Decisions break where data disconnects. We fix that partnering with organisations across sectors to turn fragmented data into aligned, decision-ready intelligence at scale.
Most businesses treat all customers the same, missing who actually drives long-term value. Without clear understanding of customer lifetime value, marketing stays generic and high-potential customers are often ignored. A leading US retailer faced this while scaling eCommerce, with no centralized data for customer profiling. ZDS built a unified view, segmented customers based on behaviour, and estimated lifetime value to help target the right customers with focused strategies.
Customer intelligence gaps quietly drain retail growth because businesses cannot identify which in-store shoppers are most likely to deepen engagement across channels and increase long-term value. One of the largest US retailers, operating nearly 5,000 stores and serving roughly 240M weekly customers, built an omnichannel lookalike modelling system using 200+ behavioural variables to identify high-potential shoppers, enabling targeted adoption campaigns and surfacing nearly 1.7M likely omnichannel customers.
"Behavioral intelligence systems that improve unclear user journeys into stronger monetization and sharper customer segmentation Most digital products struggle when user behavior stays fragmented because feature adoption, retention, and monetization decisions become difficult to scale. A global technology company launching a collaborative application built a multi-stage behavioral modelling framework to classify users, identify high-value engagement patterns, and improve paid user conversion by 24% while creating reusable customer intelligence capabilities."
Most beverage manufacturers struggle to predict bottle returns accurately, creating excess packaging injections, avoidable CAPEX pressure, and unstable production planning across markets. A global beer manufacturer hit this challenge while managing large-scale circular packaging operations, so ZDS built forecasting models that improved return visibility, sharpened injection planning, and reduced packaging waste across regions.
Agencies managing multiple brands and fluctuating client demand often struggle when revenue forecasting, resource planning, and utilization tracking operate in isolation, creating planning gaps and delayed decisions. A US-based digital media and advertising agency faced this while handling campaigns across retailers and CPG brands, so ZDS built forecasting pipelines and Tableau dashboards that unified revenue visibility, automated reporting workflows, and improved planning accuracy across teams.
Consumer purchase behavior rarely follows category structures, and brands often lose revenue when cross-category buying patterns remain invisible. A leading CPG company operating across online and offline retail channels needed a scalable association analytics framework to uncover hidden product relationships, improve promotional targeting, and help marketing teams optimize spend allocation through data-backed basket intelligence.
Forecasting new product demand without dependable analogue mapping often pushes launch teams toward inflated projections, weak planning assumptions, and avoidable commercial exposure. A global pharmaceutical manufacturer operating across OTC categories needed a scalable forecasting capability for future drug launches, so a unified modelling framework combined analogue identification, feature engineering, and predictive forecasting to improve launch accuracy, strengthen portfolio planning, and reduce dependence on opaque estimation methods.
Online shelf performance shifts fast when product visibility depends on scattered signals, weak keyword coverage, and inconsistent content quality. A leading healthcare and biotechnology company operating across consumer and pharmaceutical categories built a scalable digital shelf analytics framework that connected marketplace data, KPI modeling, and keyword intelligence to uncover ranking drivers, improve optimization decisions, and raise model accuracy to nearly 82%.
Marketing teams rarely struggle because of missing data; they struggle because disconnected metrics make campaign decisions slow, inconsistent, and difficult to trust. A global communications and advertising network managing campaigns across major consumer brands needed a centralized measurement system, so a scalable CDM-based analytics framework was built to unify partner data, benchmark performance, rank tactics, and improve campaign planning speed across brands.
Marketing campaigns often fail quietly because teams cannot isolate which tactic actually influenced customer behavior. A leading US eCommerce retailer preparing a nationwide gadget promotion built a four-cell test-and-learn framework to measure the impact of mailers, push notifications, and combined treatments, helping the business identify higher-performing campaign formats and improve campaign ROI by nearly 4%.
Customer acquisition campaigns often fail because businesses target broad customer groups without understanding who is actually likely to respond. A leading financial services provider in India wanted to grow its home insurance business, so the team built customer response models that identified high-potential motor insurance users, improved campaign targeting, and increased marketing efficiency across cross-selling programs.
"Most consumer goods manufacturers struggle with fragmented supply chain visibility, causing inventory write-offs and avoidable overhead losses. Without predictive risk assessment, obsolescence drivers remain hidden and inventory decisions stay reactive. A global beer manufacturer faced this while managing large-scale multi-region operations with rising annual obsolescence losses. ZDS built driver and risk assessment models to identify root causes, predict obsolescence exposure, and enable smarter inventory planning decisions."
Tell us about your data challenge and we'll come back with a clear, actionable plan with no jargon, no fluff, just a path forward.
