Case Studies
Predictive Customer Analytics Platform
Key Results
- 10× faster data processing
- 16% -> 5% data inconsistency
- 0%+ higher customer engagement
About the project
A global construction industry leader engaged PerformaCode to develop a customer analytics platform capable of combining web behavior, purchase history, and CRM information into actionable insights for sales and marketing teams.
The platform analyzed customer interactions across thousands of product pages and product categories, correlating browsing behavior with historical purchasing patterns. The objective was to better understand customer interests, identify buying intent, and support more personalized product and service recommendations.
PerformaCode was responsible for designing and developing the data science solution, including data preparation, predictive modeling, customer scoring, recommendation capabilities, and integration with existing business systems. The engagement also covered automation of data processing workflows and support for large-scale analytics operations.
As the project evolved, the platform expanded beyond recommendation scenarios to include customer churn prediction, behavioral analysis, and forecasting models used to support commercial decision-making across the organization.
4
engineers
9
months
T&M
delivery model
Client challenges
The client had accumulated historical customer data, but the data was not ready for recommendation, churn prediction, or scoring workloads. Signals were split across web analytics, CRM, ERP, product catalog, and cloud environments. Customer and product records had to be correlated across systems before they could be used for model training or evaluation.
Data inconsistency was high enough to affect model quality. The team had to profile, clean, enrich, and validate 55,000 customer records, 5,000 product positions, and 7,000 web pages before training and evaluation. Slow processing also limited repeatability: a pipeline taking about one hour was too heavy for frequent model iteration and daily business use.
The models had to map to sales and marketing rules, not only ML metrics. Business units needed to validate recommendation quality, churn signals, and customer scoring assumptions through business metrics and A/B testing. After the R&D stage, the solution also had to move into automated data processing, CI/CD workflows, and SAP and Salesforce integration.
Tasks performed
- Tasks performed
- Clarified customer needs for recommendation, churn prediction, customer scoring, and sales-support scenarios.
- Investigated commercial goals with business units to define useful model outputs and validation criteria.
- Analyzed historical data sources across web analytics, CRM, ERP, product catalog, SAP, Salesforce, Azure, Google BigQuery, and AWS.
- Prepared data sets covering 55,000 customer records, 5,000 product positions, and 7,000 web pages.
- Profiled and cleaned source data to detect inconsistencies, missing values, outliers, and unusable records.
- Enriched customer and product data for recommendation, churn prediction, scoring, and market basket analysis.
- Applied collaborative filtering to support recommendation scenarios based on browsing and purchase patterns.
- Developed recommendation models for customized product and service offers.
- Refactored churn prediction logic to improve an existing approach.
- Implemented customer scoring models for sales and marketing use cases.
- Defined technical and business metrics for model evaluation and business-unit validation.
- Supported A/B testing for recommendation quality and customer engagement scenarios.
- Evaluated model performance and iterated on model accuracy and reliability.
- Migrated data processing from Azure and Google BigQuery to AWS Redshift.
- Implemented data consistency checks to reduce unreliable records affecting model outputs.
- Integrated the platform with SAP and Salesforce to connect account profiles, purchase history, and sales workflows.
- Automated data gathering, ETL, storage, and daily processing workflows for repeatable platform operation.
- Automated ML learning workflows for model training and inference pipelines.
- Implemented CI/CD pipelines for application code, infrastructure code, and analytics workflows.
- Configured cloud infrastructure and containerized runtime components for deployment and operation.
- Deployed monitoring components for pipeline and platform execution.
- Automated release pipelines for the analytics solution.
Project results
10× faster processing
Data processing time was reduced from approximately 60 minutes to 5 minutes by migrating workloads from Azure and Google BigQuery to AWS Redshift and optimizing processing pipelines.
16% -> 5% inconsistency
Data inconsistency was reduced from 16% to 5% through profiling, cleansing, enrichment, outlier detection, and automated consistency checks across customer, product, and web-behavior datasets.
0.83 recommender accuracy
The recommendation model achieved 0.83 accuracy after preparing historical customer data, enriching customer and product signals, applying collaborative filtering, and refining model performance through multiple training and evaluation cycles.
65K-user reach
The completed platform supported recommendation, churn prediction, customer scoring, and market basket analysis across a customer base with a potential reach of approximately 65,000 users.
3-month MVP delivery
The recommendation system MVP was developed from scratch in three months, covering data preparation, model development, inference, performance validation, and deployment.
10%+ engagement increase
Customer engagement in sales activities and electronic communications increased by more than 10% through personalized recommendations, customer scoring, and behavior-based targeting.
Fully automated CI/CD
Application code, infrastructure code, and machine learning workflows were moved into automated cloud CI/CD pipelines, improving repeatability and reducing operational effort.
Notification system launched
A notification layer was added on top of the analytics workflow so sales and marketing teams could receive model-driven customer signals instead of manually checking processing outputs.
Unified customer data hub
Customer activity, purchase history, CRM information, and product data were connected across SAP, Salesforce, and analytics systems, enabling recommendation, churn prediction, and scoring models to operate on a consistent data set.
Value we bring
Data conditioning before modeling
Bad data becomes system behavior. Recommendation, churn, and scoring models inherit the data problems upstream. Customer records, product positions, web events, and purchase history have to be profiled, cleaned, enriched, checked for outliers, and tested for consistency before model output is worth evaluating. PerformaCode does this work early instead of trying to tune around broken inputs later.
Historical data reconstruction
Most companies sit on enormous sets of unstructured data. Large historical data sets usually contain both useful signals and potential damage at the same time. In this project, 55,000 customer records, 5,000 product positions, and 7,000 web pages had to be prepared before recommendation, churn, and scoring workloads could use them. PerformaCode works through old records, missing fields, noisy events, product mappings, and source-system assumptions before building new logic on top.
Cross-system model integration
Every client has its own park of business apps, platforms and systems. Predictive models fail in the gaps between systems. Web analytics, CRM, ERP, product catalogues, cloud storage, SAP, Salesforce, ETL jobs, CI/CD, and release workflows all have to preserve the fields the model depends on. PerformaCode handles that path end-to-end: data movement, consistency checks, processing automation, and delivery of model outputs back into existing systems.
Technologies
- Python
- Pandas
- Scikit-learn
- CatBoost
- AWS
- Azure
- Google BigQuery
- AWS Redshift
- SAP
- Salesforce
- Kubernetes
- Kubeflow
- TensorFlow
- CI/CD
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