AI & ML: Defining the New Era of Personalization at Scale

Over the last several years, personalization has become a core component of successful marketing programs. Consumer expectations for personalization are higher than ever, along with demands for transparency around how their data is being used, shared, and handled.

This year, marketers and technology leaders are also being asked to do more with less, while making their operations cost-efficient and effective. According to Gartner, 38 percent of business leaders have shifted focus from revenue growth to margin growth, while 67 percent of CFOs believe the last three years of digital spending has not met enterprise expectations.

Marketers can’t waste budget on inefficient programs that cast too wide of a net. By leveraging a customer data platform (CDP) for personalization, along with other key supportive technology platforms, companies can reduce waste and optimize operations for cost-savings and efficiency.

CDPs equipped with artificial intelligence (AI) capabilities are creating next-level customer experiences by helping businesses orchestrate the customer journey with personalized content delivered at the right moments along the buying cycle.

With valuable and relevant personalization delivered at scale, brands can now focus on nurturing high-value customers, and turning those customers into advocates with ongoing customer lifetime value. 

Quality Data Drives Personalization

Doing personalization right, and at scale, starts with quality data. If you do not understand your customers intimately, you will not be able to serve up relevant messaging, content, and experiences. 

This becomes even more critical in a B2B setting, since there are typically multiple buyers in the purchase cycle, which can last for weeks or months. And, with multiple touch points for each buyer, personalization across the buying cycle in an enterprise environment can only be handled with advanced software fed with quality data.

Companies that are serious about doing personalization at scale are using CDPs to gather customer data from multiple disparate silos, ingest that data, and integrate it into unified profiles to feed personalization systems – whether they are stand-alone or integrated into other platforms, like a digital experience platform (DXP) or a content management system (CMS).

This, along with AI-powered segmentation and next-best action capabilities, make data-driven personalization at scale a reality.

The State of Personalization in 2023

Personalization technology remains a top investment area for businesses, and will continue to grow in the future.

  • The global personalization market was estimated to be $943.25 million in 2022, and is expected to reach $1.17 billion in 2023. It’s projected to grow at a CAGR of 23.67 percent to hit $5.16 billion by 2030.
  • The recommendation engine market size is expected to reach $12.03 billion by 2025, up from $1.14 billion in 2018, with a CAGR of 32.39 percent from 2020-2025.

One of the reasons that personalization continues to grow as a key marketing technology investment is that companies are using it to acquire new customers and to build ongoing loyalty.  

Personalization can reduce CAC by up to 50 percent, and increase marketing spend efficiency by up to 30 percent. Retaining an existing customer is between five to 25 times less expensive than acquiring new ones. Loyal customers are also more likely to engage in word-of-mouth marketing and become brand ambassadors, which helps build credibility over time. 

Personalization and AI

The future of strategic personalization at scale lies in AI and machine learning (ML). AI/ML allows for more sophisticated responses based on previous user activity or intent, and learns from that data to improve recommendations. By using a CDP with AI/ML, businesses can better automate personalization strategies at scale.

However, implementing personalization at scale is tough, especially on the tech side. Thirty-nine percent of businesses report they struggle with implementing personalization technology effectively. Other challenges include lack of training, compliance concerns for data privacy regulations, poorly constructed organizational processes, and poor data quality. 

For AI to reach its potential, it must be fed with clean, quality data. The higher quality the data, the more targeted and effective the personalization efforts will be. AI/ML models need to be trained on high-quality data that is aligned with customers’ preferences and values to reduce waste and improve performance.

A CDP is designed to gather data, integrate it, and deliver it to other MarTech platforms for activation. Enterprise-grade CDPs have the data foundation you need for personalization, predictive segmentation, and advanced analytics, along with modern AI/ML algorithms to achieve strategic personalization across your organization. 

Another relevant challenge is consumers’ uneasiness with AI technology overall. It is incumbent on companies to promote transparency when it comes to AI, so consumers feel comfortable sharing their data. 

The Future of Personalization 

In the midst of a challenging economic climate for the near future, business leaders are looking to marketing and technology leaders to both cut costs and optimize their operations for efficiency. They expect to see ROI from marketing technology investments as well.

For marketers, it’s not so much about doing more with less, but about showing value and bottom-line results to the business for their efforts – all while building trust with customers to ensure successful personalization at scale.

Learn more about how you can achieve personalization at scale with a CDP here.

Brian Carlson
Brian Carlson
Brian Carlson is the Founder and CEO of RoC Consulting, a digital consultancy that helps brands establish the optimal balance of content, technology and marketing to achieve their goals.