It was back in the 1950s, in the Golden Age of Advertising, when the meaning of “marketing” transitioned from grocery shopping to the science behind the art of selling things. But before computers, customer data was hard to come by: focus groups and surveys provided the bulk of customer information, advertising meant print and broadcast, and sales figures were the primary way to measure a campaign’s success.
These days, marketers have the opposite problem: They’re swimming in customer data, which pours in from myriad sources and touchpoints — from email, e-commerce sites, and social media engagement to data aggregators and customer relationship management platforms (CRMs) populated by your account management and sales teams.
The problem has become: How can marketers turn an onslaught of valuable but raw customer data into marketing qualified leads, accurately personalized campaigns, and successfully closed deals? The answer: Start with a comprehensive data strategy.
What is a data strategy?
A data strategy is your organization’s plan for how you use data to generate value. That plan defines the technology, workflows, and data management tactics that support specific business goals.
- Technology includes all the software and platforms you currently use — and may need to add — to collect, store, analyze, and share your data.
- Change management involves gathering the relevant members of your organization and getting buy-in for your data strategy and the way you will access and use — or activate — your data efficiently and effectively.
- Data management decisions comprise what data you’ll include or ingest into your data ecosystem as well as data hygiene issues: how you’ll keep the data you collect accurate, up-to-date, compliant, and secure.
Because customer data can be put to numerous uses and monetized directly or indirectly, your strategy should begin with decisions about what you want to get out of the data you collect. For example, a data strategy might be tailored toward providing a comprehensive understanding of your prospects and customers based on information collected from every channel and touchpoint they interact with. So whether you’re looking to increase sales, boost qualified leads, or create a more predictable sales cycle, your data strategy should begin with your business goals.
The benefits of a data strategy
A data strategy enables your entire organization to easily access the data they need to make informed business decisions.
A successful data strategy framework can:
- Influence product development - Data enables your product team to develop new features based on data about how customers use your product. A data strategy can also help organizations analyze market demand to assist with product decisions.
- Inform pricing - Provide the basis for product pricing.
- Enforce data governance - With a comprehensive data strategy, your team can develop data governance policies for data collection that comply with new laws and regulations. These include statutes such as: the European Union’s GDPR, HIPAA, the Children’s Online Privacy Protection Act (COPPA).
- Ensure data accuracy - Technologies that support your data strategy should include tactics like identity resolution, which makes sure data about the same people from disparate sources is reconciled accurately.
- Bolster data accuracy, privacy, and cybersecurity - A data strategy provides the guidelines for keeping data up-to-date and secure while still ensuring it’s available to everyone who needs it.
- Make data easier to understand - With a data strategy, your organization can more easily analyze data through intuitive interfaces for report creation and analysis such as Looker or Tableau, then apply learnings to everything from business strategy to marketing campaigns.
- Maximize ad spend - You can use a data strategy to highlight which channels generate the most conversions by tracking website behavior, user journeys, and lead sources, informing decisions on ad spend.
- Improve marketing ROI - A comprehensive data strategy helps you make more informed decisions about audience building and segmentation, content personalization, and targeted marketing campaigns based on accurate demographic, firmographic, and device information.
Who needs a data strategy?
In two words: Every company.
While any organization will benefit from a data strategy, the need for one gains importance with company size and the breadth and complexity of your marketing efforts.
While even a ten-person startup relying exclusively on Facebook ads should have some strategy in place, a data strategy is absolutely essential for a medium- or large-size organization with multiple products, multiple touchpoints, and an omnichannel marketing strategy.
The risks of not having a data strategy
The lack of an enterprise-wide data strategy, especially at larger organizations, leaves you susceptible to risks that directly affect the bottom line, by hindering timely, data-driven decision-making. A few examples of these risks include:
- Alienating customers through poorly or inaccurately personalized marketing content
- Wasting an omnichannel ad budget on channels with poor conversion rates
- Misguided product development leading to decreased sales or low user-adoption rates
- The use of illegal data collection methods or data sharing that’s noncompliant with regulations
- Inefficient data collection and management using outdated tools and spreadsheets
- Overly complex data management and analysis tools that result in low adoption rates and over-dependence on IT resources
- Building business strategy and key performance indicators (KPIs) using inaccurate information or siloed data that differs from department to department
Without a data strategy, data can also get locked in team- and platform-based silos. Such disparate data sources are perhaps the biggest hurdle to effectively using customer data. When all teams don’t have access to the same, centralized data, it’s difficult to build a comprehensive — or 360-degree — view of your customers. This leaves you vulnerable to inaccuracies and reliant on manual processes for updating data. It can also impede company-wide decision-making and leave decision-makers mistrustful of the data they’re provided with.
