Personalization Engine

Personalization engines allow companies to personalize marketing, sales and other aspects of customer experience to optimize a company's relationship with each customer. If we take a website as an example, the layout, use of colors, messages on the page, promotions and products showcased on the page can be personalized to increase sales.

Since the 90s, it became clear that companies have the necessary capacity and technology to go beyond the traditional segmentation approaches that segmented customers into a few buckets. Today, companies universally aim to serve each customer with a customized approach designed to optimize the company's relationship with the customer.

Personalization pays off. For example, personalized recommendations account for 74% content watched on Netflix according to McKinsey&Company.

Personalization solutions typically leverage machine learning algorithms such as collaborative filtering which relies on the choices of similar individuals. Due to constraints of specific personalization applications such as scarcity of feedback, time sensitivity, managing a dynamic catalogue and a dynamic user base, companies test different approaches in this field including multi-armed bandits and other new approaches

To be effective, personalization solutions need to have access to data on the experiments performed and their results so they can improve based on the available data. To achieve this, personalization solutions integrate with analytics software as well as software that manages interaction with customers.

Personalization is also called individualization, one-to-one (1-1) marketing or segment of one. Personalization engines are also called personalization systems or personalization software

Innovators Specialists Leaders Challengers Market Presence Momentum
Popularity
Satisfaction
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Compare Personalization Engines
Results: 32

AIMultiple is data driven. Evaluate 32 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.

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91.88446193928914
94.00178727490959
0.030210750954451887
100
0.029365079365079365
72.82957442634144
top10
4star
Qubit Pro
4.41
100%
100%
1%
= 20 reviews
= 10 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.

88.61161923505252
90.66576509396802
7.250756056058893
96.22095190770068
0.01825396825396825
84.99575146607513
top10
top10
4star
Evergage
4.24
100%
100%
100%
= 20 reviews
= 10 employees
= 100,000 visitors

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

85.9070512496913
86.64449742982367
100
88.98030375006572
0.1003968253968254
80.96769782904413
top5 , top10
top5 , top10
4star
Optimizely
4.31
100%
100%
100%
= 20 reviews
= 10 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

82.52095750726322
76.98624881641018
10.87613277194662
81.55217256830231
0.030753968253968256
72.93725372600788
top5 , top10
top10
4star
Monetate Intelligent Personalization Engine
4.49
100%
100%
100%
= 20 reviews
= 10 employees
= 100,000 visitors

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

65.84296911185056
66.72642584716579
0
70.98459689921386
0.03015873015873016
57.89292712347802
top10
4star
Sailthru Experience Center
4.18
100%
100%
0%
= 20 reviews
= 10 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

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, personalization engine is less concentrated in terms of top 3 companies' share of search queries. Top 3 companies receive 64% (14% less than average) of search queries in this area.

Web Traffic

Personalization engine is a less concentrated than average solution category in terms of web traffic. Top 3 companies receive 72% (5% less than average solution category) of the online visitors on personalization engine company websites.

Satisfaction

Personalization engine is less concentrated than average in terms of user reviews. Top 3 companies receive 58% (0% less than average solution category) of the reviews on personalization engine company websites. Product satisfaction tends to be higher for more popular personalization engine products. Average rating for top 3 products is 4.3 vs 4.1 for average personalization engine product review.

Leaders Average Review Score Number of Reviews

Maturity

Number of Employees

Median number of employees that provide personalization engine is 110 which is 47 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. 26 companies (21 less than average solution category) with >10 employees are offering personalization 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.

IBM
Oracle
zetaglobal
acquia

Learn More About Personalization Engine

What is a personalization engine?

Personalization engine is a marketing tool that leverages data to enhance the customer journey. These engines deliver personalized content to customers on the company’s digital channels. They are most commonly used in e-commerce.

Why is it important now?

Customers expect a personalized experience thanks to their routine interactions with highly personalized digital solutions like Netflix and Amazon with their accurate content and product recommendations. According to studies they are willing to pay more for a personalized experience in many cases.

This trend creates a demand for personalization engines. According to another study, 91% of shoppers are more likely to convert with brands who understand their customers to provide content relevant to consumers daily routines. This shows how important personalization is, especially when you know almost What are the most important features?

What are the levels of personalization?

Companies can take an iterative approach in their personalization investments starting from low cost/high impact initiatives. A company could follow these steps to advance its personalization efforts over time:

  • Unpersonalized optimization: This level contains testing and optimization. With testing, organizations can create the most optimal page design while maximizing site performance. This is initially done without personalization since personalization requires additional tech investment.
  • Limited personalization: Organizations use basic targeting capabilities by leveraging contextual data such as location, device type, weather and time of interaction. Patterns in the data help organizations create a relevant digital experience.
  • Channel specific personalization: Next, companies can find value in segmenting customers by combining contextual data with CRM, Point of Sale (POS) and 3rd party data management platform data. Segmentation data can be used in outreach as well as personalization of digital properties. Though companies better understand customers thanks to this data and segmentation effort, they tend to keep customizations in one channel due to the additional cost of rolling them out in all channels. Of course, companies that manage all their channels through one system could be rolling out these customizations to all channels.
  • Omnichannel personalization: Synchronization of channels helps businesses unify experiences across web, email, app, and display ads.
  • 1-1 personalization: Finally, organizations can perform predictive 1-to-1 personalization. Marketers predict customer needs and perform marketing activities accordingly. Organizations use machine learning algorithms to optimize their results.

We benefited from this description of levels of personalization in this answer.

What are the benefits of personalization engines?

  • Drives traffic: With personalized web and email marketing, it increases the traffic to your site.
  • Increases brand loyalty: Your customers feel like they are valued as individuals due to personalized content/offers/products your engine delivers. This increases the chances of them shopping from you again.
  • Boosts ROI: Launching personal campaign can increase conversion rates. Mckinsey estimates that personalization can deliver five to eight times the ROI on marketing spend, and can lift sales by 10% or more.

What are the critical capabilities of personalization engine solutions?

Personalization engines have different capabilities and can be sold as a point solution. Capabilities can be categorized as:

Data and Analytics: Personalization starts with collecting data and performing analysis to gain insights that let you know consumers.

Testing: Your solution should be able to perform testing algorithms such as A/B and multivariate testing so that you can design your channel in an optimal way that attracts and nurtures visitors.

Behavioral predictions: Personalization engines can identify patterns of customer behavior and predict customer behavior (e.g. predict products that the customer is likely to buy).

Marketing Channel Support: Personalization engines can create and launch personalized campaigns through channels such as web, email marketing, mobile app engagement, mobile messaging, digital advertising, retargeting and paid search. This is especially relevant for e-commerce companies as they personalize their marketing outreach and their digital properties

Customer Experience Support: Personalization engines can support your omnichannel strategy by enhancing customer experience across touchpoints such as chatbots, voice assistants, interactive voice response (IVR) and call center conversations. They can also improve real-life experiences in digital kiosks or they can provide intelligence to retail sales reps via their devices (also called clienteling applications).

Measurement and Reporting: At the end of the campaign, your solution should help you track KPIs such as engagement and conversion rate to evaluate strategy via dashboards, and data visualization.

Our sources include Gartner 2019 Magic Quadrant report.