Data-Driven Marketing Strategy: Use Data to Tell Better Brand Stories

A data-driven marketing strategy is the only way to reach customers, stay competitive, and tell brand stories that others will care about. Why this matters: Today, brands and marketing teams need to strive to build a memorable customer experience, and the first step is creating relevant content. This post will explore how data-driven marketing can help you better understand your customers and inform your content programs.

The Status Quo No Longer Works in Marketing

Forrester’s 2022 B2B Marketing Survey revealed that 51% of brands that grew annual revenue by over 20% in 2021 invested a higher percentage in their marketing organization. These companies get it. Understanding your target audience is essential to any successful content marketing campaign or digital marketing strategy.

However, guessing who your customers are won’t cut it, and hoping will never be a viable marketing solution. Instead, customer data will always be the fuel that powers the performance of your programs and campaigns, helping you make smarter marketing decisions. With data-driven marketing, you can take a more scientific approach to everything you put into the marketplace.

Customer data is a potent tool for informing content strategy. It gives you an idea of what your customers care about, what they search for on Google, what they say on social channels, and which channels they spend most of their time on. If you know the trends that matter most to your target audience, you can create content around those areas, making it more likely to have a business impact.

The primary purpose of data-driven marketing is to save time by validating your work and finding white space in the market.

What is Data-Driven Marketing?

Data-driven marketing represents the future of the field, with an approach deeply rooted in analytics, testing, and optimization. Rather than intuition, data-driven marketers let customer and market data insights guide their strategy. With technology expanding access to information, harnessing these insights drives strategic advantage.

From ideation to execution, data informs every marketing decision and activity. From early market research to campaign analysis, quantitative metrics and qualitative observations at each stage shape plans with surgical precision. Data reveals both macro market forces and micro customer segments to target. Testing through A/B formats and multi-variant campaigns helps continuously refine messaging, platforms, creative, and more based on performance. With a wealth of analytics at their fingertips, data-driven marketers operate confidently, backed by evidence-based intelligence.

What’s the Data Say?

The Altimeter Report asked about data-driven marketing, “What sources of marketing data do you use to create content?” The answers were all over the place. But the good news is marketers use data to inform campaigns, messaging, advertising, and other digital marketing efforts.

• 63% of the respondents use website analytics to inform content
• 59% of the respondents are using social media metrics to inform content
• 58% of the respondents use customer surveys and primary research to inform content
• 49% of the respondents use social customer service or call center records to inform content
• 48% of the respondents use CRM systems to inform content
• 23% of respondents use data from third-party providers to inform content

The information in this report is fascinating on so many levels regarding data-driven marketing. Marketers using data is a good sign of what’s to come, especially when marketers can align marketing messages with customers’ unmet needs. I am curious, however, how many marketers are learning to measure content marketing performance using this same type of customer data. Time will tell.

Using Web Analytics to Inform Data-Driven Marketing

Web analytics platforms like Google Analytics and Adobe Experience Cloud offer invaluable data to drive content marketing strategies. Marketers gain critical insights into audience interests and preferences by tracking detailed website metrics around user engagement.

For example, Google Analytics reports on traffic drivers, visitor demographics, page views, time on site, and more. Analyzing this data reveals top-performing content that resonates based on engagement time. It also provides interest-based insights based on site search behavior and previous sites visited before landing pages.

Advanced teams can tailor experiences using this data, like serving up personalized home page content for return visitors based on past engagement. Marketing campaigns also benefit from seeing which landing pages and ads deliver conversions to refine targeting and messaging.

For a fitness company, analytics may show blog posts on weight-lifting techniques are longer on the page than cardio articles. This signifies an engaged audience of weightlifters to create more content around. Analytics also provides demographic data on this audience to inform content further.

With myriad data at their fingertips, modern marketers can confidently develop data-driven content strategies personalized to different audience interests and segments. Ongoing optimization through continual testing allows refining content and experiences perpetually based on performance.

Let me know if this rewrite sufficiently expands on leveraging web analytics to inform data-driven marketing. I’m happy to provide additional examples or modify the tone/structure further.

