Marketing playbook for 2024: Digital marketing strategies to navigate changes ahead
If there’s one thing marketers know, it’s that change is constant, and pivotal shifts are afoot in the world of marketing this year.From the phasing out of third-party cookies to the rise in adoption of AI, these changes can seem daunting to navigate. But with robust insights and strategies at hand, you can take on these changes with speed and confidence.We rounded up the most relevant digital marketing strategies and insights below, to help you find your footing quickly and scale new heights in 2024.Rethink the role of AIThe era of AI-powered marketing is firmly upon us. Brands are moving beyond early trials of using AI to accelerate their adoption and spending on the technology. However, only 30% of firms in APAC are strategically positioned to harness AI’s benefits.For your brand to be on the cutting edge, you’ll need to rethink how you’re tapping into AI to maximize its potential. Here are two ways you can do so:Get the basics downWhile AI is a powerful engine, it can’t drive marketing success by itself. To set yourself up for AI-powered success, use the AI Essentials checklist to make sure you have everything in place to unlock the full potential of AI.Embrace the right mindsetYou’re not competing against AI — you’re competing with other marketers who are using AI. So keep experimenting with new ways of integrating AI into your digital marketing strategy. Google APAC CMO Simon Kahn, for example, plans to tap into AI’s potential to transform the way his team applies marketing principles.Prepare for a post-third-party cookie worldThe ever-evolving advertising landscape will mark a watershed moment in 2024 as browsers phase out third-party cookies. In APAC, where almost 8 in 10 brands rely heavily on third-party cookies to reach customers and measure results, there’s a critical need to switch to durable measurement and audience solutions.Building a strong first-party data foundation is key to navigating a post-third-party cookie world.Below are steps you can take to set up a durable measurement strategy. It lets you gain valuable audience insights in privacy-safe ways while you measure campaign results, so you can optimize your campaign performance.Prioritize a first-party data strategyBuilding a strong first-party data foundation is key to navigating a post-third-party cookie world. The data not only lets you gain a deeper, more accurate understanding of your customers, it also fuels AI-powered solutions to help you achieve greater marketing efficiency.Meet your customers’ privacy expectationsPeople value the security of their personal information, so it’s important to make data sharing meaningful, memorable, and manageable for customers. By providing a fair value exchange, you get the audience insights you need to create relevant ad experiences that’ll help you grow brand trust and win shoppers’ preference.Unlock growth, power profitabilityConsumers might be more careful about their spending in 2024 if growth of the global economy is slower. And this could mean greater pressure on marketing budgets and investments. Indeed, 45% of CMOs say demonstrating ROI for their marketing activities is a top goal.To deliver strong business impact, you’ll need to win over undecided customers and adopt digital marketing strategies that improve the bottom line. Here’s how:Power a “confidence shift”In a climate of uncertainty, people in APAC may struggle with their purchase decisions. So you’ll need to know how to power a “confidence shift” and have them feel certain about choosing your brand. Ad solutions powered by Google AI can help you power this shift at speed and scale efficiently, whether your audience is financial customers, travelers, or shoppers.Engage high value usersNearly half of Southeast Asia’s top online spenders plan to increase their online spending in 2024,1 so make them your priority audience and optimize your engagement with them to drive profitable growth.Maximize ad revenuesIf you're looking to grow the bottom line of your app, choosing the right monetization platform can be a game changer. Platforms like AdMob let you maximize ad revenue while providing a good app experience, so you can retain users and drive sustained revenue growth.Innovate customer engagementWith an endless buffet of content available to audiences across platforms, screens, and formats, you’ll need to show up where they are to win their attention and drive sales. And you’ll have to do so creatively, since many APAC consumers expect brands to deliver more engaging experiences.To engage audiences, you’ll need to know which platforms matter to them, and show up in relevant, creative ways.Here are three things to keep in mind for increasing customer engagement:Be where it mattersAudiences today believe in curating their own viewing experiences to suit their unique lifestyle and interests; if a platform fails to meet their needs, they’ll simply disconnect from it. So know how people make their video viewing decisions, and show up on trusted platforms that matter.Tap into audience needsKeep up with the complex viewing behaviors of audiences in the region by understanding what truly engages them. Winning campaigns from the 2023 YouTube Works Awards SEA offer inspiration for how your ads can address viewers’ needs.Think out of the boxA little fun goes a long way when it comes to engaging your customers. Whether it’s riding the live stream shopping wave or adding HTML5 games to your site, consider how you can make your marketing entertaining.Cr. Lijie Huang
