Abstract
- Scalability in AI is important. Effective AI-powered marketing requires a scalable platform to handle complex, multichannel customer journeys.
- Ethics and data matter. Establishing ethical guidelines and accessing diverse data sources are essential for the successful integration of AI in marketing.
- Feedback informs the AI. Continuous feedback loops in customer interactions enhance AI’s ability to deliver more accurate and personalized marketing messages.
Marketers may be eager to leverage AI for smarter personalization, but speed is less important than effectiveness to ensure lasting benefits from AI-powered marketing. Maybe not. There are so many ways that AI can impact and transform marketing outcomes that it can quickly become overwhelming. A poorly thought-out investment can cause more problems than expected, including creepy marketing, data privacy violations, and sinking investments in shiny new tools that don't solve business problems in the long run.
So what is a more logical way to bring AI into personalized marketing and future-proof it?
Harness the power of AI-powered marketing
Unlike automated tools that you can set and forget, the world of AI-powered marketing is taking us into an ever-evolving field where AI can learn and iterate on an infinite number of complex options, and regeneratively optimize those learnings. , said Michael Cohen, global head of data and analytics at Plus Company, an international network of creative agencies.
start by clarifying
However, leveraging AI in marketing doesn’t start with finding an AI tool. Instead, define the current and potential business problems that need to be solved, and articulate a compelling business case for why and how the right AI-powered solution can best serve that need. Start by doing. As AI consultant Sarah Cornett reminds us, “AI” is not a homogeneous, simple tool that can be bought off-the-shelf and applied overnight. This is an umbrella term for a range of technologies and solutions, each serving a different purpose.
Generative AI Gateway
For most companies, the gateway to AI-powered marketing is generative AI based on large-scale language models (LLM) and natural language processing (NLP). In this context, Sreelesh Pillai, co-founder and head of business at customer experience software company Zepic, said that AI has a transformative potential for authentic conversational engagement and more nuanced and context-aware customer interactions. This suggests that it may be possible to promote a transition. According to Cornett, the attention to detail that AI brings to conversations like this is unparalleled. For example, you can instantly improve the quality of the experience by customizing the accent or language in which a particular prospect or customer wants to be addressed by your digital assistant.
Conversational marketing powered by AI
AI-powered anytime, anywhere conversational marketing can improve customer engagement and increase customer conversion rates, says David Greenberg, CMO of Conversica, an AI-powered conversational software provider. Agree. For example, AI can process customer-specific historical and preference data and real-time actions to help identify and take the next best action at any scale, even on weekends or after business hours. .
There's more
AI technologies also include machine learning (ML), which helps analyze infinite data sets based on feedback loops, learns in real-time or near real-time, and makes smarter decisions. Computer vision, another AI technology, can leverage imaging to make smarter, instant decisions in a variety of sectors, such as retail media and healthcare, without compromising customer privacy. . At the far end are complex AI such as deep learning and neural networks that aim to mimic the human brain's decision-making system.
Everything is in stacking
With so many AI technologies available today to leverage real-time data, it's important not only to find the right AI technologies and tools, but also to stack them internally to power effective marketing, says Cornet. he suggests.
Related article: AI in Marketing: Guide your team to experiment safely
3 Next Steps to Expert Recommendations for AI-powered Marketing
1. Prepare your marketing data and data strategy for AI
According to Cohen, this first step includes considerations such as:
- Build and configure data infrastructure for AI: In general, the less processing you do before feeding data to an AI training or inference service, the more information you can extract from it. This is different from the data retrieval databases/warehouses typically built and utilized in marketing analytics.
- Access or take ownership of your data: Marketing organizations need access to the data sources they need from finance/accounting, IT, supply chain, third-party sources, point of sale (consumer goods prices, quantities, discounts, displays, etc.), and advertising logs across media. . The goal is to bring it together in one place and make it useful for specific applications/use cases. This does not mean processing data into a common format or data model, but into a semi-structured format cataloged using a common data schema to link different data sources. means to put
- Sort your talents: To organize your data in a way that best suits your AI use case, in-house experts or external consultants need to develop a data-centric roadmap for AI. As AI matures within marketing organizations, roles and responsibilities will continue to evolve, so other employees need to be prepared as well.
- Establishing ground rules: Define ethics, core business values, and policies to keep customers, employees, and business safe
Related article: AI in Marketing: Balancing creativity and algorithms for marketers
2. Revisit your customer journey mapping to identify the best use cases
Pillai says effective AI-powered marketing is about creating continuous feedback loops across all customer touchpoints and interactions throughout the buying process. Feedback loops not only sharpen in-the-moment decision-making, but also continuously measure the impact of touchpoints on a buyer's purchasing behavior. This information helps AI create predictively effective messages across channels and touchpoints, and defines the best use cases for AI-powered personalization.
Pillai recommends starting with a clear use case for improving customer journey personalization, then running experiments to identify areas for improvement. For example, a newsletter for which a customer's consent has already been obtained at the time of subscription may allow marketers to create subscriber-specific offers based on, for example, her last visited webpage or her most frequently visited webpage. We offer you the perfect place to try it out. The more specific the scope of your use case, the easier it is to narrow down your requirements and measure the effectiveness of your experiments.
Greenberg agrees that starting with inbound use cases that have an immediate impact, such as increasing follow-up speed or improving lead quality down the funnel, can help build a stronger business case. Masu.
By focusing on clearly defined and measurable use cases, teams can build an iterative approach and continually refine data models and engagement strategies based on insights and feedback. Pillai said this will ensure that AI-powered personalization efforts grow more sophisticated and effective over time.
Related article: 2024 AI Roadmap for Marketers
3. Invest in solutions that can be deployed quickly and scaled seamlessly.
Business-user friendly AI is the future. While incumbents like Hubspot and Salesforce are re-architecting their AI roadmaps designed for marketers and salespeople, new entrants like Attentive and Zepic are focusing on making their AI platforms intuitive and accessible to business users. Cornett added that drag-and-drop functionality and visualization-based apps don't require deep technical expertise. He also said that AI and ML as a service makes it easier for marketers to access and adopt without the heavy investments required to set up back-end infrastructure to handle any kind of AI.
Adopting a single platform rather than a combination of multiple point solutions can accelerate feedback loops and speed iterating to the next best action, Pillai suggests. However, AI-powered personalization thrives on access to comprehensive real-time data, so evaluate how easily you can import data from different sources. With the vast amount of data available across customer profiles, interactions, devices, and transactions, identity resolution (reconciling multiple instances and versions of customer records across different organizational databases into a single “golden record”) to streamline marketing efforts The following areas are: AI can impact and improve marketing personalization in immediate and tangible ways.
Another consideration is the extensibility and flexibility of the platform. Marketers may start with small, well-defined use cases, but must scale up to address complex customer journeys across channels. Finally, Pillai said AI models such as accuracy ranges, latency, and error rates, along with tangible business outcomes such as increased conversion rates, increased ROAS, lower bounce rates, and time and resource savings. emphasizes the need to track metrics related to performance. This will tell you if your AI-powered personalization efforts are on the right track.
