Data Analytics / CASE STUDY

Finding the fault behind a purchase drop.

Journey analysis isolated payment-step friction and supported investigation by the web team.

Anonymised US telecom business

Illustrative concept for Data Analytics; not a client screenshot

FROM THE EXISTING CASE SUMMARY

45%

purchase drop investigated — not an uplift

Read the results below for context and measurement limitations.

01 / CLIENT OVERVIEW

The business behind the brief.

Anonymised US telecom business. Client identities are anonymised in the existing project summaries.

02 / BUSINESS CHALLENGES

The challenge.

Completed device purchases fell 45% against their normal trend. The team needed to distinguish traffic changes, journey friction and technical faults.

03 / GOALS AND OBJECTIVES

What success needed to look like.

Locate the source of the decline and enable ongoing funnel monitoring.

04 / STRATEGY AND APPROACH

The thinking behind the work.

Compare product-to-purchase paths before and during the issue, focusing on repeat visits and checkout destinations.

05 / SERVICES PROVIDED

The connected capabilities.

Website and Traffic Analytics, Conversion Rate Analysis.

06 / EXECUTION AND IMPLEMENTATION

How the work came together.

Mapped journey paths and found repeat payment visits rising from 19% to 41%. The web team traced a payment-confirmation bug, and two Power BI funnel measures supported daily monitoring.

07 / RESULTS AND KEY PERFORMANCE METRICS

What the evidence shows.

The source comparison shows 9,712 pre-issue hits and 7,200 during-issue hits. Purchases returned to normal after the payment-confirmation issue was addressed; no post-fix uplift percentage is supplied.

08 / FINAL OUTCOME

What the work made possible.

The investigation directed attention to a payment defect and established daily monitoring.

Project details are based on the existing QlikMatrix website. No additional client identities, financial outcomes or implementation details have been inferred. Visuals are illustrative.

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