{"id":119988,"date":"2025-01-16T09:00:00","date_gmt":"2025-01-16T17:00:00","guid":{"rendered":""},"modified":"2025-01-17T09:06:53","modified_gmt":"2025-01-17T17:06:53","slug":"how-cmos-can-personalize-marketing-and-optimize-spend-with-ai","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/industry\/blog\/retail\/2025\/01\/16\/how-cmos-can-personalize-marketing-and-optimize-spend-with-ai\/","title":{"rendered":"How CMOs can personalize marketing and optimize spend with AI"},"content":{"rendered":"\n

AI isn\u2019t just a tool\u2014it\u2019s the foundation for building competitive advantage. In a recent post<\/a>, we showed how chief marketing officers (CMOs) can move beyond AI experimentation to prove more value and return on their AI investments. To deliver more business impact, CMOs need to expand the way they think about AI from a pilot or use case perspective to a broader view. That broader view requires CMOs to rethink key business processes where many face constraints: content creation<\/strong> and customer engagement<\/strong>. These two processes are notoriously resource-intensive and costly because they require personalization at every touchpoint, but they fuel the insights needed to optimize marketing spend and grow the business. <\/p>\n\n\n\n

Copilots, agents, and AI assistants that facilitate creativity and drive productivity are becoming table stakes for marketers. Most brands have now tested generative AI for tasks like brainstorming, summarizing emails, or enhancing visual content. In fact, 78% of AI users are bringing their own AI tools to work<\/a>. But to leverage AI at a higher level and prove more value to the bottom line, CMOs need to embed AI deeper into their organization\u2019s processes and across multiple functions. And that is no overnight task.  <\/p>\n\n\n

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Microsoft Cloud for Retail<\/h2>\n\n\t\t\t\t\t
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Connect your customers, your people, and your data<\/p>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t

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Here\u2019s a three-stage roadmap for how some CMOs are rethinking business processes like content creation and customer engagement in a buy versus build framework that aligns AI investments with business outcomes. <\/p>\n\n\n\n

A new AI roadmap for CMOs <\/h2>\n\n\n\n

The Buy-Extend-Build framework extends from basic AI adoption (Buy) to full-scale transformation (Build), guiding marketers as they move from table-stakes investments to market-making, revolutionized business models. <\/p>\n\n\n\n

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  1. Buy<\/strong>:
    The Buy stage delivers operational efficiency quickly but serves as a steppingstone, not the destination. These \u201cbuilt-in\u201d solutions, <\/strong>like     Microsoft 365 Copilot<\/strong><\/a>, require no customization and help the workforce get comfortable using AI as a precursor to bigger, more customer-facing AI initiatives. These strategies can be effective at helping marketers reduce menial tasks that weigh down creative forces. In fact, the global agency network dentsu<\/a> uses Microsoft Copilot to do just that. According to Kate Slade, Director of Emerging Technology Enablement, dentsu<\/a>, \u201cOur recent analysis shows employees are saving at least 15 minutes, if not 30 minutes a day by using Copilot. We are seeing a noticeable shift in how they are using the technology, spending less time on meetings, emails, calls, or chats, using the time they get back to focus on deep work without interruptions and to invest in their teams<\/em>.\u201d  <\/li>\n\n\n\n
  2. Extend<\/strong>:
    As organizations start to mature and integrate various business processes, they need to compose their own solutions. This involves integrating generative AI tools, internal data, and models from Microsoft or open-source platforms to build brand-specific, intuitive user interfaces and personalized services. This approach enables marketers to drive more personalized experiences and precise performance measurements, allowing for faster optimizations through dynamic content changes based on campaign performance or new collaboration methods with agencies. Success at this stage requires continuous AI model training, fine-tuning, and maintaining relevant, high-quality data. The Extend stage can leverage generative AI to enhance shopper self-service, customer service, and automate internal content planning and execution processes. <\/li>\n\n\n\n
  3. Build<\/strong>:
    At this final stage, brands unlock their highest growth potential by redefining business processes end-to-end. This requires advanced AI models, change management, reskilling, and new operating models. Partners, SIs, and customers collaborate to develop self-improving models. \u2018Build\u2019 initiatives leverage AI-powered solutions like Microsoft Azure OpenAI, utilizing automatic data feedback loops from various repositories. This includes linking operational data (such as sales and inventory demand) with the marketing tech stack and customer insights. By rethinking costly business processes, the Build stage drives a compounding competitive advantage\u2014enabling continuous scaling and evolution with AI. This transformation impacts multiple business units, creating growth opportunities beyond marketing. <\/li>\n<\/ol>\n\n\n\n
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    Dynamic, adaptive content production <\/h2>\n\n\n\n

    As CMOs start to examine business processes through the lens of AI potential, they\u2019ll find that different processes require a different approach (like Buy, Extend, or Build.) Some processes will differ by cost or resource needs, and will require a change management plan. Regardless, \u2018Buy\u2019 is considered foundational to unlock more AI value. But to go even further, CMOs will need to rethink decades-old challenges. And that requires some imagination. <\/p>\n\n\n\n

