Business Analytics

Client Background: A mid-market multi-brand e-commerce retailer headquartered in New York, operating across fashion, home goods, and electronics with annual revenues exceeding $120M.

Challenge: The client’s pricing team was manually tracking competitor prices across 14 rival platforms using spreadsheets — a process that took 3 FTEs and delivered data that was 48–72 hours old. With margins thinning and Amazon repricing algorithmically, they were consistently losing the Buy Box and underperforming on seasonal promotions.

Solution: Hir Infotech deployed an AI-driven competitive pricing analytics system that scraped 14 competitor e-commerce platforms every 4 hours, structured the data into a unified pricing intelligence dashboard, and integrated it directly with the client’s product catalogue and repricing engine via API. Custom alert workflows notified category managers instantly when competitor prices moved beyond a threshold. A predictive model was built to recommend optimal pricing windows ahead of sales events.

Results: Within 90 days, the client achieved a 19% improvement in Buy Box win rate, a 14% increase in gross margin on repriced SKUs, and reduced the pricing analysis team effort by 78%. Seasonal campaign performance improved by 31% year-over-year, contributing an estimated $4.2M in incremental annual revenue.

Client Testimonial: “Hir Infotech delivered what three internal tools and two agencies couldn’t. The real-time pricing intelligence is now the backbone of our merchandising strategy — we don’t make a promotional decision without it.” — VP of E-Commerce, New York

Client Background: A mid-large German manufacturing company with operations across Germany, Austria, and the Netherlands supplying precision components to the automotive and aerospace industries.

Challenge: The client’s procurement and logistics team was operating blind — supplier performance was tracked manually in Excel, delivery delays were discovered reactively, and there was no unified view of inventory risk across their 11 European supplier relationships. This lack of visibility was costing them an estimated €2.1M annually in unplanned downtime and expedited shipping costs.

Solution: Hir Infotech built a real-time supply chain analytics platform integrating data from ERP (SAP), supplier portals, shipping APIs, and external logistics datasets. AI-driven predictive models were trained to identify delivery risk 14 days in advance based on historical supplier patterns, geopolitical logistics signals, and capacity utilization data. A supplier health scorecard was deployed to provide weekly performance ratings for all 11 vendors, visible to both procurement and C-suite leadership.

Results: The client reduced unplanned production downtime by 43%, cut expedited shipping costs by €780K in the first year, and improved supplier SLA compliance by 29%. The analytics platform paid back its full implementation cost within 6 months.

Client Testimonial: “We moved from reactive crisis management to proactive supply chain governance. The visibility Hir Infotech built for us is something our ERP vendor was quoting us 18 months and seven figures to deliver.” — Chief Procurement Officer, Munich

Client Background: A fast-growing B2B SaaS company based in London offering HR technology to mid-market enterprises across the UK, France, and Ireland, with an ARR of £18M and a sales cycle averaging 47 days.

Challenge: The marketing team was investing across paid search, LinkedIn, content, events, and SEO — but had no unified attribution model. Last-click attribution in GA4 was consistently over-crediting paid search while hiding the genuine influence of content and organic channels, causing budget misallocation of an estimated £400K annually.

Solution: Hir Infotech implemented a multi-touch data-driven attribution model connecting Salesforce CRM, HubSpot, GA4, LinkedIn Ads, and Google Ads into a single data warehouse. A custom AI model was trained on 24 months of historical pipeline data to assign influence weights to each touchpoint based on its correlation with closed-won outcomes. Dashboards were built for the CMO, Head of Demand Gen, and CFO — each with role-specific views and drill-down capability.

Results: The client identified that organic content was influencing 38% of pipeline that was previously unattributed, leading to a reallocation of £220K from paid search to content and SEO. Pipeline-to-close rates improved by 11% as sales received better qualified leads, and the CMO reported a 34% improvement in confidence when presenting board-level marketing ROI.

Client Testimonial: “For the first time, our CMO could walk into a board meeting and prove the value of every channel. Hir Infotech didn’t just build dashboards — they rebuilt how we think about marketing investment.” — Head of Revenue Operations, London

Client Background: A Netherlands-based digital lending FinTech serving SME clients across the Netherlands, Belgium, and Denmark, processing approximately 3,200 loan applications per month.

Challenge: The client’s risk scoring model was outdated, relying on static credit bureau inputs that did not capture real-time financial behaviour signals. Approval rates were low, default rates were above industry benchmarks at 6.8%, and the underwriting team was spending 40% of their time on manual reviews that could have been data-driven.

