In today’s market, brands and retailers face a significant challenge: effectively communicating with a diverse group of consumers who speak thousands of languages, come from various cultural and socioeconomic backgrounds, and make purchasing decisions based on highly personal preferences. To engage authentically with each consumer on a broad scale is no easy feat. Personalization emerges as a crucial strategy to meet this demand, allowing companies to enrich consumer experiences by tailoring messages and offers to individual needs and preferences.
New Era of Personalization: AI-Driven Innovations
Most companies currently rely on tactical, manual, and stand-alone solutions to engage their customers. However, the market is now entering a promising new era of personalization, characterized by two powerful innovations: AI-driven targeted promotions and the use of generative AI (gen AI) to create and scale highly relevant messages. These advancements are revolutionizing personalized marketing by enabling businesses to deliver bespoke tone, imagery, copy, and experiences at unprecedented volume and speed. The ability to leverage AI for such sophisticated personalization marks a significant departure from traditional methods.
To unlock the potential of these innovations, marketers should prioritize their marketing technology stack. A robust framework built on improved data, decisioning, design, distribution, and measurement is essential. Enhanced technology can provide deeper insights into customer behaviors and preferences, thereby offering better-personalized experiences and fueling long-term growth. As such, modernizing the marketing tech stack is not just a necessity; it’s a strategic imperative for any brand looking to stay competitive in today’s digital landscape.
The Promise of Targeted Promotions
Traditional promotions have typically involved broad offers to large groups of people, a method that is no longer effective. Retailers now face pressures due to economic uncertainty, changing consumer preferences, and, in some cases, declining profits. McKinsey research has shown that 65 percent of customers consider targeted promotions a top reason to make a purchase. This shift in consumer expectations underscores the need for a more personalized approach to promotions.
Retailers are beginning to see the potential of AI and gen AI to reverse these downward trends and accelerate growth. An increasing number of retailers are experimenting with AI to improve mass promotions, but the true strategic advantage lies in using AI for targeted promotions. AI can tailor discounts based on individuals’ shopping preferences or their affinity for different types of offers. By adopting a more granular approach to customer segmentation, companies can craft promotions that target specific customer life cycle stages or fulfill specific business objectives.
In a world saturated with promotions, targeted offers can help companies stand out from the competition. Retailers that implement these strategies can ensure a better shopping experience for their customers while also enjoying better margins from reduced promotional costs and increased conversions. A strategic program of targeted offers at scale should aim to use business rules and algorithms to determine the most opportune timing for offerings. Additionally, creating flexible, fit-for-purpose coupons with variable discount rates, category limitations, and specific usage periods can further enhance the effectiveness of these promotions.
Enhanced Personalized Content through Gen AI
Targeted promotions can be further enhanced by using gen AI to create tailored content that resonates more with specific consumer groups. The next step in improving the customer experience involves making the buying process more convenient and enjoyable through greater relevance. Gen AI provides the capability to produce content that speaks directly to individual consumer preferences, enhancing the buying experience.
Traditionally, reaching small consumer groups with customized content has been both cost-prohibitive and infeasible. Generative AI, however, allows marketers to develop such content at scale and at a lower cost. While many marketers are currently piloting gen AI programs, most do so manually, resulting in operational inefficiencies. Smarter integration and automation of gen AI can streamline these processes, making it easier for marketers to create and distribute personalized content efficiently.
To better understand the potential improvements in the future, it is useful to compare the current state with future possibilities across the three stages of content production. In the production and versioning stage, channel operators currently design campaigns and creative briefs, and content creators produce versions of content, tagging assets with metadata for storage in a digital-asset-management (DAM) system. With gen AI, AI can assist in brainstorming, writing text, selecting formats, identifying creative assets, and tagging final assets with metadata, thereby improving efficiency and compliance standards.
Leveraging Technology for Differentiation
To effectively leverage AI-driven promotions and gen AI-enhanced content, companies need a cohesive and advanced tech stack. McKinsey’s “4D” strategy for marketing technology includes data, decisioning, design, and distribution, with measurement as a fifth critical element. Marketers can maximize growth through personalization by ensuring these elements use the latest innovations and integrate seamlessly.
Enhancing data collection and analysis allows marketers to gain deeper insights into customer behavior and preferences. This process often involves expanding data architecture to include a promotions subject area with offer and redemption history, and a content subject area with delivery and engagement history. The use of gen AI-enabled taxonomy for automation flow, robust analytics infrastructure and MLOps, new data pipelines, prompt stores, and vector databases for large language model implementations can differentiate companies by enabling accurate predictions of customer behavior across channels.
Refreshing decision engines with new AI models is crucial for developing targeted promotions and content. These models include Promo Propensity, which predicts the likelihood of a customer making a purchase due to a promotion, and Promo Uplift, which analyzes promotion ROI by comparing customer behavior during promotion and non-promotion periods. Content Propensity predicts the likelihood of a customer responding to content, while Content Effectiveness measures content effectiveness by analyzing customer response.
An innovative design layer ensures that content is engaging and relevant. Incorporating an integrated offer management system and a centralized DAM system can streamline the process. These systems should seamlessly integrate into downstream channels, facilitating easy search, reuse, and dynamic delivery of assets. Real-time personalization requires an infrastructure that optimizes messaging across touchpoints, supported by systems like content and campaign management, dynamic modular templates, and API integrations.
Thorough measurement is vital for a complete marketing tech stack. To validate the ROI of personalization efforts, marketers should implement incrementality testing, performance metrics, and measurement playbooks. Aggregating data into a centralized reporting engine can provide self-serve dashboards for stakeholders, facilitating continuous improvement.
Conclusion: Path to Gen AI–Enabled Personalization
In today’s market landscape, brands and retailers grapple with the critical challenge of effectively communicating with a highly diverse consumer base. These consumers speak numerous languages and represent a range of cultural and socioeconomic backgrounds. Their purchasing choices are driven by deeply personal preferences and unique motivations. Engaging genuinely and inclusively with each consumer on a large scale is an exceptionally tough task for companies.
This is where personalization becomes a pivotal strategy. By customizing messages and promotions to align with individual consumer needs and inclinations, companies can significantly enhance the consumer experience. Personalization enables brands to create a deeper connection with their audience, fostering loyalty and improving customer satisfaction.
In practice, this means leveraging data analytics to gain insights into consumer behavior and preferences. Employing advanced technology, like AI and machine learning, facilitates the creation of personalized marketing campaigns more efficiently. These technologies help in predicting trends, understanding purchasing patterns, and offering tailor-made recommendations.
Furthermore, personalization is not limited to marketing alone. It extends to customer service, product development, and the overall brand experience, making it a comprehensive approach to meeting consumer expectations. In essence, personalization allows brands to speak directly to each consumer in a manner that feels attentive and unique, turning a daunting challenge into a strategic advantage in today’s competitive market.
