eCommerce

How to do Shopify Cohort Analysis to Improve Customer Retention

Sumeet Bose
Content Marketing Manager
December 26, 2025
15
min read
Understand how Shopify cohort analysis helps track retention, LTV, and CAC payback—and why it’s critical for scaling profitably.
TL;DR
  • Shopify cohort analysis shows how customer groups behave over time, revealing retention, repeat purchases, and LTV.
  • Rising CAC makes cohort-driven retention essential for profitable DTC growth across Shopify brands.
  • Acquisition, product, behavior, and channel cohorts expose true unit economics behind customer profitability.
  • Native Shopify cohort reporting lacks channel attribution, product paths, and long-term LTV projections.
  • Unified Shopify, ads, and CRM data is required to measure CAC payback and channel-level LTV.
  • Strong cohorts reveal which campaigns create high-value customers and which attract one-purchase buyers.
  • Product cohorts help identify first-purchase items that accelerate LTV and repeat demand.
  • Monthly, N-day, and lifecycle retention curves uncover when customers lose momentum or churn.
  • Advanced cohort models guide lifecycle marketing, replenishment timing, and retention-focused acquisition strategies.
  • Saras Pulse automates cross-channel cohorts, LTV forecasting, and unified customer behavior analysis for scaling brands.

Most Shopify brands track sales, not how customers behave over time, and that’s where growth quietly stalls. Rising acquisition costs, unreliable attribution, and shrinking margins make Shopify cohort analysis essential for anyone serious about retention and LTV. Cohorts expose patterns that single-number dashboards hide: who comes back, when they return, and which acquisition paths create truly profitable customers.

As brands improve data maturity, they naturally move from retention guesswork to cohort-driven growth with unified data; the shift that separates stable, scalable Shopify operators from teams constantly fighting churn. According to Harvard Business Review, even a 5% lift in retention can increase profits by 25–95%, which makes cohort analysis one of the highest-leverage growth levers for Shopify brands.

This guide breaks down how cohort analysis actually works in Shopify, where native reports fall short, how to build a reliable cohort model, and why advanced tools like Pulse unlock the real retention and LTV insights operators need to scale profitably.

What Is Cohort Analysis in Shopify?

Cohort analysis, in the simplest terms, groups Shopify customers based on a shared characteristic and then analyzes how their behavior evolves over time. Instead of looking at aggregate revenue (which often hides everything important), cohort analysis exposes the patterns that actually drive profitability.

In the Shopify context, this means tracking customers by:

1. Acquisition Cohorts (Most Common Type)

Here, customers are grouped by the week or month they first purchased. For example: customers acquired in “January Week 2” often behave very differently from customers acquired during “Black Friday Week.” One might have higher retention and LTV; the other might be discount-chasing deadweight. Shopify cohort analysis makes this visible.

2. Behavior Cohorts

These customers are grouped by actions rather than dates.

Examples:

  • Customers who purchased twice within 30 days
  • Customers who subscribed on their first purchase
  • Customers who returned an item

Also, these cohorts help operators understand early behaviors that predict long-term value.

3. Product Cohorts

You group these cutomers by the first product they purchased. This is one of the most powerful angles because the first SKU often determines:

  • repurchase probability,
  • future category expansion,
  • average order value trajectory,
  • churn risk.

Operators who understand product-led cohorts stop guessing which SKUs are “hero products”, as they see it in the retention curves.

4. Channel Cohorts

Grouped by acquisition source (Meta, Google, TikTok, Affiliates, Organic, etc.), this is where profitability analysis becomes real.  

Last-click ROAS is meaningless without cohort analysis Shopify views. A channel may look expensive on day 1 but produce the highest LTV customers by day 90.

Shopify’s native cohort tool does none of this properly, which is why most scaling brands hit a wall: they know how to acquire customers but not how to acquire valuable customers.

