May 23, 2024

AI-driven commerce accelerates conversions, lowers costs

Olin Moran

AI-driven commerce accelerates conversions, lowers costs

There’s a better way to help your customers find what they’re looking for, even if they don’t know they need it yet.

Marketers and analysts have long conducted analysis across multiple platforms and systems and stitched them together to get a better view of how users interact across different touchpoints. But the process was time-consuming and error-prone and focused solely on product recommendations based on web interactions and sales.

Recent advancements in technologies, such as customer data platforms (CDPs), cloud-based services, and AI, have made it easier to unify data and, subsequently, get a better understanding of the experience across the entire customer journey.

Leveraging the power of Omni Commerce and our marketing analytics platform, Omnicom has built an accelerator called Intelligent Commerce that enables marketers to activate effective omnichannel campaigns that drive conversions with lower acquisition costs. It does this by leveraging AI to get advanced insights, supporting omnichannel personalization at scale, and accelerating the content production process.

How Intelligent Commerce works

As customers interact, the system collects data related to their behavior and sends it to Marketing Analytics Platform, which houses second- and third-party data from Omni Commerce and various partners, such as Amazon and Acxiom. That combination of first-, second-, and third-party data delivers more comprehensive insights and a greater understanding of customer behavior.

Our AI and ML models are then leveraged to build advanced segmentation, attribution, and propensity models. Marketers can then use the chatbot feature to quickly get insights and use them to develop data-driven, personalized experiences across multiple channels.

To execute on those strategies, marketers need increasing amounts of content to create targeted, personalized experiences, but layering in different content for different audiences, as well as different languages, can become time-consuming and exponentially more expensive.

Leveraging generative AI, our model has been trained through product content and user behavior so authors can create product descriptions and specify various tones for different versions.

They can also create variations of the same content to use across different areas of the site or as content fragments in other channels, such as email or social media, supporting a highly personalized omnichannel experience.

Use case: Skin care products website

To illustrate what that might look like, we’ll use a fictitious company that sells various brands of skin care products on its website.

Let’s say a marketer logs into our MAP solution and uses the chatbot feature to retrieve sales numbers for a certain product in the United States for the past two weeks along with the numbers for the same two weeks in the previous year.

The marketer is surprised by a recent uptick in sales of that product and learns that a football player and his famous girlfriend have been vacationing on an island getaway and were photographed using it, resulting in viral interest.

Advanced insights & analytics

Wanting to take advantage of this trend, the marketer looks for additional insights to help them build a campaign. They ask the chatbot for the following:

Top 5 states in historical sales (Michigan, Maine, Florida, South Carolina, North Carolina)
Top 3 cities for historical sales in the top state (Detroit, Ann Arbor, Grand Rapids)
Social platform with the most penetration in those cities by active users (Instagram)

Given the insights gathered from the analysis and the recommended marketing channel, the marketer is better able to target their ad spend to drive traffic to the site.

Omnichannel personalization

The same data that produced the insights above enables the marketer to create advanced segments and target channels to build next-best-action decisioning.

Because they know Michigan is the state where more customers are purchasing the product, they can create an audience segment to target customers from Michigan and activate that audience to create an experience designed to increase conversions.

For example, when Jane Doe from Michigan visits the website, the system recognizes that she’s from the target audience and displays a banner ad for the product. As she navigates through product categories, those products are boosted so they’re shown first within the appropriate categories.

Content generation

As these strategies effectively increase traffic, the marketer wants to personalize the product content with different tones and languages for different audiences. They can easily do this by leveraging the generative AI functionality integrated within the CMS to create content variations for use across channels, including email campaigns and social platforms.

The bottom line

If you’re looking to create frictionless, customer-centric journeys that amplify next-best action and increase conversions and average order values, AI-powered commerce is the key.

Schedule a call with our specialists to talk about how you can leverage AI to accelerate digital commerce through best-in-class experiences across all customer touchpoints.