Marketing leaders often have a love/let down relationship with our marketing technology stacks. We love how our martech empowers transformative opportunities to drive exceptional customer experiences, unlock new revenue streams, and accelerate innovation and strategy. At the same time, we feel let down when our martech fails to deliver on promises for returns on investment (ROI), doesn’t easily integrate with our existing systems, and creates more work that results in increased inefficiencies, higher costs, and reduced agility.
Is marketing artificial intelligence (AI) the latest technology that looks great on paper, but fails to meet our expectations?
Once thought of as too futuristic, AI development has steadily progressed for over 50 years, from humble beginnings in technical research, building into proof-of-concept systems with games such as chess and Jeopardy to transform computing theory into action, to become part of our everyday lives through tools such as Amazon Alexa, Spotify, and Netflix. Today, modern, innovative marketers are leading the charge in evaluating a potential long-term relationship with AI to empower their businesses to be more competitive and boost performance.
While some marketers may have been disappointed before with unmet promises and unrealized opportunities from other futuristic technologies, it’s time to let go of the past and open ourselves up to the potential of a healthy relationship with AI in our marketing organizations.
Within our organizations, marketing has perhaps the most to gain from AI. Core marketing activities such as understanding customer needs, matching them to products and services, and persuading people to buy are all capabilities that AI can dramatically elevate. In fact, a 2020 Deloitte global survey of early AI adopters showed that three of the top five AI objectives were marketing-focused: enhancing existing products and services, creating new products and services, and enhancing relationships with customers.
Today, we see the power of AI realized in a rapidly growing number of applications including chatbots for lead development, customers, and cross/upselling; inbound call analysis and routing; product recommendations and highly personalized offers; programmatic digital ad buying; social-media planning, buying, and execution; and my personal email-geek favorite— email dynamic content optimization.
With so many opportunities, where and how should you start your AI relationship? Whether you are an AI-curious casual dater or ready to put a ring on it with an AI partner for life, here are some practical use cases you can put in practice today to support your journey together.
AI Love Language #1: Speed Dating
Use case: Identify potential customers more quickly and accurately. AI is good at continuously identifying patterns in massive amounts of unstructured data in real time. This is particularly helpful for marketers who lack good first-party data or have difficulties integrating and matching third-party data. Additionally, the self-learning aspect of AI ensures that insights are applied almost instantly to campaigns and interactions. As a result, marketers can eliminate the inefficient process of evaluating campaign results, determining necessary budget adjustments, and calling their agency to authorize changes by setting up rules in AI-powered smart-bidding systems. Meaning—they can automatically increase spending under high-performance conditions and drop spending back if campaign performance lags.
AI Love Language #2: Personalization
Use case: Deliver personalized content and product recommendations. Both B2B and B2C buyer journeys are self directed with highly empowered prospects using online and offline channels to compare and consider products before making a purchase. AI is an enabling technology that can process and analyze massive amounts of unstructured data to automatically discover and generate thousands of discrete segments to deliver personalized, relevant interactions and product recommendations. Brands that successfully harness the power of AI can not only create a personalized experience in the moment that matters to the individual customer but also identify and prepare to deliver the next best experience(s) in the customer journey.
AI Love Language #3: Engagement
Use case: Drive higher engagement with your brand through relevant moments that matter. AI’s scalability for large volumes of diverse data provides the opportunity to expand the overall depth and scope of customer interactions by operationalizing data and incorporating insights from ALL customer touchpoints, including customer-service interactions, in-store behavior, website page visits, loyalty program participation, and chat bot interactions. This makes it possible to deliver dynamic, relevant offers across all of your marketing channels that increase engagement with your brand and individualize engagement more effectively.
While the science behind AI is complicated, getting started in your organization doesn’t have to be. Roll out AI selectively by addressing tightly-defined and measurable use cases. Focus on enhancing existing programs where engagements are well understood, processes are defined, and outcomes benchmarked to fully monitor how the AI is working and driving value over traditional approaches. Programs such as dynamic email content and email subject-line optimization are great places to start your initiative.
AI will ultimately transform marketing, but just like any relationship, it’s a journey that will take time and investment.
Blog – Litmus