Recommendation Engine

Recommendation engines also called personalization engines or recommendation software, help companies recommend the right product or service to their customers based on historical customer behavior.

To be categorized as a recommendation engine, a product must be able to make personalized recommendations based on customer data

Innovators Specialists Leaders Challengers Market Presence Momentum
Popularity
Satisfaction
Maturity
Pricing
Country
Reset All Filters

Compare Recommendation Engines
Results: 14

AIMultiple is data driven. Evaluate 14 products based on comprehensive, transparent and objective AIMultiple scores. For any of our scores, click the icon to learn how it is calculated based on objective data.

Sort by:
64.99790045262046
80.00595758303196
0.030210750954451887
100
0.029365079365079365
49.98984332220896
top10
top5 , top10
4star
Qubit Pro
4.46
100%
0%
0%
= 100 reviews
= 5,000 employees
= 100,000 visitors

Qubit Pro's enterprise-grade data processing and open ecosystem allow personalization to solve some of the biggest marketing and business challenges. Bring together multiple data sources in real-time to create contextual and relevant 1:1 personalizations.

63.089272630786034
80.76889650803953
100
88.4485710318748
0.1003968253968254
45.40964875353254
top5 , top10
top5 , top10
4star
Optimizely
4.32
58%
2%
100%
= 100 reviews
= 5,000 employees
= 100,000 visitors

Optimizely is the world's leading experimentation platform, empowering marketing and product teams to test, learn and deploy winning digital experiences

61.7111345643401
75.1137782663505
7.250756056058893
92.983596579899
0.01825396825396825
48.308490862329705
top5 , top10
top5 , top10
4star
Evergage
4.22
100%
0%
66%
= 100 reviews
= 5,000 employees
= 100,000 visitors

Evergage's real-time personalization and customer data platform (CDP) enables companies to leverage behavioral analytics and machine learning

59.26490064780238
73.06602341712916
10.87613277194662
89.96916842888638
0.030753968253968256
45.4637778784756
top5 , top10
top5 , top10
4star
Monetate Intelligent Personalization Engine
4.49
30%
0%
100%
= 100 reviews
= 5,000 employees
= 100,000 visitors

Monetate is the world's most trusted experience optimization and 1-to-1 personalization platform.

53.398539244158776
65.47050367660849
0
81.28259884179236
4.444246031746032
41.326574811709065
top10
top5 , top10
4star
Adobe Target
3.97
33%
100%
0%
= 100 reviews
= 5,000 employees
= 100,000 visitors

52.87120175078753
65.14763584970667
10.87613277194662
80.06952226325906
0.04404761904761904
40.59476765186838
top5 , top10
top5 , top10
4star
Dynamic Yield
4.35
27%
0%
100%
= 100 reviews
= 5,000 employees
= 100,000 visitors

Dynamic Yield's Personalization Anywhere tech helps marketers increase revenue by individualizing each user's interactions across web, mobile and email.

47.78180513904023
57.54321747501459
0
71.9252520024984
0.03015873015873016
38.02039280306586
top10
top5 , top10
4star
Sailthru Experience Center
4.10
21%
0%
0%
= 100 reviews
= 5,000 employees
= 100,000 visitors

Sailthru is the first truly proactive marketing automation platform designed to optimize the digital experience for individual customers and for brand revenue

42.480666520191626
51.92322864525031
0.09063487714679506
64.87894156596715
0.11011904761904762
33.03810439513294
top10
top5 , top10
4star
Episerver Digital Experience Cloud
4.09
20%
2%
0%
= 100 reviews
= 5,000 employees
= 100,000 visitors

An outstanding digital experience that simply works.

29.085958661956383
35.10744828574098
0
43.88431035717623
0
23.064469038171787
top10
top5 , top10
5star
Crab
4.60
2%
0%
0%
= 100 reviews
= 5,000 employees
= 100,000 visitors

25.654511692859145
31.34520238916718
2.658609066794312
38.84567982930016
0.02797619047619048
19.96382099655111
top5 , top10
top5 , top10
4star
RichRelevance
3.91
2%
0%
24%
= 100 reviews
= 5,000 employees
= 100,000 visitors

The RichRelevance personalization technology provides personalized customer experiences seamlessly across web, mobile, in-store analytics and website

Market Presence Metrics

Popularity

Searches with brand name

These are the number of queries on search engines which include the brand name of the product. Compared to other product based solutions, recommendation engine is more concentrated in terms of top 3 companies' share of search queries. Top 3 companies receive 89% (15% more than average) of search queries in this area.

Web Traffic

Recommendation engine is a highly concentrated solution category in terms of web traffic. Top 3 companies receive 91% (18% more than average solution category) of the online visitors on recommendation engine company websites.

