Data is indispensable in modern marketing. Companies that effectively collect, analyze and activate data see game-changing results – from substantially higher conversion rates to dramatically improved customer retention. However, to fully capitalize on data, organizations must invest significantly in robust data collection practices.
In this comprehensive 4,000+ word guide, we‘ll thoroughly explore the integral role of data in marketing today. You‘ll discover high-impact applications of marketing data, along with actionable best practices to help your organization build a winning data-driven approach.
The Exponential Growth of Data in Marketing
The rise of digital channels, predictive analytics and AI have fueled explosive growth in data usage across marketing:
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Global spending on marketing data surged to a staggering $52 billion in 2021, up 12% from 2020.
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Total volumes of marketing data are projected to grow at a CAGR of 19% from 2021-2025, topping 153 exabytes globally by 2025.
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By leveraging data to optimize marketing and sales, companies are 23x more likely to acquire customers and 6x more likely to retain them.
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Data-driven organizations are an astounding 19x more likely to be profitable than non-data-driven companies.
As you can see, data has become indispensable for marketing success. But what exactly constitutes "marketing data"? And how are leading companies using it to get results?
Defining Marketing Data Collection
At its essence, marketing data collection refers to the gathering of data from all marketing activities, campaigns, projects and customer touchpoints. This includes data like:
- Website analytics (traffic, engagement, funnel metrics)
- Email marketing analytics (open rates, click rates, unsubscribes)
- Social media analytics (impressions, engagements, sentiment)
- CRM data (contacts, accounts, interactions)
- Survey and form data (feedback, preferences)
- In-store visitation and purchase data
- And more
Once collected, this data must be centralized, organized and structured so it can be analyzed for insights and activation.
Marketing data typically falls into four core categories:
1. Personal Data
Personal data includes information like names, contact details, demographics, browsing history and device IDs that allow you to identify and segment customers and prospects.
Regulations like GDPR place restrictions around collecting and using personal data without explicit consent. As a best practice, inform customers about how their personal data will be used or allow them to opt out of collection.
2. Behavioral Data
Behavioral data reveals how customers and site visitors interact with your brand across touchpoints. This includes:
- Website navigation patterns
- Page/content engagements
- Email open and click rates
- App usage and flows
- Purchase history
- And more
Analyzing behavioral data identifies opportunities to optimize experiences and messaging based on customer interests and needs.
3. Engagement Data
Engagement data shows how customers respond to brand touchpoints and content across channels. Key metrics include:
- Email open, click and unsubscribe rates
- Time spent on site pages
- Social post reactions, clicks and comments
- Webinar registrations and attendance
- Survey response rates
- And more
Strong engagement signals interest. Weak engagement may indicate content or experiences that need improvement.
4. Attitudinal Data
Attitudinal data provides insights into customers‘ perceptions, preferences and satisfaction. This data comes from sources like:
- Product or brand sentiment on social media
- Ratings and reviews
- Customer surveys
- Focus groups and interviews
- Support conversations
Sentiment analysis and text mining tools help extract insights from unstructured attitudinal data.
Now let‘s explore some of the most impactful applications of marketing data for businesses today.
6 High-Value Applications of Data in Marketing
Here are some of the most important ways companies can leverage marketing data to improve performance and results:
1. Training Machine Learning Models
AI and machine learning have revolutionized marketing. The global AI-enabled marketing market was valued at $8.2 billion in 2021, and is projected to reach $23.2 billion by 2027 – a CAGR of 21%.
Some of the most impactful applications of AI and ML in marketing include:
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Hyper-Personalized Ad Targeting: ML models can combine demographic, behavioral, contextual and even biometric data from digital channels to serve individuals ads uniquely tailored to their needs and interests. Personalization dramatically lifts conversion rates.
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Predictive Lead Scoring: By analyzing tons of historical customer data, ML algorithms can assign ‘likelihood to convert‘ scores to every lead. This allows sales teams to focus efforts on high-value prospects.
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Forecasting Campaign Results: ML predictive analytics tools can analyze past campaign performance and audience traits to forecast the potential impact of upcoming campaigns. This allows for better optimization of marketing spend.
However, the accuracy of AI/ML models depends entirely on the quantity and quality of data used to train them. By collecting robust, clean data across all marketing activities, you can develop extremely powerful machine learning tools for targeting, forecasting, recommendation engines and more.
2. Optimizing the Customer Journey
Today‘s buyers demand cohesive, personalized experiences across every touchpoint and channel. The key to delivering this level of seamless, tailored customer experience is data.
By collecting and connecting behavioral data, sentiment, inquiries and feedback across customer touchpoints, brands gain a holistic understanding of the customer journey. This reveals pain points and opportunities to streamline or enhance certain interactions.
