Generative AI such as ChatGPT is rapidly revolutionizing business operations worldwide. Not only has the global business landscape been profoundly reshaped, but CMO responsibilities have been fundamentally reimagined to fit the new paradigm. As AI continues to drive greater efficiency, effectiveness, and a new wave of innovation at scale, CMOs have the opportunity to spearhead the use of marketing AI to accelerate and enhance marketing strategies and drive tangible business outcomes, first attempting to capture The value chain integrates the full potential, benefits and opportunities of artificial intelligence. At the same time, it is imperative to create an environment of innovation defined by experimentation, test and learn, and proofs of concept—all of which allow AI to flourish within their organizations.
What Generative AI Means for CMOs
In this world, nothing is more constant than change. While a firm believer in the irreplaceable power of human influence in marketing, it is undeniable that generative artificial intelligence (AGI) such as ChatGPT is rapidly revolutionizing business operations across the globe. The AI revolution has not only profoundly reshaped the global business landscape, but has fundamentally restructured the responsibilities of the chief marketing officer (CMO) to fit the new paradigm.
AI technologies will revolutionize marketing and sales for businesses; now, generative AI is poised to disrupt new ways for B2B and B2C businesses to experience customer experience, sales productivity and growth. As AI continues to drive greater efficiency, effectiveness, and a new wave of innovation at scale, CMOs have the opportunity to spearhead the use of AI to accelerate and enhance corporate marketing strategies and drive tangible business outcomes.
To take full advantage of this exciting time, CMOs must first attempt to capture the full potential, benefits and opportunities of integrating AI across the marketing value chain. At the same time, they must create an environment defined by experimentation, test-and-learn, and proof-of-concept—all of which allow AI to flourish within their organizations.
Bringing AI into the entire customer journey
Artificial intelligence (AI) and machine learning (ML) continue to push the limits of marketing and sales. Now, as the changing evolution of generative AI continues, we see the use of open-source platforms percolating to the sales front, as well as increased investment in AGI innovations by sales technology providers. Given the increasing complexity and speed of doing business during the global economic downcycle, these technologies are becoming essential tools.
Inevitably, this will affect the way businesses operate in the marketplace, and how they connect with and serve customers. In fact, it may already be doing so. Forward-looking CMO leaders are considering how to adapt to this new landscape. In this innovation guide we outline the opportunities (and risks) of AI in marketing and sales in this rapidly changing field, and suggest a way forward.
Across industries, customer engagement models are changing: Today's customers want everything, anytime, anywhere. While they still want an even mix of traditional, remote and self-service channels (including face-to-face, inside sales and e-commerce), we see continued growth in customer preference for online ordering and reordering.
Leading players—those growing market share by at least 10 percent annually—look to leverage advanced sales technology; build hybrid sales teams and capabilities; customize strategies for third-party and company-owned markets; and achieve excellence across omnichannel E-commerce; and providing hyper-personalization (delivering unique information to individual decision makers based on their needs, profiles, behaviors and interactions, both past and predictive).
This revolutionary approach to generative AI is changing the marketing and sales landscape, driving greater efficiency and customer engagement from the very beginning of the customer journey.
CMOs welcome the benefits of generative AI
McKinsey’s latest survey of business leaders finds out how companies view AGI’s early use cases and role in broader marketing and sales. A notable finding is cautious optimism among CxOs: Respondents expect at least a modest impact for each use case. In particular, these businesses are most enthusiastic about the use cases of customer journey experience, marketing optimization, and personalized engagement (chart below).
The study found that 90 percent of business leaders expect to use AI solutions "often" within the next two years.
McKinsey Global Institute
Investing in AI grew operating income by 3% to 15%, and sales ROI increased by 10% to 20%.
