Customer Context Layer is Becoming the New Role of the CDP
As enterprises move beyond simply unifying customer data, the next challenge is making that context actionable across marketing, AI and commercial systems, says Mohamed Ali, RVP MENA at Zeotap.
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Enterprise customer data rarely suffers from a lack of information. The bigger challenge is making that information usable across the systems, markets and teams responsible for customer decisions.
As businesses invest in first-party data, CDPs, data warehouses, and AI, the definition of a unified customer view is also changing. It is no longer enough to bring fragmented records together or create a central profile.
The greater opportunity lies in making customer context available to the systems that need it, when they need it, and in a form that can drive action.
This becomes particularly complex in the GCC, where businesses operate across distinct markets, customer behaviours, regulatory environments and levels of digital maturity. Identity resolution, consent, data residency and fragmented customer journeys can quickly turn what appears to be a technical data problem into a commercial one.
For marketers, the question is therefore shifting from Do we have a single customer view? to What can we actually do with it?
At Zeotap, Mohamed Ali, RVP MENA, sees this shift as fundamental to how enterprises should approach customer data. Rather than beginning with the ambition of building a perfect customer profile, he believes organisations should start with the business outcomes they want to achieve and work backwards into the data, identity and technology required.
“The goal shouldn’t be a perfectly unified customer view. The goal should be a customer view that’s useful enough to make better decisions and take better actions,” says Mohamed Ali, RVP MENA, Zeotap.
In this interview, Ali discusses why enterprises should build their data strategy around use cases, why identity resolution in the GCC requires a balance between complexity and usefulness, how the CDP is evolving into a shared customer context layer for AI, and why organisations need to rethink what a CDP should actually do.
Excerpts from the interview;
Enterprise data rarely fails because a company lacks data. It fails because the data sits in too many places. Where do you see the biggest disconnect today between having a unified customer view and actually being able to use it?
I think the biggest disconnect is actually the use case lens.
We see businesses spend one or two years on huge data transformation programmes, bringing everything into a data warehouse and trying to create the perfect customer data foundation. That’s important, but too often the conversation about what we’re actually going to do with that data comes afterwards.
Then the business identifies a commercial use case and discovers the data isn’t structured, accessible or timely enough to support it without another layer of transformation and engineering.
I’d flip that around.
Start with the business outcomes. What are the five or ten use cases that will significantly move revenue, retention, customer experience or efficiency? Prioritise them based on impact and feasibility, and then work backwards into the data and technology required to deliver them.
The goal shouldn’t be a perfectly unified customer view. The goal should be a customer view that’s useful enough to make better decisions and take better actions.
Identity resolution is often presented as a technical problem, but in multi-market organisations, it can become a business problem too. What makes identity particularly difficult across GCC markets, and where should enterprises draw the line between complexity and usefulness?
The GCC makes identity particularly tricky because you’re dealing with distinctly different markets, even though we like to combine them into one, with different customer behaviours, regulations and levels of digital maturity within a relatively small geography.
You might have a customer interacting through an app in one market, WhatsApp in another, a loyalty programme somewhere else, and potentially multiple phone numbers, email addresses or devices across all of them. Add different consent requirements, data residency considerations and organisational structures, and identity quickly becomes much more than a matching problem. We see that in the match rates across the region.
I see businesses either over-engineer this or avoid it completely. It’s not a zero-sum game.
The ambition shouldn’t necessarily be to identify every single customer across every possible touchpoint with 100% certainty from day one. It’s a continuous process that can improve dramatically over time if you have the right technology, partner and setup.
If I can confidently recognise enough of my customers to improve retention, suppress unnecessary acquisition spend or personalise an experience, that’s already commercially valuable. From there, we can continue closing the gaps, combining deterministic and probabilistic approaches where appropriate, and improving identification over time.
Identity should be judged by the decisions it improves, not simply by the percentage of profiles you manage to stitch together.
A CDP can unify customer data, but unification alone does not create value. What needs to happen after the “single customer view” is built for it to actually change marketing, product or commercial decisions?
This is probably one of the biggest misconceptions around CDPs.
A single customer view is part of the process. It’s not the outcome or the use case.
The value starts when that data becomes available to the people and systems making decisions and driving impact. That could be a marketer deciding which audience to target, a product team deciding what experience to show next, or an AI model determining the next best action for an individual customer.
Increasingly, the important shift is from data and decisioning to include context.
We’re putting AI into almost every part of the tech stack, with different models and platforms making decisions about the same customer, often using different data and different context. That creates a new problem: you don’t want every system developing its own version of who that customer is and what matters to them.
I see the CDP increasingly becoming that shared customer context layer, providing the right data, identity, behaviours, preferences and business context to the right system or channel at the right time.
That’s ultimately how the single customer view becomes useful. Not because everyone can see it, but because the business can consistently act on it.
Zeotap works across sectors, including telco, financial services, retail and marketplaces. What differences do you see in how these industries think about customer identity, and what can they learn from one another?
The interesting thing is that each industry tends to have a different advantage.
Telcos typically have incredibly rich customer relationships and behavioural data. Financial services organisations have a strong identity and enormous amounts of transactional data. Retailers tend to be very good at translating customer behaviour into merchandising, loyalty and personalisation.
Marketplaces often have exceptional real-time signals around intent. But each also has blind spots.
A bank might know exactly who you are and what you’ve purchased, but not necessarily understand your intent. A retailer might understand your intent extremely well but struggle to recognise you across anonymous and authenticated journeys. A telco might have huge amounts of data but only activate a fraction of it.
There’s a lot these industries can learn from each other.
For me, the opportunity is combining strong identity, behavioural context and real-time intent. That’s when customer data becomes significantly more powerful.
What is one assumption about first-party data or CDPs that you encounter repeatedly in enterprise conversations, and think marketers need to rethink?
One assumption I’d challenge is that a CDP should fulfil the same role for every business.
I don’t think there’s one definition of what a CDP needs to be anymore. The value depends heavily on the organisation, its architecture, its data maturity and, most importantly, the problems it’s trying to solve.
For some businesses, particularly those earlier in their data maturity, the traditional CDP model still makes complete sense: ingest the data, resolve identity, build the customer profile, create audiences and activate them.
For others that have invested heavily in BigQuery, Snowflake, Databricks or another data platform, they don’t necessarily need another system to become the source of truth. The CDP can be the connecting tissue between the data warehouse and the activation layer, adding identity, business logic and marketer accessibility without unnecessarily duplicating the underlying data.
For another organisation, the biggest value might be orchestration: taking signals from across the business and deciding what should happen, when and through which channel.
And increasingly, particularly as AI becomes embedded across the martech stack, there’s another role emerging: the CDP as the business and marketing context graph. A common layer that gives different AI models, decisioning engines and activation platforms a consistent understanding of the customer, their history, behaviours, preferences, consent and relationship with the brand.
So rather than starting with a fixed definition of what a CDP should do, businesses should start with where customer data can create the most value, and what capabilities they need to unlock it.
That might lead to a traditional CDP, an orchestration layer, a composable architecture or increasingly a customer context layer for AI. The technology should adapt to the business, not the other way around.
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