Why Cohort Analysis Is Essential for Scaling a Shopify Brand

The higher your CAC goes (and it’s going up for everyone), the more you need to understand how quickly customers return, how much they spend over time, and which segments justify additional investment. A widely used benchmark for healthy unit economics is an LTV:CAC ratio of 3:1; meaning a customer should generate at least three times the cost required to acquire them, which is only visible when cohorts are tracked correctly.

Here’s why Shopify cohort analysis becomes non-negotiable as soon as a brand scales:

Retention > Acquisition

If your business model depends on customers buying again, you must know:

  • how soon they return,
  • how steeply cohorts decay,
  • where drop-offs occur,
  • which cohorts stabilize revenue.

Without this, retention work is just guesswork dressed up as “CRM strategy.”

CAC Pressures Demand LTV Intelligence

You can’t judge channel performance on day-1 ROAS anymore.

You need to know:

  • which channel brings customers who return within 60 days,
  • which channel never pays back,
  • and which channel’s customers expand into other categories.

That’s the whole point of customer cohort analysis Shopify; separating channels that grow LTV from channels that grow only ad spend.

Profitability Requires Longitudinal Insight

Two SKUs can generate the same revenue on day one but behave radically differently:

  • SKU A can have a high second-purchase rate
  • SKU B can have a high return rate

Without Shopify customer cohort analysis, brands don’t know which products are secretly draining margin.

A Quick Reality Check

Most Shopify brands don’t have a retention problem; rather, they have a visibility problem. They’re tracking top-line sales but not the underlying revenue engines (cohorts) that sustain growth.

This is exactly where advanced tools like Saras Pulse later come into play by giving teams unified, automated, cross-channel cohort visibility that Shopify cannot produce on its own. But we’ll come to that in the later sections.

Key Metrics Used in Shopify Cohort Analysis

A cohort table is only as useful as the metrics you track across it. Most Shopify merchants look at topline revenue and think they’re doing analysis. They’re not. The power of Shopify cohort analysis comes from tracking how value compounds (or dies) within each cohort over time.

Below are the metrics that matter for evaluating retention, customer behavior, and profitability.

1. Repeat Purchase Rate

This is the first signal of cohort health. If your month-1 to month-2 repeat rate collapses, it means:

  • your acquisition channel is attracting low-intent buyers, or
  • your product experience isn’t strong enough to justify a second purchase.

Repeat rate is the metric that exposes whether you’ve built a brand or just run a paid ads funnel.

2. Time Between Purchases

Not all cohorts buy again at the same pace. Tracking the gap between purchase 1 to purchase 2 to purchase 3 helps you:

  • time replenishment flows,
  • tighten win-back sequences,
  • build rational forecasting windows,
  • understand how “fast” or “slow” your business actually moves.

For categories like supplements, beauty, and CPG, this metric often determines retention strategy more than raw repeat rate.

3. AOV Growth

Healthy cohorts show AOV expansion over time.

When AOV stagnates or declines, it signals:

  • discount dependency,
  • poor upsell paths,
  • weak post-purchase experience, or
  • product sequencing issues.

Tracking AOV by cohort clarifies which products create long-term value and which ones cap lifetime revenue.

4. CLTV by Cohort

This is the backbone of customer cohort analysis Shopify because CLTV determines:

  • how aggressively you can scale paid acquisition,
  • when CAC pays back,
  • how predictable revenue becomes,
  • and which segments justify retention investment.

Shopify’s native reports do not provide accurate LTV curves. They show revenue, not profitability. Serious scaling requires tracking CLTV longitudinally by acquisition date, channel, product, and behavior.

Related Read: Shopify LTV

5. N-Day Retention (30/60/90/180)

Retention cannot be evaluated “in general.”

You need fixed windows, such as:

  • 30-day retention shows early habit formation,
  • 60-day retention shows whether the cohort develops loyalty,
  • 90/180-day retention reveals long-term sustainability.

Most failing brands lose cohorts before day 45 and don’t even know it.

6. CAC Payback Period

No investor or founder cares about ROAS anymore. Instead, they care about payback.

