Customer Personas in 2025: Why Yours Are Failing (And How to Fix Them)

Why Old-School Customer Persona Fall Short 

If your company’s ideal customer profile hasn’t evolved since the days when third-party cookies roamed freely, you’re not alone. Many businesses still use outdated customer persona creation methods – think internal gut feelings, anecdotal sales feedback, maybe a sprinkling of last year’s web analytics. The result is often a one-dimensional caricature of a customer that feels reassuring but is rarely accurate. In fact, customer personas have “not changed much over the years”, and companies often misuse them by inviting internal bias and stuffing them with irrelevant details (forrester.com). Too often, these personas live on a PowerPoint or poster that everyone nods at and then ignores (or worse, makes misguided decisions with).

The Problem with Internal Assumptions

What might be causing these traditional personas to be less effective now? For one, they’re usually based solely on internal data and assumptions. No fresh research or validation means the persona reflects what your team thinks – not what customers do. As Forrester analysts put it, a persona is only as good as the objective research behind it; relying on untested assumptions bakes in bias. Unfortunately, a lot of businesses do exactly that, creating personas in a vacuum and never updating them as markets shift.

The Changing Data Landscape

The data landscape has also shifted under our feet. With new privacy rules and the demise of third-party cookies, marketers lost a big chunk of easy visibility into consumer behavior. Google’s move to Analytics 4 (GA4) is a prime example: it’s redesigned for a privacy-first world, moving away from third-party cookie tracking and leaning on first-party data and machine learning instead.

The upside is better privacy compliance; the downside is marketers get less granular user data out-of-the-box. Old personas built on the rich detail from Universal Analytics or cookie-based ad data now look like dinosaurs. In short, we can’t stalk users around the web like we used to, so any persona built on those methods is rapidly losing relevance.

Finally, consider how static, outdated personas are. The world nowdays moves fast – algorithms change, trends explode overnight on TikTok, supply chains crash, and consumer priorities shift with each global news cycle. If your “ideal customer” profile isn’t keeping up, it’s probably wrong. It’s telling that only 44% of marketers even use buyer personas in their strategy (salesgenie.com), (meaning a majority either skip them or gave up on them), and among those who have personas, many haven’t updated them recently. This is a huge missed opportunity, especially when personalised marketing is more important than ever (we’ll get to that shortly).

Bottom line: clinging to a persona drawn up in a conference room last year (or five years ago) can actively hurt your marketing. It blinds you to real changes in customer behaviour and can lead to tone-deaf campaigns. To build your ideal customer persona today, we need to ditch the purely fictional profiles and embrace a data-enriched, continuously updated approach.

Meet the Modern Digital Consumer 

To appreciate why yesterday’s personas fall short, let’s look at who today’s digital consumers really are. Spoiler: they are channel-hopping, hyper-informed, and more distracted than a cat in a laser pointer factory. It’s crucial to ground your personas in this reality. Here are a few eye-opening stats.

The Always-On Digital Reality

The average internet user in 2023 spends about 6 hours and 37 minutes online each day datareportal.com – nearly a third of their waking hours. Out of that time, over 2½ hours per day is spent on social media platforms. Consumers aren’t just online briefly; they practically live there, juggling work, shopping, entertainment, and social connection.

Moreover, the typical person isn’t loyal to just one platform or device. On average, an internet user today is active on around 7 different social media networks per month. They might start their day checking Instagram, discover products on TikTok over lunch, compare prices on Google in the afternoon, and rant on Twitter by evening. This diversification of attention means your customers leave digital breadcrumbs everywhere – far beyond your website.

We’ve also hit record levels of multi-device usage. Shoppers may begin researching a product on their phone, later purchase it on a laptop, and contact support via smart speaker or chat app. Each touchpoint is a piece of the puzzle. It’s no wonder that relying on a single data source (like just your web analytics or in-store surveys) can leave your persona understanding incomplete. If you only look at one slice – say, what people do on your site – you miss that the same customers might be very different on another channel.

