CASE STUDIES
Book A CallCustomer Quality & LTV
Problem:
The client needed a reliable way to calculate Lifetime Value (LTV) to accurately assess the ROI of their marketing spend. The existing models were simplistic and didn’t account for variations in customer behavior or probability of churn, leading to inefficient budget allocation.
Solution:
Implemented a probabilistic LTV model along with a comprehensive financial reporting suite.
Approach:
1) Probabilistic LTV Model Implementation: Developed a model that forecasts expected lifetime value based on historical data, behavioral indicators, and churn probabilities. This model provided a more realistic estimate of revenue potential and risk, helping refine the company’s understanding of customer value over time.
2) Financial Reporting Suite: Created a dashboard that integrates LTV with churn rates and campaign performance metrics, giving a clear view of how marketing investments translate into long-term customer value.
Benefits:
1) Improved Contract Negotiation: Media teams could negotiate affiliate contracts more effectively by understanding the true value of customers acquired through different channels.
2) Campaign Optimization: Enhanced ability to identify high-performing campaigns and allocate resources efficiently, maximizing overall ROI.
Digital Marketing Reporting
Problem:
The digital marketing reporting process was manual and time-consuming, requiring the biddable teams to spend at least one to two full days each week on report generation. This not only led to inefficiencies but also increased the risk of errors in reporting.
Solution:
Developed a fully automated reporting suite for all digital marketing activities.
Approach:
1) Automated ETL Pipeline: Built an ETL (Extract, Transform, Load) process that pulls data from various media platforms, cleans and transforms it into a standardized format, and loads it into a reporting database.
2) Channel-Specific Reports: Created tailored reports for each major channel, including Paid Search, Paid Social, Programmatic, Affiliates, and SEO. These reports provided insights into performance metrics and trends, reducing the need for manual data extraction and manipulation.
Benefits:
1) Accuracy Improvement: Reduced the error rate in reports to below 2%, ensuring reliable data for decision-making.
2) Efficiency Gains: Cut down the time needed for report generation from days to a few hours, allowing teams to focus more on strategy and optimization.
3) Reliability: Decreased failure rate in reporting, ensuring consistent and accurate delivery of reports.
Paid Search Forecasting
Problem:
The Paid Search team needed a reliable way to forecast spend and conversions to allocate budgets efficiently and account for seasonal variations in demand.
Solution:
Developed a robust forecasting model that accurately predicts future spend and conversions.
Approach:
1) Time Series Forecasting Models: Implemented a suite of models that incorporate historical data, seasonal patterns, and market trends to forecast key metrics like spend and conversions. This allowed the team to anticipate changes in demand and adjust budgets proactively.
Benefits:
1) Optimized Budget Allocation: Improved the accuracy of budget forecasts, reducing overspend and underinvestment during key periods.
2) Higher ROI: Enabled the team to achieve better returns by aligning spend with expected performance, minimizing wastage.
CI/CD for Media Reporting
Problem:
Paid media data reporting relied on scripts running on individual team members’ computers, leading to fragmented processes, high error rates, and long turnaround times for changes.
Solution:
Implemented a CI/CD (Continuous Integration/Continuous Deployment) framework with centralized credential management and automated pipeline deployment.
Approach:
1) Credential Management: Moved all credentials to a secure manager, eliminating the risk of unauthorized access and simplifying updates.
2) CI/CD Setup: Set up automated pipelines that deploy code changes without manual intervention, reducing deployment errors and speeding up development cycles.
3) Dependency Management: Built ETL pipelines with robust dependency management to ensure data consistency across processes.
4) Monitoring & Error Handling: Implemented automated monitoring for all pipelines, with alerts for any failures or discrepancies.
Benefits:
1) Increased Reliability: Ensured data loads were consistent and accurate, reducing error rates to below 5%.
2) Streamlined Processes: Faster implementation of changes, improving team agility and responsiveness.
Anomaly Detection
Problem:
Digital marketing campaigns often suffer from tracking issues, but manually identifying anomalies across multiple channels and campaigns is time-consuming and prone to errors.