    Imagine if content marketers could assign an AI assistant to complete multiple complex workflows autonomously\u2014from automatically tagging metadata to changing the background on a display ad. Other agents could generate data-driven creative briefs for review applying copy grounded within brand guidelines, localized by region, or deliver intelligent optimization recommendations and insights based on campaign results.  <\/p>\n\n\n\n

    These are big, revolutionary concepts that require a combination of Buy, Extend, and Build initiatives. To help marketing evolve with AI, organizations need to break down common workflows and identify where AI can boost efficiencies versus where rebuilding is necessary. Not every task will need to be rebuilt. Most importantly, the vision needs clearly defined goals that impact multiple senior stakeholders so there is buy-in across IT and marketing, along with team assignments meeting specific timelines and goals. The result will be more than hyper-personalized touchpoints, higher Net Promoter Scores, or improved return on investment (ROI)\u2014it will drive overall brand growth. <\/p>\n\n\n\n

    Partners, marketing technology solution providers, and agency teams are often key to this effort. Many are already building solutions across business processes with composability in mind to fit the needs of the brand. For example, Sitecore is partnering with Microsoft<\/a> to support revolutionizing content creation and development with Nestl\u00e9. Marketers often find themselves navigating a plethora of strategic brand and marketing documents, which is overwhelming and time intensive. On Nestl\u00e9\u2019s AI transformation roadmap to revolutionize marketing processes, they co-created an AI Brand Assistant<\/a> as a part of Sitecore Content Hub<\/a>, to democratize and simplify access to brand and category knowledge, and supercharge creative partners, to ensure all AI-powered outputs\u2014from color schemes and messaging to market trends\u2014remain authentic and true to their brand.  <\/p>\n\n\n\n

    Building better shopping experiences with conversational AI  <\/h2>\n\n\n\n

    Ecommerce has remained largely unchanged in the last twenty years. It offers shoppers search, product descriptions, scrolling, and sometimes a hit-or-miss chatbot for help navigating returns or simple questions. With the advent of conversational commerce agents\u2014including virtual shopping assistants\u2014retailers are invigorated to rethink how customers experience and engage with their brand. The possibility of the future of search and online commerce includes higher customer engagement, loyalty, sales, and reducing operational costs. Retailers are rethinking what \u201csearch\u201d means to their consumers with AI. They can now hyper-personalize customer interactions at scale, increasing conversion rates, and reducing customer acquisition cost (CAC). Continuously training and fine-tuning AI models with accurate data helps ensure shopper recommendations remain relevant and contextual.  <\/p>\n\n\n\n

    Accenture\u2019s Consumer Pulse 2024 research<\/a> found that more than half (around 51%) of consumers are already open to using conversational AI solutions. For example, ASOS<\/strong> leverages this technology<\/a> to fine-tune product recommendations and deliver hyper-personalized experiences like their AI Stylist. Their Azure OpenAI-powered experience helps customers discover new looks through an easy-to-use, conversational interface, built using early access to Microsoft\u2019s generative AI tools, that reflects the ASOS brand and tone of voice authentically. <\/p>\n\n\n\n

    But personalization isn\u2019t a one-and-done task. Critical to both content generation and conversational AI are continuous data feedback loops to refine models and ensure long-term agility with AI investments. AI-powered solutions, such as Azure OpenAI models, perform best when enriched by real-time data from cross-functional systems\u2014including sales, supply chain, and customer platforms. Often this approach requires something like a unified data platform, or Microsoft Fabric<\/a>, so data is compiled in a single data layer and fed into the AI models in real-time. <\/p>\n\n\n\n

    Tools like Personalized Shopping Agent<\/strong><\/a> enhance customer interactions through conversational AI and customized recommendations, or through a combination of Microsoft 365 Copilot, Azure OpenAI, and Fabric to ensure data feedback loops are present and continually optimizing the offering.  <\/p>\n\n\n\n

    Building these systems allows organizations to move beyond incremental change and achieve compounding competitive advantages over time. <\/p>\n\n\n\n

    Three takeaways for CMOs <\/h2>\n\n\n\n
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    1. Think big<\/strong>: Don\u2019t stop at automating small tasks\u2014reimagine entire processes to unlock greater value from your investments. Ensure collaboration with IT to create data flywheels that future-proof marketing efforts.<\/li>\n\n\n\n
    2. Collaborate early and often<\/strong>: Secure alignment across the executive leadership team and potentially the board, along with external partners to drive adoption and maximize outcomes. <\/li>\n<\/ol>\n\n\n\n
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      1. Innovation is iterative<\/strong>: Success with AI isn\u2019t a one-time event\u2014it\u2019s a continuous journey of learning and refinement. <\/li>\n<\/ol>\n\n\n\n

        CMOs who act fast will capture a first-mover advantage in this new era of AI-powered marketing. AI offers CMOs the tools to optimize spend while personalizing experiences at scale, ensuring sustainable growth and customer satisfaction. By rethinking resource-intensive and costly business processes like content creation and personalized customer engagement, brands can unlock new efficiencies, deliver meaningful interactions, and achieve long-term success. <\/p>\n\n\n\n

        Learn more<\/h2>\n\n\n\n