A/B testing is dead. Read how AI testing replaces A/B testing for 1:1 personalization.

Offerfit Logo PNG 

Experimentation unleashed

Content

Ready to make the leap from A/B to AI?

Thank you

Trends in AI personalization in financial services

Read the white paper

Download whitepaper from OfferFit -- financial services

FS white paper og image

FS whitepaper square image

AI Decisioning is replacing Next Best Action in financial services marketing

See how OfferFit helps with lead nurturing in:

AI-driven personalization in financial services

AdAge social image

AI decisioning agents — not predictive models — provide a continuous feedback loop to determine which decisions drive the best and worst personalized outcomes. 

AI decisioning agents—not predictive models—provide a continuous feedback loop to determine which decisions drive the best and worst personalized outcomes. 

How to pivot from AI predictions to true AI decision-making

webinar image preview

A roundtable with MetLife CMO Michael Roberts, Former Aetna CMO David Edelman, and and former AXA Group CMO Amy Radin.

social image for AI webinar

AI-driven marketing in financial services: a CMO roundtable

Calendar vs evergreen image

calendar vs evergreen thumbnail

Evergreen campaigns give marketers two key advantages over a calendared approach. First, letting marketing assets – creative, messaging, subject lines, headers – stay in the rotation longer simply saves time and effort. But there is a more fundamental advantage. The longer a campaign runs, the more time a marketer has to gather data and measure what’s working. That makes it possible for the marketer to run A/B or multivariate to discover what's working best for their global audience, or perhaps for segments. More importantly, evergreen campaigns are a natural fit for continuous automated experimentation. 


Letting the calendar go – why evergreen marketing wins

esp blog post image

image preview for esp blog post

Marketers chasing the dream 1:1 personalization are wondering if they should rely on the systems they have, or add another product to their stack. After all, most automation platforms – meaning the platform the marketer uses to orchestrate communication with their customers – now offer suites of AI tools. Is it worth adding a dedicated platform for AI personalization?


Is it worth adding a dedicated platform for AI personalization?

Image for ESP blog post

Should you add AI testing to your tech stack?

Should you add AI Decisioning to your tech stack?

Shobeir show podcast image

"Marketers have realized that AI is real. ML models are going to be more powerful and more impactful than pretty much anyone expected." -- OfferFit CEO George Khachatryan on The Shobeir Show.

George shares his story as a refugee from the Soviet Union, how he became a mathematician & entrepreneur, and how ML is changing marketing today.

George shares his story as a refugee from the Soviet Union, how he became a mathematician & entrepreneur, and how ML is changing marketing today.

Shobeir meta image

George Khachatryan, OfferFit CEO on the Showbeir Show

The Shobeir Show

MVT blog post image

MVT image

Multivariate testing is faster than A/B testing, but otherwise, it has all the disadvantages of A/B testing: still too slow; doesn’t adapt to changing customer behavior; not personalized to each customer. There’s a radically better way to optimize your marketing campaigns, AI Decisioning agents

Multivariate testing is faster than A/B testing, but ultimately has the same disadvantages : still too slow; doesn’t adapt to changing customer behavior; not personalized to each customer. There’s a better way: AI Decisioning agents

From multivariate testing to AI Decisioning

Composability image wide

Composability image

The rise of the AI decisioning layer in the composable martech stack

Composability and the 2025 martech stack

The rise of the decisioning layer

Brinker webinar content image

Scott Brinker and George Khachatryan discuss the rise of composability and the decisioning layer in the 2025 martech stack.