Solution:
Developed an automated anomaly detection system to flag tracking issues in real-time.
Approach:
1) Data Model for Anomaly Detection: Built a data model that standardizes inputs from various sources, preparing them for anomaly detection algorithms.
2) Anomaly Detection Algorithms: Implemented machine learning algorithms that detect deviations from expected patterns in campaign performance data.
3) Anomaly Scoring System: Created a scoring system to prioritize anomalies based on severity and likelihood of impact, reducing false positives.
4) Automated Notifications: Integrated with the data pipeline to automatically notify the team of any anomalies, speeding up the response time.
Benefits:
1) Faster Issue Resolution: Reduced time to identify and fix tracking issues from weeks to days.
2) Improved Data Reliability: Ensured that campaign data was accurate and trustworthy, leading to better decision-making.
Data-Driven Attribution Modelling
Problem:
The client was using a Last Click attribution model, which overemphasized performance marketing at the expense of brand activities, leading to skewed insights and inefficient spending.
Solution:
Implemented a data-driven attribution model using advanced algorithms.
Approach:
1) Data Model Development: Built a comprehensive data model that captures all customer interactions across the funnel.
2) Algorithm Implementation: Used Shapley Value and Fractribution models to assign credit to each touchpoint more accurately, reflecting the true impact of brand and performance activities.
3) Automated Reporting Pipelines: Set up pipelines to generate attribution reports automatically, providing timely insights to the marketing team.
Benefits:
1) Accurate Attribution: Provided a holistic view of how different channels contribute to conversions, leading to more balanced investment across the funnel.
2) Cost Efficiency: Enabled the team to reduce costs by reallocating budget to under-credited but high-impact channels.
Media Mix Modelling (MMM)
Problem:
The client needed a way to optimize the mix of online and offline media investments, while also being resilient to changes in data privacy regulations.
Solution:
Implemented a Media Mix Model (MMM) to understand the combined impact of all media channels on sales.
Approach:
1) Data Model & Pipelines: Created a data model and pipelines to collect and process data from all media channels, ensuring a consistent and comprehensive dataset for analysis.
2) Baseline Model Creation: Developed an initial model to establish a baseline understanding of media performance.
3) Incrementality Testing: Conducted tests to measure the incremental impact of each channel, calibrating the model for more accurate predictions.
4) Actionable Reporting: Built a reporting suite that translates model outputs into actionable insights for the media team.
Benefits:
1) ROI Improvement: Provided clear recommendations on media mix adjustments, maximizing returns across channels.
2) Reduced Wastage: Identified underperforming channels and tactics, cutting unnecessary spend.
Automated Tracking Generation
Problem:
Manual tracking for digital marketing campaigns was inefficient, prone to errors, and difficult to manage, leading to broken links and inconsistent reporting.
Solution:
Developed an automated tool for campaign tracking generation and management.
Approach:
1) Campaign Integration: Integrated with the campaign planning tool to automatically pull campaign metadata.
2) Automated URL Generation: Created a tool that generates tracking URLs and unique campaign IDs based on metadata, ensuring consistency and reducing manual errors.
3) Data Model for Reporting: Built a data model to streamline reporting and improve time-to-insight.
4) Quality Assurance Suite: Developed a reporting suite to monitor link quality and ensure tracking accuracy.
Benefits:
1) Time Efficiency: Reduced time spent on URL creation from days to hours, freeing up the team for higher-value activities.
2) Error Reduction: Decreased broken tracking incidents from 35% to under 5%, improving data integrity and trust in reports.
Call Load Forecasting
Problem:
Accurately measuring call center queue volumes was challenging, leading to inefficient resource allocation. Call and email loads varied significantly throughout the day, week, and month, making it hard to maintain consistent service levels. Meeting Service Level Agreements (SLAs) and performance targets was difficult without precise forecasting.
Objective:
Improve achievement of performance targets and SLAs.
Optimize resource capacity planning to match demand.
Approach:
1) Forecasting Model Development: Developed a model capable of predicting call and email loads throughout the week. This model used historical data and trend analysis to anticipate fluctuations in demand.