CAC payback answers the real question:

“How long does it take for this cohort to return the money we spent acquiring them?”

If you don’t know this, you are scaling the wrong way.

This metric cannot be calculated inside Shopify, which is why most brands massively overspend on channels that have long payback windows or never pay back at all.

7. Channel-Wise Cohort Performance

Every acquisition source produces different types of customers:

  • Meta often delivers impulsive first purchases.
  • Google brings higher-intent buyers.
  • TikTok creates volatility like huge spikes or unpredictable retention.
  • Affiliates sometimes drive the highest LTV but lowest scale.

Without channel-specific cohort tracking, marketers chase ROAS instead of profit.

8. First-Product LTV Impact

Your hero product may not be your best first product. Some SKUs have:

  • low initial margin but high LTV,
  • high initial AOV but poor repurchase behavior,
  • strong subscription pull,
  • and terrible retention despite high volume.

Product-led cohorts uncover which entry points create the most valuable customers.

How Saras Pulse Helps Track These Metrics

Most Shopify stores cannot calculate these metrics accurately because data lives across Shopify, Meta + Google Ads, Klaviyo, subscriptions, third-party apps, and even spreadsheets.

Saras Pulse unifies these signals automatically and builds cohort tables with:

  • CLTV curves,
  • CAC payback windows,
  • product-path analysis,
  • channel-level cohort comparisons,
  • N-day retention tracking,
  • repeat interval visualizations.

This removes all manual work and prevents the errors that happen when teams stitch data together in Excel.

Why Shopify Brands Need Cohort Analysis

Fast-growth Shopify brands don’t scale because they hack CAC. They scale because they understand retention. Shopify cohort analysis is the operating system behind every brand that graduates from “paid ads treadmill” to predictable revenue.

Here’s what cohort analysis enables that traditional reporting never will:

1. Understand Customer Retention Patterns Over Time

Looking at total returning customers won’t make sense until you know which cohorts return, when they return, and why they stop returning. This is what reveals churn patterns and retention inflection points.

2. Identify High-LTV Acquisition Channels

A channel with 1.5 ROAS may beat a channel with 3.0 ROAS when viewed through cohort analysis Shopify. Operators only learn this after seeing:

  • which channels produce customers who buy again within 30–60 days,
  • which ones scale LTV over months,
  • which ones never pay back.

3. Track Repeat Purchase Behavior by Cohort

This helps teams understand three key elments: ideal subscription triggers, replenishment rhythms, and product sequencing opportunities. Repeat behavior is the heartbeat of DTC retention.

4. Measure Marketing ROI Across Customer Lifecycles

Cohorts also reveal which campaigns produce long-lasting customers, and not jsut not one-time spikes. This clarifies budget allocation, retargeting logic, remarketing investment, and also helps with customer journey improvement.

5. Evaluate the Impact of Campaigns and Product Changes

Price changes, new product launches, bundling experiments, and promotions show their true ROI only through cohort curves.

6. Build Stronger Retention & CRM Strategies

Cohorts even highlight who needs win-back flows, who needs replenishment nudges, and who is on track to churn. CRM becomes data-driven instead of “let’s send more emails.”

How To Do Cohort Analysis in Shopify

Most Shopify merchants know what a cohort table looks like. But very few actually use cohort analysis to drive retention, campaign performance, or LTV strategy. That’s because Shopify’s native reporting gives you a surface-level view; not the real behavioral and financial patterns inside each cohort.

These are the steps on how to perform Shopify cohort analysis using the best of what Shopify provides:

Step Description
1. Access Shopify’s Cohort Report Go to Analytics → Reports → Customer Cohort Analysis to view cohorts grouped by first purchase month and retention over time.
2. Define Your Cohorts Set up cohorts based on first purchase month or apply basic filters like location or product category where available.
3. Configure the Analysis Window Choose analysis periods (e.g., last 12 months) and metrics like repeat purchase rate, sales, or AOV.
4. Analyze Retention Over Time Review how each cohort performs month by month and identify drop-offs in purchase activity or AOV.
5. Compare Cohorts Against Each Other Evaluate which cohorts retain better and how their order rates or revenue compare over time.
6. Take Action Based on Insights Use observed patterns to adjust marketing, retention flows, and product bundling strategies.