Solution: Hir Infotech built an AI-driven credit and risk analytics engine that augmented traditional credit bureau data with alternative data signals — including payment behaviour patterns, e-commerce sales velocity, social signals, and web-scraped public financial disclosures. The model was trained on 36 months of historical loan performance data and validated against GDPR-compliant data governance standards for EU operations.

Results: Within two quarters, default rates dropped from 6.8% to 3.9%, approval rates improved by 22% on previously rejected-but-viable applicants, and manual underwriting time was reduced by 61%. The FinTech estimated the improvement in portfolio quality generated €1.4M in net revenue impact in year one.

Client Testimonial: “The difference between our old scoring model and what Hir Infotech built is night and day. We are approving better borrowers faster, and our default curve is the best it’s been since we launched.” — CRO, Amsterdam

Client Background: A regional Australian telecommunications provider serving approximately 280,000 SME and residential customers across Victoria, New South Wales, and Queensland.

Challenge: Customer churn was running at 2.4% monthly — above the industry average — and the retention team had no predictive view of which customers were at risk before they submitted cancellation requests. Reactive win-back campaigns were costly and had conversion rates below 8%.

Solution: Hir Infotech deployed a customer churn prediction and analytics system that integrated billing data, customer service interaction records, usage patterns, and NPS survey history into a unified ML churn model. The model scored all 280,000 customers weekly and assigned a 30-day churn probability, enabling the retention team to deploy proactive outreach and targeted retention offers 3–4 weeks before cancellation intent materialized.

Results: Monthly churn rate dropped from 2.4% to 1.6% within six months — representing approximately 2,240 additional customers retained per month. Proactive retention campaign conversion rates improved from 8% to 27%, and the estimated annual revenue impact of churn reduction exceeded AUD $3.8M.

Client Testimonial: “We went from chasing customers who had already decided to leave to having conversations with customers before they even considered it. This is a fundamental shift in how we manage retention.” — Head of Customer Success, Melbourne

Client Background: A Paris-based healthtech company developing digital diagnostics tools and selling into hospital networks across France, Spain, and Italy — a highly regulated and competitive market with long procurement cycles.

Challenge: The business development team had limited visibility into competitor product updates, tender activity, hospital procurement announcements, and regulatory approvals across three countries. Intelligence was gathered manually by two analysts and was weeks behind market developments — causing the company to miss tender windows and enter competitive conversations unprepared.

Solution: Hir Infotech built an AI-driven market intelligence analytics system that continuously monitored competitor websites, public tender databases (TED EU), regulatory announcement portals, and healthcare news sources across France, Spain, and Italy. Structured competitive intelligence reports were generated weekly and delivered into a dashboard that tracked product launches, pricing changes, partnership announcements, and upcoming procurement opportunities in near real time.

Results: In the first 6 months, the client identified 14 high-value tender opportunities it would have previously missed, submitted competitive bids on 9, and won 4 — contributing €2.7M in new contract value. The business development team reduced manual research time by 74%, allowing analysts to focus on strategic positioning rather than data collection.

Client Testimonial: “Our competitors are still guessing. We are watching the market move in real time. Hir Infotech built us an intelligence advantage that no internal resource could have created at this cost or speed.” — Chief Business Officer, Paris

Client Background: A Stockholm-based management consulting firm with 1,100 employees across Sweden, Denmark, and Finland, experiencing above-average attrition in senior consultant grades and escalating recruitment costs.

Challenge: The firm’s HR leadership had no predictive model for attrition risk — voluntary departures were only recognized at exit interview stage, making proactive intervention impossible. Annual attrition in the senior consultant band was running at 28%, significantly above the Nordic industry average of 18%, and recruitment cost-per-hire had risen to SEK 142,000.

Solution: Hir Infotech built a workforce analytics and attrition prediction platform integrating HRIS data, performance review history, project assignment patterns, compensation benchmarking data, and anonymized engagement survey results. A gradient boosting model was trained to assign monthly attrition risk scores to each employee, with explainability features that identified the primary drivers — compensation gap, career progression stall, or workload imbalance — for each at-risk individual.

Results: Within nine months, senior consultant attrition dropped from 28% to 19%. HR and line managers could act on risk signals an average of 11 weeks earlier than previously possible. Recruitment cost savings in the first year exceeded SEK 4.1M, and engagement scores in at-risk cohorts improved by 18 points after targeted interventions.

Client Testimonial: “We finally have the same level of data-driven decision making in HR that we offer our clients in strategy. Hir Infotech built something that our own HRIS vendor had been promising for three years.” — CHRO, Stockholm