The Marketing Leader's Guide to Predictive Personalization At Scale

In today’s marketplace, consumer relationships are a big part of brand success. By placing an emphasis on understanding your consumers, reaching them with relevant messaging, and personalizing their online experiences, your brand can encourage consumer loyalty and maintain a competitive edge.

“Slow down and listen. If you can’t meet your customers where they are, whether that’s digitally or in the store, you’ve lost.” 

- Daniel Moznett, Director of Marketing at Duraflame

What Does Personalization Mean?

Personalization is how brands tailor digital consumer experiences to the preferences of individual users. While many brands are working towards personalizing the consumer experience, most are just getting started on their personalization journey.

Most commonly, brands think about personalization simply as segmenting their audience further – the more audience segments that marketers can create, the more personalized the experience can be. While this is a good starting point, true personalization comes from being able to personalize individual experiences at scale. This comes from enabling things like dynamic audience segmentation, which automatically moves users from segment to segment based on their historical data and real-time behaviors. 

The key to achieving personalization at scale? Access to the right tools and technology. Marketers can’t personalize consumer experiences manually, and in order to create highly relevant and meaningful experiences, marketers need to focus on the bigger picture – that means implementing AI solutions that help brands leverage data science. This frees up marketers to act on insights quickly instead of parsing through row after row of data.

So, if that’s personalization, what exactly is predictive personalization? 

Simply put, predictive personalization helps marketers predict what products or content consumers will be looking for at any given moment. This is based on a number of data points, including historical data and real-time contextual information, like date, time, weather, and location. By leveraging this data, AI solutions can produce highly relevant product and content recommendations for users, with no engineering required from marketing teams.

Understanding the Stages of the Personalization Journey

Personalization is much easier to understand when you break it down into stages and think of it as a business process rather than a task to check off your list.

“Successful personalization that fosters brand loyalty can’t happen without a strong foundation and careful planning — and that starts with understanding the three stages of retail personalization.”

– Diane Keng, CEO of Breinify

There are three main stages to a brand’s journey towards predictive personalization:

• Invest in data collection and analysis. This will help you understand your consumers and provide insight into what they really care about.

• Use data to improve the consumer journey. With actionable insights at your disposal, you can use this data to rapidly optimize the consumer experience. 
• Enable fully predictive and dynamic personalization. The last part of the journey is enabling predictive personalization at scale and making dynamic consumer experiences a reality for your brand.

Of course, with any large-scale transformation, each stage has its own challenges that your brand will have to work through. Personalization is quickly becoming a core element of brand success, particularly for retail brands looking to personalize ecommerce experiences. In order to maintain a competitive advantage, regardless of your industry, it’s important to understand the stages of the personalization journey and where your brand is on the path to 1-to-1 personalization.

What Data Science Means for Personalization

Building relationships with your consumers doesn’t happen by accident, and it certainly doesn’t happen overnight. In order to reach your goals and establish valuable relationships with your target consumers, you’ll need to invest in the right tools and technology.

First things first: it’s time to get familiar with data science. Some companies might have entire teams dedicated to data science and analytics, while others might look for a partner to help them optimize their data efforts. Either way, data science is the foundation on which consumer relationships are built – you likely have all the data about your consumers that you need, but you should ask yourself a few questions. For example, how are you collecting data and what types of data are you collecting? How is it being tagged for later use? How are you planning to use it?

That being said, it’s not necessary for marketers to know how personalization algorithms work – in fact, marketers don’t need any data science background at all in order to secure the benefits of data and predictive personalization. It’s more important for marketers to understand why personalization is important in digital marketing.

Collecting first-party data is just the first step. Brands also need to think about how they are tagging it and using it effectively. For example, retail brands tend to have tons of data on their consumers, but only a few have been able to leverage it to curate personalized experiences that go beyond the basics.

When considering how to turn data into personalized consumer experiences, you’ll want to consider three key things:

• Set specific business goals, and tie personalization efforts to those
• Implement dynamic audience segmentation to personalize experiences
• Use data science and AI to enable predictive personalization and boost brand loyalty

The goal is to understand consumers on a personal level and create consumer experiences that meet these individuals where they are, no matter the channel. To do this, marketers need to rely on a strong foundation of data science – that means they need to leverage solutions that make data science accessible to their team. When marketers are able to use data science and AI to deliver predictive and dynamic experiences for users, they’re more likely to create stickiness for the brand that improves ROI and keeps consumers coming back for repeat purchases. 

Why is Personalization Important in Digital Marketing?

The key to fostering relationships with consumers is, first and foremost, having the capacity to build and maintain those relationships. Let’s be honest: if your marketing team is already stretched thin focusing on the day-to-day operations, that’s a problem. Marketing automation can help free up some of their time, but without investing in true personalization solutions, there’s still too much manual input required from marketers. 

But aren’t automation and personalization the same thing? Not entirely, but they are two sides of the same coin. Here’s why:

• Automation isn’t as advanced – think automatic emails when someone registers an account on your website, or when you A/B test and schedule campaigns through a piece of software. There’s also no AI involved in marketing automation technology. This is helpful for streamlining some manual processes, but doesn’t allow marketers to create a unique experience for each individual user.
• Personalization, on the other hand, is similar to automation with the additional benefit of artificial intelligence. This means marketers can tailor the experience to individual consumers’ needs, and free themselves up from manually creating experiences and rules for personalization. AI can identify patterns and trends that humans may not be able to, and can adjust the experience accordingly.

So, you understand the difference between personalization and automation – but why is personalization important for consumer relationships? 