Satisfaction

Recommendation engine is highly concentrated than average in terms of user reviews. Top 3 companies receive 64% (6% more than average solution category) of the reviews on recommendation engine company websites. Product satisfaction tends to be higher for more popular recommendation engine products. Average rating for top 3 products is 4.4 vs 4.2 for average recommendation engine product review.

Leaders Average Review Score Number of Reviews

Maturity

IBM
adobe
episerver
Optimizely

Number of Employees

Median number of employees that provide recommendation engine is 155 which is 101 more than the median number of employees for the average solution category.

In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 11 companies (38 less than average solution category) with >10 employees are offering recommendation engine. Top 3 products are developed by companies with a total of 501-1,000 employees. However, all of these top 3 companies have multiple products so only a portion of this workforce is actually working on these top 3 products.

Learn More About Recommendation Engine

Why are recommendation engines important?

As the competition in all industries is increasing, keeping their customers engaged is an important goal for organizations. Recommendation engines enable organizations to increase their sales by upselling (selling a higher volume of products that they buy) or cross-selling (selling new products) to existing customers. Here are some recommendation engine examples from tech leaders:

  • 35% of Amazon.com’s revenue is generated by its recommendation engine.
  • 75% of users in Netflix choose movies/tv series according to recommendation engine suggestions. Netflix executives Carlos A. Gomez-Uribe and Neil Hunt state that recommendations reduce the churn rate by several percentage points. This increases the lifetime value of existing customers that’s why they believe recommendations save them more than $1B per year.
  • Spotify first released Discover Weekly playlist recommendations in 2015 and they experienced an 80% revenue increase with 40 million Discover Weekly users(40% of total users by that time) in 2016.

How does it work?

Recommendation engines have three basic steps to make recommendations:

Data Collection

Core of a recommendation engine is consumer data. These engines collect implicit and explicit data.

  • Implicit data is the information that is gathered unintentionally from customers by checking their website history. Examples are web search history, clicks and order history.
  • Explicit data is created by customers’ inputs such as ratings and likes/dislikes.

Data storage

As the amount of data you store increases, you provide better recommendations for your customers. Organizations need to keep as much data as possible on the cloud or enterprise to analyze customers and divide them into segments.

Data Analysis and Recommendation

Recommendation engines analyze data by filtering it to extract relevant insights to make the final recommendations.

What are common approaches used in recommendation engines?

Recommendations depend on a combination of similar users' actions (collaborative filtering), products similar to those consumed by the user (content based filtering) or the context of the user (context aware filtering):

Content-based filtering

Content based filtering, as its name refers, is recommending a product that is similar to products the customer liked before. Below is an example of a movie recommendation content based filtering. Rabin is a user who mostly watches commercial dram movies and the system provides Movie A and Movie B as a recommendation. The downside of content-based filtering is product mappings are manual and depend on labelers’ bias.

Source:Medium

Another example is if the user rated a song from an artist, system recommends him another song from the same album.

Collaborative filtering

Collaborative filtering methods are divided into two categories:

  • User-based collaborative filtering: Engine recommends a product if the product has been liked by users similar to the user.
  • Item-based collaborative filtering: Based on users’ previous ratings, system identifies similar items. For example, if users A,B and C rated books X and Y, then when a new user purchases book Y, systems recommend purchasing book X as well due to the pattern created by A,B and C users.
  • Context aware filtering adjusts recommendations based on the time, place, the users' consumption right before the recommendation. For example, ice creams should be recommended more often in summer.

What are its use cases?

Recommendation systems can be useful and applicable to various industries in the B2C environment. We’ve explained those use cases before, feel free to check our article.

What are its benefits?

Recommendation engines are generally used to boost sales processes along with the relationship between the organization and customers. To learn all benefits, check out our article.

What are its challenges?

Challenges include:

  • Cold start problem: What should you recommend to new users? Should you recommend the most commonly recommended items or should you try to understand more about the user? Answers to such questions depend on the specific application.
  • Obvious recommendations: With no long tail data, recommendation systems make quite obvious recommendations which could easily be programmed by a few rules. Data is crucial for a recommendation system.
  • Static recommendations that become outdated with changing tastes: If the system is not continously learning, such a scenario is inevitable. Companies are advised to invest in continuously learning systems
  • Recommendations that violate personal privacy: Consumption data is personal data and using such data for recommendations requires care even when the recommendation is only shared by the user. A NY Times article from 2012 includes an anecdote about how Target predicted a teen's pregnancy before her father. Though we don't know if such a thing really happened, it is indeed an example of how innocent looking recommendations can violate personal privacy.