Customers expect personalized experiences—and they value them. According to McKinsey research, 76% of consumers say personalization makes them more likely to purchase, and 78% say it makes them more likely to repurchase.
Personalization has evolved from simply including customers’ names in emails to providing product recommendations in real-time based on purchase history and online behavior, seamlessly integrated across all marketing channels. Amazon is a well-known leader in delivering 1:1 personalized experiences, using the vast data collected from its members’ accounts to serve hyper-personalized product recommendations.
Delivering personalization at scale, however, has historically presented a significant challenge for many retailers. But today, thanks to the advent of artificial intelligence (AI) and machine learning (ML) technologies, companies can get much closer to delivering a 1:1 customer experience.
# What Is Marketing Personalization, and What Are Its Customized Benefits?
Marketing personalization refers to using customer demographic and behavioral data to deliver highly tailored content in real-time. Investing in marketing personalization can be strategic for companies seeking to differentiate themselves. It can significantly increase conversion rates and a customer’s lifetime value to the company. Data suggests that companies that excel at personalization generate 40% more revenue from these activities than average players.
“Customers are more likely to be loyal to a company that understands their preferences and can effectively connect them with the products and services they most desire.”
Marketing personalization strengthens the company-customer relationship.
As we covered in our blog, The Power of Customer Personalization, personalization creates better customer experiences, improves brand loyalty, and increases customers' lifetime value.
# Using AI/ML Technologies To Deliver Customer Personalization at Scale
Retailers who want to invest in personalization can begin by using AI/ML technologies in various places across their organizations and workflows. One effective strategy is to automate previously manual processes. One study found that companies that used AI-powered customer segmentation increased revenue by 4% while decreasing manual process efforts by 80%. The other major opportunity area is to improve direct customer interactions with your company systems, such as enabling better in-session responsiveness and developing more helpful assistant or chatbot conversations.
Some specific opportunities for retailers looking to leverage AI/ML tech to scale personalization include:
- Enhanced Chatbots: Future chatbots will speak personably and with a brand’s unique voice. They will also be able to deliver recommendations based on solving customers' issues, not just product searches. They’ll accomplish this by using customers' unique data profiles, empowering them to resolve basic issues without a human representative.
- Content Generation: Generative AI tools will enable faster-to-market content and generate multiple variations of that content. These tools can create subject lines, copy, and other elements in different languages and swap out atomic elements and skins to build new variations of an asset. In the future, AIs could become even more advanced and act as creative partners, suggesting aesthetic parameters and generating human models and backgrounds.
- Responsive Online Engagement: AI can help increase in-session responsiveness, where relevant images and products are surfaced to a customer based on historical and real-time data. Amazon and Spotify are doing this today, and it will continue to mature, becoming more engaging and expected by customers.
- Insights to Improve Future Campaigns: More powerful insights will be possible based on audience data, including analysis of buying history and other online behavior, leading to more accurate predictions about what a customer is likely to buy in the future. Audience segmentation will become more precise and based on categories we haven’t yet imagined. Data tagging and taxonomy to support the future might include browsing behavior or time spent with a particular campaign, for example.
- Automation of Business Processes: Campaign calendaring will become more automated and data-driven, helping brands prioritize when customers should receive what campaigns or placements within a campaign to optimize conversions.
“AI can help companies ready their data for additional AI use cases and integrations. Data readiness opportunities with AI could be centralizing disparate data sources, recommending and analyzing A/B testing, and cleaning up data sets for appropriate analysis and decision-making.”
# 5 Considerations When Integrating AI/ML Technology Into Your Marketing Organization
When embarking on a roadmap toward personalization at scale, there are five steps a company might consider:
- Define what personalization success looks like for the company — and the customer.
Be clear about what personalization means to your company as a goal, why it’s important, and what success looks like. Ideally, the way your company defines personalization aligns with the way a customer defines personalization. In other words, a company may target a placement for a particular audience segment and consider the asset personalized. However, if the content feels generic to the customer, the company may not realize significant benefits compared to not personalizing. For example, a retailer might define personalization as targeting placements based on how they segment customers across internal categories (e.g., clothing, jewelry, shoes). However, a customer may feel they shop across all categories and look for placements that find emergent connections, such as clothing and jewelry or shoes that feature Western designs or have floral elements. - Clarify investment and outcome expectations upfront.
Ensure that the level of investment your company is willing to make, the expected results, and the time frame are clear from the start. Is the goal with a personalized chatbot, for example, to enhance search, reduce call center headcount, increase stickiness, or something else—and by how much? Defining success includes setting up the right metrics so that progress can be measured throughout the project and stakeholders know what defines the end state for the transformation. - Ensure compliance with regulations and company ethics.
If your company doesn’t already have an ethical statement and supporting policies around AI usage, it’s time to start! Companies that are not transparent about how customer data is being used by AI/ML technologies and do not provide options to opt in and opt out risk losing customer trust. Additionally, as this is an emerging field, regulations are rapidly evolving, and companies need to keep up-to-date to ensure compliance. - Enhance the customer experience.
Improving the customer experience is crucial for companies to ensure they build long-term loyalty. A personalized marketing approach that focuses on providing engaging content and products and services the customer is likely to want can achieve this. However, it’s important to avoid intrusive or manipulative personalization tactics. To create better customer experience, companies need to innovate, experiment, and conduct A/B testing. - Ensure impacted teams, customers, and partners are brought along.
As with any major transformation, it’s essential that impacted teams are engaged with the changes and being brought along with transparent communications, appropriate learning, and other support to ensure they are successful in the future state. For companies that partner with other brands, education and dialogue will be necessary so that brands understand what is changing and how personalization will impact their revenue and other goals. Finally, companies need to be transparent with their customers about where AI/ML technologies are being used and the implications for both a better customer experience and data privacy.
“Delivering personalization at scale requires a deep understanding of the tools, people skills and capabilities, processes, and governance required to run marketing campaigns from start to finish. Investing in this work up front is essential for decision-makers and for bringing along impacted teams.”
# Getting Started With AI-Powered Marketing Personalization
AI-powered marketing personalization allows businesses to forge deeper connections with customers. When skillfully managed by humans, AI tools can learn what content resonates with customers and deliver it automatically. Companies that seize AI/ML technology opportunities to provide personalized content will likely deliver a more engaged and satisfying customer experience. Customers are eager to feel understood by the brands they frequent and will repay those efforts with loyalty.
We are here to help as you embark on your marketing personalization journey.
