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Personalization marketing is not just a passing trend, it is now expected by consumers. A vast majority (79%) of consumers agree that the more personalization tactics a brand uses, the more loyal they are to that brand, according to data from our latest customer loyalty study.
And personalization marketing yields real results for the brands that use it. About 90% of leading marketers told Econsultancy and Google that personalization significantly contributes to profitability, and 89% noted that personalization contributes directly to revenue growth.
So, how can businesses be sure they’re meeting consumers expectations for individualized offers, and optimizing their revenue by doing so? The key is a personalization engine.
What is a personalization engine?
Personalization engines, also called personalization platforms or personalization recommendation engines, are what drive individualized marketing. These technology tools analyze customer data – from internal and third-party sources like browser cookies, as well as information about ongoing customer behavior. The insights derived from this data then enables businesses to curate and tailor experiences that are unique to each customer. Personalization engines, according to Gartner, "apply context about individual users and their circumstances to select, tailor and deliver messaging such as content, offers and other interactions through digital channels in support of three use cases — marketing, digital commerce and customer experience."
Personalization engines automate marketing efforts, including the processes of segmenting, testing and distributing the personalized offers designed to inspire customers to action. The customer data management technology can operate as stand-alone software, or integrate with other business process tools, such as web content management systems, content marketing and multichannel marketing hubs, and digital commerce platforms, Gartner noted.
Types of Personalization engines
Personalization engines are intended to gather, store and optimize customer data to generate actionable insights. Personalization tools are broken down into two categories:
Collaborative Filtering: This type of personalization tool uses the information gathered from every customer's previous interaction with your business and compares the results of similar customers to offer personalized recommendations.
Content-Based Filtering: This personalization software is slightly different because recommendations are based on visitors' keywords and browsing history to describe products or services.
These two personalization engines will offer results to serve your varied business needs. In addition, both will help determine the tech stack you'll need to support your personalization efforts.
Customer data platform (CDP) vs. personalization engine
Some may use the terms customer data platform (CDP) and personalization engine interchangeably. But these solutions are actually very different when you view their capabilities more closely.
Gartner defines a CDP as “a marketing system that unifies a company’s customer data from marketing and other channels to enable customer modeling, and optimize the timing and targeting of messages and offers." CDPs focus on the known customer, tracking and unifying data.
Personalization engines go even further, helping marketing teams to combine both known user data and anonymous data, then deliver personalized content, offers, and other experience to existing customers and potential customers via digital channels, all based on predictive modeling. Personalization platforms are typically powered by artificial intelligence (AI) and machine learning (ML) to achieve a level of personalization and scale that is not possible using traditional marketing methods or tools.
|Customer Data Platform (CDP) vs. Personalization Engine|
|Customer Data Platform||Personalization Engine|
|Tracks known customer data||Tracks known customer data and anonymous data|
|Allows marketers to have full control of the data collected||Utilizes artificial intelligence and machine learning|
|Creates a snapshot of an individual customer||Provides more context surrounding user behavior and circumstances|
|Useful for targeting known customers||Useful for targeting anonymous users based on their previous behaviors or behaviors of audiences with similar habit|
How do personalization engines work
Based on data science, personalization recommendation engines use AI, ML, data insights and data visualization techniques to support decision making. AI is one of the most critical components for personalization engines because it enables the technology to continue learning and make adjustments in order to better predict customer intent – the key to personalization. A personalization engine should be able to develop a unique profile for each customer, and adapt that as the customer shops or more data is acquired about his or her decision making process.
As the amount of data available continues to grow exponentially, personalization engines also must be scalable. This is necessary for ingesting data about thousands of customers and processing millions of queries per minute, then delivering results in a timely manner.
There are three types of personalization engines, each of which operate differently, according to WhatIs.com:
Collaborative filtering engine, which gathers data about customers' interactions with a business (past purchases, where and when they made their purchase) via cookies and other means to determine when the customer is most likely to buy again.
Content-based filtering engine, which focuses on keywords that customers use when searching for products or services, making recommendations based on browsing behavior.
A hybrid that uses both collaborative and content-based filtering, which is often viewed as the most effective method since more customer data is incorporated.
Benefits of personalization engines
Using a personalization engine to better understand your customers' unique interests, as well as what they're going to buy now, and in the future, can yield tangible results. This type of 1:1 approach can:
Improve customer satisfaction and loyalty - By gaining a clear understanding of customer preferences, personalization engines can help provide suggestions for other similar products or deliver offers of interest. Relevant 1:1 marketing will keep customers coming back and loyal to your brand because they feel heard and understood.
Increase conversion rate - Relevant and right-timed offers or suggestions will spur customers into action. This is the difference between customers being a browser and a buyer.
Grow revenue - With greater loyalty and increased conversion rates, personalization marketing will yield greater revenue. Customers will buy products, and keep coming back for more from their favorite brands that deliver a personalized experience. And long-term customer loyalty will continue to increase customer lifetime value.
Must have personalization engine features
If you’re in the market for a personalization engine, Gartner notes that some of the critical capabilities you should look for include:
- Data and analytics
- Targeting and triggering
- Marketing channel support
- Testing and optimization
- Measurement and reporting
- Digital commerce support
- Customer experience support
The research and advisory firm also noted that personalization engines should “unify customer data across different customer experience channels; produce and deliver customized user experiences through various channels; enable users to create customer personalizations; and
incorporate machine learning, segmentation and A/B testing when creating customer profiles.”
If you are looking to add more 1:1 individualization to your marketing efforts, deployment of a personalization engine is an ideal customer data management tool to support your goals. This technology can rev up marketing campaigns, giving customers the individual attention they crave, while strengthening your company's bottom line by increasing conversion rates. When evaluating various solutions, the ideal personalization engine should have the ability to learn, using ML to leverage AI to augment human intelligence; and can scale dynamically to handle the vast amounts of data continually being generated.
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