As retail marketers dig into attribution and revenue reporting, they may find each of their analytics tools is competent on its own. But taken together, they’re still ineffective as a single source of truth.
This is often because of data silos and platform fragmentation. For example, your marketing automation platform may credit a sale to an email campaign, whereas your ad platform might claim the same order as revenue from a paid click. Then, if that same person makes a repeat purchase in store, that data might live only in your POS and nowhere else.
This kind of data fragmentation happens when your sources aren’t feeding into a shared customer profile. When fragmentation is your default as a retailer, you either spend too much time reconciling data for accurate attribution or you’re unable to trust your attribution models at all because they’re constantly double counting revenue.
In this guide, we teach you what retail marketing analytics is, how multi-touch attribution works, and how to use different kinds of analytics and reporting to track and even predict customer behavior.
What is retail marketing analytics?
Retail marketing analytics is the practice of collecting, unifying, and interpreting data across all active purchase channels to measure what drives revenue and decide what to next to increase it.
Retail marketing analytics typically spans a few categories of data:
- Product and merchandising performance: product sell-through, category performance, product affinity, basket composition, cross- and up-sell performance, markdown performance, and best-/worst-performing products
- Traffic and acquisition: percentage of visitors who come from different channels (organic search, paid ads, email, social, referral), customer acquisition cost (CAC)
- On-site behavior: pages and categories browsed, time on site, cart adds, cart abandonment, funnel drop-off points, wishlisted items
- Sales and revenue: conversion rate, average order value (AOV), revenue per visitor/recipient, sales by product/category, discount code performance
- Retention: customer lifetime value (LTV), repeat purchase rate, cohort retention, overall retention
- Marketing performance: return on ad spend (ROAS) and return on investment (ROI) by channel, email and text message engagement, attribution across channels
- Customer segmentation: revenue, growth, and engagement performance by demographic, lifecycle, behavioral, and predictive segment
- Omnichannel performance: online vs. in-store sales, cross-channel purchasing, store performance, and online-to-store conversion/vice versa
Accurate omnichannel reporting is only possible when all your active channels across online and in-store sales are part of the same system that houses your customer data platform (CDP). This is how retail marketers can understand how their channels are working together to influence revenue.
How omnichannel, multi-touch attribution works for retail marketers
Customer journeys aren’t linear, and they don’t happen on only one channel. Shoppers may be influenced by 5 online interactions before they ultimately decide to make an in-store purchase. They may also browse in store, scan a QR code to sign up for your newsletter, and make a purchase after 3 more online interactions.
Omnichannel, linear multi-touch attribution can track these types of customer journeys and, most importantly, capture truths about how customers buy and what happened before their purchase to influence their decision.
Depending on your business model, you may want to customize your attribution model to account for any truths you already know about your business. Here are some basic attribution models to consider:
Model | How it assigns credit | Useful for |
|---|---|---|
First-touch attribution | 100% to the first customer interaction | Measuring what drives awareness |
Last-touch attribution | 100% to the last customer interaction before a purchase | Measuring what closes sales |
Linear attribution | Assigns equal credit across all customer interactions | Measuring influence across the entire customer lifecycle |
Time-decay attribution | Assigns more credit to more recent customer interactions | Measuring customer journeys where recency drives revenue |
You may want to lean on one attribution model over the others or use a combination of all of them at different times. When designing your attribution model, you should also consider customizing which types of messages count as meaningful touchpoints. For example, you may want to exclude transactional emails (like order confirmations) or exclude bot clicks on an email.
Here’s how to set up multi-touch attribution in your marketing analytics platform:
- Choose a platform with built-in marketing analytics, like a B2C CRM with a built-in customer data platform (CDP). This is your data infrastructure that centralizes every customer interaction with your brand. When this data lives in one place, your attribution models are more accurate because they’re pulling from complete data.
- Install your on-site tracking script. This may include a custom snippet on the check-out page specifically, since that's where conversion data is ultimately tied back to a session.
