metrics: Each squad follows a lightweight, method-driven process, including discovery, design, build, test, and release. They run experiments and rely on data to inform decisions; you can predict impact using early indicators and pilot results. Time-to-market improvements, sales lifts, and store adoption are tracked, with income and margin metrics reported to the leadership group. In the latest year, several squads delivered impressive results, driving millions in incremental revenue and improving customer satisfaction scores. A documentary-style progress report shows the path from idea to value, while the springone insights feed into the next cycle.

People and culture: The model rewards collaboration across business and tech functions, and encourages accountability through clear roles and transparent dashboards. Roles include product owner, squad lead, engineer, QA tester, UX designer, data analyst, business analyst, and integration specialist. They collaborate with store managers and category teams to ground the work in real-world needs. Time-bound demos and weekly standups keep every member aligned, while a formal plan review ensures alignment with the overall business plan.

Integrating product data into associate workflows for on-the-floor decisions

Implement a centralized, real-time product-data feed delivered to associates on handhelds and screens to guide stock decisions and customer interactions, reducing on-floor delays.

The merchsearch series aggregates data from site catalogs, retailers’ inventories, and sales history to surface a single feature that guides the associate. It shows availability, size runs, and related models, helping them respond to customers in seconds.

The rollout uses a method starting with a short pilot in 15 stores, then expanding to a broader set over years. The rise in data coverage goes from 20% in year one to 70% after two years, boosting replenishment speed and search accuracy. This transformation has kept the focus on speed and accuracy, despite data complexity.

Associates receive on-screen prompts for sports and athletes categories, enabling rapid decisions on exact models, sizes, colors, and complementary items. This also reduces back-and-forth with customers and accelerates sales, while the site and physical store stay in lockstep with certain customer needs.

williams, president, said the approach is impressive and aligns with the rise in sales in the sports segment over the years; the team noted that the data-driven method supports a clear transformation across the companys footprint, both online and in stores.

To sustain momentum, adopt a phased plan: standardize data models, maintain merchsearch-curated models, train associates, and monitor results by short cycles. The method should go around quarterly reviews, with adjustments made to the features and the series as merchants gain proficiency.

Data sources, quality controls, and decision metrics for merchandising

Recommendation: Establish a centralized data fabric that ingests POS, ecommerce, supplier feeds, and marketing signals; the process continues to refresh dashboards every 12 hours and goes beyond quarterly reviews to guide focused assortment and pricing decisions. Dont rely on siloed data; leverage near real-time signals to drive success for customers and the retailer. It also communicates clear actions, and a short feedback loop keeps adjustments aligned with their goals.

• Data sources
  • Point-of-sale and online orders from the latest systems; capture sales, returns, channel attribution, and times of peak demand.
  • Ecommerce platform data and marketplace signals; conversions, add-to-cart, checkout abandonment, and search terms.
  • Supplier feeds and catalog data; availability, cost, lead times, and promotional programs.
  • Marketing campaigns and creative performance; lift per SKU, channel impact, and period-over-period trends.
  • Inventory and replenishment data; current levels, in-transit stock, and weeks of supply.
  • Customer signals; loyalty segments, baskets, reviews, and post-purchase feedback.
  • External trend data; sports seasonality, regional events, weather, and competitive promotions.
  • Operational data; returns issues, carrier performance, and fulfillment times.
  • White-space visibility: dashboards designed for transparency; avoid black-box forecasts where possible.
  • Historical data spanning years; built with clean history to detect patterns and seasonality, enabling smarter search and forecasting.
  • Dont ignore data from smaller suppliers or niche categories; they can unlock margins in underserved segments.
  • Latest data sources that go beyond routine reports to capture real-time signals and opportunity windows.
• Quality controls
  • Data governance: named owners for each source, clear data definitions, and defined retention periods.
  • Standardization: uniform product identifiers, units, currencies, and time buckets across channels.
  • De-duplication and cleansing: remove duplicates, reconcile overlapping records, and correct anomalies.
  • Validation rules: range checks, negative values, cross-source reconciliation (inventory vs. sales), and anomaly alerts.
  • Data lineage and audits: trace decisions to source events; williams champions cross-functional governance and accountability.
  • Archives kept for multiple years to support trend analysis and long-horizon planning.
  • Periodical audits: quarterly validation of 1,000+ SKUs and top 20 suppliers; issues kept under 2% of rows.
  • Data security and access: tiered permissions, logging, and privacy controls for customer-related insights.
  • Latency management: short-cycle feeds; target under 15 minutes for critical streams and under 24 hours for non-critical data.
  • Issue resolution: a named owner and SLA; dont let data issues stall merchandising cycles.
• Decision metrics
  1. Sell-through rate by week and SKU; target > 75% across core categories within a 4-week window.
  2. Inventory turnover and weeks of supply; aim for 4–6 turns per year in core categories and keep weeks of supply under 8 during growth periods.
  3. GMROI and margin realization; track gross margin return on investment per category and supplier; target > 1.8.
  4. On-shelf availability (OSA) and stock-out rate; maintain OSA > 98% and stock-out rate < 2% in stores and online.
  5. Promotional lift and incremental sales; measure uplift versus baseline controls; expect lifts > 15% for major campaigns.
  6. Pricing accuracy and rate elasticity; monitor price realization and markdown rates; keep markdowns below 10% of revenue for core lines.
  7. Forecast accuracy; compare forecast to actual demand with MAPE < 15% for most categories; track bias by period and adjust inputs accordingly.
  8. Channel contribution and mix; analyze revenue by store, ecommerce, and mobile; ensure online growth aligns with in-store objectives.
  9. Customer impact metrics; average order value, repeat purchase rate, and basket size by segment; prioritize high-potential customers.
  10. Supplier performance; on-time delivery rate, fill rate, and defect rate; targets: on-time > 95%, fill rate > 98%.
  11. Search and discovery metrics; monitor site search conversion, query relevance, and navigation depth; align merchandising with latest customer behavior.
  12. Operational risk indicators; flag issues around peak times and regions; implement contingency plans for high-demand periods.

