What is a Personalization Engine? Your 2023 Guide
Consumers have an attention span of 8 seconds, which can make it more challenging for brands to connect and engage with consumers.
This is where personalization engines come in. Personalization engines are behind the tailored experiences you encounter daily on websites and apps. They’re the brainpower behind product recommendations that seem to know your taste better than you do or the content suggestions that make you click 'read more.' Personalization engines are the secret sauce of lifecycle marketing and engagement strategies.
In this post, we'll be taking a deeper look at how they really work, the benefits they hold for agile marketers, and the best tool for implementing a personalized marketing experience.
What are personalization engines?
Before we get into the definition of personalization engines, let’s first review what personalization means. Personalization strategies aim to deliver a relevant and engaging experience to each prospect or customer, ultimately increasing customer satisfaction and driving conversions.
However, the term “personalization” tends to be overused when it comes to marketing to customers. When software or marketers refer to "personalization," they’re often talking about rules-based personalization. In this approach, marketers define specific rules or criteria to determine how to customize interactions with customers. For example, sending a reminder if a product is in a customer's cart for a certain number of days.
This rules-based personalization is not true one-on-one marketing. It relies on predefined segments and rules, which group customers based on limited criteria (e.g., all customers who bought product X). This approach lacks the depth of personalization that can be achieved with more advanced methods. While segmentation can be effective to some extent, it doesn't fully harness the rich first-party data that businesses have about their customers.
So, though personalization engines are systems designed to collect and analyze customers' behaviors to create a customized user experience, be wary about the software you’re considering. Of course, some personalization engines, like OfferFit, actually support personalized outreach for every customer. OfferFit uses automated experimentation driven by reinforcement learning models to continuously test and learn from data, adapting their recommendations and communications in real time.
The platform goes beyond simplistic personalization by considering a multitude of factors, including past interactions, purchase behavior, demographics, and even real-time actions. This holistic understanding allows personalization engines to tailor content, product recommendations, and marketing messages with remarkable precision for every single customer–not groups or segments.
Personalization engine example
To grasp the concept better, let's explore some real-world examples.
OfferFit customer Brinks Home set out to enhance contract renewals, aiming to maximize both contract duration and NPV (net present value).
Before adopting OfferFit, their strategy relied on manual A/B testing and substantial discounts. However, this segmented approach fell short of providing a genuine one-to-one customer experience. Moreover, managing this process manually was neither efficient nor scalable.
Today, they leverage automated experimentation through OfferFit to pinpoint the most suitable renewal offers for individual customers. This approach enables them to identify customers who exhibit lower price sensitivity, requiring less significant discounts for contract renewal.
The results speak volumes. Brinks Home's new strategy led to an astounding 450% growth in the value of contract extensions, equating to an annual benefit of $5 million.
This success story exemplifies how personalization at scale can substantially benefit your business. It's not limited to contract renewals; personalization has the potential to revolutionize various aspects of your operations.
Dive deeper into our case studies to explore the myriad possibilities of implementing personalization at scale.
Personalization engines vs. personalization software tools vs. personalization platforms
Personalization Engines: The best and most successful personalization engines conduct automated experimentation which is powered by advanced AI and reinforcement learning models. They focus on making 1:1 decisions at scale, providing hyper-personalized experiences for individual customers. They continuously learn from data and adapt in real time. However, most engines do not have this capability.
Personalization Software Tools: Personalization software are tools that help businesses offer some form of personalization, whether it’s email marketing software with custom subject lines, personalized video software where marketers address customers by name, or landing page software that changes depending on the source link. These tools typically follow predefined rules set by marketers or business owners. For example, if a customer abandons a cart, the email marketing tool might send an automated follow-up email with a generic discount. While useful, these tools can lack the adaptability and depth of personalization engines.
Personalization Platforms: Personalization platforms are essentially the same as personalization software. Therefore, not all personalization platforms employ advanced AI and reinforcement learning models like personalization engines.
How personalization engines work
Personalization engines operate through a multi-step process that begins with the collection of customer data and ends with the delivery of highly personalized recommendations and communications.
1. Customer profiles At the heart of every personalization engine lies the creation of detailed customer profiles. These profiles are built using a wide array of data sources, including purchase history, browsing behavior, demographics, and more. The goal is to capture a holistic view of each customer, enabling the engine to understand their motivations, needs, and behaviors.
When it comes to capturing customer profiles, there are three primary types of personalization engines:
Collaborative Filtering Engine: This engine focuses on gathering data about all customer interactions with a business. It tracks actions such as past purchases, shopping channels (online or in-store), and timing of purchases. By analyzing this data, the engine identifies patterns and similarities between customer profiles. This enables the engine to predict when a specific customer is most likely to make a purchase, offering personalized recommendations at opportune moments.
