Personalization at Scale: The Ultimate Guide 
In the ever-evolving landscape of digital marketing, one concept has risen to the forefront: personalization at scale. Personalization at scale is a term used to describe the deliverance of hyper-personalized customer experiences.
Consumers are receiving a higher-than-ever volume of marketing outreach and businesses are interacting with their customers through more channels than ever. To survive in today’s marketing economy, personalization is no longer a nice-to-have; it’s become the consumer’s expectation.
In fact, a McKinsey report found that 80% of consumers indicate that a more personalized experience is important to them. And while marketing professionals understand the importance of personalization…the same report revealed that only 20% believe they are doing it effectively.
This comes as no surprise, since providing personalized experiences to every single customer can be challenging to scale.
In this guide, we’ll dive into the world of personalization at scale by defining true one-to-one personalization, highlighting its importance with real-world examples, and finally equipping you with the knowledge and skills to start your own journey toward scaling your personalization strategy.
What is Personalization at Scale?
Personalization at scale refers to the process of analyzing vast amounts of customer data to deliver extremely relevant customer experiences at every customer touchpoint.
However, with the proliferation of emerging technologies, personalization has become a bit of an industry buzzword. Often, the term “personalization” is applied to tools and software that use outdated approaches, such as rules-based strategies or segmentation. These strategies rely on predefined rules, such as sending a follow-up email to all customers who left something in their cart.
These methods fall short of true one-to-one (1:1) personalization because they don’t tailor experiences to each customer. Personalization is no longer about simply addressing a customer by their first name in an email. In an era where consumers are bombarded with marketing messages from all angles, they have come to expect a much more tailored experience.
For better results, marketers need to adopt one-to-one marketing, where you customize interactions based on each unique customer's behaviors and needs. However, this requires a lot of data and a lot of analysis to be successful and scalable.
Gathering data isn’t usually the issue: with a myriad of data points likely piling up in your CRM, the data is likely growing faster than your ability to analyze it. This is where the challenge lies: there is simply too much data to analyze and too many variables to test. This undermines your ability to scale your personalization efforts and reach new levels of revenue.
This is where emerging artificial intelligence technology plays a critical role. Advanced personalization software leverages AI and machine learning to automate the aggregation and analysis of this data and provide actionable insights into each customer’s needs.
OfferFit accomplishes this by leveraging automated experimentation to determine the best offer for every single customer, experimenting with variations in messaging, creative content, incentives, channel delivery, and timing. The platform continuously learns from each interaction to maximize your chosen key performance indicators, ultimately allowing you to accomplish personalization at scale.
Benefits of Scaling Personalization in Marketing
Let’s break down a few of the key benefits of true 1:1 personalization at scale.
Improved Customer Connection & Engagement
Customers in the post-pandemic online economy are flooded with promotions and marketing notifications. Forbes estimated that consumers may receive as many as 10,000 brand messages every day. To stand out, it’s necessary to demonstrate to the customer that you understand what will get them to convert.
By leveraging individual data, you can craft content and offerings that customers are more likely to engage customers. This leads to an overall increased satisfaction with the brand. A Bond Brand Loyalty study found that there is a 6.4x lift in member satisfaction when companies effectively leverage personalization strategies.
However, the same study indicated that companies that were not using personalization effectively did not see the same results. Companies must go beyond simply sending emails to remind customers that they left something in their cart. Customers must feel that your brand understands their individual needs, leading to a higher likelihood that they engage with your content and offerings.
Higher Conversion Rates
By tailoring your messages and offerings to each customer’s unique interests, you can significantly enhance the likelihood of converting leads into customers and clicks into purchases.
Epsilon research indicated that 8 out of 10 consumers were more likely to make a purchase as a result of one-to-one marketing. When you discover the best message, channel, and cadence to reach your customers, they are more likely to take the desired action.
More Customer Retention & Brand Loyalty
Customers who receive personalized experiences are more likely to remain loyal to your brand. Personalization and brand loyalty are two sides of the same coin. Customers who feel that the experience is tailored directly to them are more likely to have a positive experience and receive relevant information and recommendations.
Personalization plays a pivotal role in retaining existing customers and building long-term customer relationships. As personalized marketing becomes the norm, brands that excel will gain a competitive advantage. Customers are more likely to stick with brands that consistently deliver relevant, personalized experiences.
Ultimately, the goal of any marketing effort is to drive revenue. Personalization at scale can significantly boost your revenue by increasing customer engagement, conversion rates, and customer retention.
It’s a strategic investment that yields tangible financial results -- according to 451 Research, that increased revenue as a result of personalized recommendations totaled $5.6 billion. And Statista indicates that these numbers are likely to continue to increase over the coming years.