Let’s be clear: personalization is important for marketers. Consumer experience is everything these days. Consumers not only want personalized experiences, they expect them. If your brand can’t provide that, they’ll be quick to switch over to another brand. 

Did you know that just 15% of CMOs think their company is on track to enable personalization? That leaves 85% of CMOs who aren’t as sure about the way forward.

The most important thing is to get started sooner rather than later. AI and personalization are constantly developing and changing – that means it’s tough to wait until you’re an AI expert to get started with personalization. Instead, get started now and develop your understanding as you go. As you work your way through the stages of the personalization journey, you’ll become more familiar with how AI can help drive your business forward. 

Examples of Personalization at Scale

Sometimes, it’s easiest to learn when you can see things in action. Here are a few examples of brands that leverage predictive personalization to offer top-tier digital experiences to consumers.

• Netflix

Netflix is the gold standard of personalization. Think about it – out of the millions and millions of users that Netflix has, no two people have exactly the same home screen and recommendations for what to watch next. Netflix collects and uses its data so effectively that it can figure out exactly what you’re going to want to watch next. And each time you watch something, it gets even more accurate.

• Amazon

Amazon is a great example of a brand that’s providing highly personalized experiences to consumers. With each product a user views or purchases, Amazon is able to personalize the experience on a more granular level, providing recommendations for products, deals, and other content.

Sure, those two are giant corporations with seemingly bottomless marketing budgets. With that in mind, here are a few examples from our real customers that illustrate the power of personalization:

• BevMo!

BevMo! is the largest alcohol retailer in the US, with stores concentrated on the West Coast. The numbers don’t lie – by personalizing experiences across their website, email, and SMS campaigns, BevMo! generated $125M in new sales revenue, increased e-commerce sales by 5.3% during the pandemic, and improved year-over-year sales by 51%.

• Duraflame

Cowboy Charcoal is a subsidiary of Duraflame, and recently started leveraging data science to enable personalization on their website. Within two months, the brand saw tangible ROI on their investment: AI-recommended recipes received nearly 70% more clicks than manually-curated ones, and recipe views saw 8.9% month-over-month growth. Cowboy Charcoal also improved data collection practices, gaining access to more insights and opportunities to rapidly improve the digital consumer experience.

• Claro

Claro Colombia is Colombia’s largest telecommunications provider, with 34 million subscribers (that’s more than half of the country’s population). In order to improve checkout rates for top-ups on their prepaid cell service packages, Claro invested in its personalization efforts. With 180+ variations in copy and personalized push notifications, Claro saw total sales for top-ups increase by over 31% and total checkouts for the brand improve by 16.4%.

Picking the Right Personalization Vendor

As with many transformations on a large scale, it can be difficult to figure out exactly how to get started. The most important piece of information to take away from this: just get started. It’s good to have a plan in place, but you don’t necessarily have to have everything figured out to start making progress towards enabling predictive personalization at scale.

It’s no secret that data science is one of the most powerful tools for marketers when it comes to creating highly relevant and personalized experiences for their consumers. Most brands, however, aren’t looking to build out a full data science department in-house just to help the marketing department analyze data and extract actionable insights. With the right AI solution, marketers won’t have to do any heavy lifting. 

When looking for the right personalization vendor, ask yourself a few questions: 

• Does this solution go beyond the basics?

You’re looking for a solution that does more than just analyze the data. This solution should use AI and machine learning to parse through your data sets and make predictions in real-time based on that data.

• Is it scalable?

To be successful in today’s online marketplace, marketers need to get beyond 10-15 audience segments, and work towards personalization at scale. You’ll need a solution that can handle massive volumes of individual consumers (and their data). If you’ve got one million consumers, find a solution that can automatically create one million different consumer experiences.

• Do the features meet my needs? 

Investment in personalization solutions won’t yield great results unless the vendor can take you to the next stage in your personalization solution. If you’ve already got stellar data collection and analytics capabilities, look for a partner who can enable dynamic content at scale.

• Does the algorithm fit my industry?

Personalization is based on algorithms, and those algorithms are built using the demographic and behavioral data of specific customer bases. If your vendor’s algorithms don’t match your industry, the resulting product and content recommendations likely won’t be as effective as you want them to be.

How Breinify Can Help

Breinify streamlines the implementation process by offering an AI-powered predictive personalization platform that can integrate in less than two weeks. We’re not here to automate marketing roles, we’re here to help marketing teams leverage data science and achieve predictive personalization at scale. Our solution is great for:

• Analyzing data. Recognize patterns and trends at scale, and easily extract actionable insights that drive marketing teams forward. The right insights lead to quick wins and improved ROI for the consumer journey.

• Implementing real-time dynamic content. With our algorithms, create variations of your content for every single consumer. If you’ve got one million consumers, our solution can create one million variations, allowing you to personalize on an individual level.

• Driving business outcomes with predictive personalization. Set your goals, and let our solution do the rest. Boost cart value, conversions, CRM registrations, and more through predictive personalization at scale. 
• Integrating with any existing tech stack. As a flexible, fully end-to-end solution, Breinify’s plug-and-play solution can integrate on its own or help you fill the gaps in your tech stack, working with your existing technologies to cover all your bases.

Still curious about how your brand can make the most of predictive personalization at scale? Our team of experts is here to help. Breinify’s AI platform can integrate in under two weeks, and start producing real ROI for your business in weeks, not months.

Want to learn more about predictive personalization, data science, and marketing AI? Take a look at our other blog articles, or check out our podcast for insights from real marketing leaders.

Ready to enable predictive personalization at scale for your business? Get in touch with us today to learn more!