Bash: Omnichannel Retail Product Intelligence (TFG)

Overview

Bash: Omnichannel Retail Product Intelligence is a retail product analytics case study built around the real operating shape of a high-traffic commerce platform. The work focuses on where the user journey breaks between browse and purchase, how fulfillment reliability affects support demand, and where returns begin to distort what looks like healthy product performance.

Hero

Omnichannel retail product analytics case study focused on conversion pressure, fulfillment reliability, and returns-led commercial risk.

Intelligence Layer

Retail teams can see sessions, orders, and campaign spikes every day. That does not automatically reveal where the actual product pressure sits. Bash needs a view that connects discovery, checkout, delivery, and returns so product, trading, and operations can make sharper weekly decisions.

Problem

The core problem is not traffic volume. It is the lack of a joined-up operating view across the full retail journey. Without that, teams risk pushing more acquisition into a weak checkout path, treating delivery issues as a logistics-only concern, and over-celebrating categories that are quietly losing value through returns.

Data / Signals

Analyst Objective

Build a decision-ready product intelligence layer that helps teams:

• read the funnel from session to purchase,
• isolate where mobile and channel friction is breaking checkout,
• measure the commercial effect of late fulfillment,
• and identify which categories carry the heaviest returns pressure.

Stakeholders

• Product teams improving mobile and checkout experience.
• Growth teams deciding where extra traffic will actually convert.
• Trading and merchandising teams reviewing category performance.
• Fulfillment and CX teams managing post-order friction and support load.

Key Questions

• Where does the Bash funnel lose the most momentum by device?
• Is mobile web a traffic problem or a checkout problem?
• How much do late orders lift support contact and return risk?
• Which categories require better fit, expectation-setting, or product detail before more demand is pushed through them?

KPI Framework

• Funnel: sessions, PDP reach, add-to-cart rate, checkout start rate, purchase rate.
• Device quality: session-to-purchase conversion by mobile app, mobile web, and desktop.
• Fulfillment: late-order rate, support-contact rate, split-delivery exposure.
• Returns: category return rate, refund share, top return reasons, net revenue after returns.

Insight

• The modeled funnel converts 62,959 sessions into 4,186 purchases, a 6.65% session-to-purchase rate.
• Mobile app converts at 8.78% versus 3.30% on mobile web, which points to downstream checkout friction rather than weak demand.
• Late orders represent 12.1% of orders, but lift support-contact rate to 31.1% versus 9.8% for on-time delivery.
• Footwear carries the highest modeled return rate at 22.9%, with size_issue emerging as the most common return reason overall.

Implication

• Bash should prioritize mobile web checkout fixes before buying more top-of-funnel traffic.
• Delivery reliability belongs in product review, because late fulfillment lifts both customer effort and return risk.
• Mixed-inventory and split-delivery exposure should be treated as conversion-protection work, not only back-office ops work.
• Footwear needs sharper sizing, fit guidance, and expectation-setting before heavier promotional pressure is justified.

Closing

Deliverables

• Synthetic Bash-shaped datasets across users, sessions, events, orders, fulfillment, and returns.
• Pandas-based funnel, checkout, fulfillment, and returns analysis.
• Exported CSV outputs for KPI review and supporting visuals for stakeholder storytelling.
• Hosted walkthrough report plus readable project documentation in GitHub.

Outcome

The case demonstrates how omnichannel retail analytics can move beyond traffic and revenue reporting into product intelligence that is more useful for weekly operating decisions.

Key Visuals

Mobile app converts far better than mobile web in the modeled funnel, which points to later-stage experience friction rather than weak demand.

Promotional and acquisition-heavy channels show the weakest checkout completion, reinforcing the need to protect downstream experience before scaling traffic.

Late delivery increases both support demand and return risk, making fulfillment reliability a commercial KPI rather than a pure logistics KPI.

Returns pressure is concentrated rather than evenly spread, with Footwear carrying the heaviest modeled risk in this run.

Recommendations

• Fix mobile web checkout and payment friction before increasing acquisition spend.
• Reduce split-delivery exposure on mixed-inventory orders.
• Add tighter delivery-promise communication when order complexity is high.
• Focus category-level content improvements first on Footwear, especially fit and sizing clarity.

Embedded Project

Link

• Hosted walkthrough: Bash Product Intelligence
• GitHub repo: bash-product-intelligence