---
title: "Merchandising and raising order value | Optimize All Academy"
description: "What merchandising means online Merchandising is how you present and arrange products so shoppers find what they want and discover more. In a physical…"
url: https://optimizeall.com/learn/ecommerce-marketing-and-growth/merchandising-and-aov
updated: 2026-10-05
---

E-commerce Marketing and Growth · Merchandising, pricing, promotions and consumer rules · lesson 4 of 20 · 11 min

# Merchandising and raising order value

## What merchandising means online

**Merchandising** is how you present and arrange products so shoppers find what they want and discover more. In a physical shop, it covers windows, shelves and displays. Online, it includes navigation, collections, search, product sorting, recommendations and bundles.

## Navigation and collections

- Organise categories the way **customers** think, not how your warehouse is organised (for example, "Gifts for her" and "Under AED 100" alongside product types).
- Keep top navigation simple; use mega-menus sparingly on desktop and clear, tappable menus on mobile.
- Create **curated collections** for occasions and needs: "Eid outfits", "Back to school", "Home office essentials", "New arrivals", "Bestsellers".
- Use collection pages with helpful introductions and filters (size, colour, price, availability).

## Sorting and ranking

The default order of products in a collection matters. Options include bestsellers, new arrivals, highest margin, or manual curation. A common approach is to feature **bestsellers and hero products first** (social proof and conversion) while mixing in new or high-margin items. Always hide or push down out-of-stock items.

## On-site search

Shoppers who search often convert at higher rates because they have clear intent. Improve search by:

- Supporting synonyms and common misspellings (including transliterations, for example Roman Urdu or Arabic spellings).
- Showing product suggestions as users type.
- Reviewing **zero-result searches** weekly and fixing them (add synonyms, products or redirects).
- Using search data to inform buying and content decisions.

## Raising AOV

| Tactic | How it works | Watch out for |
|---|---|---|
| Bundles | Sell related items together, often at a small saving | Margin; bundle relevance |
| Cross-sells | "Frequently bought together", "Complete the look" | Relevance over randomness |
| Upsells | Premium version or larger size | Don't hide the cheaper option |
| Free-shipping threshold | Free delivery above a set order value | Set threshold using AOV data and shipping costs |
| Tiered offers | "Spend 300, save 30" | Margin at each tier |
| Gift with purchase | Free item above a threshold | Cost of the gift |
| Post-purchase offers | One-click add-on after checkout | Keep it relevant and optional |

## Setting a free-shipping threshold

A common approach is to set the threshold somewhat above your current AOV so that many customers add an item to qualify. Example with illustrative numbers:

```
Current AOV: 180
Average shipping cost to you: 20
Option: free shipping above 220

If a meaningful share of orders between 180 and 220 grows to 220+, AOV rises.
Check: does the extra gross margin from added items exceed the shipping cost you absorb?
```

Test and monitor: conversion rate, AOV and contribution margin per order.

## Merchandising calendar

Plan homepage and collection changes around the retail calendar: new season launches, gifting occasions, local holidays and sales events. Keep the homepage fresh and relevant to what customers are shopping for this week.

## Worked example: a Dubai electronics accessories store

AOV is low because most orders are single phone cases. Changes:

- "Complete your setup" cross-sells on phone case pages (screen protector, charger).
- Bundles: case + screen protector at a small saving.
- Free shipping threshold set just above the price of a case plus protector.
- Search synonyms added for model names customers type differently.

The team monitors AOV, attach rate (share of orders with cross-sold items) and margin per order.

## Hands-on: choose a free-delivery threshold with your own order data

A threshold works when it nudges a meaningful share of orders slightly upward without giving away delivery on orders that would have been large anyway. Start from your order-value distribution (export from Shopify, WooCommerce or your OMS):

```python
import csv
from statistics import median

values = []
with open("orders_last_90_days.csv", newline="", encoding="utf-8") as f:
    for row in csv.DictReader(f):                 # expects a column 'order_value' (net of discounts, excl. tax)
        try:
            values.append(float(row["order_value"]))
        except (KeyError, ValueError):
            continue

values.sort()
print(f"Orders: {len(values)}  median: {median(values):.2f}")
for threshold in (150, 175, 200, 250):          # candidate thresholds in your currency
    just_below = sum(1 for v in values if threshold * 0.8 <= v < threshold)
    already_above = sum(1 for v in values if v >= threshold)
    print(f"{threshold}: {just_below / len(values):5.1%} of orders within 20% below "
          f"(nudge candidates); {already_above / len(values):5.1%} already above (cost of free delivery)")
```

A common starting point is a threshold somewhat above the median order value, where a good share of orders sit just below it. Then check the maths: the extra margin from nudged orders must exceed the delivery costs you absorb. Test or monitor AOV, conversion rate and contribution margin — not AOV alone.

