Retail & E-commerce Data Analytics
Turn customer data into revenue
From RFM segmentation to multi-channel profitability analysis, we build the analytics that help retailers and e-commerce companies understand their customers, optimize pricing, and grow margins.
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Retail & E-commerce Analytics Services
Paid Media Performance Dashboard
Centralized campaign monitoring across Amazon, Target, Walmart, and other platforms. Track ROAS, ACOS, conversion rates, and spend by category and region - all in one place, replacing scattered platform reports.
Customer Segmentation and RFM Analysis
Group customers by recency, frequency, and monetary value. Identify your best customers, big spenders, at-risk buyers, and dormant accounts to target marketing spend where it counts and automate personalized campaigns.
Multi-Channel Profitability Analysis
Compare true margins across Amazon, wholesale, DTC, and marketplace channels. Understand profitability after fees, shipping, returns, and advertising costs per channel to reshape distribution strategy.
Online-to-Offline Strategy
Bridge online sales data with in-store operations. Identify trending products online and optimize physical store placement, inventory levels, and cross-selling bundles using data-driven recommendations. Read our deep-dive on online-to-offline retail analytics.
Read more →Multifactorial Sales Forecasting
Predict demand at the SKU level by combining historical sales, seasonality, weather, reviews, ratings, and external signals like housing starts. Reduce stockouts and overstock while improving cash flow.
Marketing Attribution and Campaign Analytics
Connect ad spend to actual revenue across paid search, social, email, and affiliate channels. Multi-touch attribution models reveal which campaigns drive real conversions and which waste budget.
Pricing and Promotion Optimization
Analyze price elasticity, competitor pricing, and promotion lift to find optimal price points. Automatically identify overstocked and obsolete products and trigger targeted campaigns to accelerate their sale.
Customer Lifetime Value Modeling
Predict how much each customer will spend over time. Use CLV models to set acquisition budgets, personalize retention offers, and prioritize high-value accounts for targeted outreach.
Product Recommendation Systems
Deploy collaborative and content-based filtering models that surface the right products to the right customers - increasing average order value and cross-sell rates both online and in physical retail.
Retail & E-commerce Case Studies
Explore real projects where we delivered measurable outcomes in this industry.
Showing 5 case studies
Thanksgiving & Black Friday Sales Analytics: Real-Time Campaign Monitoring
A real-time Tableau dashboard on Salesforce and SQL Server data helped a retailer monitor hourly Black Friday sales and plan next year's strategy.
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Affiliate Marketing Dashboards: Unified Performance Tracking
Learn how affiliate teams replaced spreadsheets with unified dashboards to track performance across networks, identify profit leaks, and optimize ROAS.
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Multi-Channel Retail Profitability: Amazon vs Wholesale Analytics
A US retailer used Tableau to compare Amazon and wholesale profitability, uncovering margin differences that reshaped their distribution and pricing strategy.
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Amazon Ads Reporting with Power BI for Food & Beverage
How a snack manufacturer replaced manual Amazon Ads tracking with automated Power BI dashboards to optimize spend, measure ROAS, and guide marketing decisions.
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Marketing Automation with RFM Segmentation for a Coffee Chain
How we helped a coffee shop chain connect POS data, build RFM segmentation, and automate SMS campaigns that reduced churn by 10% and grew revenue 12%.
Read case study →TESTIMONIALS
Witanalytica has been an excellent partner in managing and optimizing our Tableau environment. Their team’s technical expertise and proactive support have streamlined our reporting processes, improved dashboard performance, and provided valuable insights to our business. Their responsiveness and deep understanding of data analytics make them a trusted extension of our own team.
Mark Lack
Director of Data Analytics and AI, The Ubique Group
Witanalytica’s expertise in big data engineering and visualization complements our digital media audit and customer analytics services. Collaborating with them allows us to deliver end-to-end analytics solutions and services, without the risks and investments associated with building these capabilities in-house.
Silviu Toma
Senior Partner, Microanalytics
Your Goals, Our Expertise
We start from your strategic objectives and work our way back to the right mix of solutions and technologies, not the other way round.
Book a Consulting CallOur Approach to Retail Analytics
We identify the specific data-driven initiatives that will move your top and bottom line - from reducing cart abandonment to optimizing channel mix.
We connect POS, e-commerce platforms, CRM, marketing tools, and supply chain data into a single warehouse - giving you one source of truth for all decisions.
We deliver dashboards and models that answer the questions your teams actually ask - not generic reports, but operational tools tuned to your business.
We automate reporting pipelines, build alerting for anomalies, and train your teams to operate the analytics independently as your business grows.
We identify the specific data-driven initiatives that will move your top and bottom line - from reducing cart abandonment to optimizing channel mix.
