The internet turned marketing from an art to a science.
In the early 2000s, digital marketing brought forward a set of previously unmeasurable metrics: website sessions, click-through-rates, purchase attribution, and the ever-yearned-for ROI. Those three little letters took on a near universal presence in marketing teams across the world. You want to start a podcast? What’s the ROI? ABM is showing a strong ROI? By all means, have some additional budget. For the first time, the impact of marketing strategies and individual tactics emerged from the fog of immeasurability and gut-feelings.
Marketers have mixed feelings about this shift. On the one hand, it became harder to demonstrate the value of some of the tactics we loved best. Sure that flash-dance activation was creative – it caught eyeballs – but did it drive pipeline? On the other hand, getting metrics was precisely what gave marketers more authority in their organizations.
As digital marketing and advertising became measurable and things that could be optimized, marketing budgets grew. In 2011, Gartner predicted that by 2017, CMOs would spend more money on technology than CIOs. That prediction proved true and then some. In 2014, CIO magazine bemoaned, “CIO-to-CMO Transition of Power Is Becoming a Reality.” Marketing as a career ballooned. Fields like growth marketing, performance marketing, SEO, demand gen, ABM and product marketing bloomed and diversified. With almost every new piece of content, we could demonstrate how much value we were driving for the company. There was comfort in the data and direction.
Over the last year, marketing has undergone another consequential shift as generative AI entered our workflows and changed the way people discover and buy online. At this point, Gartner estimates that the majority of marketing teams have begun experimenting with generative AI at this point. The case for piloting AI is a clear one: the ability to create more quickly means tangible efficiency gains for marketing teams facing heavy content demands.
But are efficiency gains enough?
Performance over speed
Increasingly, I hear from marketing leaders that while they appreciate AI’s efficiency gains, it needs to drive more than time and cost savings to be truly transformative for their teams and for it to earn a permanent spot in their tech stack. It needs to drive better marketing results.
Taylor Hollenbeck, senior director of global revenue marketing at Lilt, jumped right to the point in a recent article on what marketer’s need next from AI when he said: “I want early trend identification in all of its forms. Increased traffic to a page from a specific keyword or ad? Immediate identification and small ad test balloon to amplify. Opportunities lagging at stage two when they come in from a given source? Identify them for me, push the results, and maybe even suggest some causes. I know all this is possible, but this is the exact type of analysis that falls through the cracks when you’re working on the 100 other things.”
In short, AI needs to move from a cost saver to a business driver. For most marketing leaders, AI has been a fascinating addition to their tech stack. But for it to be a lasting and transformative one, it has to enable them to identify trends early, pinpoint problem areas, and suggest potential optimizations. It's clear that for AI to be as transformative as it’s been promised to be, it must be as performance-data-driven as marketers themselves have become.
What should AI-assisted performance insights look like?
It should be conversational and queryable
The marketing copilots of the future should be designed to comprehend and respond to casual, unstructured language. This shift towards conversational AI will enable marketers to ask about performance in a much more natural, intuitive manner. Rather than wrestling with complex algorithms or parsing dense reports, users could simply ask their AI assistant questions like they would a colleague: How are our email campaigns performing this quarter? or What's our most successful content piece on social media?
This level of user-friendly interaction opens up data analysis to a broader base within an organization, including those without technical expertise. By democratizing access to insights and making them as easy as having a chat over coffee, these future AI tools can empower teams to make more informed decisions and quickly adapt. Most importantly, these tools should provide actionable recommendations based on real-time data analysis and performance feedback.
[.blog-quote]“For AI to be as transformative as it’s been promised to be, it must be as performance-data-driven as marketers themselves have become.”[.blog-quote]
It shouldn’t wait for us to ask
That said, these copilots should also proactively point us to opportunities without us needing to ask. They should be able to scan our marketing portfolios, and patterns of customer signals outside of our organization, to surface leverage points we may have missed. For example imagine a copilot that could identify correlations between certain types of content and higher conversion rates for our business. Or a copilot that could surface the key people in your social media community who have been most influential in driving demand for your products. AI should also be able to recognize patterns in vast amounts of disparate data that may have gone unnoticed by human eyes.
It should follow us everywhere
The insights generated by AI shouldn’t be locked in an isolated application. Through integrations and extensions, they should be accessible anywhere marketers work and within the piece of content being referenced. Imagine logging into your analytics or business insights platforms and getting an AI interpretation of your data in actionable, natural language overlaid on the dashboard. Picture viewing your product page on Amazon or your website and getting data-based direction on how to improve it. Through secure integrations, AI could be the glue that brings greater performance context to every aspect of your strategy.
It should inform future outputs
Marketers should not be the only ones getting smarter through AI insights — the underlying AI model should improve on past performance as well. Let’s say your copilot helps you brainstorm testable variations of your ad copy or email subject lines. It should be doing so by first analyzing past campaigns to find patterns in higher performing ads and emails. Then it should base its recommended alternatives on what has historically worked well based on your company’s data. Data-driven brainstorming can be far more beneficial than strategizing based on educated guesses.
These tenets are the foundation of how we're building performance analytics and insights into Jasper. Querying data and presenting insights is the first step you'll see AI tackle, but we think this is only the beginning. In building this functionality, we're being intentional to not be overly reliant on the structures of legacy platforms. Instead, we pull our direction from real conversations with marketing leaders about how they want to engage with data.
As we step into the future, we need to ensure that AI in marketing progresses from being a novel time-saver to a reliable business driver. The dream of a conversational, proactive, omnipresent, and self-educating marketing copilot is ready to be realized. We are excited to build it.
Jasper is getting AI analytics and insights! Learn more about this evolution in our analytics webinar.
