Is It The Marketers Or The Machine?
Martech has been buzzing about all things AI, but the new age of marketing doesn’t come without its hesitations. From countless scandals in the news to unsettling sci-fi films, AI can feel like uncharted territory for marketers.
But the good news is, AI isn’t taking over your role. While AI can help us scale customer-centricity like never before, it does the complete opposite of edging out the marketer. Instead, the additional time and resources create an opportunity for marketers to do the more strategic, innovative, and creative work that needs the human touch.
But how can marketer and machine work in tandem to ensure optimal effectiveness? It begins by answering five simple questions.
Who Should We Talk To?
New campaigns usually begin with merchants and marketers discussing what products they want to sell, with segments being created after the goals and KPIs have already been set.
While marketers are familiar with this process, there’s room for improvement. Currently, average click-through-rates sit between 0.7-2.3%, meaning about 99% of customers are ignoring everything brands have to say. Something has to change.
Marketers need to rethink their strategies with AI as the new foundation. That begins with flipping the script: starting with a customer, not a product.
What Should We Talk About?
Knowing customers deeply as individuals can sound like a pipe dream. And manually, it is—it’s virtually impossible to scale personalized messaging that adapts with customers over time. But with an AI-driven approach, marketers can get past a first-name basis with their customers and start to add real value with hyper-relevant communications:
Understand Customer Preferences
There is no one “ideal” customer because every individual is unique. Just because two people share the same demographics, their needs, preferences, and desires are vastly different.
Movable Ink AI applies this same idea. Rather than attempting to group different customers into several pre-labelled buckets, the AI analyzes existing customer profile and transactional data (including products/categories purchased, product/category share of wallet, etc.) and learned responses (engagement data such as opens, clicks, conversion, etc.) to generate traits or “facets” of a customer over time, and map out the ideal version of each customer, that becomes the basis for purposeful discovery throughout your product catalog. As a result, every customer profile is completely unique from one another—to reflect their one-of-a-kind preferences. This is the first step to truly understanding your customers and discovering what content will resonate.
Assess Promotional Affinity
Once the AI has identified which product/category is going to best resonate with the customer, it then analyzes their motivations and determines how dependent they are on promotions to convert.
Balance Short-Term Gains With Long-Term Objectives
Your ultimate goal is to maximize every customer’s lifetime value. But knowing when to sell versus nurture towards a larger opportunity is impossible at scale. That’s where AI comes in.
Using AI, marketers can analyze and understand where customers are during their individual journeys and tailor the content accordingly - ensure each customers’ needs are met, and that the business best balances short vs. long-term lift, engagement vs. selling, and nurturing to higher AOV vs. over-focusing on a one-off trivial sale.
With this approach, brands are off to a great start in their AI-journey. The last piece of the puzzle is the human touch. No matter how sophisticated the AI model, marketers need to set the rules to ensure relevance and empathy —such as respecting legal constraints, known customer preferences, or brand policies — before the AI can generate anything.
When Should We Engage?
Once the message is in place, marketers need to decide when to send it to customers. Previously, meticulously planned email calendars relied on guess-work and arbitrary dates. But with an AI-driven approach, the calendar can turn into another opportunity for further relevance.
For example, rather than sending full-file emails at predetermined times throughout the day, using AI, marketers can automate their emails to deploy whenever a customer is most likely to engage.
But to perfect the approach, marketers step in to apply necessary parameters, such as start and end times based on their business needs.
How Frequently Should We Interact?
Getting too many emails is the number one reason why customers unsubscribe from a mailing list, making optimal frequency a crucial issue.
With AI, the software can now analyze each customer’s habits to determine the frequency that is best for them. But ensure the machine doesn’t go rogue, marketers need to define two crucial factors:
Engagement Frequency Rules limit the overall number of sends during a specific timeframe - e.g. do not send a customer more than 3 messages a day, 21 a week, and/or 60 a month. These rules are designed to control costs (every message sent costs money after all) and reduce churn risk by hitting that sweet spot with every customer.
Creative Reuse Rules limits message fatigue by controlling how often a single creative is shown to a customer based on their activity levels. For example, marketers can tell the AI to only show a particular piece of content in the hero 3 times, and if the customer still does not engage, it’s time to move on and try something new. Or even if that customer does engage, marketers can limit reuse based on how far they get in their purchase journey (e.g. after click, wait 7 days before reusing that creative). That way brands are not continuing to show a customer something when their context has clearly changed, and it’s time to move on.
How Can We Optimize Going Forward?
The final step to AI-powered prowess allows marketers’ to show their skills at their finest. In this world, AI has automated the mundane tasks, freeing up marketers to take on higher-level, more strategic initiatives to innovate going forward. Let’s break it down:
Know Your AI
Not all AI is created equal. When it comes to choosing an AI tool, marketers need to note their key differences:
Predictive analytics is a type of AI that operates from a static model and requires manual updating as customers, environments, and goals change, making it difficult to scale.
Machine learning is a form of AI that automatically learns about customers’ situations and adapts over time - ensuring every interaction is relevant and allowing brands to scale to heights that were never possible before.
But even with a Machine Learning approach, marketers still need to provide the creative inputs for the machine to arbitrate between. So the question becomes, how do marketers know what to create?
Audit Performance
For success with AI, marketers need to take an audit of where they stand today and continue to analyze moving forward.
Audit your content library and determine what categories need more content, and which ones are collecting dust. From here, marketers can know where they need to concentrate the resources of their creative teams.
Audit within your categories and take stock of your current creatives. While you may need more clothing creative, you may not need more content for jeans specifically. Additionally, be sure to analyze the performance of each creative for another angle of insight when determining where to devote your time and resources.
Audit performance with a fine tooth comb. If a creative performs well, take a deep dive into the breakdown. Did it perform well in clicks, opens, conversions? This detailed look will further inform your upcoming strategies and optimize efficiency.
Create Your Marketer-Machine Dream Team
What this five-step approach to AI-Optimized experiences reveals is that the best marketing needs the marketer and the machine, not one or the other. While AI is the fuel you need to achieve your business goals, the marketer is the one driving the car. With this in mind, it’s time to take your marketing to new heights with a powerful AI-optimized strategy.