Mammoth engaged with a net new unified customer model build for Attribution App. Previously, there was no single view of a customer or invoicing details at an account level, preventing robust B2B marketing activities and analysis.

We integrated six different data sources onto a common schema, centralizing on key account/business name. Architecture design was delivered within 2 weeks of the initial kickoff.

A new Snowflake instance and dbt project were instantiated, and basic architecture built within the first 2 weeks. Within the following 2 weeks, Mammoth Growth fully productionized bronze, silver, and gold tables, along with new ingest and transformation processes. 

Allow C-Suite to deeply understand top customers, proactively anticipate churn rates, and user hierarchies. This enabled them to quickly identify accounts where overages were not being billed correctly, users that were no longer active and understand revenue across 3 different go to market strategies. 

We partnered with ecoATM, a leading electronics trade-in solutions provider whose Salesforce Marketing Cloud contract was expiring in four weeks. Our goal: migrate customer data to Iterable while enhancing data structures for targeted marketing campaigns.

Leveraging Snowflake as the central data hub and Iterable's Smart Ingest capabilities, we designed a solution to unify fragmented customer data across both physical kiosk transactions and online trade-ins, with a focus on robust data models that would transform raw transaction data into actionable customer insights, as well as establishing a data taxonomy that would support audience segmentation.

In just four weeks, we built a migration framework with enterprise-grade Snowflake data modeling, custom event structures, a unified customer profile strategy, Smart Ingest configurations, and streamlined API documentation.

By segmenting customers based on their behavior, the client’s marketing team can now move from generic to targeted communications, delivering precisely timed messages at each key milestone of the customer journey.

We partnered with TUBI TV’s data and growth teams to address inconsistencies in attribution models limiting cross-functional insights and making it difficult to optimize media spend and improve acquisition strategies.

Our plan involved build a roadmap to evaluate all existing systems against our best practices of a structured, scalable attribution framework and best-in-class measurement approaches able to support complex cross platform marketing campaigns. 

We audited existing analytics systems, tools, data sets and business logic supporting 3 diverse attribution models documenting key business logic, metrics and needs with a focus on simplification, consolidation and unification of customer data in a centralized data warehouse. 

Leadership teams were equipped with the key data & infrastructure inputs to inform strategic decision making to unify attribution modeling across 3 overlapping growth & marketing teams and create a comprehensive foundation for robust incrementality testing.  

We partnered with Shipt a leading grocery delivery service to optimize data-driven marketing. Their main objective was to enable timely, targeted campaigns and eliminate frequent failures in daily/hourly data jobs.

Our plan leveraged Snowflake for scalable warehouse and dbt for streamlined transformations for a new unified customer data model.  We audited billions of rows, documented business logic, and established new modeling strategies to ensure reliable, high-frequency automations.

We completed data backfills to lifecycle marketing platforms, validated data quality, and replaced fragile processes with robust, error-proof highly performant data sets powering precise, real-time insights.

Within the first two months, the marketing team launched dynamic abandoned-cart campaigns fueled by fast, trusted data. Efficiency soared, campaign accuracy improved, and the organization realized a huge ROI through modernized data-driven customer analytics.

We partnered with MSG networks to address involuntary churn for it’s direct to consumer streaming apps. To tackle this, we aligned on a clear objective to unify all touchpoints in the subscription journey and automate re-engagement.

We proposed Snowflake as the customer data warehouse, dbt to standardize business logic, and Braze for seamless campaign triggers. This design enabled rapid campaign experiments, ensuring timely re-engagement.

Using Snowflake & dbt, we designed & build customer data models and loaded them directly into Braze to trigger personalized reminders and recapture efforts based on real time payment failure events.

Our cross-functional collaboration delivered critical realtime user data directly to marketing teams in Braze resulting in improved payment recovery and reduced churn. 

We partnered with TED who needed a unified data strategy to leverage engagement insights across diverse platforms and replace siloed, Google Analytics–centric reporting with a scalable, holistic infrastructure.

We recommended Snowflake as the centralized warehouse and introduced a new tracking schema with standardized metrics. Our approach leveraged cost effective high scale solutions ensuring flexible but consistent behavioral insights across current and future data sources.

