Case Studies
How a Biopharma Company Eliminated 30,000+ Manual Hours and Reduced Supply Chain Communication Latency by 60%
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Successful marketing teams need efficient data matching and pairing processes to succeed. This was a big challenge for one of our FinTech clients, who was aggregating data from various sources and running advanced analytics models on local machines.
Altimetrik created a machine-learning model that provided better data in a timely matter, ultimately making our client more agile and nimble with more robust data, deeper insights, faster ability to take action, and better feedback loops.
Aggregating data from various sources without a reliable collation mechanism and running advanced analytics models on local machines led to a cumbersome data matching and pairing process for this Fintech marketing team. Building out data models took 6-10 months and even the most basic uses of the data–list management and analysis–were manual, complex, and prone to error.
With a Big Bang approach, a significant amount of money is invested in building a large data lake project. The roadmap planning team knew that it might take months or years to realize any true business insights or results. The Altimetrik team took a lean approach–creating a machine-learning model with just enough data to allow the team to seed the data lake while further enrichment would enable the build-out of additional models.
Elements of the makeover included:
Robust data and deep insights that are relevant to current / near-future business cycles, and better feedback loops provided improved ROI and dramatically increased revenue.
“Decisions – small and large – are made with a combination of analytics drawn from data and augmented by the business experience. Decision-makers now have the ability to gather deep insights and create predictive tools and analytics that drive real business outcomes.”
Head, Analytics Coe
A leading global chemical producer wanted to generate additional revenue from its top 25 customers. However, account managers lacked visibility into more than one million products distributed across approximately 20 planning units, limiting their ability to identify relevant cross-sell and upsell opportunities.
Altimetrik created a Single Source of Truth that unified product, customer, sales, and market data. Machine learning algorithms analyzed this information alongside geographic restrictions and customer spending capacity, generating actionable recommendations through an intuitive dashboard.
A US refrigeration equipment manufacturer manages temperature monitoring across more than 2,500 retail stores manually. The approach drove high equipment downtime, escalating maintenance costs, and left the client unable to control business-critical operations remotely. Altimetrik built a SaaS store monitoring platform with multi-site control, real-time temperature tracking, and detailed alarm/activity history. It added secure role-based remote diagnostics and CRM integration that auto-generates work orders from business rules, replacing manual monitoring with centralized, data-driven operations across the store estate.
