MurafaDigital OÜ looks at why so many campaigns that claim to be personalized end up feeling anything but, and how the data feeding them is usually the problem nobody wants to talk about.
Tallinn, Harju Sep 16, 2026 (Issuewire.com) - MurafaDigital OÜ, a digital marketing company focused on personalized marketing strategies and tailored campaign execution, has published findings on a pattern it observes repeatedly across the industry: brands investing in personalization at the campaign level while relying on data that is, in practice, far too broad to support it. The result is a gap between what a campaign claims to be doing and what it is actually doing, and that gap tends to be wider than most teams realize.
The Personalization Problem Nobody Talks About
Personalization has become one of the most used words in marketing, and one of the most inconsistently applied. A campaign gets labeled personalized because it uses a first name in a subject line, or because it targets a broad demographic segment, or because it shows different creatives to different age groups. None of that is wrong exactly, but it is a long way from what personalization is supposed to mean.
The deeper issue, and the one MurafaDigital OÜ's analysis focuses on, is that personalization is only as good as the data it draws from. A campaign built on audience segments that were defined months ago, using behavioral signals that have since changed, or based on third-party data that was never specific to the brand's actual customer base, is not really a personalized campaign. It is a generic campaign dressed up in personalization language. The user on the receiving end can usually tell the difference, even if they couldn't explain exactly why.
What the Data Shows
MurafaDigital OÜ's analysis found that 71% of brands surveyed described their campaigns as personalized, but fewer than a third of those brands were using first-party behavioral data as the primary input for audience segmentation. The rest were relying on demographic profiles, purchased data sets, or platform-level interest categories that apply to millions of people at once. These are useful tools, but they are not personalization. They are targeting, and the distinction matters when a brand is making promises about relevance that its data cannot actually support.
The analysis also found that campaigns using genuinely first-party data, built from actual interactions with a brand's own audience, produced engagement rates that were on average 43% higher than campaigns using third-party or platform-defined segments alone. The difference was most pronounced in retargeting and re-engagement contexts, where the specificity of the data had the most direct bearing on whether a message felt relevant or intrusive.
Where Generic Data Creeps In
The reasons brands end up with generic data feeding personalized campaigns are fairly predictable once you know what to look for.
Building a first-party data strategy takes time. Defining the right signals, setting up the collection infrastructure, cleaning and segmenting the data, and then building campaigns around it is a longer process than simply using what a platform already has available. When timelines are tight, the platform data tends to win by default, and the campaign gets labeled personalized anyway.
- The second is scale pressure.
Truly personalized segments are smaller. A campaign that targets 500 people who have done something very specific is harder to justify to a stakeholder asking why the reach numbers are low, even if those 500 people are far more likely to respond. The temptation is to broaden the segment until the numbers look more comfortable, at which point the personalization has been quietly diluted.
Even brands that start with good first-party data often fail to keep it current. A customer who bought something eighteen months ago, visited a pricing page six months ago, and hasn't been back since is not the same customer they were at any of those points. Campaigns that treat historical data as a permanent proxy for current intent are personalizing against a version of the audience that may no longer exist.
Why This Keeps Mattering
MurafaDigital OÜ's analysis is not an argument against using platform data or broad segmentation. Both have their place, particularly in awareness-stage campaigns where reach is more important than precision. The issue arises when generic data gets used in contexts where precision actually matters, such as re-engagement campaigns, high-consideration decisions, or moments where the message needs to feel specifically relevant to be effective.
In those contexts, a campaign built on generic data does not just underperform. It can actively damage the relationship between a brand and its audience by sending a message that feels tone-deaf or badly timed. McKinsey research found that 76% of consumers get frustrated when their interactions with a brand don't feel personalized, which puts a real number on what's at stake when the gap between personalization promises and data reality gets too wide. MurafaDigital OÜ shares this analysis as a reference for marketing teams that are serious about closing the gap between what their personalization strategy promises and what their data can actually deliver.
About MurafaDigital OÜ
MurafaDigital OÜ is a digital marketing company working with brands and businesses on personalized marketing strategies and tailored campaign execution. The company's work is built around the idea that effective personalization starts with understanding what a brand's audience is actually doing, rather than what a platform's data says they might be interested in.