5 generative AI misconceptions
Stay up to speed on transformative trends in generative AIBusiness leaders are buzzing about generative AI. To help you keep up with this fast-moving, transformative topic, each week in “The Prompt,” we’ll bring you observations from our work with customers and partners, as well as the newest AI happenings at Google. In this edition, Philip Moyer, Global VP, AI & Business Solutions at Google Cloud, discusses five misconceptions customers have about generative AI.As you can imagine, I’ve been in a lot of meetings this year with Fortune 500 leaders discussing how they want to bring generative AI into their business. It’s hard to think of a time when there has been so much interest by enterprises in a new technology so quickly. Generative AI is significantly more approachable than previous generations of AI, and I'm excited by all the possibilities.But matching this excitement, of course, is a lot of hype, so I’ve had to debunk as many ideas as I’ve had to put forward. In this first edition of “The Prompt,” I want to share some of these misconceptions and help demystify this fast-evolving technology.Misconception 1: One model to rule them allIt is a myth that a single defining large language model (LLM) or other type of generative AI model will define all use cases. Many corners of the technology market have come to be defined by a handful of companies. The nature of generative AI, especially for enterprises, suggests we will be looking at thousands of models or more. The reasons vary, but it is already clear that some models are good at summarization, others are good at bulleted lists, others are good at reasoning, and so forth. Industries, lines of business, and companies have very different editorial tones for expression of knowledge. All these should be considered when choosing your models.Misconception 2: Bigger is betterGenerative AI models consume large amounts of computing resources. The large funding rounds required for companies creating foundation models is just one testament to these costs. Potentially high compute costs are one reason why using the right model for the job is so important. The larger the model, the more it costs to query.Your enterprise model doesn’t need to know the words to every Taylor Swift song to generate a summary report on next quarter’s sales goals by region. Context is king and you need to be selective in just how much IQ a model requires for your use case.Misconception 3: Just me and my botJust as past “bring your own device” and “bring your own app” movements raised “shadow IT” concerns, some financial institutions I work with have shut down access to publicly available generative AI for fear that models could leak proprietary information.Some public generative AI services may leverage user data for future training sets, potentially exposing proprietary data. Let’s say a bank is exploring a merger for a large industrial client, and someone in the M&A department queries a public model, asking, “What are some good takeover targets for XYZ Industries?” If that information contributes to the model’s training data, the service could become trained to answer this question for anyone. By default, Google Cloud AI services do not use private data in this way. Most customers I speak to are worried about the security of the questions they ask models, the content they train on, and their models’ outputs. And they probably should be. Misconception 4: No questions askedThe accuracy and reliability of generative AI has been one of the biggest topics around the new technology. An algorithm is designed to give an answer no matter what, and in some cases, generative AI models can produce answers that aren’t true. Every company I know has deeply invested in creating verifiable facts and data. It’s essential for enterprise customers to use models and a technology architecture that is grounded in the factuality of their data. Most generative AI models punt on this enterprise data requirement. It is essential, especially in regulated industries, to not punt.Misconception 5: Ask me any question?Enterprise customers have many information sources: pricing, HR, legal, finance, etc. But I dont know of any company that allows open access to all of this information.Some business leaders are increasingly interested in building all their information into a large language model, so it can conceivably answer all questions, whether that’s at the organizational level or the global level.After a company thinks through how they can keep their information private and factual, they quickly realize the next step: How do I manage who can ask questions of my models, and at what level?" Some business leaders are interested in building their information into an LLM, so it can conceivably answer all questions, whether at the organizational level or the global level. "Cr. Philip Moyer Global VP, AI & Business Solutions at Google Cloud
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