2) Resource Calculation: Calculated the Full-Time Equivalent (FTE) resources required to handle the predicted load while maintaining the desired SLAs. The model factored in different demand patterns and allowed for dynamic adjustments based on real-time data.
Benefits:
1) Better SLA Compliance: Improved ability to meet and exceed SLA targets by ensuring the right number of staff were scheduled at peak times.
2) Efficient Resource Allocation: Optimized staffing levels, reducing costs associated with overstaffing and minimizing the impact of understaffing on service quality.
Data Automation and Reporting
Problem:
Reporting was manual, time-consuming, and scattered across multiple systems, resulting in a fragmented view of performance.
Legacy systems complicated the automation of data extraction and integration, making it difficult to streamline processes.
The manual nature of reporting increased the time to insight and error rates.
Objective:
Automate data extraction and integration.
Create a centralized reporting suite for better visibility and control.
Develop a presentation format that can be easily updated for regular management meetings.
Approach:
1) ETL Pipeline Creation: Developed an ETL pipeline that integrated data from multiple sources, including Excel files, legacy applications, and database queries. This pipeline automated the data extraction, transformation, and loading processes, reducing manual intervention.
2) Reporting Dashboard: Designed a dynamic dashboard that visualizes key metrics and performance indicators, automatically updating as new data is ingested. This provided a single source of truth for all performance reporting.
3) Automated Presentation: Created an automated PowerPoint presentation that pulls critical metrics from the reporting layer, allowing for easy and consistent updates during management reviews.
Benefits:
1) Time Savings: Reduced the time required to generate reports from 4 days per month to just half a day, freeing up resources for strategic tasks.
2) Reduced Errors: Lowered the error rate in reported metrics, improving confidence in the data presented to stakeholders.
CRM Database De-duplication and Cleaning
Problem:
The CRM database was cluttered with inaccurate, incomplete, and duplicate records, making it difficult to manage and reducing the effectiveness of marketing and sales efforts.
High maintenance costs and poor-quality data negatively impacted lead quality and campaign performance.
Objectives:
Deduplicate, clean, and augment CRM records to improve data quality.
Establish processes to prevent future pollution of the CRM database.
Approach:
1) De-duplication with ML Algorithms: Employed exact and fuzzy matching machine learning algorithms to identify and merge duplicate records, significantly reducing redundancy in the CRM database.
2) Data Cleaning and Augmentation: Cleaned and enriched CRM records using third-party data sources and merged records to ensure high data quality.
3) Preventative Measures: Developed and implemented data validation processes to clean and validate records before they are added to the CRM database, preventing future data pollution.
Benefits:
1) Significant Database Reduction: Reduced the database size from approximately 500,000 records to around 250,000 high-quality, validated records, lowering storage and maintenance costs.
2) Improved Data Quality: Enhanced the quality of key fields, such as addresses and contact information, leading to more effective communication and higher campaign performance.
Lead Reporting to Dealerships
Problem:
There was a disconnect between the lead generation process and conversion tracking at dealerships, leading to a lack of visibility into the effectiveness of lead management.
The manual reporting process was time-consuming and error-prone, making it difficult to assess the quality and conversion of leads.
Objectives:
Connect the entire lead journey from initial contact through to conversion at the dealership.
Automate the reporting process to improve accuracy and reduce manual workload.
Approach:
1) Data Integration: Integrated data from lead generation forms with dealership management systems, creating a seamless flow of information from lead capture to conversion.
2) Pipeline Development: Built data pipelines to extract, transform, and join data from various sources, providing a unified view of lead performance and conversion metrics.
3) Integrated Reporting: Developed a comprehensive reporting suite with key performance indicators (KPIs) to evaluate the lead journey, providing actionable insights to improve lead management and conversion rates.
Benefits:
1) Visibility Across the Funnel: Enhanced understanding of the lead-to-conversion process, allowing for better optimization of marketing and sales efforts.
2) Reduced Errors: Automated reporting reduced manual errors, ensuring that dealerships received accurate and timely information on lead performance.