Case study on how to increase AOV by highlighting volume discounts

Step 1: Create a Behavioral Challenge

The first step in the study was to identify and define the central behavioral challenges Sevan faced. This laid the foundation for focusing efforts where the potential for positive change was greatest.

Question 1: Behavioral Challenges

Initially, three overarching challenges were identified:

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B2B E-commerce

Get B2B customers to add more complementary products or order larger volumes via data-driven recommendations.

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Recipe Inspiration (Retail)

Inspire store consumers to buy more Sevan products via recipe recommendations

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Environmentally Smart Choices (Retail)

Get environmentally conscious consumers to actively choose Sevan by clarifying 'environmentally smart' properties.

Question 2: Potential for Change

The challenges were evaluated based on user motivation and the company's ability and opportunity to influence behavior. This provided a score for prioritization.

The chart shows the total score (Motivation x Ability & Opportunity) for each behavioral challenge.

Question 3: Project Goal Based on Potential for Change

The challenge "Increase AOV through complementary products/volume (B2B)" was chosen due to high potential and best measurability.

Chosen Behavioral Challenge:

"How can we get our B2B customers who place an order to add more complementary Sevan products or order larger volumes based on data-driven and relevant recommendations (e.g., product pairings, volume benefits) during the order placement process on our B2B e-commerce platform?"

Concrete Project Goal:

8%

Increase in AOV (Target)

6 Mon

Test Period (Q3-Q4 2025)

Introduction of a new data-driven recommendation engine on Sevan's B2B e-commerce platform.

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During a 6-month test period (Q3-Q4 2025).

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Increase average order value (AOV) by 8% for customers who interact with the recommendations.

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Measure the proportion of orders containing at least one recommended product.

Step 2: Understand the User's Reality

To design effective nudges, a deeper understanding of B2B customers' current behaviors, drivers, and barriers in the e-commerce environment was required.

Question 4: Data Collection Methods

Two primary methods were used to collect data:

📊Analysis of Transaction Data (Quantitative)

Systematic analysis of order history, interaction data with recommendation systems (clicks, conversion), and customer segmentation data from the B2B platform.

Rationale: Provides hard, quantitative data on actual purchasing behavior and enables A/B testing.

🗣️Qualitative Interviews

Semi-structured interviews with a selection of B2B customers (purchasing managers) to understand the *why* behind their actions and experiences with the system.

Rationale: Provides in-depth insights into perceived relevance, barriers, and motivating factors.

Question 5: Behavioral Insights, Barriers, Motivators & Decision Situations

The analysis resulted in several key insights:

Potential Behavioral Insights:

Industry-specific product pairings & recommendations with clear economic benefits (e.g., volume discounts) may have the highest conversion.
Time savings & discovery of genuinely relevant new products are appreciated.
Clicks without purchase may be due to price, uncertainty, or non-immediate need.

❗Barriers:

Perceived irrelevance of recommendations.
Distrust of the system's intentions.
Cluttered user experience (UX).
Existing budget and purchasing routines.

👍Motivators:

Clear benefit (time saving, cost saving).
Simplified decision-making process.
Help in meeting their own customers' needs.

🎯Effective Decision Situations for Recommendations:

When customers have almost completed their order ("don't forget" items).
When they are actively selecting key products ("complete the solution with").
When they are close to a benefit threshold (discount, free shipping).

Step 3: Create Behavioral Interventions (Nudges)

Based on insights into the users' reality, several potential nudging interventions were developed. These were then evaluated to select the most promising one.

Question 6: Potential Nudging Interventions

🎁1. Smart Bundle Suggestions

Present well-thought-out product combinations (e.g., "Complete Meze Starter Pack") when a key product is added to the cart. Can be offered with a bundle benefit.

Nudge Tools:

Frame information ("easy solution")
Show the way (towards a complete order)
Change placement (relevant timing)

📈2. Volume Benefit Visualized

Dynamically visualize volume discounts when the customer enters quantity (e.g., "Add Y more items for Z% discount!"). Price per unit updates.

Nudge Tools:

Visualize the effect (economic benefit)
Provide feedback (immediate price change)
Frame information ("smarter purchase")

👍3. Collectively Validated Complements

Show which other products are often bought together by similar customers (e.g., "Restaurants that bought X often ordered Y").

Nudge Tools:

Provide social proof
Show the way (proven combinations)

Question 7: GAME Evaluation and Prioritization

The nudge interventions were evaluated according to the GAME framework (Genomförbar (Actionable), Användarfokuserad (User oriented), Mätbar (Measurable), Etisk (Ethical)).

Nudge Intervention	G	A	M	E
1. Smart Bundle Suggestions	Medium	High	High	High
2. Volume Benefit Visualized	High	High	High	High
3. Collectively Validated Complements	Medium	Med-High	High	Med-High

Prioritization: "Volume Benefit Visualized" (Nudge 2) emerged as the strongest, followed by "Smart Bundle Suggestions" (Nudge 1).

Step 4: Chosen Behavioral Intervention & Study Design

Based on the evaluation, a specific nudge was chosen for implementation and testing. A detailed design and a concrete study plan were formulated.

Question 8: Choice of Behavioral Intervention - "Volume Benefit Visualized"

This nudge aims to transparently and encouragingly show B2B customers how they can achieve better prices by ordering larger volumes.

Mock-up: On Product Page (Interactive)

Product: Hummus Original 1kg

Quantity: pcs

Progress towards the next discount level.

Mock-up: In Shopping Cart

Product: Olive Oil Extra Virgin 5L

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Save more! Next price level at 10 pcs.

Click to see details or change quantity.

Order Summary

You save 150 SEK through volume purchases!

Key Design Details:

Purpose: Transparently show price benefits for larger volumes.
Interaction Flow: Directly visible, immediate feedback.
Tonality: Positive, helpful ("Optimize your purchase!").
Technical: Requires correct volume price lists, frontend logic.

Success Metrics (beyond AOV):

Proportion of orders with volume-discounted prices.
Average increase in units/product with volume discount.
Click-through rate on "show more info about discount".
Customer feedback (surveys) on utility and clarity.

Question 9: Description of Measurement

The effect of the "Volume Benefit Visualized" nudge will be carefully measured:

Primary Metrics:
1. Average ordered quantity/product item.
2. Proportion of orders utilizing volume discount.
(Measured via backend order data)

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Secondary Metrics:
1. Impact on total AOV.
2. Interaction rate with the nudge element.
(Measured via platform logs)

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Implementation:
Directly on product pages (at quantity field) and in the shopping cart.

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Evaluation: A/B Test
Test group (exposed to nudge) vs. Control group (existing interface).

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Time Period: 3 months.

Question 10: Hypothesis

The hypothesis is that the nudge will lead B2B customers in the test group to increase their order volumes and more frequently utilize volume discounts.

Specific Expectations (vs. Control Group, 3 months):

≥10%

Increase in average ordered quantity (for products with nudge)

≥5pp

Increase in proportion of orders activating volume discount

A positive, measurable effect on the total average order value (AOV) is also expected.

⚠️Potential Confounding Factors:

1. External Market Changes

Unexpected shifts in raw material prices.
Aggressive campaigns from competitors.
Significant changes in customers' industries.

2. Other Concurrent Sevan Initiatives

Other major sales campaigns.
Product launches with their own strong incentives.
Major changes to the e-commerce platform.

These factors can make it difficult to isolate the specific effect of the "Volume Benefit Visualized" nudge.