Content-Based Filtering Engine: In contrast to collaborative filtering, content-based filtering centers on keywords used to describe products or services. Customer profiles are created by identifying the types of products or services a buyer is likely interested in, based on the keywords they've searched. Recommendations are then generated based on previous purchases. This approach ensures that recommendations align with the customer's expressed interests.
Hybrid Engines: Some personalization engines combine the methods of both collaborative and content-based filtering. This hybrid approach incorporates a broader range of customer data, making it highly effective. However, it can face challenges when dealing with limited data, known as the "cold-start problem." This occurs when the engine lacks sufficient initial data to provide meaningful recommendations.
2. AI model learning
Once customer profiles are established, personalization engines can employ powerful AI and machine learning algorithms to analyze data and identify patterns, correlations, and trends. Over time, the engine continuously learns and adapts, ensuring that recommendations become increasingly accurate and relevant.
3. Personalized recommendations & communications
The true magic of personalization engines comes to life in their ability to deliver personalized recommendations and communications. Whether it's suggesting products, content, or marketing messages, these engines take into account the individual's unique purchasing behaviors.
This level of personalization leads to higher customer engagement and conversion rates.
Benefits of personalization engines
Let’s take a look at some of the benefits personalization engines can provide.
Tailored Experiences: By analyzing vast datasets and understanding individual customer behaviors, personalization engines enable businesses to craft tailored experiences for each visitor. This goes beyond mere segmentation and allows for truly unique interactions.
Dynamic Content: Content customization is a key aspect of personalization engines. They can dynamically generate content, whether it's product recommendations, blog articles, or email messages, to match each customer's interests. This personalized content is so important to customers that a significant 71 percent of them anticipate personalized interactions from companies and 76 percent express disappointment when this expectation is not met.
Multichannel Consistency: Personalization engines ensure that the same personalized experience is delivered across various channels, from a website to a mobile app to email campaigns. This consistency enhances the customer journey by strengthening the brand-consumer relationship, reducing friction, and maximizing the utility of customer data.
Improved engagement, conversion rates, and retention
Higher Engagement: Customers are more likely to engage with content and recommendations that resonate with their interests. Personalization engines help ensure that every touchpoint is relevant, increasing overall engagement.
Boosted Conversion Rates: Personalized recommendations, communications, and offers lead to higher conversion rates. According to a study by Forrester Consulting, content personalization initiatives boost conversion rates by 67%. When customers feel that a business understands their needs, they are more likely to convert and make a purchase.
Enhanced Customer Loyalty: Personalized experiences foster a sense of loyalty and satisfaction. Customers who receive personalized recommendations are more likely to return and make repeat purchases, contributing to long-term customer retention.
Reduced Cart Abandonment: Personalization engines can target customers who have abandoned their shopping carts with tailored reminders or incentives, reducing cart abandonment rates.
Increased Sales: Personalized product recommendations often lead to higher sales. When customers are presented with products that align with their interests and needs, they are more likely to make a purchase. According to a report by McKinsey, high-growth companies derive 40 percent more of their revenue from personalization compared to their slower-growing counterparts.
Cross-Selling and Up-Selling: Personalization engines excel at cross-selling complementary products and up-selling premium options. They can identify opportunities to suggest additional items or upgrades, increasing the average transaction value.
Reduced Churn: By continuously adapting to customer actions and delivering relevant content, personalization engines can help reduce customer churn. Customers who receive personalized experiences are less likely to switch to competitors.
Data-Driven Insights: Personalization engines provide valuable insights into customer behavior. This data can inform marketing strategies, product development, and inventory management, further optimizing revenue generation.
The bottom line on personalization engines
Personalization engines are the future of marketing and ecommerce. They empower businesses to move beyond the generic "personalization" label and engage in true one-to-one marketing or "segment of 1 marketing." The power of these engines lies in their ability to understand and cater to individual customers, driving higher engagement, conversion rates, and revenue generation.
If you're ready to unlock the ultimate personalization experience and overcome the limitations of traditional personalization methods, it's time to explore OfferFit. As a leader in automated experimentation powered by reinforcement learning models, the platform makes daily recommendations for each customer so you can maximize your chosen success metric.
With faster experimentation, actionable insights, and a clear ROI, OfferFit is a no-brainer for marketers looking to revolutionize their success with true personalization.
Ready to make the leap from A/B to AI?