Limitations of Inbuilt Shopify Cohort Analysis

Shopify’s cohort reporting is fine for a quick glance. It is not designed for operators scaling retention, predicting LTV, or optimizing CAC. Here are the biggest blockers.

Limitation What It Means
1. No Marketing Channel Attribution Shopify cannot reveal which acquisition channels, campaigns, or ad sets produce high-LTV cohorts, fast repeat purchasers, or one-time buyers. The platform doesn’t store or blend attribution data deeply enough to answer these questions.
2. No Product-Level Cohort Comparison There is no visibility into which first product drives higher LTV, which SKUs foster long-term loyalty, which bundles boost repeat purchases, or which low-margin SKUs attract poor-quality cohorts. This blocks product-path and merchandising optimization.
3. No Revenue Attribution to Campaigns Shopify shows revenue across cohort periods but cannot tie that revenue back to marketing spend, campaign types, keywords, or audience segments, making lifecycle ROI nearly impossible to measure accurately.
4. No LTV Projections Shopify provides no ability to model long-term value, forecast payback periods, estimate contribution margin over time, or predict retention curves. Teams are forced to rely on guesses instead of data-backed projections.
5. No Ability to Merge Shopify + Ads + CRM Data Shopify is not built as a data warehouse, so it cannot unify signals from Meta Ads, Google Ads, Klaviyo, Recharge, Gorgias, or affiliate platforms. This fragmentation makes cross-channel cohort analysis unachievable.

Without unification, cohort analysis remains fragmented and unreliable.

These limitations are exactly why most brands abandon cohort analysis after the first attempt.

How Saras Pulse Solves These Limitations

Shopify shows you “what happened.” Saras Pulse shows you why it happened, how valuable each cohort is, and what to do next. Pulse doesn’t just rebuild Shopify’s cohort report — it redefines it into an actionable decision system.

Here’s how.

1. Automated, Unified Cohorts Across Shopify + Ads + CRM

Pulse merges Shopify, Meta, Google, Klaviyo, subscriptions, and CRM signals automatically.

This lets you analyze cohorts by:

  • acquisition channel,
  • campaign,
  • creative,
  • keyword,
  • email touchpoint,
  • subscription behavior.

Exactly the granularity marketers never get in native Shopify reporting.

2. Product-Path & First-Product Cohort Analysis

Pulse shows:

  • which first product creates the strongest LTV curves,
  • which bundles lead to faster second purchases,
  • which items attract discount-dependent shoppers,
  • which SKUs bring “one-and-done” buyers.

This drives more intelligent acquisition and merchandising decisions.

3. Accurate CLTV Curves and Payback Models

Instead of static revenue, Pulse builds:

  • LTV curves over time,
  • CAC recovery timelines,
  • contribution margin per cohort,
  • profitability windows.

This is the data finance and growth teams actually need to scale safely.

4. Cohort Performance by Channel, Ad Set & Campaign

Pulse breaks down LTV and retention by the actual marketing unit that drove the acquisition; not with last-click hacks or Shopify’s generic source/medium guesswork. This allows marketers to scale high-LTV pathways and not just high-ROAS channels.

5. Automated Reporting & Real-Time Cohort Refresh

Pulse updates cohorts automatically as new data flows in, which means no more CSV stitching, no more manual dashboards, and no more misaligned attribution windows.

You get:

  • daily cohort updates,
  • product-level cohort charts,
  • retention visualizations,
  • second-purchase interval analytics.

Step-by-Step Framework for Building a Cohort Analysis Model

A real cohort model is structured, consistent, and tied to business decisions. Here’s the operator-grade framework used by brands that take retention seriously.

Step 1: Define the Cohort Type

Cohorts only work when you’re clear about the question you're trying to answer.

