It's time AI shifts from being a cost-saver to a business driver.
And for that, we're going to need performance data. Performance data changed everything for marketers. It elevated CMOs, took us into the board room and showed once and for all the impact that marketing could have. More than that though, it gave us a better way to make better decisions.
Today's generative AI tools have shown us how to produce faster, but not necessarily better. For that we're going to need those same tools to have a lot more context about the outcomes of their own outputs. This is why we built analytics into Jasper, but just having analytics isn't enough. Zach Anderson, our head of product and CS and I were talking last week about what shape performance data needs to take in AI copilots and here are a few of the bigger themes we hit upon.
--It should be conversational and queryable--
Marketing analytics today tell you that "x" is up or down week over week. They fail to tell you why. Or whether this should be concerning. Or what on earth you could do about it. Charts may be nice to look at, but they can't handle follow-up questions. This is where AI can shine. This level of user-friendly interaction opens up data analysis to a broader base within an organization, including those without technical expertise. And we need to get there.
--But 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.
-- It should follow us wherever we create --
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.
-- It should learn from the past. --
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.
Now, Jasper is further ahead in some of these areas than others, but it is worth sharing where we're headed an getting your thoughts on if we're thinking about it the right way. If you want to see more about our progress on integrating analytics and insights into the copilot, there's a session with Zach and me on Thursday - link in comments. Thanks as always for helping inform where we take Jasper and AI for marketers next.