How Web Analytics Contributes to Data-Driven Marketing

Simplifying Big Data for Social Media Analytics

While social media generates vast amounts of customer data, marketers must distill these insights into actionable direction. Leveraging social analytics tools enables collecting, filtering, and applying data to inform data-driven marketing strategies.

Platforms like Sprinklr, Sprout Social, and Khoros help manage expansive datasets through options to isolate, segment, and visualize data. Rather than getting overwhelmed by sheer volume, marketers can extract specific patterns and insights. For example, filtering Facebook analytics by gender and age range may reveal the highest engagement coming from millennial women.

Publishing a social media content calendar provides further learning opportunities from engagement metrics. Tracking impressions and engagement rates by post allows determine ideal content types, angles, and narratives for your audience. A software company may find that persona-driven posts highlighting customer success stories drive the most shares and engagement.

Identify key questions to inform content and channel strategy, then analyze data for answers. Easy-to-use social analytics tools democratize access to intelligence that drives strategic optimization. Marketers must distill “big data” into targeted insights through careful filtering and strong analytical skills.

Using Social Media Analytics & Tools

Leveraging Primary Research to Enhance Marketing

Primary research provides marketers with an invaluable avenue for gathering targeted insights directly from their core audiences. Surveys, interviews, and focus groups allow asking specific questions to reveal granular data on interests, behaviors, values, and preferences.

For example, a software company could survey developers to learn which programming languages, frameworks, and platforms they use most. Asking about their preferred media outlets, publications, forums, and influencers also provides key data. This psychographic and demographic data informs content formats, messaging angles, and distribution channels to align with the target audience tightly.

Combining primary inputs with website analytics and social listening validates survey responses. If developers prefer text-based tutorials, but YouTube analytics show higher engagement on instructional coding videos, this insight drives more video content creation.

For an athletic apparel company, surveys could gauge consumers’ exercise habits and attitudes toward sustainability. The data may reveal an emerging audience of eco-conscious runners looking for environmentally friendly gear. Messaging and content can specifically target this niche based on their stated preferences.

Primary research gives marketers the intelligence needed to hyper-personalize campaigns. However, it must connect to broader analytics frameworks to weigh insights against behavioral data. An integrated research approach ensures messaging strikes the right chord with each unique audience.

Use Cases for Primary Research & Analytics

Leveraging Customer Service Data to Enhance Marketing

Customer service interactions hold insights to inform data-driven marketing strategies beyond resolving support issues. Direct consumer conversations on calls, chat transcripts, forum posts, social media, and more provide invaluable context into questions, pain points, motivations, and language choices.

For example, a customer calls Apple support needing help resetting an iPhone. The transcript of the conversation containing verbatim phrases like “how to reset iPhone” presents a content opportunity. Creating a step-by-step tutorial article or forum thread using that exact user language targets searchers with a ready answer.

Scaling this, support teams can funnel aggregated insights to marketing teams to identify top questions and knowledge gaps. FAQ content then targets consumer searches by mirroring their vocabulary. This content also decreases future support contacts and costs.

To activate this, brands need workflows facilitating smooth information sharing from service to content teams. But the benefits are plentiful: increased visibility and engagement, lower support costs, and amplified brand advocacy by directly answering consumer needs.

This data integration expands the marketing toolkit to include a treasure trove of insights within customer service conversations. With strategic processes, marketing content and experiences can directly reflect the consumer’s voice for greater relevance. Customer support thus evolves from a cost center to a high-value marketing asset.

Example Use of Customer Service Data

Using Audience Data from 3rd Party Databases

While owned first-party data provides the most direct customer and content intelligence, third-party data sources offer ways to fill gaps and expand reach. Numerous research firms and data brokers aggregate robust consumer demographic, psychographic, and behavioral data. Tapping these vast databases allows marketers to enrich profiles and find prospective lookalike audiences.

For example, Acxiom provides highly segmented customer data across categories like age, income, interests, purchase habits, and more. APPX allows targeting online ads to specific device IDs based on real-world data. This enables reaching consumers with relevant messaging while retaining user privacy. Data aggregators like LiveRamp also match first and third-party data to support personalized omnichannel marketing at scale.

