Increase ROI and profit by building internal applications and reports which bring open-source data science and analytics to e-commerce data.


Modern e-commerce analytics extends far beyond standard KPIs and general data consolidation. It encompasses sophisticated statistical methodologies, such as churn prediction, Customer Lifetime Value (CLTV) estimation, and Market Basket Analysis, all of which have the potential to substantially increase ROI and profit. However, the real challenge lies in whether marketing or growth teams have a robust data infrastructure and the necessary expertise to develop internal data products that can generate and disseminate these high-value insights.

All too often, these teams find themselves in a predicament, having to choose between archaic Business Intelligence platforms and specific point solutions. Both options are typically rigid, lacking customization, and involve time-consuming, costly integration processes.

Result: instead of producing statistically-grounded reports and insights that truly add value, these teams are left waiting for IT departments to incorporate data into outdated software systems.

Python and the wider open-source ecosystem present the most powerful and versatile means of data analysis, which can extract value from transactional, CRM, and web data. However, it remains out of reach for most end-users and nearly impossible for teams to utilize operationally.

NStack's Platform bridges this gap. It offers everything modern e-commerce companies need to construct and deliver internal marketing analytics products. Here's how it can enhance your operations:

  1. Seamlessly integrate with e-commerce platforms and internal data sources such as Shopify, Segment, or your own files and databases.
  2. Choose from a vast library of pre-built e-commerce analytics modules, all customizable in Python. Alternatively, you can create your own modules from scratch, employing your preferred libraries and tools.
  3. Programmatically craft ad-hoc or automated reports that can be directly shared with non-technical end-users.
  4. Develop interactive web applications that offer self-service capabilities with dynamic inputs, forms, and file uploading features.
  5. Automate report distribution and surface insights through various channels like email, Slack, and Teams, ensuring timely information delivery.

Result: marketing and growth teams are empowered to build and operationalize advanced people science data applications which can be consumed by the entire company and make an outsized impact.

Example Reports and Metrics


  • What are the key drivers of churn for each customer cohort?
  • Which customers are most likely to churn and should be sent a voucher or sales outreach?

Market Basket Analysis

  • Which products are most commonly purchased together?
  • What bundling and cross-selling strategies should we employ to increase AOV for each cohort?
  • How can we build a custom data-driven way to benchmark employees using other internal datasets?

Subscription Analytics

  • How do we convert single purchasers to subscribers?
  • Which users are predicted to churn from their subscription?

Sentiment Analysis

  • What are customers saying about my products or brand on social media and review sites?
  • How do customer sentiments correlate with sales or returns?
  • How can I address negative feedback or capitalize on positive feedback from customers?

Shipping and Logistics Optimization

  • How can I optimize shipping routes and delivery schedules to minimize costs and maximize customer satisfaction?
  • How can I predict and manage potential delays in delivery?
  • What is the impact of delivery performance on customer satisfaction and repeat purchases?

Data and Integration

Unrestricted by proprietary connectors, NStack's data layer can connect to any internal database, data warehouse, file, or directly into systems of record such as:

  • Amazon
  • Shopify
  • Recharge
  • Snowflake
  • Amazon Redshift
  • Google BigQuery
  • Postscript
  • Google Analytics

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Featured modules

CLTV Prediction

Predict CLTV of users based on transactional history.

Customer Segmentation

Divides customers into different groups based on shared characteristics, enabling targeted marketing campaigns.

Subscription Churn Prediction

Predict which users are most likely to churn from a subscription, and what are the key drivers of churn.