Picture this: Your manager leans in, curiosity sparking in their eyes, and they inquire, ‘Can you share with me what your AI strategy is for the upcoming campaign?
It’s not just a casual conversation. Leadership craves answers and results. This is your pivotal moment – to either maintain the status quo or rise as the AI hero of your organization.
Before you divulge your AI game plan, there are options to consider. You must dig deeper and clarify if leadership wants generative AI or predictive AI.
If the answer is ‘generative AI,’ resources from our partners at Google Cloud await you. Yet, chances are, your boss will be seeking clarity: ‘What distinguishes these two?’ they’ll ask. This is when you seize the opportunity to demystify the AI landscape.
- Generative AI: Generative AI utilizes public internet data or, for optimal results, datasets you’ve tailored to your needs. It’s potent, but relatively new. To truly shine, we recommend training your AI model using your unique voice and assets. This holds particular significance for brand-driven companies, as it can mean the difference between merely testing subject lines and calls to action, or achieving more substantial gains in performance.
- Predictive AI. Predictive AI is a more mature and proven branch of artificial intelligence that excels in foreseeing outcomes based on your historical data and patterns. By identifying correlations and trends within the data, predictive AI can offer valuable insights, allowing businesses to make informed decisions, optimize processes, and enhance their strategies. Yet too often, many organizations still grapple with overwhelmed internal data science teams bottlenecking their AI and machine learning endeavors.
Why do internal teams get stuck?
While well-intentioned, internal data science teams often face challenges when assisting marketing teams. They may focus too much on intricate details, slowing down the implementation and scalability. Additionally, they can be deprioritized in favor of other projects. Alternatively, AI-in-a-box platforms provide transparency and functionality but can be tricky to set up, appearing too complex for marketers and too restrictive for data scientists who crave more hands-on involvement.
Are out-of-the-box audiences in your ESP enough?
Today’s marketing user likely has some out-of-the-box predictive analytics in their ESP, but these capabilities are usually black box. This limits the explainability of the solution, and you lose the ability for insights about the solution. Furthermore, their multi-channel flexibility is limited to the activation platforms the ESP is compatible with.
Many marketers also experience setbacks when changing ESP or when they use multiple ESPs since the predictive AI won’t transfer to a new system with you.
There is a path for you
There are solutions that are customized to your first-party customer data that offer rapid time to value. Predictable is one of those solutions. We offer explainable insights sub 2-week implementations, and we’re very cost-effective. It doesn’t require a data scientist – only access to your data in your ESP or your data warehouse. And it works. Clients who implement Predictable see 25-40% improvements in CPA, and we identify up to a 40x lift in email engagement. So can you be an AI hero?
Be an AI hero, with these 4 steps:
- Prioritize time to value. Don’t spend months using internal resources building something that may or may not provide value to your business. Consider a solution that offers rapid time-to-value and can be customized to your specific use cases. An approach that prioritizes quick wins – like improving CPA, finding more engaged customers, and extending lifetime value – can rapidly gain momentum and prove valuable.
- Test against your current best performing campaigns. Why would you test AI, if not for the performance benefits? Pick a campaign where you can easily A/B test the performance. Here’s an example of one client who tested an audience of high propensity users (link) against their best-performing remarketing campaigns. This retailer improved CPA by 40% and migrated all of their remarketing campaigns to Predicable’s propensity-based audiences.
- Move aggressively to scale campaigns. After proving the efficacy of predictive AI , move your campaigns over to a data science-based approach that leverages 1st party customer data. Develop a method to score the utilization of that data, and make sure you continue to track improvements to CPA, CAC, and LTV.
- Share wins with management. Compare the timeline and costs of your solution against the alternative. Report back:
- How quickly were you able to deploy predictive analytics? It’s likely that a solution like Predictable can provide much more rapid time to value. We had scored dozens of brands. We don’t share data between our clients, but we have learned lessons that drive much faster time-to-value.
- What results did they generate? The results speak for themselves. One furniture retailer say a 38x increase in CTR. A women’s apparel retailer improved CPA by 40%. Predictable can help drive lift to make you an AI hero.
- How does this compare to prior efforts? If you’ve already gotten to 40% improvements inside of a month, it would be surprising if other efforts did better faster. Nevertheless, you should have a measurement plan in order to assess AI’s impact on CPA, email engagement, and CAC.
When challenged with the task to innovate with AI, you have the choice to rise as the AI hero your organization needs. Consider the benefits of working with a partner who directly leverages your 1st party customer data, has experience in your field, and does so at a fraction of the cost of building the capability in house.