Why companies are failing to create a single customer view (and how to fix it)

Imagine you visit an ATM and check your account balance. There’s no reason you wouldn’t trust the number it shows, right? 

You visit another ATM later in the day, but it says you have more money than the last. Now you’re confused. Did you receive an unexpected payment? Can you splurge on dinner?

You check your mobile account, and it shows yet another balance. Something is clearly not right, and you don’t know which number to trust. 

Although an unlikely real-world scenario (the bank would lose its customers fast!), this is what it feels like when organizations fail to unify customer data across sources. Siloed systems contain fragments of data, meaning no team has a complete view of the customer — or knows which data to trust. This data disconnect also destroys the ability to deliver personalized, timely, and accurate customer experiences.

The solution is to build a single customer view. The trouble, however, is that many organizations believe they have this… when they actually don’t. 

To help get your data foundation in place and empower every team to make full use of your customer insights, let’s examine what it takes to build a single customer view — and where organizations often fall short.

What is a single customer view?

A single customer view (SCV) is essentially a one-stop shop for organizational teams to access any information about their customer base. The concept is commonly called a few things:

• Customer 360

• Single source of truth

• Unified customer view (UCV)

• Unified customer profile

No matter what your team calls it, the single customer view provides a complete profile of individual customers and their personally identifiable information (PII). The SCV offers real-time, aggregated insights across every customer channel and touchpoint, such as calls to a customer service team, email marketing engagement, and paid media activities. 

An SCV empowers every team in your organization, including business decision-makers who rely on customer data to create strategies, measure results, and forecast the business pipeline. 

Why should teams care about an accurate, single view of the customer?

Many teams struggle to build a complete customer view and connect the many systems that rely on customer data. This causes some serious operational inefficiencies, wasted money, and, ultimately, sub-par customer experiences. 

Of course, the impact of inconsistent or disjointed customer data depends on its use. At a minimum, internal teams will likely disagree on business reporting if different systems say different things. 

In the customer’s experience, a disconnected stack shows up in a few different ways:

• A retailer sends the customer a coupon for a product they purchased in-store only a week earlier, a mistake caused by POS data not connecting with online purchase data. Ultimately, this hurts the relationship with a customer and the potential for upsells or cross-sells.

• An internet provider advertises fiber internet plans to prospects whose neighborhoods do not have those cables available, because the provider was relying on an outdated list of customer zip codes.

• A sports team sends exclusive handwritten cards inviting people to become season ticket holders… but those people have already renewed for the season. The team got a few of the names wrong, too. 

Mixing up a customer’s product preferences for a social media ad campaign isn’t relationship-breaking, but some scenarios can quickly destroy your customer’s trust (like a bank reporting different account balances).  

The business value of accurate customer data

Teams need access to accurate and real-time customer data to deliver truly personalized experiences, especially as AI helps automate and supercharge their efforts. 

Whether customers call for support or receive promotional messages, your team needs to trust your data and have access to all the relevant information necessary to provide the best experience at that moment. 

Recent surveys and studies reinforce how a single customer view enables business growth: 

• 83% of customers are likely to purchase if a brand’s outreach shows specific products they recently browsed.

• 64% of customer support agents say that having a single view of a customer’s interactions across all channels would help them do their job better.

• 40% more revenue is generated by companies if they prioritize personalization to drive sales and loyalty.

Common methods for creating a single customer view that don’t actually work

Companies seeking to acquire a customer 360 view and a single source of truth with their data typically fall into two groups: Those bound by legacy or homegrown systems and those who have adopted an array of modern technologies.

Each presents its own challenges.  

Why legacy systems are ineffective for creating a single customer view

Many large enterprises and companies in regulated industries use legacy systems (often ones built in-house) that were likely written decades ago. Legacy systems often have outdated business logic that nobody has questioned; it has just been running quietly in the background. 

The challenges with legacy systems can spiral quickly:

• On-prem hardware requires additional staff to maintain, compared to cloud technology 

• Engineers become inundated with maintenance tasks as the system fails to support new technologies or integrations

• Internal cybersecurity teams may be unfamiliar with modern tools and, therefore, skeptical of their security as they integrate with the legacy system

• Batch processing means teams cannot access the real-time data needed to deliver the agile experiences customers demand

Unless your organization has significant resources to invest in ongoing system updates and maintenance, it is likely best to switch to modern solutions that support the creation of a single customer view. 

It’s helpful to hire an internal data team member familiar with the modern data stack or hire a consulting company to guide the transformation from a legacy system to a modern one.  

Teams will likely be uncomfortable with any discrepancies in the new business reporting — the legacy system reports one thing, the new system reports another — so test your system and confirm its logic is sound. Be sure to shut down the legacy system when the new system is validated, too. Otherwise, teams may continue to use the legacy system, continuing the problem of siloed data. 

Marketing clouds, siloed tech, and packaged CDPs duplicate customer data

Other companies have adopted modern architecture with cloud-based tools, but their data is often siloed between different platforms: Email marketing teams use the email marketing platform data, paid advertising teams use their paid advertising platform data, etc.

When no team is working with full customer insights, there is an inevitable disconnect in the customer experience. Systems need a centralized location to pull data from, which should be the company’s data warehouse. (We’ll explain how to best position the data warehouse below.) 

Organizations using marketing clouds and other modern tech may also have a traditional customer data platform (CDP), which they consider their single source of truth. Traditional CDPs (also called packaged CDPs) presented major advantages compared to other customer data options. However, they now introduce inefficiencies. 

A traditional CDP is another platform where you send all your data. And if you send your data from your data warehouse to this traditional CDP, you technically have two “sources of truth.” As with other data silos, this means teams are working from different data and may be sending misaligned or duplicate messages to customers. 