💡 For example, a customer might rarely open your marketing emails but engages with your brand’s Instagram daily; another might lurk on your site without buying until they see a retargeting ad with a discount.

What Modern Customer Personas Must Account For

The modern consumer is well-informed and expects convenience. Studies show over 50% of consumers now expect companies to anticipate their needs and offer relevant suggestions before they even reach out. And impatience is high – if you blast them with generic offers that don’t fit, a majority will tune you out or even feel annoyed.

The takeaway for persona-building: your ideal customer isn’t a static sketch with one channel and one need. They are multi-dimensional. Any useful persona today must account for the complexity of digital behaviour – the many platforms, the constant connectivity, and the heightened expectations. It also means internal data alone (like just purchase history or just Google Analytics) paints an insufficient picture. To truly “build your ideal customer”, we have to assemble data from across their digital lives, with respect for privacy and consent, of course.

The 360° Customer View Gap (and Why Data Is So Fragmented)

If modern consumers scatter their data across dozens of touchpoints, how can we ever get a complete picture? This is the classic 360-degree customer view challenge – and it’s where most e-commerce businesses struggle. The irony is that many businesses are sitting on piles of data about their customers, but it’s all fragmented in different systems. Your e-shop has web analytics data, your CRM has email engagement and purchase history, your social media team has audience demographics from Facebook or TikTok, your payment processor or POS has transaction details… and these pieces rarely talk to each other. It’s like trying to solve a jigsaw puzzle when all the pieces are stuck in different boxes.

This fragmentation isn’t just an IT headache; it’s killing marketing insights. When data is siloed between platforms, meaningful segmentation and personalization become nearly impossible.

One study found that companies lose 20–30% of annual revenue due to inefficiencies caused by poor data management (ouch!) and a whopping 84% of sellers say fragmented data prevents them from delivering a seamless customer experience (linkedin.com).

Think about it – if your email system and your website analytics don’t share data, you might email a customer a promo for a product they already bought yesterday (because your email tool didn’t know about the purchase). Or you might treat a high-LTV repeat buyer the same as a one-time tire-kicker because their behaviors live in separate silos. These missteps translate to lost sales and weakened loyalty.

Why Customer Data Gets Fragmented

• Multiple Platforms for Different Functions: An e-commerce business might use one platform for the online store, another for email marketing, a separate CRM for customer support, and maybe an analytics tool or two. Each generates its own dataset. Unless proactively integrated, they remain isolated.
• Different Consumer Behaviours by Channel: Customers behave differently on your site versus social media versus in-store (if you have brick-and-mortar). Each touchpoint captures a different aspect of the person (browsing history, social interests, credit or payment info, loyalty status, etc.). Without integration, you get a fragmented persona – like blind men describing an elephant from different angles.
• Legacy Systems and Lack of IT Resources: Smaller businesses often can’t afford a fully unified tech stack. Piecemeal solutions accumulate over time. Integrating them requires technical expertise (APIs, data warehouses) and ongoing maintenance. Not every company has a dedicated dev team or data engineer to do this heavy lifting.
• Privacy and Data Restrictions: Ironically, tighter privacy rules can increase fragmentation. With cookie restrictions and opt-outs, third-party data that used to flow freely is now curtailed. You might have some first-party data, but gaps where you used to rely on third-party tracking. Unless you replace those with new strategies (like asking users directly or leveraging logged-in data), you’re left with partial views.

Why Customer Data Gets Fragmented

All of this leads to an uncomfortable truth: Most e-commerce brands do not truly have a 360° view of their customer, even if they believe they do. You might have 36 different views in 36 different places. No wonder building a truly accurate persona feels daunting!