McKinsey Global Institute
Simon Kahn, Google's vice president of marketing, highlighted the important role of generative AI in marketing " In marketing, I see two immediate opportunities for generative AI: 1) to increase productivity and 2) to complement creativity. I see to my team using Bard for everything from streamlining briefings to brainstorming co-creation solutions. A tool like Bard can be a powerful co-creator by getting repetitive tasks done faster, improving our communication, and by overcoming Starting the creative process from zero hurdles gives us momentum. Like others, we are just beginning to touch the transformative potential of AGI and are excited to continue engaging the field to explore as we identify new opportunities."
Overall, the most effective businesses are prioritizing and deploying advanced sales technology, building hybrid teams, and enabling hyper-personalization. They are maximizing e-commerce and third-party marketplace platforms through analytics and artificial intelligence. Successful businesses tend to:
- There is a clearly defined AI vision and strategy.
- Over 20% of digital budgets are invested in AI-related technologies.
- Hire a data science team to run algorithms to inform rapid pricing strategies and optimize marketing and sales.
- Enterprise strategies are looking to the future and starting with simple AGI use cases.
These trailblazers have realized the potential of AGI to enhance their operations.
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Customer Operations: Improve customer experience and customer service efficiency
Generative AI has the potential to revolutionize the entire customer operations function, improving customer experience and service productivity through digital self-service and augmenting and augmenting agent skills. The technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language. The study found that at a company with 5,000 customer service operations, generative AI applications increased problem resolution rates by 14% per hour and reduced time to resolve issues by 9%. It also reduced agent attrition and requests to speak with managers by 25%. Crucially, productivity and service quality improved most among less experienced agents, while AI assistants did not increase, and sometimes even decreased, productivity and quality metrics for more skilled agents. This is because AI can assist less-experienced agents to communicate using techniques similar to their highly skilled counterparts.
AI’s Productivity Improvements to Customer Operations
38%
global spending
$404 billion
Economic Value
The following are examples of customer service operations improvements that generative AI can make for specific use cases:
- Customer self-service . AGI-powered chatbots can provide instant and personalized responses to complex customer inquiries, regardless of the customer's language or location. Improving the quality and effectiveness of interactions through automated channels, AGI can automatically respond to a higher percentage of customer inquiries, enabling customer service teams to handle inquiries that can only be resolved by human agents. About half of all customer contacts at North American banking, telecommunications, and utilities are already handled by bots, including but not limited to AI, the study found. It is estimated that AGI can further reduce the number of human contact customers by up to 50%, depending on the existing level of automation of the enterprise.
- First contact needs to be resolved . AGI can instantly retrieve a company's data about a specific customer, which can help human customer service representatives more successfully answer questions and resolve issues during initial interactions.
- Improve response time. AGI can reduce the time it takes for human sales reps to respond to customers by providing assistance and recommending next steps in real time.
- Increase sales. Thanks to its ability to quickly process data on customers and their browsing history, the technology can identify product recommendations and deals tailored to customer preferences. Additionally, AGI can enhance quality assurance and coaching by gleaning insights from customer conversations, identifying what can be done better, and coaching agents.
McKinsey research estimates that applying AGI to customer service functions could increase productivity with a value ranging from 30% to 45% of the current function's cost. This analysis only captures the direct impact that AGI may have on customer operational productivity. It fails to take into account the potential knock-on effects the technology could have on customer satisfaction and retention through improved experiences, including better understanding of customers to help agents provide more personalized assistance and advice.
Marketing: Increase Personalization, Content Creativity
AGI is rapidly taking hold in the marketing function, where text-based authoring and communication and mass personalization are the drivers. The technology enables the creation of personalized messages tailored to individual customer interests, preferences and behavior, as well as tasks such as producing first drafts of brand ads, headlines, slogans, social media posts and product descriptions.
Introducing AGI into the marketing function requires careful consideration. First of all, the language model trained on public data does not have sufficient protection against plagiarism, copyright infringement and brand recognition, and may infringe intellectual property rights. Due to limited or biased training data, virtual try-on applications may produce biased representations of certain demographic characteristics. Therefore, conceptual and strategic thinking tailored to the needs of each business requires significant human oversight.
