Feature
The Gist
- AI scalability vital. Effective AI-powered marketing requires scalable platforms for handling complex, multichannel customer journeys.
- Ethics and data crucial. Establishing ethical guidelines and accessing diverse data sources are essential for successful AI integration in marketing.
- Feedback informs AI. Continuous feedback loops in customer interactions enhance AI's ability to deliver more accurate and personalized marketing messages.
Marketers may be raring to leverage AI for smarter personalization, but to ensure sustainable advantages from AI-powered marketing, speed may not be as important as effectiveness. There are so many ways that AI can impact and transform marketing outcomes that it can very quickly get overwhelming. Poorly considered investments can lead to more problems than bargained for, including creepy marketing, data privacy violations, and sunk investments in shiny new tools that don’t solve business problems in the long term.
So, what’s a more logical path to deploy AI for personalized marketing today so it’s still paying off in the future?
Harnessing the Power of AI-Powered Marketing
Unlike set-and-forget automation tools, the world of AI-powered marketing takes us into an always-evolving field where AI can learn, iterate, and optimize those learnings regeneratively over an infinite set of complex options, says Michael Cohen, global chief data and analytics officer of the Plus Company, an international network of creative agencies.
Begin With Clarity
However, leveraging AI for marketing doesn't start with finding an AI tool. Begin instead with clarity about the current and potential business problems that need to be solved, and a compelling business case for why and how the right AI-powered solution can best serve those needs. As AI consultant Sarah Cornett reminds us,"AI" is not some homogenous and simplistic tool that can be bought off-the-shelf and applied overnight. It is an umbrella term for an array of technologies and solutions, each serving a different purpose.
The Generative AI Gateway
The gateway into AI-powered marketing for most companies is generative AI, based on large language models (LLMs) and natural language processing (NLP). In this context, suggests Sreelesh Pillai, co-founder and business head at customer experience software company Zepic, AI can power a transformative shift to trusted, conversational-style engagements and more nuanced and context-aware customer interactions. Per Cornett, the attention to detail AI can bring to such conversations is unmatched. For example, customizing the accent or language in which a particular prospect or customer may prefer to be served by a digital assistant can instantly elevate the quality of the experience.
AI-Powered Conversational Marketing
AI-powered, anytime, anywhere conversational marketing can improve customer engagement and amplify customer conversion rates, agrees David Greenberg, CMO at AI-powered conversational software provider Conversica. For instance, AI can help process customer-specific historical and preference data and real-time actions to identify and execute the next best action even during weekends and off-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 power smarter decisioning. Computer vision, another AI technology, can leverage imaging to make smarter in-the-moment decisions in fields as diverse as retail media and health care, without violating customer privacy. At the far end, we have complex AI such as deep learning and neural networks, which aim to mimic the decisioning systems of the human brain.
It's All in the Stacking
With so many AI technologies to leverage the real-time data available today, it’s not just about finding the right AI tech or tool, but stacking them together under the hood to power effective marketing, Cornett suggests.
Related Article: AI in Marketing: Guide Teams to Safely Experiment
Learning Opportunities
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Driving Customer-Centricity in the Financial Services Industry
Discover how financial organizations are leveraging generative AI technologies.
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An Irish Roundtable in the Valley: The Economic Impact of AI on the World of Work Stanford 2024
Exploring the Impact of AI On the World of Work with Irish diaspora leaders and tech business champions from the North of Ireland
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3 Expert-Recommended Next Steps for Your AI-Powered Marketing Journey
1. Get Your Marketing Data and Data Strategy Ready for AI
According to Cohen, this primary step involves considerations such as:
- Building and configuring data infrastructure for AI: Generally, the less processing before data is fed to AI training or inference services, the more information you can extract from it. This is different from the data retrieval databases/warehouses typically constructed and utilized in marketing analytics.
- Gaining access to or ownership of data: The marketing organization needs access to the necessary data sources from across finance/accounting, IT, supply chain, third-party sources, point of sale (price, volume, discount, display, etc., for consumer goods), ad logs across media channels, etc. The goal is to get it in one place and make it useful for particular applications/use cases. This does not mean processing it into a common form or data model, but rather, putting it in a cataloged and semi-structured form with common data schemas to link the various data sources.
- Lining up the talent: Organizing data in a manner most optimal for your AI use cases requires internal experts or external consultants to lay out a roadmap for data-centricity for AI. Other employees must also be prepared since roles and responsibilities will continuously evolve as AI matures in the marketing organization.
- Establishing the ground rules: Define the ethics, key business values, and policies to ensure the safety of customers, employees and the business
Related Article: AI in Marketing: Balancing Creativity and Algorithms for Marketers
2. Revisit Your Customer Journey Mapping to Identify the Best Use Cases
Building ongoing feedback loops into every customer touchpoint and interaction across the buying journey will be crucial for effective AI-powered marketing, says Pillai. Feedback loops not only sharpen in-the-moment decisioning but also continually measure the influence of touchpoints in the buyer’s journey. This information helps AI create predictably effective messaging across channels and touchpoints and define the best use cases for AI-powered personalization.
Pillai recommends starting with a clear use case to improve personalization in the customer journey, and then running experiments to identify areas for improvement. For example, with customer consent already acquired at subscription, newsletters offer marketers a great place to experiment with subscriber-specific offers, say based on their last or most commonly visited web pages. The more specific the scope of the use-case, the easier it is to narrow down the requirements and measure the effects of the experiment.
Greenberg agrees that starting with an inbound use case where an immediate pickup in impact, such as faster follow-up speed or better lead quality down the funnel, can be seen helps build a stronger business case.
By concentrating on well-defined, measurable use-cases, teams can build an iterative approach, continuously refining data models and engagement strategies based on insights and feedback. This, says Pillai, ensures AI-powered personalization efforts grow more sophisticated and effective over time.
Related Article: The 2024 AI Roadmap for Marketers
3. Invest in Solutions That Can Be Deployed Rapidly and Scaled Seamlessly
Business-user friendly AI is the future. While established players such as Hubspot and Salesforce are rebuilding AI roadmaps designed for marketers and sales people, new entrants like Attentive and Zepic are focused on making AI platforms intuitive and accessible to business users. The drag-and-drop capabilities and vizualization-based apps don’t demand deep technical expertise, says Cornett, adding that AI and ML as a service will ease access and adoption for marketers without the large investments needed to set up the backend infrastructure to handle all the different types of AI.
Adopting one platform rather than cobbling together multiple point-solutions could accelerate the feedback loop and speed up iterations to the next best action, suggests Pillai. However, evaluate how easily data can be imported from various sources, as AI-powered personalization thrives on access to comprehensive, real-time data. With the huge volume of data available across customer profiles, interactions, devices, and transactions, identity resolution (reconciling multiple instances and versions of a customer record across various organizational databases into a single "golden record") to streamline marketing efforts is an area where AI can impact and improve marketing personalization in immediate and concrete ways.
An additional consideration is the scalability and flexibility of the platform. Marketers may start with small, well-defined use cases but will need to scale up to handle 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., alongside tangible business outcomes like higher conversion rates, higher ROAS, lower bounce rates, and time or resource savings. This will indicate whether AI-powered personalization efforts are moving in the right direction.
