Customer data strategy

How to create a holistic customer data strategy

In the past, organizations often operated with a siloed approach to customer data. Whether because of the size of the business, operational realities, or as a result of tool and technology limitations, it’s not unusual to find companies with partially or even completely separated data streams within business units.

To meet the demands of today’s customers and stay competitive in a fast-paced business environment, you can’t afford a piecemeal approach to your customer data. Successful companies adopt a 360° view of customer data and a holistic customer data strategy, which provides greater value to the business and dramatically improves customer experience.

A 360 degree customer view provides value to the business through sales opportunities, efficiencies in marketing spend, and AI/ML-readiness. It also provides value to the customer in the form of relevant recommendations, personalized experiences, and improved customer service.

Unifying your customer data strategy

It can be overwhelming to pool data from your sales, marketing, customer service, IT, and product teams together into a usable format, reporting structure, and decision-making resource. If your company is like most organizations, you’re likely to run into challenges like:

  • Fragmented and inconsistent data
  • Lack of stewardship and governance
  • Missing integrations between systems and data sources
  • Unclear customer privacy and consent policies

You may not be able to find a single solution that solves every problem, meets all your internal and external data requirements, tracks all the right metrics, and allows for a complete range of analysis and reporting. On the road to becoming a strategic customer data powerhouse, you’re likely to run into some roadblocks.

While every organization is different, we laid out a roadmap for achieving a holistic customer data strategy that minimizes the potential for wrong turns. Using this guide, your organization can align around common objectives, understand your gaps, and make informed decisions to build, test, and iterate a customer data strategy that works for your unique business needs and objectives.


The Ultimate Guide to Creating Your Customer Data Strategy

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How to create a holistic customer data strategy

Step 1: Take a snapshot of your current state

A 360-degree customer view includes data you collect across channels and business units as part of your customer data strategy

The first step toward a unified customer data strategy is a comprehensive audit of how your organization currently collects, stores, and uses customer data in any form and any business unit. Whether you opt to handle the audit internally or look for an external partner to facilitate discovery, your customer data audit should include:

  • The current size of your customer data set: Which business units collect customer data, how much do they collect, and where is it stored?
  • The current scope of your customer data set: What types of data are collected, and how are they obtained? Do you have a mix of internal and external data? First, second, and third-party data?
  • The current state of your customer data set: Is the data primarily structured or unstructured? How consistent is the data across business units and tools?
  • The current technology solutions in use: Is your customer data collected, stored, and analyzed in one central solution, or are those functions distributed across the business? Do those technology solutions currently interface? Are any advanced or emerging technologies or analytics solutions in place?
  • The current policies, processes, and safeguards in place: What data security, data privacy, and governance policies are in place, and how closely does the business comply?

Step 2: Identify data gaps and opportunities

As you build your customer data strategy, what does your information tell you, and what is missing?

Now that you have a baseline understanding of your customer data status, it’s time to analyze potential gaps and opportunities in your data collection and use.

Using your audit findings as a starting point, consider whether you might need to expand your data by exploring new means of collection and/or new sources. To build a truly holistic view of your customers, you’ll need to bring diverse data domains together in your customer data pool.

Customer data sources fall into three main categories:

First-party data

To enhance your collection of first-party data, make a plan to build customer trust so that users feel comfortable sharing their data with your company. Map out ways to add real value when information is exchanged, and use clear, transparent language to explain how the data you collect is used to improve the customer’s experience.

Second-party data

Internal data will always be a lagging indicator. To deepen your understanding and get more real-time insight, research and test second-party data strategies within larger customer ecosystems like Amazon, Facebook, and Walmart.

Third-party data

If your company relies on third-party cookies as a data source, you need to identify new ways to collect that information.

Step 3: Align goals and requirements across the business

It probably goes without saying, but your data, technology, governance, skills, and operations need to align with your overall business strategy. Then again, as management guru Stephen Covey observed, common sense is not always common practice. To set your customer data strategy up for success, consider bringing together stakeholders from your marketing, data, customer service, and technology teams, as well as key business leaders, for a half-day alignment session. An alignment workshop could cover:

  • Audit findings
  • Key opportunities
  • Requirements for technology solutions
  • Future-focused goals
  • Potential use cases for an improved customer data infrastructure

Step 4: Assess the technology landscape

With audit, analysis, and alignment results in hand, your team is equipped to research technology solutions that would best meet your needs. Whether you plan to integrate legacy systems in new ways, add new platforms, or explore new functionality, keeping your gaps and goals in mind can help you make an informed decision that benefits workflows across the business.

When it comes to customer data, you’ll find four key types of technology solutions. Understanding each type’s pros, cons, and connectivity will benefit your vendor consideration.

Customer Relationship Management (CRM) Tools

Your organization may already use a CRM, although maybe not to the full extent needed to satisfy your holistic strategy. Intended to give the business visibility into customer contacts from customer service and sales perspectives, a CRM consolidates data collected from customer interactions to help businesses manage relationships with customers and prospects. Some CRM solutions can integrate with a company’s martech stack to allow automated outreach, nurture, retention, and cross-selling campaigns.

CRM Pros:

  • Customer data is directly available in the CRM
  • Data unification is less expensive

CRM Cons:

  • CRMs lack true master data management capabilities
  • External integrations are complex
  • Licensing costs can be prohibitive

Customer Data Platform (CDP) Tools

Unlike a CRM, a CDP contains all customer touchpoints across the business and is primarily used to drive marketing decisions and applications. A CDP can provide a single view of the customer, enabling personalized and targeted marketing across a multi-channel customer journey. Customer Data Platforms integrate with CRMs and martech solutions.