This is why organizations are shifting toward composable CDPs, which offer a zero-copy architecture and pull directly from the data warehouse to activate data across your marketing and sales channels. 

How to create a single customer view that works

Because organizations rely on several, if not dozens or even hundreds, of marketing and sales tools to achieve specific objectives, you’ll likely end up with multiple “sources of truth.”

The best solution I’ve seen is to have a centralized data warehouse where you ingest data from every relevant source and apply business logic to perform identity resolution and various data cleansing processes — thus providing you with customer 360. 

The data warehouse is central to a SCV

With all customer data in the data warehouse, you can route only the minimum data necessary to end destinations, where your teams interact with and activate the data.

There are many benefits to having a data warehouse at the center of your tech stack and using that as your single source of truth for all your data:

• Security - A data warehouse sits inside your own cloud infrastructure, so you have full control over it. If you send data to other platforms, your data leaves your infrastructure, which puts you at the mercy of that vendor’s security protocols. Considering that 60% of customers believe companies are falling short in their data security efforts and 70% won’t buy from a company they think has sub-par security measures, this is a major advantage. 

• Cost - Many customer data and marketing platforms charge by usage, sometimes by the number of rows of data they ingest. Instead of sending data on every transaction to the end platform, you can perform the necessary aggregation and filtering in the data warehouse to send only what’s necessary to the external system.

• Privacy - When exposing highly sensitive data, like when you’re performing identity resolution and could be de-duplicating entries based on social security numbers, it’s safer to perform that resolution in your data cloud. 

Consider this example: You’re emailing customers whose birthday is this month to offer them a discount. In a composable architecture, you can route an audience whose birthday is this month to your end destination without sharing every line of customer information. You won’t even need to share the birthday, because you will know the audience you crafted in your data warehouse reflects users whose birthday is this month.  

5 steps for creating a single customer view

Regardless of your existing customer data toolkit, follow these steps to create a single customer view: 

1. Identify upstream data sources. Create an inventory of every customer data source, going directly to the data source, such as your point of sale system and survey or feedback platforms. 

2. Perform data mapping to guide your data transformation. This optional step can help organizations visualize how data will flow across systems and how it should be transformed for optimal use across systems. 

3. Centralize the data in a warehouse. Ingest data into your warehouse and perform a series of identity resolution and de-duplication activities to ensure there is a single source of truth on each customer.

4. Review your data and conduct reconciliation exercises to ensure everyone is comfortable with the new reporting. Reconciliation exercises compare two sets of data to identify and resolve any discrepancies so they are aligned and report the same numbers. Automate data quality checks to flag potential mismatched rows for human reviews. 

5. Terminate the connections from the source systems, such as the CRM (which should instead connect to the data warehouse).

What to remember when you’re creating a single view of the customer

Teams encounter a few common pitfalls when building their single customer view. Follow these best practices to accelerate your success. 

Rely on upstream systems

Connect your data warehouse to the highest source system possible, such as the point of sale, email platform, or survey and feedback solutions. Teams commonly connect their marketing cloud or customer relationship management (CRM) platform to the data warehouse, but multiple source systems feed into those systems. Connect those source systems directly to the data warehouse. 

Integrate paid media insights

Many organizations overlook touchpoints with paid media data, partly because it can be difficult to gain user-level data from those sources. Create tags associated with your paid media posts to ensure your SCV is as comprehensive as possible. 

Follow ELT, not ETL

Sparing a deep technical explanation, it’s best to extract (E), load (L), and then transform (T) data instead of extract, transform, load. ETL was a prominent method for decades because of traditional row-based storage databases. However, modern columnar-storage cloud data warehouses can manage much larger volumes of data, so it is best to move your untransformed data into your warehouse to then perform the transformation to avoid losing the original data.

Think of it like editing a photo. You want to save your original photo before cropping or filtering it so you can access the original if needed.

Be comfortable with data variance during the switch

The new data reporting will likely differ from the old numbers. Reconciliation exercises — which can be led by data teams who understand how to validate data — help get your stakeholders’ buy-in and establish trust in the data, accepting that a 0% discrepancy likely will not happen. You may have a 3-5% discrepancy, and your comfort threshold will depend on your industry. Accounting organizations should be very close to the original numbers, whereas retail organizations could be comfortable with a 5% difference in the initial switch. 

Use a third-party identity spine

A common challenge for teams is having the same customer stored in three different systems with different pieces of their information, like phone number, email address, and physical address. A third-party identity spine can help unify data and identify the separate entries as a single customer. Identity resolution platforms and third-party audience providers often serve as this spine. 

Appoint data advocates for ongoing refinement

Organizations should have a centralized data team and designate one person from each department (marketing, finance, product, etc.) to be the liaison for their team and executive’s needs. These data advocates help streamline resources and protect the data team from becoming flooded with questions or requests. 

The data liaisons don’t need to be data experts, but they should have some knowledge of the organization’s data ecosystem. Their role is to voice their team’s concerns or identify areas the data team should look into and help the data team understand how they can best add value to the business. 

Composable architecture facilitates and preserves customer 360

Keeping your organization’s data comprehensive, clean, and accurate is essential for scaling your success and unlocking the full potential of your customer data.

Watch KPIs like customer acquisition, retention, revenue uplift, and overall satisfaction to identify whether your efforts are adding business value — and whether specific moments in the customer journey need to be fine-tuned.

Adopting a data warehouse and using a composable CDP to activate your marketing and preserve your single source of truth is a winning strategy built for today's and tomorrow's technologies.