The essential
- AI scalability is vital. Effective AI-driven marketing requires scalable platforms to manage complex, multi-channel customer journeys.
- Ethics and data are crucial. Establishing ethical guidelines and access to diverse data sources are essential for successful integration of AI in marketing.
- Feedback informs the AI. Continuous feedback loops in customer interactions improve AI’s ability to deliver more accurate and personalized marketing messages.
Marketers may be eager to take advantage AI for smarter personalization, but to ensure lasting benefits from AI-driven marketing, speed may not be as important as efficiency. AI can impact and transform marketing outcomes in so many ways that it can quickly become overwhelming. Poorly thought-out investments can lead to more problems than expected, including scary marketing, data privacy violations, and sunk investments in shiny new tools that don’t solve long-term business problems.
So what is the most logical path to deploying AI for personalized marketing today so that it is still profitable in the future?
Harnessing the Power of AI-Driven Marketing
Unlike automation tools set and forgotten, the world of AI-powered marketing takes us into an ever-evolving field where AI can learn, iterate and optimize those learnings in a regenerative way across an infinite set of complex options, says Michael Cohen, global head of data and analytics at Plus society, an international network of creative agencies.
Start with clarity
However, leveraging AI for marketing don’t start by finding a AI tool. Instead, start by clarifying the current and potential business problems that need to be solved, along with a compelling business case for why and how the right AI-powered solution can better meet these needs. As Sarah Cornett, AI consultant, reminds us For us, AI is not a homogeneous and simplistic tool that can be bought off the shelf and applied overnight. It is an umbrella term for a range of technologies and solutions, each serving a different purpose.
The generative AI gateway
The gateway to AI-driven marketing for most businesses is Generative AIbased on large language models (LLM) And natural language processing (NLP). In this context, suggests Sreelesh Pillai, co-founder and business head of a customer experience software company. Zepic, AI can drive a transformative shift toward trusted, conversational engagements and more nuanced, contextual customer interactions. According to Cornett, the attention to detail that AI can bring to such conversations is unmatched. For example, customizing the accent or language in which a particular prospect or customer prefers to be served by a digital assistant can instantly improve the quality of the experience.
AI-powered conversational marketing
Anytime, anywhere AI-driven conversational marketing can improve customer engagement and amplify customer conversion rates, agrees David Greenberg, chief marketing officer at the AI-driven conversational software provider . Conversica. For example, AI can help process historical and customer-specific preference data as well as real-time actions to identify and execute the next best action, even during weekends and after hours, at any scale.
And there’s more
AI technology also includes machine learning (ML), which can help analyze infinite data sets for real-time or near-real-time learning based on feedback loops and enable smarter decision-making. Computer vision, another AI technology, can leverage imagery to make smarter, in-the-moment decisions in areas as diverse as retail media and healthcare, without violating customer privacy. At the extreme end, we have complex AI such as deep learning and neural networks, which aim to mimic the decision-making systems of the human brain.
It’s all in the stack
With so many AI technologies to leverage real-time data available today, it’s not just about finding the right AI technology or tool, but stacking them under the hood to power effective marketing, suggests Cornett.
Related article: AI in Marketing: Guide Teams to Experiment Safely
3 Expert-Recommended Next Steps for Your AI Marketing Journey
1. Prepare your marketing data and data strategy for AI
According to Cohen, this primary step involves considerations such as:
- Creating and configuring a data infrastructure for AI: Generally, the less processing that occurs before data is passed to AI training or inference services, the more information you can extract from it. This is different from the data retrieval databases/warehouses typically built and used in marketing analytics.
- Access or own data: The marketing organization must access the necessary data sources from finance/accounting, IT, supply chain, third-party sources, point of sale (price, volume, discount, display, etc., for consumer goods), advertising newspapers in all media. channels, etc. The goal is to bring it together in one place and make it useful for particular applications/use cases. This does not mean processing it in a common form or data model, but rather putting it in a cataloged and semi-structured form with common data schemas to link the different data sources.
- Align talents: Organizing data in the most optimal way for your AI use cases requires internal experts or external consultants to establish a roadmap for data centralization for AI. Other employees must also be prepared, as roles and responsibilities will continually evolve as AI matures in the marketing organization.
- Establish ground rules: Define ethics, key company values and policies to ensure the safety of customers, employees and the company
Related article: AI in Marketing: Balancing Creativity and Algorithms for Marketers
2. Revisit your customer journey map to identify the best use cases
According to Pillai, creating continuous feedback loops in every customer touchpoint and interaction throughout the buying journey will be crucial for effective AI-driven marketing. Feedback loops not only sharpen decision-making in the moment, but also continuously measure the influence of touchpoints in the buyer’s journey. These insights help AI create effective, predictable messages across all channels and touchpoints and define the best use cases for AI-driven personalization.
Pillai recommends starting with a clear use case to improve customer journey personalization, then running experiments to identify areas for improvement. For example, with customer consent already acquired upon subscription, newsletters offer marketers a great place to experiment with subscriber-specific offers, for example based on their latest web pages or most frequently visited web pages. visited. The more specific the scope of the use case, the easier it is to refine requirements and measure the effects of the experience.
Greenberg agrees that starting with an inbound use case where one can see an immediate impact, such as faster follow-up speed or better quality of leads throughout the funnel, helps build a more business case. solid.
By focusing on well-defined, measurable use cases, teams can build an iterative approach, continually refining data models and engagement strategies based on insights and feedback. According to Pillai, this ensures that AI-driven personalization efforts become more sophisticated and effective over time.
Related article: The 2024 AI Roadmap for Marketers
3. Invest in solutions that can be deployed quickly and scale seamlessly
Business-friendly AI is the future. While established players like Hubspot and Salesforce are rebuilding AI roadmaps designed for marketers and salespeople, new entrants like Attentive and Zepic are working to make AI platforms intuitive and accessible to professional users. Drag-and-drop capabilities and visualization-based applications don’t require deep technical expertise, says Cornett, adding that AI and ML as a service will make it easier for marketers to access and adopt without the significant investments required to set up the backend infrastructure. manage all different types of AI.
Adopting one platform rather than cobbling together multiple point solutions could speed up the feedback loop and speed up iterations toward the next best action, suggests Pillai. However, evaluate how easily data can be imported from various sources, as AI-driven personalization thrives on access to comprehensive, real-time data. With the enormous volume of data available on customer profiles, interactions, devices and transactions, identity resolution (reconciling multiple instances and versions of a customer record across various organizational databases into a single “data record”) ‘gold’) to streamline marketing efforts is an area where AI can make an impact and improve marketing personalization in an immediate and concrete way.
An additional consideration is the scalability and flexibility of the platform. Marketers can start with small, well-defined use cases, but will need to scale to manage complex customer journeys across channels. Finally, Pillai highlights the need to track metrics related to AI model performance, such as accuracy range, latency, error rates, etc., as well as tangible business outcomes such as conversion rates higher ROAS, lower bounce rates and savings in time or resources. This will indicate whether AI-driven personalization efforts are moving in the right direction.
