Engaging with product recommendations can significantly boost revenue, generating 280% higher per visit compared to non-engaged users. Utilizing diverse recommendation strategies and personalizing recommendations based on user behavior are key to maximizing conversions and overall revenue. Optimizing recommendation widgets for revenue metrics rather than click-through rates can lead to better outcomes.

5. All Users Recommendation Clickers
Visitors who engage with Product Recommendations generate 280% higher
revenue per visit compared to all users
RevenuePerVisit
$20
$15
$10
$5
$0
+280%
Users with 2+ PV’s
Key Insight:
Product Recommendations are an integral
part of the shopping experience.
Maximize exposure to recommendations by
optimizing widgets with the most effective
number of items, layouts and strategies.

6. Conversions
2.3X AOV
1.2XRPV
2.8X
Revenue = Conversion Rate x Average Order Value
Compared to all site visitors,
users that engage with product recommendations yield:

7. RevenueAfterClick
ClickRate
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10 Day 11 Day 12 Day 13 Day 14 Day 15 Day 16 Day 17 Day 18 Day 19 Day 20 Day 21 Day 22 Day 23 Day 24 Day 25 Day 26 Day 27 Day 28 Day 29 Day 30
$4
$6
$8
$10
$12
$16
$18
$14
9.6%
9.0%
8.4%
7.8%
7.2%
6.6%
6.0%
Revenue After Click
Click Rate
Key Insight:
Instead of optimizing
recommendation widgets for CTR,
optimize for revenue metrics like
Revenue per Visit, Average Order
Value, Lifetime Value, etc.
Higher CTR ≠ Higher Revenue
(In Product Recommendations, higher CTR does not entail higher revenue)

8. Diverse Recommendation
Strategies
Only Similarity-Based
Strategy
Only Popularity-
Based Strategy
+14.7%
+11.2%
ConversionRate
Conversion Rate by Recommendation Strategy
7.0%
6.6%
6.3%
5.9%
5.6%
Key Insight:
Use multiple strategies in a single recommendation widget in order to maximize revenue.
Combine different recommendation strategies to identify the highest yield specific to each product category.
Similarity-based
recommendations
(e.g. bought together)
Popularity-based
recommendations
(e.g. hottest, most viewed etc.)
Example of diverse recommendations
(using multiple strategies within the same recommendation widget)
Multiple Recommendation Strategies in a single widget produces better results

9. Diverse / Contextual
Strategies
Personalized
Strategies
Diverse / Contextual
Strategies
Personalized
Strategies
Conversion Rate for the Average User
Conversion Rate for Engaged User Segments
i.e. with rich purchase history
-7.3%
+7.7%
Personalized recommendations refers to a class of algorithms that takes the user’s past site behavior,
purchase behavior and/or CRM data into account
6%
5.5%
5%
4.5%
4% 6%
7%
8%
9%
10%
For engaged users, personalized strategies outperform all other strategies
Key Insight:
Onboard all available transactional
and behavioral data - online,
offline, CRM - to develop a rich
data set and tailor your
recommendation strategies.

10. No Price Consideration
Price has no impact on
recommendation algorithm
Revenue Per User
+13.77%
-3.34%
Moderate Price Consideration
Price has moderate impact on
recommendation algorithm.
Medium range products are
pushed more in this approach.
Aggressive Price Consideration
Price has heavy impact on
recommendation algorithm.
More expensive products are
included in this approach.
$80
$70
$60
$50
$40
Take price into consideration when recommending products, but don’t overdo it
Key Insight:
Test price consideration (medium
to high) to identify the sweet spot
that maximizes revenue.

12. Algorithmic Fusion
Combine multiple recommendation strategies within a single widget to maximize performance.1
2
3
4
5
Dynamic Yield’s Personalized Product Recommendations
Contextual Recommendation Layouts
Change the layout of recommendation widgets based on user’s visit context, traffic source, purchase history, etc.
(e.g. for 1st time users with no history, show widgets with more products to infer interest)
Omni-Channel Recommendations
Place product recommendations on any page or any channel.
(e.g. on Homepage, category pages, product pages, cart pages, pop-ups, navigation menus, mobile apps, email & more)
Data-Driven Merchandizing
Create recommendation rules that automatically update widgets according to product availability, price changes,
real-time behavioral interactions, local weather forecast, etc.
Flexible Merchandizing Rules
Give power to your merchandizers by giving them a flexible and easy-to-deploy merchandizing rule builder that
allows you to pin, suppress and exclude specific items in the automated results.

13. Our recommendation engine
assesses the level of valuable
data about an individual visitor
and deploys the most
appropriate strategy.
Gathered information per user
Revenue
Popularity
Predictive
Personal
Automated Segment-Driven Strategy Selection
New Users Occasional Users Frequent Users Savvy Users

14. Global retailers who have implemented Dynamic
Yield’s recommendations have seen a substantial
uplift in revenue within the first 90 days.
Your site can do it too.
SEE A LIVE DEMO