To illustrate, lets imagine one customer personas example – customer Jane Doe. Your Shopify store knows Jane as buyer #1234 who bought two items last month. Your email system knows her as jane.doe@gmail.com who clicks on 10% of your newsletters. Facebook knows her as user @JaneDoe who liked your last post. Your customer service software knows her by phone number as the person who called about a return. Each system holds a piece of Jane’s puzzle. If you never assemble those, your “persona” of Jane will be incomplete at best, or totally misleading at worst.

This disjointed data also makes old persona-building exercises (often done on paper) almost laughable – teams end up guessing what customers do because they can’t easily see the full story. Or they ignore swathes of data because it’s too hard to consolidate. The result is a persona that might reflect one slice (e.g., website behaviour) but not the whole customer journey.

So, how do we bridge this gap? Large enterprises throw money at the problem – massive data lakes, fancy Customer Data Platforms (CDPs), armies of data scientists – but that’s not feasible for everyone. In fact, small businesses frequently spend thousands of dollars per user per month on various sales and marketing tools and still end up with fragmented, unproductive data silos. Ouch. Clearly, we require more intelligent and accessible methods to link the disparate data.

The encouraging news is that solutions do exist to tackle data fragmentation and build richer personas – from no-frills tactics like surveys to high-tech platforms. The next sections will explore these persona-building solutions and how you can leverage them, whether you’re a lean e-commerce startup or a larger online retailer. The goal is to achieve that elusive unified customer understanding without completely breaking the bank (or your sanity).

Solutions: Modern Methods to Build Personas

Building a data-driven marketing persona might sound high-tech, but it boils down to combining common sense customer research with smart use of technology. Here we outline a range of approaches – you might use one or a mix of several. Each has its pros, cons, and costs. The key is to move beyond guesswork and leverage real, current data about your customers. Let’s dive in.

1. Regular Surveys and Customer Research (Voice of Customer)

One of the simplest ways to keep a persona fresh is just to ask your customers about themselves. Surveys, interviews, feedback forms – these traditional research methods are still valuable. They provide qualitative insights that raw data might not, like why a customer behaves a certain way or what problems they’re trying to solve by using your product.

💡 For example, you might send a post-purchase survey asking how the customer found you, what almost stopped them from buying, and what they care about (price, quality, sustainability, etc.). Over time, patterns emerge that inform your persona’s motivations and pain points. You can also do one-on-one interviews or usability tests to watch real customers interact with your site or product.

Strengths: Surveys and interviews give you direct-from-the-horse’s-mouth insights. They can validate (or invalidate) your assumptions. This is especially useful to combat internal biases. It’s also relatively low-cost – tools like Google Forms or SurveyMonkey are inexpensive, and incentives like a small coupon can boost response rates. In a privacy-centric world, surveys are “zero-party data” (the customer willingly provides info) – no cookies or spying needed.

Weaknesses: The downsides are that surveys are self-reported (people don’t always remember or tell the full truth about their behaviour), and you may only hear from a small, vocal subset. It’s also a snapshot in time; consumer sentiments can change, so you’d need to do surveys regularly. And frankly, response rates can be a challenge – many people ignore surveys unless highly motivated. So while this method is excellent for qualitative depth, it won’t give you the complete quantitative picture. It also won’t automatically unify data—you’ll get insights that you have to integrate with what you see in analytics.

Best Practice Tip: Make customer research an ongoing habit. Even a short quarterly survey or a few customer calls per month can surface new trends. Just remember to act on what you learn – feed it back into your persona profiles and share with your team. Don’t let it become another report that gets filed away.

2. In-House Development and Data Science Teams

At the other end of the spectrum from DIY surveys is the heavy-duty approach: using your own technical team to unify and analyse customer data. This means tasking developers or data scientists to connect all your disparate systems and crunch the numbers for insights. For example, your team might build a data warehouse that pulls in data from your e-commerce platform, email tool, ad campaigns, customer support logs, etc., and then use analytics or machine learning to identify distinct customer segments (personas) from that unified data.