From pilot to enterprise rollout: milestones, risks, and governance gates

Recommendation: Launch a concise pilot in california stores focused on core transactions, investing in data pipelines and essential modules, and then scale through governance gates to enterprise adoption, leveraging their investments and cross-functional support. The short, impressive initial footprint will raise credibility around planning and build momentum because the period after a successful run tends to rise quickly, while keeping focus on serving athletes and shoppers alike.

Milestones should be organized as a tight series that aligns with the original plan. Start with a planning phase to define objectives, metrics, and a clear service model, then move to building the core capabilities, run a limited test, and use the results to decide whether to expand around the peak season. This approach keeps the scope manageable and shows value fast, which encourages ongoing investing and buy‑in from the vice president and their team.

Risks are concentrated in data quality, inter-system compatibility, and staff adoption. Because stores operate in a fast-moving environment, misalignment between point‑of‑sale, inventory, and loyalty feeds can slow momentum. Also, security controls and operational support must be ready before broader use. In the short term, the team should plan around potential disruption to service during the test and ensure governance workflows are clear, because clear accountability reduces friction and builds trust with leadership.

Governance gates define decision points that protect value and safety. Gate criteria should include objective measurements, documented risk controls, and a plan for service continuity; owner roles must be explicit, with reports to the vice president of technology and the chief operations officer. This cadence prevents scope creep and makes the transition from pilot to scale a measured, data‑driven process.

Training, enablement, and ongoing support for store teams

Recommendation: roll out a two-tier training cadence for associates: fast-start onboarding delivering core processes in 4 weeks, followed by ongoing monthly refreshers totaling 8 hours per quarter. Target 90% completion within the first 60 days and 95% retention of key features by month 6. Rather than one-off webinars, combine hands-on coaching with short simulations that mirror the buy-online and in-store pickup flow, which reduces issues at checkout and drives a rise in customer satisfaction rates.

Enablement toolkit: deploy a digital toolkit across devices and in-store terminals that uses micro-learning, scenario-based practice, checklists, and real-time feedback. It avoids telling scripts in favor of contextual prompts, so associates stay authentic. The library ties to buy-online and ecommerce workflows and aligns with marketing campaigns, which supports telling prompts to associates instead of rigid scripts, improving authenticity. Heading the rollout, the program uses a single source of truth to guide every module, ensuring consistency across the world of sporting retail.

Ongoing support: establish a regional training squad that rotates through stores every 6–8 weeks, providing hands-on coaching, refresher sessions, and rapid issue resolution. Create a ticketing channel with SLA of 24 hours. Launch a quarterly conference to share models and best practices; continue to refine search paths and the feature set based on real-store data. The team should keep content fresh for months and ensure the feature set continues to evolve with customer expectations in ecommerce.

Measurement, cost, and fiscal impact: allocate an investing plan for training materials and tool access. Track cost per store and total program spend, according to store size. Monitor income lift from buy-online adoption and in-store conversions, with rates that rise quarter over quarter. Tie outcomes to the fiscal picture, with a conservative forecast that investment yields payback within 9–12 months, signaling a clear gamechanger for associates’ performance and overall team effectiveness.