Which AI Model Do Marketers Choose?
AI’s explosion in sophistication and adoption during the last year is undeniable. Everywhere marketers turn, there’s either a new solution on the scene or their existing tech tools are getting an AI facelift. The trend makes perfect sense, as AI has a proven track record for streamlining workflows, scaling content, and generating deep insights. However, marketers must be strategic when choosing an AI to commit to. Not all AI solutions are made equal, and discerning between AI-washed products and those that deliver true value is crucial.
To do that, marketers need to understand the model behind the AI—or, better yet, the ensemble of AI models that work together for top-notch results.
Why Should Marketers Embrace an Ensemble Approach?
In AI, an ensemble approach indicates several models operating simultaneously in every output. The difference between a single model and an ensemble model approach is similar to that of a single violin versus an entire orchestra. While each instrument delivers value, several working together create something truly magical.
The more models, the more marketers can do with their AI. However, simply acquiring multiple AI-powered tools is not going to cut it. While different solutions can generate copy or images, they won’t be able to work together for a seamless, cohesive output.
This is one of the greatest strengths of ensemble models: here, marketers can produce composite pieces of content with on-brand copy and images that are personalized to the customer. With every model in sync, marketers can produce effective, relevant content that takes every context into account.
Stereotypes, biases, and inaccuracies can slip into the output with any AI. Why? Because AI outputs are based on whatever datasets and models are given; if the data isn’t perfect, the outputs won’t be either.
Think of any archetype—while these associations are not accurate, the sheer number of biased inputs could skew whatever content the AI generates. This is why it’s so crucial to opt for an AI solution that uses diverse data sets and an ensemble of models that are constantly being updated and trained to account for bias. By not leaning on a single type of AI, marketers can mitigate their risk of biased content.
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Da Vinci AI's Ensemble Approach
For these reasons, Movable Ink Da Vinci embraces an ensemble approach to AI and empowers marketers with several models that work in tandem to create strategic, engaging content:
The Prediction Model
One model, three components. The Prediction Model uses a diverse combination of features—content decisioning, send time personalization, and frequency management—to ensure that each customer receives the right content at the right time.
Content Decisioning predicts and deploys the perfect blend of design, layout, and branded messaging that will resonate with individual customers most.
Send Time Personalization automatically sends emails when customers are most likely to engage, releasing marketers from the confines of the campaign calendar.
Frequency Management creates a personalized interval of messaging for each customer, controlling the total number of emails sent and preventing fatigue.
The Vision Model
The Vision Model uses algorithms to analyze the metadata within content, allowing for in-depth analysis that can’t be completed through human effort alone. This model uses two key components, image and language classification, to process each creative element:
Image Classification uses computer vision to analyze, process, and extract information from every creative element, which allows the AI to identify and understand different elements within visual content.
Language Classification discerns meaning and context from every element within a creative using Natural Language Processing (NLP).
The Generation Model
Using the power of scalable personalization, the Generation Model creates subject lines in the blink of an eye. Not only does the copy automatically generate, this AI ensures that the output aligns with both brand guidelines and customer’s individual preferences.
The Insights Model
The Insights Model uses three groundbreaking components to power personalized messaging. With the combination of customer facets and model effectiveness scoring, marketers can understand customers’ aggregate traits and deliver content that gets increasingly effective over time.
Customer Facets create one-of-a-kind customer profiles by analyzing characteristics over time. This not only develops an in-depth view of customer behavior, it becomes increasingly accurate as customers continuously interact.
Model Effectiveness Scoring predicts the revenue potential of a single creative. Marketers no longer have to use guesswork to understand why an email performed well; now they can have an accurate view of which creatives are resonating with customers.
With all of these models working together, marketers can develop a holistic view of their customers and understand what content is resonating. This knowledge is the key to producing relevant, revenue-driving messaging all in one automated process.
Challenges and Considerations
Incorporating any new tool is an adjustment for marketers. No matter how seamless or user-friendly, a learning curve will always remain. While an ensemble approach to AI can face the same challenges, looking ahead and preparing accordingly will minimize the adjustment period.
The ensemble approach to AI is effective but complex. Learning how to navigate the various models, components, and functions will require training time for marketers. Additionally, while an AI-powered email process will be automated in the long run, positioning brand guidelines, setting benchmarks, and ingesting datasets are all tasks that need to be dealt with first.
Simply put, more models mean that there’s more for marketers to learn. As each AI model has its own functions and ultimate goals, it stands to reason that more implementation time is required. While this may seem like a detriment to the ensemble approach, keep in mind that any new tool will involve growing pains—it’s simply inevitable. Rather than avoiding as much martech onboarding as possible, marketers need to focus on finding the solutions worth the time and resources spent. With Movable Ink Da Vinci, for example, the ROI is undeniable with clients seeing a $20M in payback within six months.
More Models, More Powerful Results
The sum of AI models is greater than its parts. By incorporating several models that work in tandem with one another, marketers can generate content that is more effective, personalized, and engagement-driving.