## Merchandising with AI search and recommendations

- **Site search** increasingly uses semantic and AI-assisted matching (for example Shopify Search & Discovery, Algolia, Klevu, Bloomreach, Constructor). It understands "gift for dad under 200" better than keyword search — but still needs synonyms, merchandising rules and zero-result monitoring.
- **Recommendations** ("frequently bought together", "complete the look") work best with clear business rules: exclude out-of-stock items, respect margin, avoid recommending returns-prone products.
- **Conversational shopping assistants** on your site must use live catalogue data (prices, stock, delivery) and approved claims.

## Before and after: a collection page

```
BEFORE  Default sort: "Newest"; 3 filters (size, colour, price); out-of-stock items shown first on page 1
AFTER   Default sort: "Featured" (bestsellers with stock, then new arrivals); filters by use case
        ("for hot weather", "for work"), fabric, fit, delivery speed; out-of-stock pushed to the end;
        badges only where true ("Low stock" from inventory, not permanent)
```

## Common mistakes

- Navigation mirroring internal categories customers don't understand.
- Random or irrelevant recommendations.
- Out-of-stock products at the top of collections.
- Free-shipping thresholds set without margin analysis.
- Ignoring zero-result searches.

## Personalised recommendations

Many platforms and apps offer automated recommendations ("customers also bought", "recently viewed", "you may also like"). They can raise AOV and discovery, but check that suggestions are relevant, in stock and appropriate for the customer's market and language. Review recommendation performance monthly: click-through, add-to-cart rate from recommendations and the margin of recommended products. Where automated suggestions are weak, curate manual rules for your top products.

## Merchandising checklist

- [ ] Navigation and collections based on customer language and needs
- [ ] Bestsellers and heroes prioritised; out-of-stock items demoted
- [ ] Search synonyms and zero-result review in place
- [ ] At least two AOV tactics running and measured
- [ ] Merchandising calendar aligned with the retail calendar

## Video lecture: Merchandising and raising order value

Lecture coming soon · 14 chapters · about 8 minutes. Read the full transcript below.

1. Merchandising and order value
2. Why merchandising matters
3. Navigation and collections
4. Sorting and filtering
5. On-site search
6. Raising AOV honestly
7. Simple example: Dubai phone accessories
8. Realistic example: UK homeware threshold (illustrative)
9. Watch me do it: threshold from data
10. Rules for recommendations and AI
11. Badges and labels
12. A merchandising calendar
13. Common mistakes
14. Recap

## Lecture transcript

### Merchandising and order value

Walk into a well-run shop and you'll notice something: the right products are in the right places. Bestsellers near the entrance, accessories by the till, seasonal items where you'll see them. That's merchandising. Online, it's even more powerful, because you can change it instantly and measure every result. In this lecture you'll learn navigation and collections, sorting and search, how to raise order value honestly, how to set a free-delivery threshold from your own data, and how AI search and recommendations fit in. Then you'll watch me pick a threshold with a short script.

### Why merchandising matters

Why does merchandising matter? Because shoppers can only buy what they find. A great product buried on page four of a collection, sorted by newest, might as well not exist. And average order value is one of the most profitable levers you have: a bigger basket usually adds revenue without adding another delivery or another ad click. Good merchandising helps shoppers find what they want faster, and discover things they'll genuinely value.

### Navigation and collections

Navigation and collections come first. Organise the menu the way customers think, by use, occasion, room or problem, not only by your internal product categories. Keep top-level items few and clear. Create collections for real shopping missions, like gifts under two hundred, workwear, or summer essentials. And test navigation with customers, for example with a tree test, before a big restructure. The Conversion Rate Optimization course covers those methods.

### Sorting and filtering

Sorting and filtering decide what shoppers see first. The default sort matters most, because most people never change it. A sensible default is featured: bestsellers that are in stock, then new arrivals. Push out-of-stock items to the end. Offer filters that match decisions, like size, fit, fabric, price and delivery speed, and for some categories, use cases like for hot weather. And only show badges that are true: low stock should come from real inventory, not a permanent label.

### On-site search

On-site search is where high-intent shoppers go. They typed exactly what they want. Modern search tools use semantic and AI-assisted matching, so they understand gift for dad under two hundred better than old keyword search. But they still need care: add synonyms and local spellings, like abaya and abaya dress, or English and Roman Urdu variants; monitor zero-result searches every week; and pin the right products for your top searches. Searchers who find what they want often convert better than browsers.