  • If you want to know “Which month brings our highest-LTV buyers?”, use acquisition cohorts.
  • If you want to understand product-path behavior, build product cohorts.
  • If you want to evaluate paid performance, use channel or campaign cohorts.

Many brands skip this step and end up with a dashboard that looks pretty but answers nothing.

Step 2: Extract and Unify Data

Shopify alone cannot power advanced cohorts because it sees only Shopify activity. Actual cohort accuracy requires merging:

  • Shopify order data
  • Meta + Google campaign sources
  • Subscription + CRM events
  • Refunds, returns, and shipping timelines
  • Email and SMS engagement

This is exactly where Saras Daton becomes critical. Daton automates all data replication from Shopify, ads, email, subscriptions, and CRM tools into one clean warehouse.

Step 3: Choose Cohort Tracking Windows

Shopify defaults to monthly windows, but that’s insufficient for DTC behavior. Category dynamics dictate the structure:

  • Fast-moving consumables: weekly or 30-day windows
  • Apparel: monthly windows with 60/90-day LTV checkpoints
  • Subscription brands: billing-cycle cohorts
  • High-AOV brands: 180-day retention curves

Cohorts must match purchase cadence. Otherwise, patterns get distorted, and teams chase ghosts.

Step 4: Analyze Retention, Revenue, and LTV Trends

Once cohorts are defined and structured, the real analysis begins.

It helps you uncover truths like:

  • Certain first products consistently produce higher LTV (your real hero SKUs)
  • Meta prospects might produce faster second orders than Google, even if CAC is higher
  • Customers acquired during promos often have weak post-purchase retention
  • Some cohorts collapse after order #1, thus signaling lifecycle flow failures

Saras Pulse does this automatically by visualizing cohort curves, LTV progression, product-path journeys, and drop-off windows.

Step 5: Take Action on Cohort Findings

Cohort analysis without action is just reporting. Experienced operators use cohort insights to reshape strategy:

  • If a cohort has fast 1→2 reorder timelines, adjust replenishment flows.
  • If a product-path cohort has superior LTV, increase spend for that SKU.
  • If a channel produces low-LTV cohorts, reduce budget even if ROAS looks good.
  • If customers drop off after order #1, fix post-purchase education or fulfillment.

Pulse supports this step with automated insights, product-level comparisons, campaign-level LTV tracking, and risk indicators that highlight cohorts needing intervention.

Cohort Analysis Use Cases for Shopify Stores

Persona How They Use Cohorts
Growth Marketers Cohorts expose what ROAS hides: which campaigns generate profitable customers over time. Marketers can scale channels with strong payback windows, cut sources driving low-LTV buyers, and judge Meta vs. Google based on lifetime revenue instead of short-term dashboards.
Retention Teams Cohort curves pinpoint where customers lose momentum due to delayed second orders, poor replenishment timing, subscription skips, or weak onboarding. Retention teams can rebuild lifecycle journeys based on observed behavior rather than assumptions.
Founders & Operators Cohorts clarify contribution margin, inventory timing, and budget allocation. Operators can identify the segments that actually sustain the business and the ones eroding margin. Forecasts stabilize because they reflect real cohort performance, not optimistic projections.
Agencies Cohorts let agencies report performance clients genuinely trust. Instead of vanity ROAS metrics, they can demonstrate which campaigns produce long-term value, shape retention-focused strategies, and justify decisions using LTV curves rather than weekly fluctuations.

Common Mistakes Brands Make in Cohort Analysis

  • Focusing on revenue instead of LTV, which hides weak long-term cohort performance.
  • Ignoring acquisition source, making retention analysis useless without channel attribution.
  • Relying only on Shopify’s native view, which lacks spend, product-path, and subscription data.
  • Stopping cohort tracking after 30 days, even though real retention patterns emerge much later.
  • Skipping A/B tests tied to cohort outcomes, leaving growth decisions based on guesswork.

