No marketing technology series would be complete without discussing measuring success, or key performance indicators (KPIs). I’ve written about KPIs and ROI throughout my career, with the topic becoming particularly hot and heavy in the dawn of social media. I remember discussing this topic at a digital marketing dinner about 15 years ago, where after several glasses of wine, the bold consensus was to forget about direct ROI and instead find inherent value in the new relationships we could build on social media.
Today, no one disputes the importance of building relationships with influencers on social platforms, as the ROI and KPIs of social media marketing are now well established in strategic frameworks. I wish it were that simple when it comes to AI.
New technologies always meet with resistance
There are similarities between the birth of social media marketing and the latest advancements in generative AI. In both cases, a brand new technology has seen phenomenal success, giving marketers the opportunity to reach their audiences in new ways.
As with anything new, there were some who were skeptical about its long-term value. Back in the social media boom, these clients insisted on seeing a direct sale or conversion to find value in social media campaigns. It was my job to gently explain to these skeptics why their target audience was on the social platform in the first place and to see the opportunity to meet their needs in a new way—not just by promoting the product.
The advent of social media created a need for valuable content, and content marketing has since come to the forefront, with standard content consumption and engagement key performance indicators (KPIs) to help judge its effectiveness.
AI ROI Solution: “It’s Complicated”
AI and predictive analytics, on the other hand, have been playing a leading role in all aspects of marketing for decades, not just outreach and external communications. Recent advances in generative AI and tools like ChatGPT are forcing marketers to ask themselves how to prove ROI and measure success. The answer is simple: it depends.
If you followed this whole series of articlesyou know that generative AI can be used in all areas of marketing and impacts every task we do on a daily basis. This is where AI differs radically from social media and why it becomes much more complicated to determine the metrics of success.
Success metrics are directly related to the work the AI machine needs to do and can be categorized into several categories, such as productivity KPIs, cost reduction KPIs, audience engagement and campaign KPIs, and production and creative quality KPIs.
Start with strategy
The golden rule for establishing AI success metrics is to start with a clear strategy. Clearly and concisely define the project goals, the roles and/or users impacted by the goal, and finally, a set of key performance indicators that will help you determine whether the project was successful. previous article In this series, I have provided more information on creating an AI-powered marketing strategy and a comprehensive list of marketing tasks that can benefit from AI.
Let’s roll up our sleeves and look at some concrete examples, shall we?
AI for Personalization and Engagement: The Art of AI According to Intel
Intel reached out to me to find new ways to engage attendees at an upcoming global conference. The company wanted to reach conference attendees in person and online and show them the potential of new generative AI technologies powered by Intel.
In this case, AI needed to create a “surprise and delight” moment to capture the attention of conference attendees, leaving them inspired and excited about how AI could impact their industry.
After presenting several creative concepts, the team decided to move forward with an AI art exhibition that demonstrated the power of AI through interactive AI art installations.
Here is the short list of project KPIs:
- Number of conference participants, in person and online, who visited the exhibition.
- Number of participants who shared their co-created personalized artwork on social media.
- Number of badge scans from in-person attendees.
- Number of visits to the exhibition microsite.
- Number of mentions of the brand and experience on social and digital networks.
- Share of voice at conference for AI-related terms compared to competitors.
- Analysis of feedback and comments capturing trends and general impressions of the experience.
In this case, generative AI enabled personalization and interactivity in the form of a one-of-a-kind, AI-co-created artwork offered as a takeaway experience. All three AI art exhibitions demonstrated a different aspect of generative AI while providing a completely new and engaging experience.
AI for productivity: PIA, my client-coach and AI tutor
This second example is about extending and scaling my expertise, allowing clients and students to access my knowledge 24/7, without constant monitoring of my inbox. In this case, AI should help me save time by automating the process of answering repetitive questions and save my clients time by providing them with quick and easy information anytime, anywhere.
The solution was a privately trained AI chatbot called PIA that is accessed via a web browser on any smartphone, tablet, or laptop. PIA was trained on this entire series of MarTech articles as well as transcripts of my AI Marketing Revolution Challenge Video Tutorialscontent of my AI strategy courses, my blog, my website and my entire professional history.
This tool was recently launched, so stay tuned for a full case study describing PIA’s performance in the coming months. In the meantime, here are the KPIs for this project:
- Number of questions answered successfully.
- Number of customer engagements with PIA.
- Number of hours saved each week (i.e. time I can devote to more meaningful client work).
- Sentiment analysis of conversations with PIA.
- Results of the post-PIA survey determining customer satisfaction with the tool.
Explore, experiment and engage
These are two very different examples of aligning AI KPIs with a specific goal, and I hope they help you understand my strategic approach. I recommend launching your AI projects with a spirit of exploration and experimentation. We are still in the early stages of these powerful technologies, we are all pioneers navigating uncharted territory.
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