CDP Pros:

  • Allows a 360° customer view
  • Enables multi-channel marketing

CDP Cons:

  • A CDP may overlap capabilities with other technology solutions
  • CDPs are exclusively marketing focused
  • CDPs lack true master data management capabilities

Master Data Management (MDM) Tools

An MDM provides a single point of reference for data throughout the organization. By identifying, collecting, and repairing data to create a high-quality master reference point, an MDM ensures data uniformity, accuracy, consistency, and accountability. Many organizations integrate an MDM with a CRM and the martech stack.

MDM Pros:

  • An MDM provides a single source of truth for customer data
  • An MDM ensures dedicated data quality

MDM Cons:

  • Marketing needs may be less supported than with a CDP
  • Most MDMs are complex to integrate
  • MDMs may come with high costs

Data Management Platform (DMP) Tools

A DMP is a data warehouse that consolidates and standardizes data from second- and third-party sources in a central repository, structuring it to facilitate advertising and retargeting. These solutions support large amounts of business data and can integrate with an existing CRM and martech stack.

DMP Pros:

  • A DMP allows for segmentation of large data sets
  • A DMP can easily scale as data needs grow

DMP Cons:

  • The DMP does not provide access to real-time data
  • DMP solutions are significantly larger and more expensive than alternatives
  • These solutions are highly dependent on ongoing IT support

Step 5: Design your data infrastructure

Once you’ve selected the right tools and technology for your use cases, it’s time to turn your data into insights so you can maximize the value of every customer touchpoint. As you architect your new data infrastructure, there are three key considerations to keep in mind:

Data storage:

Depending on your business model and requirements, as well as the volume of information you plan to collect, you could choose to store your customer data on-premises, in the cloud, or both.

Data processing:

Whether your data is entirely structured or includes partially unstructured information, and depending on the types of questions you’re asking, you may need advanced processing tools to surface actionable insights. Emerging technologies like AI, computer vision, natural language processing, and prediction engines can help you make the most of your customer data.

Data analysis:

To put your data to work, you’ll need to establish processes and capacity for analysis. Depending on your internal skillsets, this may be a function you democratize across the business, or you might create dashboards to deliver insights along pre-scripted pathways. Some organizations alternatively outsource data analysis to third-party consultants or platforms.

Step 6: Manage the switch

Change – even positive change that enhances customer experiences and accelerates business goals – is hard. When it comes to implementing your customer data strategy, you’ll face challenges and complexity from at least three fronts:


Technical integration and implementation of a new tool or platform with your legacy architecture may present significant challenges. In many cases, these tools have pre-defined integration points, while in others you may need to build custom APIs to meet functionality needs, regulatory requirements, or privacy standards.


Any time you’re dealing with customer data, security should be top of mind. From data privacy to protecting information in storage or in transit between systems, your customer data strategy requires an in-depth look at applicable laws and regulations, as well as the structure and format of your consent management and opt-in features.

Human nature:

Be sure to bring your teams along for the transition between old ways of accessing data and your new and improved processes. Rollout plans need space for notification and training on specific platforms and dashboards as well as overall data literacy.

Step 7: Unleash your customer data

From the start of your pilot project through enterprise scale-up, you can refine and enhance your reporting and outcomes. Even above your business-level goals, the ultimate objective of any customer data strategy is to enable data-driven decisions that improve customer experiences and build business results. No matter how your customer data strategy comes to life, customer data can lead to meaningful business outcomes:

Audience-building and segmentation:

The more you know about your customers – drawing from data you collect internally and obtain from external sources – the more granular your segmentation becomes. This enables your team to connect with prospects, both known and unknown to your organization, with better specificity and impact.

Customer experience personalization:

Data insights also help to drive personalization, so the content a customer receives across digital channels and personal touchpoints are directly relevant, meaningful, and impactful to their stage of the buyer’s journey.

Internal alignment:

Greater access to customer data from across the business can help encourage a data culture and enhance data literacy within your teams, giving rise to more efficient internal alignment. Better data and reporting, combined with good data storytelling and understanding, often deliver appreciable improvements in efficiency and connection.

Step 8: Stay change-ready

Your customer data strategy must continue to evolve to meet the demands of business and regulatory requirements and today’s rapidly changing customer expectations. A policy of iterative improvement and commitment to continual growth will ensure that your customer data strategy stays relevant and continues to deliver impact, regardless of what the future might hold.

Unsure? Stuck? Not sure where to begin?

Our Customer Data Strategy Workshop meets you where you are. Fusion Alliance helps companies navigate changing customer data environments with a unique methodology that fosters collaboration, transparency, and shared ownership of digital transformations.

Your Customer Data Strategy Workshop brings your marketing, data, and technology stakeholders together. Whether you’re just getting started or stuck in the messy middle of an enterprise rollout, we help you unpack your processes and partnerships to identify risks and opportunities so you can take the right next steps toward a future-focused customer data strategy.

Tailored directly to your organization’s goals and priorities, the workshop leads to three key deliverables to kickstart or level-up your transformation:

  • Comprehensive Data Map: An illustrated visualization of the teams, processes, and technologies impacted by your current data collection, storage, and analysis efforts.
  • Risk Scorecard: A quantified evaluation of monetary risk, process impact, data security, and business-specific factors of your current-state customer data operations.
  • Customer Data Strategy Roadmap: An overall approach and concrete steps to modernize your customer data program.

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