Get Actionable Insights from Shopify Cohort Analysis with Saras Analytics

The entire value of Shopify cohort analysis lies in visibility, accuracy, and actionability. Shopify provides a starting point, but scaling brands need deeper, unified, and automated cohort intelligence. Saras Pulse brings all of that into one place.

If you want your retention, acquisition, and revenue strategy grounded in how your customers actually behave over time, talk to our data team. We’ll help you turn cohorts into one of the strongest operating levers in your Shopify business.

Frequently Asked Questions (FAQs)

How is cohort analysis different from segmentation in Shopify?
+

Cohort analysis tracks how groups of customers behave over time, revealing retention and LTV patterns. Segmentation groups customers by shared traits in a single snapshot. Cohorts show lifecycle performance; segmentation shows static audience attributes.

How much does Shopify Analytics cost?
+

Shopify Analytics is included in all Shopify plans, but advanced reports like cohorts require the Shopify, Advanced Shopify, or Plus tiers. There’s no separate fee, but data depth is limited unless combined with external sources.

What is a good retention rate for Shopify stores?
+

Most eCommerce brands average 35–40% retention. Anything above 45% is strong, and 50%+ is elite. The ideal rate varies by category, purchase frequency, and subscription behavior.

How do I compare cohorts across different marketing channels?
+

You must merge Shopify data with ad-platform data to compare LTV, repeat rates, and payback windows by channel. Native Shopify reports can’t attribute cohorts to Meta, Google, or email campaigns, so cross-channel linkage is required.

Do I need third-party tools to run advanced cohort analysis?
+

Yes, if you want channel attribution, LTV projections, product-path analysis, or unified Shopify + ads + CRM data. Shopify’s native view is too limited for forecasting, payback modeling, or channel-level cohort comparisons.

+

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Learn how to calculate and improve your Amazon ROAS to boost ad profitability. Discover key strategies and tools for better returns on ad spend.

Amazon TACoS: What It Is & Strategies to Improve It (2025)

Learn what Amazon TACoS is, why it matters in 2025, and strategies optimize it to improve ad efficiency, boost profits, and scale sustainably.
April 16, 2025

Why a Single Source of Truth for E-Commerce is More Important Now Than Ever?

Learn why a Single Source of Truth is vital for e-commerce today—enabling AI accuracy, better insights, and smarter, unified decisions.
April 16, 2025

Unmasking the Hidden Ghosts: The Known and Unknown Costs of Shipping, Churn, and Waste

Use the Rumsfeld Matrix to uncover hidden inefficiencies in D2C operations across shipping, churn, inventory, and marketing.
April 16, 2025

The Culture of Intelligence: Beyond Data, Toward Smart Decision-Making

Discover how D2C brands can move from data chaos to clarity by creating a culture of intelligence through aligned people, processes, and technology.
April 16, 2025

Data Maturity: Why your data is still not an advantage to your D2C Brands?

Discover the 5-stage D2C Data Maturity Model and learn how brands can evolve from gut-driven decisions to data-led, cross-functional business growth.
April 11, 2025

Why Fivetran Pricing Can Get Expensive: Challenges with MAR Pricing for High-Volume Data

Struggling with Fivetran’s MAR pricing? This guide breaks down the hidden costs and shows how Daton helps eCommerce teams save on data integration.
April 10, 2025

TikTok Shop Analytics: Complete Guide for 2025

Discover why TikTok Shop analytics matter, common data challenges businesses face, and solutions to access accurate, actionable insights.
April 10, 2025

Complete Guide to TikTok Shop Seller Center 2025

Explore TikTok Shop Seller Center, its core features, benefits, and the common data challenges that seller face plus smart solution to overcome them.
April 9, 2025

Customer Profitability Analysis: Metrics, Steps + Strategies (2025)

Learn how to track, analyze, and improve customer retention using analytics. Discover key metrics, strategies, and tools to reduce churn and boost growth.
April 9, 2025

Customer Retention Analytics: A Comprehensive Guide (2025)

Explore what customer retention analytics is, why it matters, how to conduct it, key metrics to track, and best practices to reduce churn and boost loyalty.
April 7, 2025

Ecommerce Customer Value: How to Calculate & Improve It (2025)

Learn what eCommerce Customer Lifetime Value (CLV) is, why it matters, how to calculate it, and data-driven strategies to improve CLV and profitability.
February 12, 2025

How D2C Brands Can Build Resilience in ‘Trump-tariffs Era’ With Supply Chain Uncertainty?