Strengths: When done right, this approach can yield the richest, most tailored intelligence. You essentially create your own mini-Google-Analytics-on-steroids that’s specific to your business. In-house teams can craft exactly the queries and analyses you need (e.g., “show me high-value customers who browse on mobile at 2am and respond to push notifications”). You’ll own the solution and the data, which is great for privacy compliance and flexibility. If you have data science capabilities, you can even get predictive – forecasting customer lifetime value or churn risk and incorporating those attributes into personas.

Weaknesses: The obvious drawback is cost and complexity. Integrating multiple data sources is notoriously difficult – expect countless hours integrating APIs or databases. It’s not uncommon for such projects to run into the tens of thousands of dollars in developer time or require hiring specialists. Maintaining these pipelines is also non-trivial (system updates or API changes can break your data flow). Essentially, this approach is often feasible only for larger companies or tech-savvy businesses that can invest heavily. If you’re a small to mid-sized e-commerce, you might not have the budget or the people to do this internally. Even if you do, it could take months to get results.

There’s a trade-off: build it yourself and get exactly what you need (but pay the price), or use third-party tools that might not fit perfectly (but are ready-made). Many growing e-commerce firms start trying in-house data unification, only to realise it’s a massive undertaking. It’s okay if you can’t go full “DIY data science”. The positive news is, there are tools to help – which brings us to the next method.

3. Customer Data Platforms (CDPs) and All-in-One Solutions

Over the past few years, Customer Data Platforms have emerged as the holy grail for unifying customer information. A CDP is basically a software that sucks in data from all your sources (website, mobile app, email, ads, CRM, etc.), stitches together unified customer profiles (resolving that Jane Doe on your site is the same as Jane D. in your email list), and often provides tools for segmentation and activation (like creating persona-based audiences you can send to Facebook Ads or email). Examples include Segment (Twilio Segment), Adobe Real-Time CDP, Socialscore Light CDP, Treasure Data, mParticle, and many others.

For an e-commerce business, a CDP can theoretically give you that coveted 360° view by serving as the central brain. It might track that User X is the same across devices, has opened 5 emails, visited product A three times, purchased twice, and has a customer service ticket open – all in one profile. You can then define personas or segments within the CDP (e.g., “Bargain Hunters” vs “High-Spenders” based on behavior patterns) and push personalized campaigns accordingly.

Strengths: The obvious strength is unification and actionability. A good CDP will solve the data fragmentation issue by design – it’s built to integrate data sources and keep profiles updated in real-time. Many CDPs also have machine learning features to discover customer clusters or predict attributes (like likelihood to buy). Once set up, marketers can often use a CDP with minimal IT help, pulling up segments or exporting audiences with a few clicks. This is a huge win for agility. In short, CDPs promise to be a one-stop-shop for turning fragmented data into coherent, usable personas and segments.

Weaknesses: Two big ones: cost and implementation effort. CDPs, especially the enterprise-grade ones, are not cheap. The minimum investment is often in the range of $4,000–$12,500 per month for a CDP solution, putting it out of reach for many small businesses. And that’s just licensing – you also might need a solutions engineer or partner to implement it. It can take months to fully implement a CDP and integrate all your data sources. If your data isn’t clean or consistent, a CDP won’t magically fix that; garbage in, garbage out. For SMBs, there are lighter CDP-like tools or even CRM systems (like HubSpot, which we’ll discuss later) that include some CDP features at lower cost, but they still require a commitment.

Additionally, using a CDP effectively means your team needs to learn it and actively maintain those integrations. It’s powerful, but if underutilised, it can become an expensive piece of shelfware. We also have to note that while CDPs unify first-party data (your data), they don’t inherently bring in new external insights beyond what you feed them. If you lack data in some areas (say, you have no idea about customers’ social media interests), a CDP by itself won’t fill that gap; it will just organise what you have.

