Few consumer brands now sell through a single channel.

A customer may first see a product on social media, browse an online storefront, ask a question through a messaging app, make a purchase through a marketplace, and later return through a loyalty offer or an in-store promotion. Each interaction can produce useful customer data, but the information often sits in separate systems.

Such fragmentation can make it harder for merchants to understand what is driving sales, which campaigns are worth repeating, and how customers move between channels.

In a written interview with TNGlobal, Pavan Govindan, Co-Founder and CEO of Trozo, said the challenge for many brands is shifting from customer acquisition toward customer understanding. Trozo says it provides tools for direct-to-consumer brands covering brand analysis, content creation, publishing, social tracking, and sales attribution.

A customer journey spread across platforms

For merchants, the problem is not necessarily a lack of data. Customer and transaction information may already exist across a brand’s store, social channels, customer service tools, loyalty systems, and marketing platforms.

The challenge is turning that information into a timely decision.

“Most businesses still see these interactions as disconnected events,” Govindan said. “The result is fragmented customer journeys, inconsistent engagement, duplicated marketing spend, and limited visibility into what actually drives revenue.”

That is especially challenging for smaller teams. Marketers may have to export reports, combine spreadsheets, define audience segments, prepare content, launch campaigns, and assess results across several platforms. The work can be slow even before a campaign reaches customers.

For Govindan, the more useful role for AI is to reduce those handoffs between data, planning, and execution.

“The issue is rarely a lack of data,” he said. “The challenge lies in converting data into action quickly enough to influence outcomes.”

AI may help small teams move faster

AI is already being used across content creation, customer segmentation, campaign planning, and analytics. The practical value, Govindan said, lies in helping smaller marketing teams handle work that previously required more specialists.

He pointed to behavioral audience segmentation, AI-assisted campaign content, and predictive prompts as areas where brands may benefit. A system could, for example, identify repeat customers who have not made a recent purchase, recommend a relevant offer, and help create the content needed for a campaign.

That does not remove the need for marketers. It changes where they spend their time.

“AI’s greatest value is not replacing marketers,” Govindan said. “It is giving small teams enterprise-level capabilities.”

The usefulness of those capabilities will depend on the quality of a brand’s customer data and the degree to which its systems can share information. Adding a standalone AI product to an already crowded marketing stack may simply create another layer for teams to manage.

The need to reduce complexity

Many consumer brands already rely on separate tools for email, social media, messaging, customer support, loyalty programs, analytics, and e-commerce operations. AI products are entering each of those categories.

Govindan said merchants should first consider whether a new tool reduces operational complexity or adds to it.

“The first question should be: does this tool reduce operational complexity or increase it?” he said.

He suggested that brands assess AI tools based on whether they can bring together customer information, automate useful execution, and demonstrate measurable commercial outcomes.

For merchants, that is a more practical test than adopting AI because it is currently fashionable. A platform that creates more content but does not improve targeting, workflow speed, or revenue visibility may have limited long-term value.

Attribution remains difficult

Attribution is another persistent challenge, particularly when customers engage with a brand through multiple channels before buying.

A shopper might first discover a product through social media, revisit it through a website, receive a message or loyalty notification, and then complete a purchase later. Traditional last-click attribution gives most of the credit to the final interaction, even though earlier touchpoints may have influenced the decision.

Govindan said brands need a broader view of the customer journey.

“The goal is not simply identifying the final click,” he said. “The goal is understanding the entire decision-making process that led to conversion.”

AI may help brands analyze a wider range of customer interactions and identify patterns across channels. Still, attribution models are only as reliable as the underlying data. Merchants need consistent tracking, connected systems, and a clear view of how each platform records customer activity.

Brand controls and customer trust

As generative AI makes it easier to produce marketing content at scale, brand consistency can become harder to maintain.

A consumer should not encounter a different tone, message, or customer promise when moving from an Instagram post to an email, messaging campaign, or storefront. Govindan said AI should work within a brand’s established voice and values rather than produce content without direction.

“The risk with generative AI is not that it creates too little content,” he said. “It is that it creates too much inconsistent content.”

Brands also need to consider how automation affects customer trust. Relevant recommendations and useful updates can improve the customer experience. Repeated or poorly timed messages can feel intrusive.

Govindan said personalization should be guided by timing and context, rather than a simple push to increase message volume.

“Consumers appreciate relevance, but they dislike intrusion,” he said.

Preparing for adoption

For smaller brands, Govindan said AI adoption should begin with operational basics rather than technology alone.

That includes maintaining clean customer data, setting clear brand guidelines, and defining the business outcomes a team wants to improve. Those outcomes might include higher repeat purchases, faster campaign preparation, more consistent content, or clearer attribution.

AI can help merchants process information and automate parts of marketing execution. It does not remove the need for disciplined data practices, human oversight, or clear customer communication.

As consumer journeys become more fragmented, the opportunity for brands may lie less in adding more tools and more in making their existing systems work together.

This written interview has been edited for clarity and length.

SHOPLINE, Trozo partner to offer AI marketing tools to consumer brands