Discover how D2C brands can navigate tariffs, supplier price hikes, and supply chain disruptions with real-time profitability insights and proactive strategies.
April 1, 2024

Data Dominance in eCommerce: A CEO's Blueprint for 1000x Triumph

Discover how CEOs can harness data-driven strategies to achieve 1000x growth in eCommerce. Learn actionable tips for scaling and driving innovation.
September 28, 2023

Best Data Analytics Company for eCommerce Brands and Agencies in Austin

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December 5, 2025

eCommerce Analytics 101 | What is eCommerce Analytics

eCommerce Analytics: Metrics, Best Practices, Case Studies, and Use Cases for Driving Sales and ROI
February 27, 2023

Top 75 Ecommerce KPIs to track in 2025 for Business Growth

Discover the top 75 eCommerce KPIs to track in 2025 across sales, marketing, finance, and operations. Optimise your business with data-driven insights.
September 28, 2022

Key Ecommerce Metrics Explained- RoAS vs CAC vs LTV

Learn the difference between RoAS, CAC, and LTV, and why metrics like LTV-based RoAS and the LTV:CAC ratio matter for long-term marketing success and profitability.
December 16, 2022

Amazon Business Reports 2025

Use Amazon Business reports to track sales, analyze data, and boost growth. Learn how to leverage these reports for smarter decisions in 2025.
December 12, 2022

The Ultimate Guide to Shopify Reports (2025)

Shopify Reports for eCommerce Analysis: Learn more about your store performance and get insights into your Shopify business' sales and order performance
July 29, 2022

Various Paid and Non-Paid Channels in Google Analytics

Google Analytics analyzes overall traffic flowing into your website from Paid and Non-Paid Channels in Google Analytics.
April 1, 2024

How Amazon Plans Its Customer Retention Strategy

How Amazon Plans Its Customer Retention Strategy, which they use to hold on to existing customers. Learn the strategy to grow your business.
July 29, 2022

How Some Sellers Are Getting More Out of Amazon Ads

Amazon ads are ideal for better conversions with higher intent audiences. Reduce your ad spend by utilizing data from the Amazon Ad platform using Daton.
May 22, 2023

Building a Scalable Data Warehouse and its Maintenance

Learn about this scalable, data warehouse and its maintenance. It covers everything that one should know to create a scalable data warehouse end to end
July 29, 2022

Ways to Improve Data Analyst Productivity

Ways to Improve Data Analyst Productivity, Leaders in growing businesses often rely on a data analyst to look into the business performance
August 21, 2022

Best Practices for Data Modeling

Comparison of traditional and modern best practices for Data Modeling with recommendations to drive key business decisions with the best practices
July 29, 2022

Data Scientist Or Data Analyst: Who Is The Best for Your Business?

In-house CRO teams play a significant role in building a company's development team. Who is best suited for your business: Data Scientist or Data Analyst?
July 29, 2022

Learn the Cross-selling Steps to Grow your Business

Learn the Cross-selling Steps to Grow your Business to Google BigQuery, Snowflake, AWS Redshift, ADW, Amazon S3, GCP MySQL, GCP Postgres, RDS Postgres, RDS
July 29, 2022

Learn The Art of Customer Retention Strategy with Google Analytics

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July 29, 2022

How Predictive Analytics can Enhance your Marketing

Learn How Predictive Analytics can Enhance Your Marketing and overall performance of the business.
July 29, 2022

Top 3 Essential Drivers for Cloud Data Warehouse Adoption

Consolidate silo'ed data into a cloud data warehouse adoption and leverage for growth. Get running with a data warehouse in minutes to unlock the secrets hidden
July 29, 2022

How Important Product Sequencing is to the World of Ecommerce

Have you ever wondered how to display all your products in a listing for faster conversions? Product Sequencing is an effective way.
July 29, 2022

How to use Inventory Data Effectively to Drive Business Growth?

