Apr 11, 2024

AI-powered commerce: Optimize customer journeys and drive conversions

Olin Moran

Olin Moran

AI-powered commerce: Optimize customer journeys and drive conversions

It’s no surprise that artificial intelligence was front and center at Adobe Summit 2024 in Las Vegas–a recent Forrester report showed that 83% of companies consider AI a top priority.

AI has been part of digital commerce for years, as the space is at the top of the list in early adoption. Businesses are primarily focused on applying AI to improve the customer experience, but that’s expanding beyond the web experience to include the entire customer journey.

When I started my career as a digital commerce analyst, a lot of data was available to us, but multiple systems and spreadsheets were required to make sense of it all, and analysis took weeks. I then had to work with a different company for email, media, social, and web to coordinate campaigns across all our different channels. By the time we made a change, we had lost a ton of revenue. While AI alone doesn’t enable marketers to get to market faster, if leveraged correctly, it can be a great contributor.

Faster time to market is becoming increasingly important because customers are having more interactions across multiple digital channels and expect a seamless experience. So we’re seeing that the best investment of AI dollars in commerce is clear—improving the omnichannel experience. As was on full display at Adobe Summit, there’s a lot of opportunity to leverage AI in the commerce space and make the most of all those interactions.

Primary areas of AI in commerce

Research shows that chat functionality has dominated the AI space for some time, and recent advancements in Gen AI make it a clear front-runner. That said, other areas with a lot of potential are quickly gaining traction.

Analytics & insights

We’re seeing companies go from siloed, manual data analysis to integrated predictive analytics, driven by data science. Earlier this year, 1 in 4 decision-makers told Forrester they want to expand their use of analytics–and that’s on top of the 20% who believe they already have a good foundation.

Companies are getting better at collecting and centralizing data, using data lakes, CDPs, and other technologies that are faster, cheaper, and easier to use. However, according to Forrester, almost 1 in 3 companies continue to list data quality as a big concern because they’re trying to gain insights from data that is structured differently (and a lot of times poorly) and lives in siloed systems. It’s imperative to have a strategy around data pipelines and execution: bad data in, bad insights out.

AI can improve the quality of insights by helping us make better sense of unstructured data. It can also help in identifying anomalies and trends, building out propensities, and determining next-best-action recommendations, all of which can be leveraged to improve conversion rates.

For example, you can take disparate data sources, such as marketplace data (e.g., Amazon), media performance data (e.g., Trade Desk), and first-party data (e.g., web interactions), and layer in AI to build out advanced segmentation, detect anomalies, and identify propensities. You can then integrate it with Adobe Experience Platform (AEP) to activate on those insights across all available channels.

Omnichannel personalization

Historically, personalization has been synonymous with product recommendations, and those recommendations were mostly driven by web signals like pageviews, product data, and transaction data.

We’re now seeing companies want to go from sales-driven product recommendations to omnichannel 1:1 personalization, meaning personalization must become more sophisticated. Additional data sources are needed to optimize algorithms and personalize the entire customer journey with targeted content and promotions.

Leveraging such data sources as the ones described in the Analytics & insights section above, you can produce next-best-action recommendations based on elements like product, geography, and activation channel. That next-best-action decisioning can then generate segments within AEP that can be used for activation across multiple channels.

Content generation

That level of sophistication comes with the potential for the cost of content to grow at an exponential rate, and we’re seeing companies go from manual content generation to automated content generation. Within the commerce space, product data is king, and as you enable more personalization use cases, you’ll need to generate more product content and find ways to improve your content processes to keep up with demand.

AI solutions can help keep the costs and time required to produce all that content in check. For example, Adobe Firefly can be leveraged within Adobe’s creative suite to generate images, and other solutions can build specialized models for SEO or language translations.

The bottom line

Identifying AI opportunities is not hard–they are everywhere. Building out prototypes isn’t very hard, either. What is hard is taking a prototype to production and using it successfully with your customers.

You need a robust architecture, sophisticated dev ops, and proper governance to successfully deploy AI to production. At Credera, we have a quote we use often: “There is usually more engineering and development in AI than there is AI.”

The companies that are doing this well are typically investing in AI and dev ops, and in their architecture, often partnering with consultancies like Credera to make sure they get the most out of those investments. Schedule a call with one of our specialists to talk about leveraging AI to optimize your company’s customer journeys and drive more conversions.

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