AI's productivity gains for marketing
10%
global spending
$463 billion
Economic Value
Potential operational benefits of using AGI for marketing include the following:
- Efficient and effective content creation . AGI can significantly reduce the time required for ideation and content drafting, saving valuable time and effort. It also promotes consistency across disparate content, ensuring a consistent brand voice, writing style and format. Team members can collaborate through AGI, an artificial intelligence that can integrate their ideas into a cohesive work. This will allow the team to significantly enhance the personalization of marketing messages for different customer groups, geographies and demographics. Mass email marketing campaigns can be instantly translated into as many languages as needed, with different images and messages depending on the audience. The ability to generate AI-generated content with varying specifications can increase customer value, engagement, conversion and retention over a lifetime at a scale beyond what is currently possible with traditional technologies.
- Enhanced use of data . AGI can help marketing functions overcome the challenges of unstructured, inconsistent and disconnected data, such as data from disparate databases, by interpreting abstract data sources such as text, images and different structures. It can help marketers better use data such as territory performance, consolidated customer feedback and customer behavior to generate data-informed marketing strategies such as targeted customer profiles and channel recommendations. These tools can identify and synthesize trends, key drivers, and market and product opportunities from unstructured data such as social media, news, academic research, and customer feedback.
- SEO optimization . AGI can help marketers achieve higher conversion rates and lower costs through search engine optimization (SEO) of marketing and sales technology components such as page titles, image tags, and URLs. It can synthesize key SEO tokens, support experts in SEO digital content creation, and distribute targeted content to clients.
- Product discovery and search personalization . With AGI, product discovery and search can be personalized through multimodal input of text, image and voice, and a deep understanding of customer profiles. For example, technology can leverage individual user preferences, behavior, and purchase history to help customers discover the most relevant products and generate personalized product descriptions. This will allow CPG, travel and retail companies to improve their e-commerce sales by achieving higher website conversion rates.
McKinsey’s 2023 analysis estimates that AGI could increase the productivity of the marketing function, with a value between 5% and 15% of total marketing spend. Analysis of the potential use of AGI in marketing does not take into account knock-on effects beyond the immediate impact on productivity. AGI-powered synthesis can provide higher-quality data insights, leading to new ideas for marketing campaigns and more targeted customer segments. Marketing functions can shift resources to producing higher quality content for owned channels, potentially reducing spending on external channels and agencies.
Produce tailor-made content at scale
AGI tools can leverage existing documents and datasets to drastically simplify content generation. These tools can create personalized marketing and sales content for specific customer profile data and history, as well as a variety of alternatives to A/B testing. In addition, AGI can automatically generate template documents, identify missing documents, and scan for relevant regulatory updates to create alerts for relevant shifts.
Selling Commerce: Reshaping Customer Engagement and Commerce Productivity
New McKinsey analysis suggests that implementing AGI could increase sales productivity by approximately 3% to 5% of current global sales spend. This analysis may not fully account for the additional revenue that AGI may bring to the sales function. For example, AGI’s ability to identify leads and follow-up capabilities can uncover new leads and facilitate more effective lead interactions, which can lead to additional revenue. Additionally, the capabilities of AGI can save sales reps time to invest in higher-quality customer interactions, increasing sales success.
AI's Productivity Improvement for Sales Business
4%
global spending
$486 billion
Economic Value
AGI can change the way B2B and B2C companies operate sales. Here are two sales use cases:
- Increase the sales order rate. AGI can identify and prioritize sales leads by creating comprehensive consumer profiles from structured and unstructured data and recommending actions to employees to improve customer engagement at every touchpoint. For example, AGI could provide better information about customer preferences, potentially increasing conversion rates.
- Improve lead generation. AGI can help sales reps nurture leads by synthesizing relevant product sales information and customer profiles and creating discussion scripts to facilitate customer conversations, including up- and cross-selling talking points. It can also automate sales follow-ups and passively nurture leads until the customer is ready to engage directly with a human sales agent.
