Should I show recently viewed items in the cart?

Question

As an e-commerce business owner, I'm exploring ways to improve my cart page's conversion rate and provide a better user experience. I've heard about showing recently viewed items in the cart, but I'm unsure about the potential benefits and best practices. Should I implement this feature? Will it distract customers from completing their purchase, or could it actually help increase average order value and reduce cart abandonment?

Muhammed Tüfekyapan · Accepted Answer

How Do I Personalize Email Marketing for E-Commerce?
Email personalization goes far beyond using someone's first name. True e-commerce personalization uses behavioral and purchase data to make each email feel like it was written specifically for that person - recommending products they're likely to buy, addressing their specific stage in the customer journey, and sending messages that are relevant to their demonstrated interests.

Personalization Levels and Their Impact

Personalization LevelElements UsedRevenue Lift
  
    Level 0 (None)Generic broadcast to allBaseline
    Level 1 (Basic)First name only+15-20%
    Level 2 (Behavioral)First name + product recommendation from browsing+40-60%
    Level 3 (Predictive)Purchase history + LTV prediction + next best product+60-100%
    Level 4 (Full 1:1)Dynamic content blocks per individual+80-150%

Practical Personalization Implementation

Product Recommendations from Browse Data
    When a subscriber views a product but doesn't buy, trigger a browse abandonment email featuring that product 24 hours later. "Still thinking about [Product Name]?" with a photo and direct link. Klaviyo and Omnisend both support this natively.

Category-Based Content Blocks
    In newsletter campaigns, show different product blocks based on past purchase category. Skincare buyers see skincare content; makeup buyers see makeup. Same email template, different content blocks - requires dynamic content support in your ESP.

Replenishment Timing
    Calculate average product use time (30ml moisturizer at 2 pumps/day = ~90 day supply). Send replenishment email at day 75-80. "Your [Product] should be running low" with reorder link. Converts at 15-30% - highest rate of any triggered email.

LTV-Based Upgrade Paths
    Use Klaviyo's predictive LTV to identify high-LTV customers and send them premium product recommendations, VIP programs, and exclusive offers. Low-predicted-LTV customers get retention-focused messaging.

Personalization Data Sources in Shopify

Data SourceSignals AvailablePersonalization Use
  
    Order historyProducts bought, frequency, AOV, categoriesNext product recommendations, replenishment
    Browse activityProducts viewed, categories visited, time on pageBrowse abandonment, relevant newsletter blocks
    Cart dataItems added, abandonment patternsCart abandonment emails with specific products
    Email engagementOpen/click patterns, content preferencesSend time optimization, frequency adjustment

On-Site Personalization Parallel
  Growth Suite's Advanced Behavioral Targeting applies the same personalization logic on-site in real time - adapting exit-intent offers based on what specific products the visitor viewed, their cart contents, and their engagement depth. The behavioral data collected on-site can be passed to your email platform via integrations, creating a unified personalization layer where on-site behavior informs email content and vice versa.

Should I show recently viewed items in the cart?

TL;DR - Quick Answer

Complete Expert Analysis

How Do I Personalize Email Marketing for E-Commerce?

Personalization Levels and Their Impact

Practical Personalization Implementation

Personalization Data Sources in Shopify

On-Site Personalization Parallel

Turn This Knowledge Into Real Revenue Growth

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Muhammed Tüfekyapan

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Use Cases

Personalization Level	Elements Used	Revenue Lift
Level 0 (None)	Generic broadcast to all	Baseline
Level 1 (Basic)	First name only	+15-20%
Level 2 (Behavioral)	First name + product recommendation from browsing	+40-60%
Level 3 (Predictive)	Purchase history + LTV prediction + next best product	+60-100%
Level 4 (Full 1:1)	Dynamic content blocks per individual	+80-150%

Data Source	Signals Available	Personalization Use
Order history	Products bought, frequency, AOV, categories	Next product recommendations, replenishment
Browse activity	Products viewed, categories visited, time on page	Browse abandonment, relevant newsletter blocks
Cart data	Items added, abandonment patterns	Cart abandonment emails with specific products
Email engagement	Open/click patterns, content preferences	Send time optimization, frequency adjustment