### Raising AOV honestly

Now order value. Honest ways to raise it: bundles of products that genuinely go together, like a skincare routine set. Complete-the-look and frequently bought together recommendations. Quantity breaks for consumables. A free-delivery threshold. And accessories offered at the right moment, like a case with a phone. What to avoid: pre-ticked add-ons, confusing multi-buy pricing, and recommendations that push returns-prone products just to raise the basket.

### Simple example: Dubai phone accessories

A simple example. A Dubai electronics accessories store sells phone cases at a modest average price. Most orders are one case. The team creates a protect-your-phone bundle with a case, a screen protector and a cable, priced a little below buying separately. They show it on product pages and in the cart. They also add a frequently bought together row, excluding out-of-stock items. The bundle is a genuine convenience, the price is honest, and average order value rises without any tricks.

### Realistic example: UK homeware threshold (illustrative)

Now a realistic scenario with illustrative details. A UK homeware store has a median order of about a hundred and forty pounds and a free-delivery threshold of a hundred, so most orders already qualify. Delivery costs are eating margin, and the threshold isn't nudging anyone. The team analyses the order-value distribution and tests a higher threshold, around a hundred and seventy-five, where a meaningful share of orders sit just below. They show a progress bar in the cart: you're twenty pounds away from free delivery. They watch average order value, conversion rate and contribution margin together, because a threshold that lifts AOV but reduces conversion too much isn't a win.

### Watch me do it: threshold from data

Watch me pick a threshold with data. I export the last ninety days of orders with order value, net of discounts and excluding tax. A short Python script loads the values, prints the order count and median, then tests several candidate thresholds. For each, it shows the share of orders sitting within twenty percent below the threshold, those are my nudge candidates, and the share already above it, which is the cost of giving free delivery to orders that would have been large anyway. I look for a threshold with a good share of nudge candidates and a manageable share already above. Then I check the margin maths, and plan to monitor conversion and contribution after launch.

### Rules for recommendations and AI

Recommendations and AI assistants deserve rules. Recommendation engines, like frequently bought together or complete the look, work best with business rules: exclude out-of-stock items, respect margin, avoid products with high return rates, and keep recommendations relevant to the product being viewed. Conversational shopping assistants on your site must pull prices, stock and delivery from live catalogue data, and use approved claims only. An assistant that promises next-day delivery you can't provide creates angry customers.

### Badges and labels

Let's talk about badges and labels, because they're small merchandising tools with big effects. Bestseller, new, low stock, back in stock, and exclusive online. Used honestly, they help shoppers decide quickly, especially on mobile where people skim a grid in seconds. Bestseller should be based on real sales in a stated period. Low stock should come from inventory, and disappear when stock is replenished. New should mean recently launched, not three years old. And limit how many badges appear on one tile, because when everything shouts, nothing is heard. One honest, relevant badge beats three decorative ones.

### A merchandising calendar

Plan merchandising as a calendar. Rotate featured collections for seasons, events and launches: Ramadan and Eid, back-to-school, summer, White Friday and Black Friday, and national days. Prepare collection pages, banners and search pins ahead of time. After each season, review which collections and bundles performed on revenue per visitor and margin, and record it for next year.

### Common mistakes

Common mistakes. Menus organised by internal categories. Newest as the default sort. Out-of-stock items on page one. Ignoring zero-result searches. Permanent fake low-stock badges. Thresholds set by guesswork. Recommendations that ignore stock and returns. And AI assistants that invent prices or delivery promises.

### Recap

Recap. Merchandising helps shoppers find and discover. Organise navigation the way customers think, choose a sensible default sort, invest in search with synonyms and zero-result checks, and raise order value honestly with bundles, recommendations, quantity breaks and a data-driven free-delivery threshold. Give recommendation engines and AI assistants clear rules and live data. Try this now: run the threshold script from the lesson text on your last ninety days of orders, and review your top twenty site searches for zero results.

## Key takeaways

- Merchandising is how products are arranged and presented so shoppers find and discover more.
- Organise navigation and collections around customer needs and language.
- Improve on-site search by fixing zero-result queries and adding synonyms.
- Raise AOV with relevant bundles, cross-sells and margin-checked thresholds.

## Try it

Review a store's navigation, search and AOV tactics, and propose one bundle, one cross-sell and a free-shipping threshold with the margin maths.

- [Previous: Product pages that convert](https://optimizeall.com/learn/ecommerce-marketing-and-growth/product-pages-that-convert)
- [Next: Pricing, promotions and discount codes](https://optimizeall.com/learn/ecommerce-marketing-and-growth/pricing-promotions-and-discount-codes)
- [All lessons of E-commerce Marketing and Growth](https://optimizeall.com/learn/ecommerce-marketing-and-growth)