While acknowledging its promise, business leaders and CMOs are often unclear about where they should apply AI to reap the biggest rewards. Businesses that frame the right questions, start small, and scale AI across business functions as they mature, stand to gain the most from AI’s potential.
Artificial Intelligence has significantly altered the marketing landscape as we know it. AI and machine learning are already indispensable in many marketing activities and their capabilities are rapidly growing. For most companies, AI is already integral to marketing — from chatbots for lead generation and customer support to automated marketing campaigns, personalised product recommendations, social-media planning, website optimisation and more.
AI makes it possible to understand customer needs, match these needs to products and services, and persuade people to buy — at a scale human marketers would struggle to match. It’s no surprise then that marketing is one of the domains where AI can contribute the greatest value according to a cross-industry study by McKinsey.
Experience Design is the process of creating meaningful experiences both online and offline, centred on the user. It’s a much broader umbrella than user experience (UX) design. To design an experience, you might need information architects, interaction designers, user experience designers, user interface designers, visual designers and developers, but you also might need the assistance of researchers, architects, social scientists, psychologists, ergonomists, marketers, customer experience experts, data analysts and more, depending on your product or space. Think of a resort reception, a client presentation or a pitch, each experience includes the spatial, tactile, auditory, olfactory — and good experience design ensures these are the result of careful consideration, not accident.
Pinpoint the Problem You’re Trying To Solve
AI can replicate more complex human tasks and decision-making, so it’s important to have a clear understanding of the problem you’re trying to solve and the objective of the exercise. For many online retailers, this often means developing a closer understanding of customer preferences based on previous browsing and purchase behaviour, and the probability of buying a specific product or category in the near future.
Preference algorithms can also help with targeting. Say a brand launches multiple products within the span of a week. The algorithm helps market the right products to the right audiences, minimising message fatigue and improving conversion for every marketing dollar spent. This works from online purchases to electronic direct mailers. Active email subscribers don’t necessarily translate to buyers, and some loyal customers never open emails. By learning customer behaviour over time, brands can better at offering personalisation at scale, especially as they launch into new product categories.
AI’s ability to segment audiences based on preferences and inventory makes it more efficient to showcase and sell the right products, and proves enormously helpful as brands break into new categories as they grow.
For sustainable label Everlane, algorithms help unite marketing and merchandising with “reverse recommendations” that segment audiences based on inventory, targeting customers with what they’d like to see, but also what’s currently available. By linking these capabilities to supply chain management, AI-empowered decision making helps eliminate overstocking and minimises waste across the supply chain.
Simple to Complex: Task Automation to Machine Learning
AI applications in marketing have been around for far longer than we realise, and range from Facebook’s Messenger chatbots to email automation systems. They can be integrated into a company’s platform to handle and route inbound calls or automate CRM-linked marketing. More complex machine learning can range from brand-specific apps like Olay’s Skin Advisor to programmatic digital ad buying and ecommerce product recommendations.
One of the key opportunities AI-powered marketing offers over traditional alternatives is its ability to scale with personalisation — and relevance. Marketing teams and data scientists must communicate well to frame the right questions, direct AI to solve the right problems, and tune into AI’s ability to make real-time, high volume “micro-decisions” to enjoy higher returns on their AI efforts.
Olay’s Skin Advisor for instance is a “supervised learning system” that uses AI to make predictions about ageing and skin care needs. As users share their selfies the system becomes smarter for subsequent users and (ideally) more accurate in its ageing predictions.
The tool responds to multiple challenges faced by consumers in the beauty industry — 30% of potential beauty customers return home empty-handed because they aren’t sure which products are right for them, and traditional norms meant that men were embarrassed to be seen shopping for beauty products in public.
“By combining privacy and personalisation, AI-assisted marketing tools offer scalability, while breaking down the norms in industries traditionally marketed mostly to one demographic.”
AI-driven Programmatic Advertising
Google Marketing Platform tool Custom Algorithm uses machine learning to increase the number of viewable impressions bought on premium placements. By making sense of historical data, it increases the probability that ads are served to the most relevant audience. When compared to other campaigns that don’t use the tool, impressions on premium inventory went up 3X and viewable CPM fell 34%.
The Economist used AI-driven advertising to identify reluctant readers and lookalike audiences with a 10:1 ROI
Programmatic advertising learns from traffic data and online targeting to offer impressions more accurately, efficiently and at scale, which means better ROI for both advertisers and publishers.
The Economist leveraged the process of AI-driven programmatic advertising to buy and sell targeted adverts autonomously. Over the course of the campaign, the team were able to capture and analyse consumer data in detail. The publication zeroed in on an audience of “reluctant readers”. The AI analysed web and app usage, drilled down to specific reading habits and preferences, discovering more effective ways to approach prospects online. They also unearthed new segments and created lookalike audiences by matching cookie, subscriber and additional data sets. The results were stellar and engaged over 3 million new readers, achieving an overall return on investment of 10:1 from the initial revenue generated from these prospects.
A Sign of AI maturity
How do you recognise an AI leader? For most part, mature AI adopters usually deploy AI across their business, not just sales and marketing, but customer service, operations, supply chain management, and more.
It typically begins with an early AI pilot project. If successful, business leaders look for new areas to apply AI. Artificial Intelligence-powered tools whether for marketing, pricing, or customer service are more approachable than ever before. It’s easy to try an AI-powered solution for under US$ 20 a month.
Artificial intelligence systems will evolve over time, as businesses grow confidence in their ability to deploy it usefully. No matter the minutiae of the AI strategy, experts agree that it’s critical to think of AI within the organisational system, with human managers held fully accountable.
While many believe that AI will ultimately transform marketing, the road is one that might take decades. In the meantime, marketing teams can begin by identifying potential use cases, testing available AI tools and identifying risks, before designing an AI strategy that works for your business — depending on what’s possible and desirable, and the appetite for risk. One solution doesn’t fit all.
Looking to generate new leads or design an impactful AI-powered marketing strategy but don’t know where to begin?
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