Reinventing the Customer Interaction Model
Consumers are increasingly looking for customization, from clothing and cosmetics to curated shopping experiences, personalized outreach and food, and generative AI can improve that experience. Generative AI can aggregate market data to test concepts, ideas and models. Stitch Fix, which uses algorithms to suggest style choices to customers, has tried DALL·E, which visualizes products based on customer preferences for colour, fabric and style. Using text-to-image generation, the company's stylists can visualize clothing based on consumer preferences and then identify similar articles in Stitch Fix's inventory.
Retailers can create apps that provide shoppers with a next-generation experience, creating a significant competitive advantage in an era when customers expect a single natural language interface to help them choose products. For example, generative AI could improve the process of choosing and ordering ingredients or preparing food for a meal — imagine a chatbot that can pull up the most popular tips from the comments attached to a recipe. There is also a great opportunity to enhance customer value management by providing personalized marketing campaigns through chatbots. Such applications enable human-like conversations about products, increasing customer satisfaction, traffic, and brand loyalty. Generative AI offers retailers and CPG companies many opportunities to cross-sell and up-sell, gather insights to improve products, and increase their customer base, revenue opportunities, and overall marketing ROI.
Insights for quick resolution and enhanced customer service
The growth of e-commerce has also increased the importance of effective consumer interaction. Retailers can combine existing AI tools with generated AI to enhance chatbots so that they can better mimic the interaction styles of human agents—for example, by responding directly to customer queries, tracking or Cancel orders, offer discounts, and upsell. Automating repetitive tasks allows human agents to devote more time to complex customer questions and gain context.
Dynamic Audience Targeting and Segmentation
Generative AI can combine and analyze large amounts of data, such as demographic information, existing customer data, and market trends, to identify additional audience segments. Its algorithms then enable businesses to create personalized outreach content easily and at scale.
Instead of spending time researching and creating audience segments, marketers can leverage generative AI’s algorithms to identify segments with unique characteristics that might otherwise be overlooked in existing customer data. Without knowing every detail of these niches, they can ask a generation of AI tools to automatically draft tailored content like social media posts and landing pages. Once these are refined and vetted, marketers and sales leaders can use AI generation to generate further content, such as outreach templates matching sales campaigns, to reach out to prospects.
Embracing these technologies requires some openness to change. Organizations will need a comprehensive and aggregated dataset (such as an operational data lake pulled into disparate sources) to train AI models capable of generating relevant audiences and content. Once trained, the model can be run in commercial systems to streamline workflows while being continuously refined through agile processes.
Finally, adjustments to marketing organizational structures and operating models may be required to ensure appropriate levels of risk oversight are in place and performance reviews are conducted in line with new ways of working.
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6 steps to start marketing AI innovation journey
In the past few months, generative AI has become the brightest beacon in this post-epidemic market downturn and sluggish demand environment. Our innovation consulting team promotes AI innovation projects in the field of marketing not only internally but also with multiple external customers. For example, we have always adhered to the principle of lean innovation , quickly building MVP and operating experiments from 5-6 weeks to 10-12 weeks, and realizing marketing quickly. value. How to do it? Here is our recommended 6-step approach to AI innovation:
In addition to taking immediate action, CMO leaders can start thinking strategically about how to invest in AI-enabled marketing excellence over the long term. It is important to identify which use cases are worth investing in and which can help you differentiate your value in the market. Then prioritize based on impact and feasibility.
The AI landscape is rapidly evolving, and today's winners may not be viable tomorrow. Small start-ups are good innovators, but may struggle to scale and produce sales-focused use cases that meet enterprise needs. Carry out experiments and iterations with different external innovation partners, and at the same time, innovations that are linked to the main business marketing and sales can often win quickly. It is recommended to strengthen strategic partnerships with external technology and innovation partners who are open, able to iterate rapidly and expand capabilities.
