Synthetic data: Empowering marketing growth in the AI era

CEOs are leaning more than ever on their marketing leadership to partner with them to drive growth—more so in these times of growth-hacking. Marketers, in turn, are adjusting strategies, tweaking tactics, and pruning operational excesses to nimbly respond to the call to drive growth in the near-term and prepare the brand to flourish in the sunnier times to come.

Marketers already know that AI - especially gen AI—with a wealth of insights, power to automate, and even create, can amplify them on the job.

What a vast majority of them are still coming to appreciate is how one of AI’s less-glamorous, relatively less-discussed boon for brands—synthetic data (i.e. AI-generated data mimicking authentic data from real sources)—can do the same.

Unlike real-life data that’s hard to get in some segments, is sometimes biased, takes the time and costs of arduous surveys, or may need to be kept private due to data laws, synthetic data is faster to access, better-balanced and comes in handy on the amplification mission. It has the potential to boost marketing creativity, personalisation, and campaign efficacy practically on-demand. Let’s look at how.

Intuition to shape creatives

Bringing campaigns that cut across the clutter means developing novel ideas and compelling messaging. These must, however, be tested and refined before they are scaled, and it must all be done with speed-to-market. Brands can now predict the potential appeal and effectiveness of their messaging, ideas, and campaigns by testing it effectively on synthetic populations before real-world deployment. 

Marketers can also conduct A/B testing and refine content and segmentation strategies, ensuring that campaigns—pathbreaking and surprising as they may be—deliver predictable favorable outcomes. Today retailers < the link has nothing to do with retailers sharing data> are already sharing representative copies of consumer data or synthetic data with manufacturers and advertisers so they can better-manage messaging and outreach. Insights aside, brands have also made rather clever use of synthetic data media (in the form of deep fakes) directly in their campaigns.

It was the talk of marketing circles, when Dove put out videos of mothers repeating toxic rhetoric from influencers. The real mother and her daughter then viewed the deepfake video; resulting in personal reflection and inspired action to #DetoxYourFeed.

Intelligence for personalisation

Leaders like Anthem, the American healthcare major, started early-on to generate synthetic data— medical histories, insurance claims, and other healthcare data—to help them personalise care for their customers.

Marketers can train algorithms on synthetic data, similarly, to personalize marketing content and deepen personalisation of customer experiences. This can prove invaluable especially in a world where cookieless personalization will be the only way forward for marketers and brands looking to craft a privacy-compliant, future-proof strategy for delivering highly relevant brand experiences.

Insights for efficacy

Data-led insights from surveys and polls are crucial for marketers to sense changes in customer behavior and make decisions like talent, channel, messaging, and budget rearrangements in a timely manner. Creating moments that matter for customers requires a powerful insights engine and discovery of customer intent, interests, and unmet needs at a consistent fast clip.

With massive quantities of data being generated, it’s exceptionally hard to reliably achieve the velocity and focus along with the massive budgets to get it all done. Synthetic data offers a great way for brands to stay in touch with real world dynamics and for marketers to allocate critical resources to the most value-driving programs and campaigns. It’s not very different from how Provinzial, the German insurer, uses synthetic data to identify the needs of over a million customers and predict what services and products they might buy next.

As with all of AI, synthetic data is not without a downside. The data and models are only as good, as unbiased, as the prompts used to create them, and they are not, in any intrinsic way, better than traditionally collected data.

So, if you already have good data, keep using that because while the speed of accessing synthetic data is greater, it still takes elaborate planning and oversight to execute with it. Using synthetic data, responsibly, to help close data information gaps is how most value is created.

This article first appeared on Performance Marketing World.