- Use accurate UTM tags. Since attribution relies on UTM source/medium, campaigns with missing or inconsistent tags might be labeled as unknown.
- Update your attribution model settings. Choose which attribution model makes the most sense for your brand, and set your attribution window accordingly. You should also customize which types of messages count as meaningful touchpoints (you might, for example, exclude transactional messages or bot clicks).
Retail analytics tip: Use omnichannel, linear multi-touch attribution with funnel analysis to zero in on where and why people drop off before moving to the next stage of the customer lifecycle.
For example, dogwear brand Ruffwear uses funnel analysis to track how sign-ups from their partner giveaways convert over time. This empowers them to discontinue partnerships that drive sign-ups, but not revenue.
How RFM analysis improves retail discount strategies and ROI
“RFM” stands for recency, frequency, and monetary value. Use these 3 important metrics to analyze customer behavior and identify high-value customers.
Here’s a quick breakdown of what RFM analysis measures and how you can start to interpret the data:
Signal | What it measures | What it means |
|---|---|---|
Recency | How recently someone bought | If someone hasn’t bought in a long time, they may be at risk of churning completely. |
Frequency | How often someone buys | If someone who buys on a set schedule increases their frequency, they may want to join a loyalty program. |
Monetary value | How much someone spends when they buy | High-value customers are VIPs who should receive special offers, whereas customers who don’t spend as much should receive up-sell offers. |
As a cluster of retail analytics, RFM analysis helps retail brands identify high-value customers, reduce churn, and develop discount strategies that don’t waste spend.
For example, shoe retailer Marc Fisher uses RFM segments to send more targeted offers. They reserve discounts for customers at risk of churn while taking a more full-price, brand-first position with their most loyal customers.
Predictive analytics for retail: forecasting customer behavior
Predictive analytics uses historical purchase and behavior data to forecast future customer behavior. It includes metrics like predicted number of orders, expected date of next order, predicted LTV, and churn risk.
When you can predict this kind of customer behavior, you can get ahead of it by sending messages that are more likely to resonate closer to when they’re likely to happen.
Here are 3 ways you can use retail predictive analytics to boost revenue:
- Use churn risk to flag at-risk customers who may need certain types of offers to purchase again.
- Use expected date of next order with geography data to personalize offers for shoppers who live close to a retail location but have never bought in store.
- Use predicted LTV to estimate lifetime spend, so you can target high-value shoppers with special in-store or online incentives.
With predictive analytics, The Willow Tree Boutique started sending their campaigns to customers whose AI-predicted next purchase date was in the last or next 30 days. They also focused on promoting more expensive apparel to customers with a predicted LTV over $500 or an AOV over $150.
“After we started sending campaigns to segments created with predictive analytics, all our metrics improved, and our revenue improved drastically,” says Jade Richardson, email strategist for the boutique’s agency. “It taught us so much about the subscriber base—when they shop, how they shop, etc.”
In 90 days, 17.1% of the brand’s Klaviyo-attributed revenue came from predictive analytics segments. “It has been fundamental to nailing down best practices for the brand,” Richardson says. “It really opened up a whole new world to us.”
Drive more cross-channel retail revenue with Klaviyo Analytics
Klaviyo is the autonomous B2C CRM that brings together real-time customer data, analytics, marketing automation, customer service, and built-in agentic AI, so retailers can measure and act on their data from the same place.
With Klaviyo, you can:
- Build advanced reports for online and in-store sales. Use unlimited groupings, custom profile properties, and combined flow and campaign reporting without a business intelligence tool.
- Customize attribution. Measure attribution by joining two or more metrics or filtering for specific events across your POS and online store.
- Sync your data warehouse with Advanced KDP. Reformat, standardize, and clean your data for consistency and accuracy without coding and without waiting.
- Build segments directly from funnel analysis. Target customers at their exact conversion drop-off point in one click.
- Act on your data, faster. Access pre-built recommendations and flow templates to act on your real performance data without starting from scratch.
Start measuring and acting on your data from one place with Klaviyo. Sign up