A leading U.S. telco sought to quantify the impact of network improvements on its existing customer base, new customer acquisition and identify network sites or area anomalies to identify network upgrades and precision deployments.

Tredence developed a data foundation on DBX, Azure to store and analyze network topology, telemetry data, customer usage, customer sentiment in the region, competitive landscape from a network standpoint, pricing, and promotion data. Tredence deployed a causal model based on this framework for individual markets to calculate the impact of network improvement on net activation and define the RoI for capex on network.

A top US telco leader wanted to understand key drives of customer disconnect to reduce attrition rates, which cost it millions in lost revenues and profitability. The telco sought to launch targeted interventions, offering high-risk customers relevant products and offers to keep these accounts.

Tredence built a model on ADF and Snowflake to identify at-risk customers and their disconnect drivers. The propensity models assign customers a disconnect score, align key drivers with recommended actions, and provide trend analytics. It integrates with existing enterprise resource planning (ERP) and customer relationship management (CRM) systems to enable account-level actions and provides a playbook that categorizes accounts and actions to reduce churn.

The wireless company sought to leverage journey analytics to uncover behavioral shifts and optimize marketing strategies.

Tredence built and deployed a customer journey analytics and insights tool. The new tool enables cross-functional teams to track and analyze shifts in customer behavior to identify key trends and drivers and monitor and visualize customer journeys to pinpoint and address issues. Teams can then create targeted marketing briefs and segmentation strategies using these insights.

A SaaS company sought to understand the impact of marketing spend on converting leads into sales prospects through the marketing funnel and across paid marketing channels. The company’s leaders wanted to create insights and integrate marketing and sales systems to reduce wasted spend and convert more leads into customers.

Tredence developed extract-transform-load (ETL) data routines and an API to harmonize spend and impression data from partners. We implemented a self-serve insights platform to generate attribution, channel, and campaign insights, spanning 10 spend channels and three global regions. A what-if scenario builder helps teams optimize budgets across channels to reach their target goals.

A US publishing firm lacked insights and analytical applications to track audience engagement and conversion. Its leaders wanted to build a recommendation engine for the company’s media sites that would ultimately scale across 400 brands, 725 websites, and 42 different verticals. Marketing measurement datasets exceeded 800 million rows.

Tredence enabled a common AWS data platform account with cloud best practices. We then built and deployed a recommendation engine powered by AWS Personalize and Python for 100+ media sites.

A leading gaming company grappled with fragmented on-premise systems, limited data accessibility, and delayed insights—hindering agility and growth. They needed a modern, scalable foundation to unlock real-time visibility, streamline reporting, and improve compliance.

Tredence’s partnership with this interactive gaming company began with a Google Cloud Platform (GCP) data modernization effort but evolved into a transformative multi-cloud initiative. Tredence solidified GCP’s role as a growth driver by enabling them to maximize its data potential while increasing business flexibility.