When Do You Need Retail & E-commerce Analytics?
- You sell across multiple channels and cannot compare true profitability between them.
- Marketing spend is increasing but you cannot attribute revenue to specific campaigns or channels.
- Customer retention is declining and you lack visibility into churn patterns.
- Inventory planning is reactive - you are either overstocked or running out of key SKUs.
- Pricing decisions are based on competitor copying rather than data-driven elasticity analysis.
- Your team spends days building reports in spreadsheets instead of acting on insights.
Why Choose Witanalytica for Retail Analytics?
Retail and E-commerce Experience
We have delivered analytics for multi-channel retailers, marketplace sellers, DTC brands, and food & beverage companies across the US and Europe.
Full-Stack Data Capability
From data engineering and warehouse design to BI dashboards and machine learning models - we handle the entire analytics stack, not just the visualization layer.
Platform Agnostic
Shopify, Amazon Seller Central, WooCommerce, Magento, SAP, NetSuite - we integrate data from any platform into your analytics environment.
Data Science That Drives Revenue
Our recommendation systems, CLV models, and segmentation algorithms are designed to generate measurable business impact - not just interesting charts.
Proven Case Studies
From RFM-based marketing automation for a coffee chain to multi-channel profitability analysis for a US retailer - our work delivers documented results.
Flexible Engagement Models
Whether you need a one-time analytics build, ongoing retainer support, or a dedicated data team - we structure engagements around your needs and budget.
Our Retail & E-commerce Analytics Pricing Models
Transparent pricing built for long-term partnerships, not one-off transactions.
On-Demand Expertise
All tasks are tracked, and the corresponding invoice of the delivered services is billed monthly.
| Activity | Hourly Rate |
|---|---|
| Data Engineering & Database Administration | $110 |
| Business Intelligence Reporting | $90 |
| Data Science | $120 |
Reserved Capacity Agreement
- Pre-purchase a package of monthly working hours that guarantees reserved capacity and priority availability, regardless of our workload.
- Because this capacity is exclusively allocated to you, unused hours do not carry over to the following month.
| Hours Package | Price |
|---|---|
| Every 50 hours | $4,500 10% savings |
Alternatively, we also offer project-based pricing
For well-defined engagements, we scope the full project upfront and agree on a fixed fee, so you know exactly what to expect.
Retail & E-commerce Analytics FAQs
We work with multi-channel retailers, Amazon and marketplace sellers, DTC brands, food & beverage companies, and subscription-based e-commerce businesses. Our clients range from growth-stage companies to established enterprises.
Yes. We build integrations with Shopify, WooCommerce, Magento, Amazon Seller Central, Square, Lightspeed, and custom POS systems. We consolidate all sources into a central data warehouse for unified analytics.
RFM analysis segments customers by Recency, Frequency, and Monetary value. This lets you target high-value customers with retention campaigns, re-engage lapsed buyers, and stop wasting budget on unresponsive segments. Our clients typically see 2-3x improvement in campaign conversion rates.
Yes. We build collaborative filtering, content-based, and hybrid recommendation engines that integrate with your e-commerce platform to increase average order value and cross-sell rates.
We primarily work with Power BI, Tableau, and Domo. Tool selection depends on your existing infrastructure, team capabilities, and specific reporting needs.
We build demand forecasting models that account for seasonality, promotional calendars, and historical patterns. Real-time dashboards during peak periods let your team monitor performance and adjust strategies on the fly.
Transaction/order data (even basic CSV exports work initially)
Customer records from your CRM or e-commerce platform
Product catalog with categories and pricing
Marketing channel spend and campaign data
Inventory levels and supply chain data (if applicable)
We offer two engagement models with transparent pricing.
On-Demand Expertise
All work is tracked and billed monthly at hourly rates:
- Data Engineering & Database Administration - $110/hr
- Business Intelligence Reporting - $90/hr
- Data Science - $120/hr
Reserved Capacity Agreement
- Pre-purchase a 50-hour monthly package at $4,500 (10% savings)
- Guaranteed priority availability regardless of our workload
We also offer project-based pricing for well-defined engagements.
Contact us to discuss the best fit for your needs.
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RFM Segments: Using Customer Behavior to Define High-Value Groups
Not all customers have equal value. Learn how to use purchase frequency, recency, and monetary data to define RFM segments and prioritize your marketing spend.
Data-Driven Segmentation for Personalized Marketing Campaigns
RFM segments reveal who your customers are. Learn how to turn those segments into targeted campaigns with personalized messaging, offers, and timing.
RFM Analysis Explained: Segment Customers by Value and Behavior
RFM analysis groups customers by recency, frequency, and monetary value. Learn how to build segments, interpret scores, and apply them to marketing strategy.