--- title: "What does your customer view actually need? A look into the Enterprise Martech Outlook" id: "97835" type: "post" slug: "what-does-your-customer-view-actually-need-a-look-into-the-enterprise-martech-outlook" published_at: "2026-10-15T18:38:41+00:00" modified_at: "2026-10-15T18:40:52+00:00" url: "https://tealium.com/blog/partners/what-does-your-customer-view-actually-need-a-look-into-the-enterprise-martech-outlook/" markdown_url: "https://tealium.com/blog/partners/what-does-your-customer-view-actually-need-a-look-into-the-enterprise-martech-outlook.md" excerpt: "A customer once told me they needed to run an abandoned-cart campaign. The catch was that they had about three minutes to act. Once someone left their app, that person could be buying lunch somewhere else within three to five..." taxonomy_category: - "Customer Data Platform" - "Data Warehouse" - "Partners" --- Customer Data Platform # What does your customer view actually need? A look into the Enterprise Martech Outlook Zack WentheOctober 15, 2026 A customer once told me they needed to run an abandoned-cart campaign. The catch was that they had about three minutes to act. Once someone left their app, that person could be buying lunch somewhere else within three to five minutes. An offer arriving an hour later would miss the mark entirely. A business can know a great deal about a customer and still be unable to use the few pieces of information that matter while the customer is deciding. I shared that example recently in my conversation with Keanu Taylor for The Martech Weekly’s *Enterprise Martech Outlook 2026*. The research combines analysis of 417 enterprise martech stacks with a survey of 127 brand-side martech leaders. There is plenty to explore across budgets, AI, operating models, and measurement, but today I want to focus on one idea: the move from an all-knowing Customer 360 to what the authors call a “Contextual Customer View.” ## **Centralizing data leaves work to do** The report finds that 59.1% of surveyed organizations consider the cloud data platform fairly or very central to their martech stack. Bringing customer data together creates a foundation for analytics, measurement, and a more consistent understanding of the customer. But the same section shows how much work remains. Nearly half of respondents, 47.2%, describe their customer view as partial: key structured data sources are centralized, yet the view remains incomplete and inconsistent. Another 21.3% still have customer data trapped in application silos. The partial category is particularly interesting. It describes organizations that have already done some of the hard work of bringing data together, but they still have gaps. For those teams, the next investment needs a more specific objective than making the profile larger. As I put it in the report: “Data being central to your architecture is not the same as data being useful in the customer's moment.” The practical question is what happens after the data arrives. Can the systems serving the customer use it? Is it current enough for a decision? Can the team responsible for that experience understand and act on it? ## **Give the customer view a specific job** I like the report’s definition of a Contextual Customer View because it gives teams a more useful target. The aim is to assemble the customer information needed for a particular decision, interaction, or measurement requirement. That gives teams a way to decide which missing data matters first. The key is deciding what must be known for that experience to work. A purchase event might tell you to stop a recovery message. An unresolved service issue might change whether you should send another offer at all. The customer view needs to bring together the information that could change the decision. Return to the lunch example. A useful response might require knowing what the person just considered, whether an order was completed, whether they are eligible for an offer, whether they've responded to offers in the past, and whether the proposed contact is permitted. Those requirements give the team something concrete to build and test. A longer purchase history might improve the response. It might also add nothing useful to that particular decision. You have to establish which information would actually change what you do. Consider a customer walking into a jewelry store. That interaction may justify more historical context, including previous purchases and preferences, and there may be more time to retrieve it. The acceptable balance between speed and detail depends on the experience. This is where I think customer-data projects can become more disciplined. “We need a complete customer profile” leaves the scope open indefinitely. “We need to recognize that this customer has already ordered before sending a recovery offer” creates a clear requirement. You can identify the source, establish how fresh it needs to be, assign responsibility, and test whether the experience changes. ## **The handoffs deserve as much attention as the profile** A narrower use case still requires dependable data. Identity, permission, accuracy, and freshness all affect whether the information is suitable for the action you want to take. It also requires agreement between the people involved. Marketing exploration can easily become a queue of requests to data engineering. Marketing asks for an audience. Someone produces a result. The result prompts another question, which becomes another request. Eventually, the campaign deadline determines how much exploration the team can afford. Teams need a workable process for developing the use case, followed by reliable handoffs that let it respond within the customer’s time window. For the abandoned-cart example, marketing, data, and channel teams need to agree on what counts as abandonment, how they check for a completed purchase, and what happens if the information arrives too late. Someone needs to own the handoff when a customer’s status changes. This is the work behind making a customer view actionable. It includes the route from a new signal to an updated decision, and from that decision to the system delivering the experience. Every step consumes part of the available time. At Tealium, this is why I keep coming back to the outcome the customer-data work is supposed to support. Starting there makes it easier to decide which capabilities matter and where responsibilities belong. ## **Choose one decision and prove the improvement** The concept is to pick a customer decision that is currently being made with missing or outdated information. Document what happens today. Identify the data that would change the decision, the time available to use it, and the team accountable for the result. Then define how you will measure improvement before building. For an offer-suppression use case, that could mean measuring how often an offer reaches someone who is already ineligible, how quickly eligibility changes reach the delivery system, and the associated discount cost. Where possible, use a controlled comparison to establish whether the change produces the expected business benefit. That gives the next phase of customer-data investment a clear purpose. It also creates evidence for expanding the approach to another use case. For 2027 planning, I’d start with a use case where better customer context can prevent a specific cost or improve a specific outcome. That gives the business something concrete to fund, measure, and build on. [Download Tealium’s reprint of The Martech Weekly’s Enterprise Martech Outlook 2026](https://tealium.com/resource/analyst-report/the-martech-weekly-executive-martech-outlook-download/) for the full research, peer benchmarks, and our conversation about putting customer data to work. ## Related Content [Powered by Tealium](https://tealium.com/blog/teal-on-teal/how-we-used-our-own-stack-to-personalize-tealium-com/?db_surface=web&db_slot=0) ### Loading Learn more ### Loading Learn more ### Loading Learn more ### Submitted! Thank you!