Integrating external data must complement internal analytics frameworks. Testing lookalike targets against actual site visitors and high-value customers ensures relevance before deploying campaigns. As with any data source, third-party information requires critical examination to determine utility. But used judiciously, it expands audience understanding and campaign reach exponentially.

Augmenting owned media data with third-party sources provides unmatched scale and granularity for audience targeting. But precision and optimization remain paramount when leveraging these expansive consumer databases for marketing.

3rd Party Database Use Cases

Using Data to Improve the Customer Experience

For both B2B and B2C brands, providing a stellar customer experience should be the north star guiding all strategies. Robust data and analytics empower marketers to align content and messaging with consumer insights to deliver ongoing value.

Below are three reasons why data-driven marketing is vital, and technology makes it easy for marketers and advertisers to collect data.

Table: How Data Can Optimize Integrated Marketing Strategies

Data-Driven Marketing Delivers Content Relevance

Careful audience research helps brands craft relevant content and experiences tailored to customers’ interests and needs. For example, a fashion retailer can engage their style-conscious target audience by leveraging social data to identify trends and design aesthetics that resonate.

This allows the retailer to tailor looks and products on their website and social pages that align with audience taste profiles. Landing pages personalized to reflect individual browsing histories foster deeper engagement.

Video and native ad formats also better engage modern consumers on sites like Facebook and Instagram compared to disruptive display ads. By understanding cultural conversations and consumer language through data, brands can craft social posts and ads in formats and with messaging that speaks directly to the target user in an authentic way.

In essence, data-driven insight allows shaping content, experiences, and campaigns to match the specific desires of the intended audience for superior relevance. This hyper-personalized approach drives meaningful engagement across channels.

Data Helps Find Market White Space

Data reveals the white space in a given market by surfacing unmet audience needs and desires. For example, a software company may find through search trend analysis that business decision-makers are increasingly asking questions and having discussions online about data analytics tools and how to implement them.

However, the search results and content available don’t adequately address their concerns and information needs based on high search volumes around certain unanswered topics. Identifying this market gap for more educational, tactical content and resources is invaluable.

The software company can then develop tailored thought leadership content like how-to guides, conference workshops, and whitepapers to provide the in-depth analytics guidance target users seek. Providing this hard-to-find information establishes the brand as an authority and ahead of the curve.

Data illuminates gaps in the marketplace between audience needs and available solutions. Seizing these opportunities with targeted content development enables brands to support customers and strengthen strategic positioning.

Content Is Just One Element of Data-Driven Marketing

While crafting great content is critical for engagement, data optimizes and connects broader initiatives across marketing, comms, and sales. For example, search engine marketing (SEM) campaigns can be optimized by aligning paid keywords to high-value content themes that attract qualified traffic.

Public relations outreach can spotlight data-backed thought leadership content and place contributor articles in targeted industry publications. Email nurture streams can deliver different content matched to prospect engagement signals.

Ongoing testing and measurement of all strategies and tactics provide ROI insights for continual optimization. A/B testing content variants or subject lines identifies high-performing options. Evaluating multi-touch attribution models ensures integrated campaigns maximize outcomes.

Data enhances every element of digital marketing programs – from messaging to channels – by pinpointing what connects with audiences and drives measurable business results. An analytics-first approach results in a strategy backed by evidence, not assumptions.

Speed to Insight With Data-Driven Marketing Campaigns

A key advantage of data-driven marketing is enabling brands to glean insights quickly and act on them in real time to optimize campaigns. Having nimble processes to test, measure, and iterate based on analytics is essential to stay competitive.

For example, an e-commerce retailer may monitor real-time engagement metrics across social ad variations. Once data reveals a specific creative resonates best with their audience, they can rapidly expand investment in the high-performing option to maximize results.

Likewise, analyzing site analytics like click-through rates can show which on-site content best moves customers through the conversion funnel. Doubling down on more content in those popular categories increases engagement.

Essentially data-driven marketing informs agile decision-making to improve marketing programs, content, and the customer experience through constant incremental testing and refinement. Speed to insight allows brands to stay tuned to their audience and swiftly respond to ever-changing market dynamics.

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