Personalization at Scale Examples
From Netflix to Amazon, tech giants are always using personalized recommendations based on individual consumer data to keep consumers engaged and increase conversions.
Let’s look at a few businesses that have taken the leap from the outdated segmented approach to true one-to-one personalization at scale. The following examples showcase how true 1:1 marketing goes far beyond surface-level efforts and leverages individual data to boost engagement and maximize ROI.
Streamotion’s goal was to increase reactivation by encouraging past subscribers to resubscribe.
Prior approach: Before using OfferFit, they relied on sophisticated A/B testing. As mentioned previously, while this strategy may be somewhat effective, it relies on segmentation, which is not true 1:1 personalization.
Current approach: Now they use OfferFit to leverage automated experimentation to test thousands of variables at once, discovering the best creative, message, channel, and cadence for each individual.
Result: The CEO put it best. The use of augmented experimentation was like putting the data “on steroids”.
Brinks Home’s goal was to secure contract renewals, maximizing both contract length and NPV (net present value)
Prior approach: Prior to using OfferFit, they relied on manual A/B testing and significant discounts. First, this segmented approach does not provide a true 1:1 experience. Furthermore, a manual approach is not truly scalable.
Current approach: Now they use automated experimentation with OfferFit to find the best renewal offer for each customer, and identify customers who are less price sensitive and need less significant discounts to renew.
Result: This strategy resulted in over 450% growth in the value of contract extensions for a $5 million annual benefit.
Securing contract renewals and winning back subscribers are a small sample of ways personalization at scale can help your business thrive.
Explore more case studies to discover the myriad of possible applications of personalization at scale.
How to Build Personalization at Scale
Personalization at scale needs to be an essential component of your marketing strategy. Most digital marketers know this already: 98% agree that there is a high cost to ignoring personalization.
The next step is understanding where to begin. Here are the key steps to get you started:
1. Determine KPIs for Improvement
The easiest way to fail is to start without defining your goal and success metrics. Begin by defining key performance indicators (KPIs) that align with your personalization goals. Ensure that your KPIs are clear and specific so you can accurately measure the effectiveness of your efforts.
For example, are you seeking to cross-sell or upsell, increase renewal and retention, win back former subscribers, or maximize purchase and repurchase rates? Your KPIs might include metrics like click-through rates, conversion rates, or customer retention rates.
2. Identify Personalization Opportunities
Identify the touchpoints in your customer journey where personalization will make a significant impact. What are the dimensions along which you’d like to test? You might include personalization in your website interactions, email marketing, or product recommendations, testing the offer, subject line, creative, channel, timing, or cadence.
3. Use Scalable Personalization Technology
The sticking point of your personalization strategy is how to scale your efforts. Finding the right technology is paramount: McKinsey found that, among companies outperforming the market, 50% feel that they have leveraged the correct tech tools–compared with only 16% of their poorer-performing contemporaries.
Outdated technologies boasting personalization have traditionally used A/B testing to segment customers and provide targeted marketing to each segment. Yet, savvy marketers have begun turning to machine learning to achieve true 1:1 personalization.
With today’s rapid advancements in self-learning AI, marketing teams are leaving behind the limited success of A/B testing and turning to technologies that can provide true one-to-one marketing. OfferFit uses reinforcement learning to automate the process of experimenting and scale one-to-one marketing.
The AI provides daily recommendations for each customer to maximize the chosen success metric, learns from every interaction, and then applies these insights to the next day’s recommendation.
Automated experimentation is more than a powerful tool to scale personalization. It is a fundamental shift in the possibilities of one-to-one personalization at scale.
4. Track & Optimize Your Personalization Campaigns
To determine the success of your personalization campaigns, track the performance and use data-driven insights to make optimizations. To do this effectively at scale, agile marketers need automated experimentation.
Self-learning AI refines the strategies and delivers even better results continuously, learning from its own product recommendations to make more effective recommendations the following day. Those who can leverage the tools that can effectively learn from the data to optimize upcoming campaigns will be the ones to win customers and outperform the market.
Drive More Engagement with Personalization at Scale
In today’s competitive landscape, scaling your personalization strategy is the key to rising above all the noise. You’re able to connect with your customers on a deeper level by understanding their unique needs and delivering a tailored experience.
By investing in the right technology and following a structured approach, you can increase conversion rates, build stronger customer relationships, foster loyalty, and ultimately boost your revenue.
OfferFit can help you start your journey toward more personalized, impactful marketing. The platform makes daily recommendations for each customer, learns from customer interactions, and then applies these insights to the next day's recommendations. You put testing and learning into hyperdrive so you can solve the problems you want to solve and optimize the success metrics you care about.
With OfferFit, marketers are finally breaking through the bottlenecks of data integration, variant creation, and experimentation to access true 1:1 personalization at scale.
Ready to make the leap from A/B to AI?