Prevent an imbalance in sales and poor customer experience by learning how to use inventory data effectively for driving business growth.
July 29, 2022

5 Benefits of Automated Data Ingestion

Automated data ingestion into data warehouses using self-service ETL tools enhance analytics, allows data cleansing and increase employee productivity.
May 4, 2023

What is Right Latency for Data Analytics

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July 29, 2022

How to Pitch Your Management to Adopt Data Analytics & Business Intelligence?

How to Pitch Your Management to Adopt Data Analytics & Business Intelligence? A handy guide that will help you convincing management.
July 28, 2022

Top 5 Free ETL Tools for MySQL

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July 28, 2022

Pros and Cons of Amazon Redshift

Learn more about the pros and cons of using Amazon Redshift, a petabyte-scale data warehouse. Know the technical limitations & benefits of using Amazon Redshift
July 28, 2022

How Sales & Marketing Team Use Google Sheets for Data Analysis

Google Sheets for Data Analysis using functions like charts, pivot tables, what-if analysis, conditional formatting, Vlook Up, Hlook Up, sort
July 28, 2022

Top Snowflake ETL Tools in 2025: Features, Pricing & Pro Tips

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July 28, 2022

Data Analysis Using MS Excel

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July 28, 2022

Amazon RDS Pros and Cons – A Detailed Overview

Learn the Amazon RDS pros and cons. The article also focuses on a detailed overview and extensive features of Amazon's fully managed, open-source cloud database
July 27, 2022

How Reporting and Analytics Can Grow your Business

Reporting and Analytics play a major role in transforming business data into useful insights to achieve goals. Learn how to use it.
July 27, 2022

Amazon Seller Central vs Amazon Vendor Central

Learn what is Amazon Seller Central vs Amazon Vendor Central. Many brands aren't aware of the long-term implications of choosing one program over the other.
July 27, 2022

Amazon Vendor Central Guide [2025]: Benefits, Features & How to Join

Explore the 2025 Amazon Vendor Central Guide – Learn how it works, who can join, key features, benefits, reports, and how it compares to Seller Central.
July 27, 2022

Amazon Sponsored Products vs Amazon Sponsored Brands 2025

Discover the key differences between Amazon Sponsored Products vs Sponsored Brands to optimize your ad strategy and boost sales effectively on Amazon.
July 27, 2022

Complete Guide on Amazon Seller Central

Learn about Amazon Seller Central and all the features it offers to sellers to help run your online business smoothly in this comprehensive guide
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Amazon PPC Advertising Guide

This Amazon PPC Advertising Guide tells you everything you need to increase your profits on Amazon. Learn about Amazon Ads, benefits, KPIs, and more!

Amazon KPI Guide 2025

To assess and obtain marketplace success and compete in the Amazon ecosystem, you need to grasp and comprehend the top 24 Amazon KPIs.
July 27, 2022

Amazon Buy Box Guide

Amazon Buy Box Guide shares the important details such as benefits, criteria, and impact on sales by Amazon Buy Box and the top winning factors
July 27, 2022

Amazon Brand Registry Guide

Amazon Brand Registry Guide on getting started with Brand registry benefits, and eligibility requirements, for manufactures and seller to ace their eCommerce brand.
January 10, 2025

Amazon Brand Analytics: A 2025 Guide to Smarter Selling with Data

Discover how Amazon Brand Analytics helps sellers gain insights into customer behavior, optimize search terms, analyze competitors, and drive business growth
January 5, 2023

Amazon Attribution Guide 2025

Amazon Attribution- Learn in-depth about its uses & benefits, and how it tracks and measures the impact of your non-amazon traffic on your Amazon products.

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