Forecasting
Forecasting Marketing Leads Using Website Data
Client
BMW, Critical Mass
YeAr
2024
Category
Machine Learning & Predictive Modeling
Service
Predictive Insights & Forecasting
Tools / Languages Used
- R
- SQL for data extraction from Google Analytics and CRM systems
- Tableau for visualization and reporting
- Jupyter Notebook for experimentation
Technical Skills
- Time series forecasting and regression modeling
- Feature engineering and lag variable creation
- Model evaluation (MAE, RMSE, MAPE)
- Data visualization and dashboard design
Soft Skills
- Translating business goals into measurable models
- Communicating forecasts and confidence intervals to non-technical teams
- Collaborating with marketing and sales on data-driven planning
- Iterative problem-solving and model refinement
Step 1: Exploratory Data ANalysis
- Collected 18 months of website performance data (sessions, bounce rate, form fills, traffic sources).
- Joined data with CRM lead outcomes to identify conversion relationships.
- Found that organic traffic and repeat visits were strong predictors of lead volume, while paid traffic had high variance.
- Detected seasonality patterns — spikes during product launches and quarter-end marketing pushes.
Step 2: Solution Design
- Defined goal: Forecast weekly lead volume for the next quarter.
- Created lagged features for page views, conversions, and campaign spend.
- Tested multiple models: ARIMA, Deep AR and XGBoost.
- Deep AR performed best for capturing seasonality, while XG Boost captured nonlinear campaign effects.
- Built a hybrid forecasting approach combining Deep AR trend predictions with regression-based residual adjustments.
Step 3: Model Assessment
- Evaluated on a 3-month holdout period using MAE and MAPE.
- Achieved MAPE = 7.8%, outperforming baseline rolling average by 26%.
- Visualized predicted vs. actual lead counts to validate forecast accuracy and seasonality alignment.
- Included prediction intervals to quantify uncertainty and guide marketing pacing.
Step 4: Results / How It’s Used
- Forecast model enabled marketing team to anticipate lead surges and adjust ad spend proactively.
- Sales team used projections to better allocate SDR resources by week.
- Integrated model outputs into a Tableau dashboard showing historical performance, forecasted leads, and confidence ranges.
- Forecasting pipeline scheduled to update automatically each week, ensuring near real-time decision support.
- Forecast model enabled marketing team to anticipate lead surges and adjust ad spend proactively.
- Sales team used projections to better allocate SDR resources by week.
- Integrated model outputs into a Tableau dashboard showing historical performance, forecasted leads, and confidence ranges.
- Forecasting pipeline scheduled to update automatically each week, ensuring near real-time decision support.