100% user-oriented
Start with the customer and end with the customer. Some CMOs may try to primarily use AI technologies to reduce costs and increase efficiency for their teams, especially given the current economic downturn. CMOs need to ensure that deployments always come back to “How does this improve the experience for our customers and employees? Remember, this is a new moment that is redefining how marketers and brands interact with customers.
Creative apps are just the beginning
Early adopters are already using the latest artificial intelligence tools to achieve dizzying creative effects, such as generating new creative images with the click of a button. The winners in AI marketing don’t stop there, though. Businesses will take a holistic approach, leveraging the power of AI to personalize marketing, improve operational processes, improve marketing measurement, real-time marketing experimentation, and enhance marketing decisions by understanding unstructured data. Pragmatic innovations include using unstructured data for precise audience targeting or updating marketing execution with real-time campaign data. Another benefit of adopting systemic marketing AI innovations: improved collaboration with other departments.
Quick wins and complex projects must run in parallel
Some marketing teams are making early headway by deploying generative AI in managed pilots, such as in employee-facing settings, or where employees can review AI-generated content before it reaches customers. Rather than wait to solve the toughest deployment challenges, companies must take these small steps now to build their expertise and achieve quick, confidence-building wins. But realizing the full long-term potential of AI will also need to start working in parallel on complex projects, especially those connected to customer data lakes — such as personalized marketing, proactive user engagement for customer retention predictions. We encourage CMOs to open up capabilities for the boldest innovations to transform experiences and value propositions, such as Spotify’s AI-powered DJ and Duolingo’s conversational role-playing capabilities for language learning.
CMOs are ideally suited to be AI innovation enablers
CMOs need to carefully execute their role as brand guardians, manage risk and put in place guardrails in areas such as intellectual property and data protection (in collaboration with legal teams), while building risk response mechanisms to respond effectively when AI-customer interactions go awry. But they must also ensure that this brand guardianship does not end up stifling innovation.
Marketing can be one of the earliest business scenarios where generative artificial intelligence can reshape work capabilities. Instead, the rollout of generative AI can be a showcase for marketing teams eager to demonstrate their ability to contribute to adjacent functions like product management and customer experience.
Start by Cultivating a Culture of Experimentation
Software vendors such as Google, Microsoft, and others are moving quickly to build generative artificial intelligence into their products. This can simplify some forms of productivity work where CMOs should not have to focus on custom solutions. However, a different approach is required in the more specialized areas of corporate marketing and sales innovation, such as achieving real competitive advantage and differentiation in areas such as customer acquisition and active engagement. This will require more customized business capabilities, and these functions must be retained and self-built within the enterprise.
By fostering an environment that encourages AI innovation teams to pilot, test and iterate, CMOs can position their organizations as leaders in the rapidly evolving marketing landscape, driving AI innovation and achieving better business outcomes.
To remain competitive with marketing AI, CMOs must take a proactive approach, take considered risks, and foster a culture of experimentation that enables the organization to learn from both success and failure. In the unknown landscape at the frontier of artificial intelligence, one must rely on the timeless principles guiding agile management of change: test, learn, and iterate.
Effectively navigate the complex, unfamiliar complexities of marketing AI with a clear vision and unwavering confidence by encouraging a culture of experimentation. This thoughtful approach paves the way for transformative innovation, success, and limitless possibilities in AI.
At the end of the day, while AI is a powerful technology, it cannot replace the human creativity, empathy, and strategic thinking required for great marketing. As we continue to experiment and push the limits of artificial intelligence, I remain convinced that the human touch is critical and necessary to drive impactful, purposeful marketing in the long run.
Artificial intelligence is changing at an alarming rate, and while it is difficult to predict the course of this revolutionary technology, it will certainly play a key role in marketing and sales in the future. Leaders in the space are succeeding, turning to generative AI to maximize their marketing and commerce operations by taking advantage of advances in personalization and internal marketing excellence. How will your industry react? Welcome to thread discussion.
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