What Does Personalization Mean In Marketing?

Today’s consumers expect highly individualized experiences more than ever. They’re looking for brands to build 1:1 relationships with them. Consumers want to be marketed to, but in a way that is very subtle and meets their needs. Delivering relevant and personalized marketing experiences is key to building long-lasting consumer-brand relationships.

What is personalization?

Personalization in digital marketing is a one-to-one marketing strategy that leverages consumer data to provide highly relevant, unique experiences based on individual customer needs, behavior, interests, and beliefs. It’s the ability to know what your consumers are thinking and acting on those insights to provide them exactly what they’re looking for.

Consumers leave behind a lot of valuable data when they interact with your brand, essentially telling you how to better serve them. Marketers can pull actionable insights from this data to inform an effective personalization strategy.  This goes beyond just using customers' names in the header of the same marketing email that goes out to every customer. It's about delivering the highly relevant experiences and messaging your consumers want to see when they interact with your brand.

An effective digital marketing personalization strategy is one that’s scalable. Organizations must be able to deliver targeted experiences to each consumer across every touchpoint.  The majority of consumers, 70% to be precise, expect personalized experiences. Fortunately, digital technologies enable marketers to employ real-time data analysis to deliver the right messaging instantly.

Let’s say you have a hair and beauty company with an e-commerce store, and a customer purchases the same shampoo every month. An example of using historical data to personalize their consumer journey would be to send email reminders at the same time every month to purchase another bottle. This is a simple yet effective level of personalization, but you can personalize even more and  improve the consumer experience. Based on the individual’s location and the weather, you can make more contextually relevant recommendations that complement their usual shampoo, such as anti-frizz products or moisturizing conditioners. If you really wanted to encourage a purchase, you could offer a bundle of these products at a discount as well, and increase their basket size.

The ability to provide personalized experiences with contextual and historical data in mind can improve this customer’s experience and increase brand loyalty. While this may sound complex, it doesn’t have to be with predictive personalization.

How to make personalization predictive

Predictive personalization means that you are using both historical and real-time data and taking into account a number of different contextual factors to offer subtle yet tailored experiences. Making personalization predictive is a journey, and the personalization journey can be broken down into three stages that aren’t always linear:   

1. Collect data and pull actionable insights 
2. Optimize the consumer journey with different simple use cases
3. Build on this foundation and provide predictive and dynamic experiences at scale 

Many marketers struggle because they don’t prioritize the right data. With increasing consumer privacy concerns, and with Apple and Google giving consumers more control over who can access their data, third-party cookies are getting phased out. As a result, brands must prioritize first-party data in their marketing efforts. Privacy concerns are limited when it comes to first-party data, and it is also the most reliable data type for predictive personalization. However, you need to effectively collect and organize this data to pull actionable insights—identify patterns or outliers that can inform your personalization strategy.

Collecting the right data and pulling actionable insights is one thing. Scaling those efforts is another. Having a foundation of data science and integrating machine learning and artificial intelligence is a great starting point.  This is because it is too complex and time-consuming to implement predictive personalization manually. If you have millions of consumers, as many brands do, analyzing individual data and curating experiences for each one is simply not feasible. But with the right AI solutions, you can identify patterns and insights more effectively and produce millions of consumer experience variations in real-time.

Benefits of Predictive Personalization

Besides ensuring your consumers have a great experience, predictive personalization has several benefits to your brand. Here are just a few:

Increased sales and conversions

A big part of increasing sales and conversions is matching consumer expectations, addressing their unique needs, and building brand loyalty. Predictive personalization that uses AI and machine learning identifies and addresses individual consumer needs effectively and at scale. It also curates unique experiences that match consumer expectations. Brands that speak consumers' language will enjoy increased loyalty and ultimately increase sales and conversions.

Increased marketing ROI

Traditional personalization can get really tedious because it requires manual processes that are difficult to scale and, therefore, creates high implementation costs that ultimately result in reduced ROI. On the flip side, predictive personalization that utilizes machine learning and AI can not only show improved sales and conversions but also eliminate manual processes and save costs.

Anonymous personalization

Personalization requires quality data. The more you know about your consumers, the more you can curate unique and engaging experiences that drive customer loyalty. While most brands believe there’s no data to create personalized experiences for anonymous first-time visitors, AI-driven solutions can help you access data points that’ll inform relevant experiences for first visits.

Most visitors to a website are anonymous, but you can still create personalized experiences for them. Using AI solutions to track data, such as the fact that they’re first-time visitors, channel source (where they’re visiting from), geolocation, initial intent, etc., brands can make visitors feel welcomed and confident that they are in the right place.

Examples of Personalization in Marketing

Here are some easy use cases to activate personalization.

1. Hyper-specific product recommendations

   This type of personalization uses machine learning and AI solutions to a very high degree of accuracy to determine the most likely customer interests at a particular time and then tailor recommendations accordingly. Top brands, like Amazon, apply this approach to recommend hyper-specific content to users based on products users view or previous purchases.

2. Dynamic Content 

   While personalization is widely associated with product recommendations and e-commerce, it can also be used for content and layout. Marketers are using dynamic content to increase time spent on websites and keep customers engaged. By applying machine learning and AI algorithms, predictive personalization leverages historical data (purchase history, products viewed)  and real-time contextual data (time, weather, and location) to not only deliver the most relevant products but also create tailored content and layout.

   Understanding your audience is key to increasing the relevancy of dynamic content. One way to ensure the right information is to prioritize first-party data.

3. Dynamic audience segmentation
   

   Dynamic audience segmentation uses real-time data to update segments continuously as consumers fall in and out based on changing user behavior and needs.  For example, once a consumer meets the criteria for your repeat customer segment (making weekly purchases), they are automatically added to the segment.

   This approach saves time and drives more personalized messaging. By launching campaigns based on customer behavior, brands can increase engagement and boost ROI.

Today, personalization is no longer seen as a means to build a competitive edge. Rather, brands must offer personalized experiences to remain relevant. Using artificial intelligence and machine learning to enable predictive personalization can take your marketing efforts to the next level. Brands that can accurately anticipate their consumers’ wants and needs will enjoy the benefits of personalization: increased sales, conversions, marketing ROI, and customer loyalty. 

Ready to learn more about predictive personalization and data science in digital marketing? Check out Breinify’s other blog posts or get in touch.