How do I get started with a data strategy?
When building a data strategy, it’s critical to start with your business goals and work backward to determine your data strategy’s architecture. Creating a data strategy can be broken into five phases:
1. Define your business objectives, such as optimizing marketing qualified leads (MQLs), setting KPIs, identifying customer journey pain points, or informing product development.
2. Identify your data sources and decide which ones are relevant to those objectives. Those sources can include:
- Point-of-sales systems
- Online shopping carts
- Website analytics that track traffic source, downloads, time on page, and user journeys
- Customer relationship management platforms like Salesforce or Hubspot
- Campaign-based email open and response rates
- Second-party data partners and third-party data vendors
- Social media channels
- Facebook and Google ads, and other digital advertising
3. Establish data governance models to ensure compliance with data collection laws, especially in highly regulated industries like healthcare and financial services.
4. Build out a technical architecture to support data collection, storage, access, and maintenance.
5. Make the data accessible to your entire organization, preferably through easy-to-use visualization and activation tools that empower non-technical users.
Who’s responsible for creating a data strategy?
Large companies may have a chief data officer who builds a strategy based on input from key stakeholders in marketing, sales, customer service, product, and so on. At smaller companies, developing a data strategy may be the domain of marketing operations.
Regardless of company size, a good data strategy should encompass your entire organization to eliminate data silos and ensure every team is working with the same information.
What are the primary components of a data strategy?
The technical architecture for a data strategy should encompass the entire data lifecycle, from collection through activation. This technology performs the following functions:
Ingestion and storage
Data points generated through customer interactions on websites or other touchpoints, or purchased from third-party vendors, must be collected and then ingested into some kind of data storage. Data strategies benefit from storing data in a central, comprehensive data warehouse, which is often cloud-based. Common enterprise data warehouses include Google BigQuery, Amazon Redshift, and Snowflake. Platforms like FiveTran and Snowplow are used to populate data warehouses with data from first-, second-, and third-party sources. Technologies like accelerators developed by DBT and Google Cortex speed the transfer of huge quantities of data among data sources.
Transformation and modeling
Data aggregated from different sources may be duplicative or inaccurate. Transformation involves data hygiene — making sure the data is clean, formatted consistently, arranged according to uniform business logic, and de-duplicated. . Access, analysis, and visualization
Software with this functionality enables stakeholders to slice and dice data and generate easy-to-understand reports that help them monitor their data-driven business goals. Some common data visualization tools include Power BI, Tableau, and Looker.
Activation
These are the tools that allow marketers to act on the data they’ve collected and create value by reaching out to prospects and customers through intelligent audience segmentation, channel advertising, and personalized marketing campaigns targeted to specific user personas.
Tools for data activation
For marketers, the success of a data strategy is often measured by how easily and effectively it enables them to activate their customer data to marketing and sales channels. More and more, they are turning to composable customer data platforms (CDPs) to help them do so, because these platforms sit on top of the cloud-based data warehouse where their data is being stored and transformed, and which functions as a single source of truth for the entire company.
Traditional vs composable CDPs
Traditional CDPs must store a copy of your company’s data within the platform. This means that your company must juggle two or more data repositories, which must then be synced — often through manual data dumps — and in practice often become siloed. Traditional CDPs may also lock you into a standard set of reporting and proscribe to which channels you can activate your data.
A composable CDP fits into your company’s existing tech stack. You can use a composable CDP with the data warehouse of your choice, and activate that data to the channels of your choosing. Because they’re more flexible, they can integrate a broader set of data inputs, giving you the most comprehensive view of your customers and prospects
Composable CDPs are therefore a key part of any effective data strategy because they prioritize a single source of truth and a modular technology infrastructure.
A data strategy is essential to a successful business strategy
Customer data is key for shaping business strategy. Marketers in particular know that customer data is the cornerstone for successful content personalization and targeted marketing campaigns. But there is so much customer data coming from so many sources that it must be harnessed manageably before it can be activated successfully.
A comprehensive, enterprise-wide data strategy that includes a centralized data repository, ensures the right data is being collected in compliance with changing laws and regulations, and employs the right technology to keep that data accurate and accessible to all who need it is essential for putting customer data to profitable use.