We rapidly implemented Snowflake, orchestrated data ingestion from key platforms and built a structured data layer that integrated video, relational, and social data propelling TED towards a modern data stack.

By centralizing siloed data and standardizing definitions, TED quickly unlocked cross-platform reporting. With automated ingestion, defined feature requests, and a scalable warehouse, TED transformed data access, fueling deeper insights and faster decision-making..

We partnered with the client to identify critical challenges in CRM, subscription management, and revenue reporting, and aligned on a key objective—building a unified customer data environment to support their shift to end-to-end services. 

We proposed a scalable, SaaS-based architecture leveraging Snowflake for centralized data storage, dbt to transform and document core business logic, and Hightouch for eventual data activation across the customer journey. 

With a tight timeline, we rebuilt all revenue and conversion reporting using unified data sources in Snowflake and modular dbt models to centralized business logic, data governance and usability eliminating prior points of failure.

Our work resulted in clear revenue & conversion insights that supported a platform rebuild and brand/product repositioning ensuring marketing & growth teams drove efficiency platform engagement.  

We partnered with the client to align on a key objective—connecting marketing, PLG, and SLG teams to improve audience targeting and drive expansion sales.

Using BigQuery, dbt, and Hightouch, we designed a solution to deliver trustworthy user and account data for real-time audience targeting and A/B testing.

In 3,000 hours, we built 57 high-performance customer data models, creating a unified view that supports seamless collaboration across marketing, PLG, and SLG teams.

The company reduced infrastructure costs by hundreds of thousands while improving PLG-to-SLG expansion sales, using accurate, real-time data to target and test audiences more effectively.

We partnered with the client to align on a key goal—providing unified customer & user insights across 900+ operating P&Ls. Together, we identified critical business questions and the need for a scalable, data-enabled future.

Using Snowflake and dbt, we designed a solution to unify thousands of data sets. The plan focused on building high-performance user data models to deliver real-time, actionable insights.

In 11,000 hours, we built 350 user data models, giving hundreds of employees access to unified insights across millions of users—a first in the company’s history.

With a new data foundation, the company gained transformative insights and action plans to improve performance across 900+ P&Ls, becoming data-enabled for the first time.

We partnered with Siemens’ Next Gen Marketing Automation (NGMA) division to align on a singular objective: unify data for personalized, data-driven marketing across complex stakeholder needs. 

Using Snowflake & Segment’s rETL, we designed a multi-phase plan to integrate Salesforce & LinkedIn data by building a custom Amazon S3 source for lead gen capture, enabling advanced marketing automation in Eloqua.

We built end-to-end Segment ↔ LinkedIn integrations and implemented real-time data flows into Outreach.io. These low maintenance high impact integrations supported reporting, predictive modeling & automated workflows.

Empowered with a unified data framework, Siemens leveraged new insights to optimize campaign targeting, expedite reporting, and reduce missed opportunities and improve marketing ROI.

We partnered with Nutrafol, the top dermatologist- recommended hair growth brand who was struggling with data spread across multiple platforms. We aligned on creating unified, consistent customer data to boost customer insights & empower marketing.

Mammoth Growth proposed a robust analytics stack featuring Snowflake, dbt, Tableau, Segment, Mixpanel, Iterable to unify customer data through seamless collection, ETL, rETL reporting & activation.  

We deployed & integrated all tools to build a robust unified customer data set in Snowflake enabling real-time audience targeting and management across ad networks & platforms.  

With reliable data and synchronized marketing, Nutrafol unlocked a 60% revenue surge leading to a successful Unilever acquisition in 2022 and proving the transformative power of our structured approach.

Tempo, a leading provider of Jira-based plugins, needed to unify customer data across six+ products to capture leads and create consistent user identity resolution to run targeted cross-sell / up-sell campaigns. 

We proposed a complete tech stack refresh leveraging Snowflake, dbt, Segment and Braze to integrate seamlessly with existing Salesforce CRM and sales processes.  

Our team designed & implemented a unified customer & account data set that tracked users and aggregated user activity, captured new leads, prioritized only the best leads for sales and synced to Salesforce for immediate outreach. 

With unified user & account data Tempo’s sales & marketing teams saw a 20% increase in lead gen conversion rates and rapidly deploy highly personalized campaigns across all six products driving efficient cross-sell / up-sell opportunities.