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 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 brands be sure they’re meeting consumers expectations for individualized offers, and optimizing their revenue by doing so? The key is a personalization engine.
Personalization engines are what drive individualized marketing. These technology tools analyze customer data – from internal and third-party sources, as well as information about ongoing customer behavior. The insights derived from this data then enables companies 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 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.
Some may use the terms customer data platform (CDP) and personalization engine interchangeably. But these solutions are actually very different when you look more closely at their capabilities.
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 user, tracking and unifying data.
Personalization engines go even further, helping marketers combine both known user data and anonymous data, then delivering personalized content, offers and other experience via digital channels, all based on predictive modeling. Personalization engines 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.
Based on data science, personalization 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 user 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 information 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:
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:
If you’re in the market for a personalization engine, Gartner notes that some of the critical capabilities you should look for include:
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 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. 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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