“There’s a broad array of capabilities you can unlock when you’re sharing data seamlessly across your platforms,” Gopinath says. “You’re not just sending emails or push notifications — you’re orchestrating a truly omnichannel experience. It’s incredibly powerful.”
A strategic foundation
Working with implementation partner Bounteous, a digital transformation services consultancy, Gopinath and his team created a personalisation strategy for CCETH that involved four key steps:
- Setting the team structure to enable collaboration
- Centralising data to gain a clear picture of customers
- Defining use cases to serve as a strategic foundation
- Executing on the strategy through the use cases
Gopinath, who has a deep background in digital marketing, knew the technology team needed to be involved from the beginning. Rather than a traditional handoff approach in which the business team sends ideas to the technology team to execute, he brought everyone together for joint planning, execution and iteration throughout the process. That also meant designing a system for cross-continental collaboration with team members on the other side of the world.
The next step was to break down silos by centralising data from multiple sources. The team needed a way to combine ecommerce data from Adobe Commerce — including behavioural actions like adding items to a basket, back-office data like order history and profile data — with information from other sources like the company’s ERP and CRM. They used the Adobe Commerce Data Sharing extension to flow the data into Experience Platform and Real-Time CDP, where it was standardised into unified customer profiles.
“With the Commerce integration, we’re able to capture every consumer touchpoint,” Gopinath says. “It helps us build a true consumer profile from the moment they first land on the site.”
Equipped with the ability to create rich customer profiles, Gopinath and his team were ready to dive into personalising customer journeys. They prioritised the possible use cases according to consumer interactions, marketing initiatives and personalisation tactics, producing a framework of three pillars:
- Use cases that would drive revenue
- Use cases that would boost engagement and retention
- Use cases that offered the opportunity to analyse what worked and iterate
A true omnichannel experience
The team started by tackling a classic engagement use case — basket abandonment. Before the data integration, they had to wait up to 48 hours for data to indicate whether a CCETH customer had finished checking out. With data now flowing in real time, they were able to start sending immediate, personalised email reminders to users who didn’t check out within an hour, resulting in increases of 36% for email open rates, 21% for click-through rates and 8.5% for conversion rates.
The team was also able to go beyond email, delivering reminders through pop-up messages on the website and with push notifications, including a new WhatsApp integration. Journey Optimizer helped determine the right frequency and channels for communication based on customer behaviour, AI propensity scores and other information.
“Journey Optimizer has unlocked a lot of channels,” Gopinath says. “It’s empowered us to orchestrate a true omnichannel experience.”
Access to real-time information also allowed CCETH to use Adobe Customer Journey Analytics to better understand the ordering behaviours of its bottlers and warehouses. In addition to immediately seeing which products were running low, Gopinath’s team could trigger customised communications through Journey Optimizer to bottlers based on post codes where they expected demand to increase because of seasonal factors.
The team also used analytics to understand purchase trends and correlate them with product recommendations that drove more revenue. “The products we recommend have to be backed by revenue numbers — otherwise, it’s difficult to justify what we’re doing,” Gopinath says. “Customer Journey Analytics made it easy for business stakeholders to visualise what we were accomplishing through the dashboards we created.”
Customer Journey Analytics also helped the team find new ways to re-engage and delight low-frequency customers — for example, by sending personalised coupons during a customer’s birthday month or in response to a potentially negative event, such as an attempt to use an expired promo code. At the same time, the team could also understand which customers were least likely to make a purchase so they could be more efficient with media spending.
The power of personalisation
Gopinath and his powerhouse global team created a playbook for personalisation that’s reshaping Coca-Cola’s strategy in markets worldwide. They’ve already started rolling it out to the Coca-Cola Store in the US, where personalised product discovery powered by Adobe Sensei , Adobe’s AI and machine-learning technology, has delivered immediate value.
Early results showed that 1-to-1 product recommendations based on behavioural actions and shopper affinities drove a 117% increase in clicks and a 36% increase in revenue. The shop also saw a 17% click-through rate for its “Frequently Bought Together” cross-sell recommendations. Conversion rate from on-site search hit 19%, with the top three results generally containing what customers were looking for.