In summary, CDPs are fantastic for organisations that are data-rich and ready to invest in infrastructure to use that data. If you’re smaller, don’t despair – you can still get many benefits by carefully choosing a more affordable platform that covers your primary channels or by using the next approach: third-party data enrichment.

4. Alternative Data Providers and Enrichment Services 

Another path to building richer personas is augmenting your data with external sources. Think of this as plugging gaps in your customer knowledge by bringing in outside information. This is where providers like SocialScore come in (among others). These services gather consumer data from various alternative sources – often public or aggregated data from social media, online behaviour, demographics, etc. – and use it to enrich the profiles you have.

For instance, SocialScore can take something like an email address or phone number from your customer list and look up that person’s public social media profiles, interests, and even digital footprint across hundreds of websites. Suddenly, you might learn that customer Jane Doe is very into outdoor hiking and follows several eco-friendly brands online – insights you never got from your own site analytics. SocialScore describes its mission as helping businesses make smarter decisions using alternative customer data for analysis, prediction, and insight. In practice, that means they pull in data from 300+ websites and social platforms, scoring audiences on things like interests and online behavior.

What does this look like?

Let’s say you have a persona of “Eco-conscious Emma” for your sustainable products line, but all you know from your data is that Emma buys your bamboo toothbrushes. An enrichment service could tell you that “Emma” (real customers fitting that profile) also likely subscribe to certain eco-lifestyle blogs, spend a lot of time on Pinterest, have an interest in yoga, and tend to use iPhones. Now your persona is multidimensional: not just what she buys from you, but what her lifestyle and preferences are more broadly.

One e-commerce case study showed a brand using SocialScore to gather data from over 60 social networks and sites, pulling details like user bios, interests, and communication preferences to build detailed customer profilessocialscore.io. This allowed them to personalize marketing based on hobbies and communication channel preferences – things they’d never know from internal data alone.

Strengths: Data enrichment can give you a fuller 360° view without you having to collect everything yourself. It’s like turbocharging your personas with societal and behavioral context. It is especially valuable for smaller companies that don’t have resources to track users across the web – these providers have done the legwork. It’s also typically faster to implement than a full CDP; you send over some customer identifiers and get back enriched profiles. Services like this can be more affordable than building a giant stack – some operate on pay-per-match or subscription models that scale to your size. They can also provide scoring models (e.g., a “social influence” score or “purchase power” estimate) that help segment customers in new ways.

Weaknesses: There are important caveats. Firstly, data quality and privacy must be considered. You need to ensure the data source is compliant (check GDPR, etc., and ensure you’re allowed to use that data for marketing). Customers might find it creepy if you suddenly target them based on info they never gave you – so use enriched data smartly and ethically (e.g., to guide strategy, not to directly say “we know you love hiking” if they never told you). Furthermore, external data can sometimes be outdated or inaccurate for specific individuals – treat it as probabilistic.

Another weakness is that using enriched data incurs additional costs and creates a dependency on a third party. And it won’t solve internal silos by itself; you’ll still need to merge this enriched data with what you have (though many providers output data in an easy-to-import format, and some can plug into CRMs or CDPs directly).

Use case: Alternative data shines when you want to evolve your personas beyond the obvious. It’s not about replacing your first-party data but completing the picture. For example, if your internal persona is “High-Spending Hannah” based on purchase value, enrichment might reveal there are two types of Hannah: one that is career-focused and active on LinkedIn and another that is a socialite active on Instagram – and you’d approach those sub-personas differently. Without outside data, you’d market to them the same way and miss the nuance.

Each of these solutions, such as surveys, CDPs, and data enrichment, addresses a specific aspect of the persona. You don’t necessarily have to pick just one. In fact, the best approach is often a combination: use surveys to get qualitative feelings, use your analytics/CRM for quantitative behaviour, and maybe plug in a data enrichment service to layer on extra insights. The goal is to turn your persona from a static sketch into a living, breathing data-informed profile.

Next, let’s see how a persona can evolve in practice when you apply some of these methods.

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