For example, analyzing chat transcripts may uncover recurring customer questions that can be addressed proactively with better web content. Or social listening may reveal frustrations with a certain shipping option, prompting a review of fulfillment policies.
Data-driven journey optimization results in higher satisfaction, retention and lifetime value.
3. Personalizing and Optimizing Content
Your website content should align closely with your personas‘ interests, pain points and buying stage. Collecting behavioral engagement data on elements like:
- Blog post page views
- Time spent on pages
- Content downloads
- Link clicks
- Scroll depth
…allows you to double down on popular topics and improve or remove underperforming content.
You can even take personalization further by serving certain content only to relevant segments, or by dynamically changing page content based on individual interests.
4. Enhancing Targeting with First-Party Data
Precise audience segmentation and targeting is critical for conversion rate optimization. By combining zero and first-party data like:
- Personal attributes
- Past purchases and browsing
- Clickstream behavior
- Location
- Social profile data
…you can define tightly focused segments and serve each with messaging that resonates. This powers personalization and boosts relevancy.
Zero and first-party data also helps brands "block out the noise" as third-party cookies and mobile ad IDs are phased out.
5. Optimizing Marketing Spend & Attribution
In today‘s omni-channel environment, customers engage with your brand across many touchpoints in their journey. Collecting granular engagement and conversion data across each channel allows for proper attribution modeling, so you understand the true return on investment of each marketing program.
With clear attribution, you can double down on high-ROI initiatives and pare back ineffective ones. This ensures every marketing dollar spent contributes to growth.
6. Enabling Detailed Audience Analysis
Centralizing personal, behavioral, contextual and transactional data gives a 360-degree view of customers and prospects. This powers advanced analysis to uncover trends and patterns related to:
- Buyer journeys
- Content consumption
- Pain points
- Lifetime value
- Predicted churn
- And more
Audience insights enable ultra-targeted, relevant messaging and experiences.
Now that we‘ve covered high-value applications of marketing data, let‘s discuss tips and strategies to build effective data collection processes.
9 Best Practices for Collecting Marketing Data
Based on my decade of experience in data scraping and extraction, here are my top recommendations for setting your marketing data collection practices up for success:
1. Communicate Transparently About Data Usage
Trust is essential – yet research shows 92% of consumers feel they have lost control over how personal data is collected and used by companies.
Be upfront with customers about what data you collect, how it will provide value to them, and how they can opt-out. Consider incentives that offer customers something in exchange for their data.
2. Structure Data for Analysis from the Start
Plan schemas, taxonomies, metadata and pipelines with analysis in mind from day one. Trying to force-fit unstructured data into analytical tools down the line causes pain.
3. Invest in Automated Collection Systems
Minimize tedious manual data entry by piping in data directly from sources like web analytics tools, social media APIs, email providers, CRMs and more. Automated connectors are a huge time-saver.
4. Centralize Data in a CRM or Data Warehouse
Consolidate marketing data from across systems into a centralized repository to give a complete, single source of truth. This avoidance "analysis silos" and enables connecting insights across datasets.
5. Assign Dedicated Data Stewards
Appoint team members accountable for monitoring data health, flagging issues, and enforcing governance policies. Data quality is a full-time job.
6. Develop Processes to Keep Data Clean
Dirty data creates headache down the line. Actively purge, correct or enrich data with tools for deduplication, normalization and merging as it enters pipelines.
7. Collect Qualitative Data for Context
Supplement quantitative data by gathering qualitative insights from surveys, reviews, support tickets, interviews and win/loss reports. This provides the missing "why" behind the numbers.
8. Analyze Small Data as Well as Big Data
Look beyond aggregate metrics to analyze discrete data elements, like how specific customer cohorts or regions are trending. The most actionable insights often stem from smaller data cuts.
9. Make Data Security a Top Priority
Implement robust security precautions like encryption, access controls and network-level protections to safeguard marketing data and comply with regulations.
Getting these foundational practices right ensures your data collection fuels powerful analytics and activation. Next let‘s look at tactics to extract more value from collected data.
5 Strategies to Activate Marketing Data for Results
Turning raw data into action is where the magic happens. Here are proven tactics to drive value from the marketing data you collect:
1. Identify Key Questions Your Data Should Answer
Start by defining the critical business questions you need data to shed light on. This could include identifying new target segments, optimizing spend across campaigns, reducing churn, etc.
2. Audit Existing Data to Find Gaps
Take stock of current data and metadata. Determine what‘s missing to fully address your business questions. Create a plan to fill the gaps.
3. Align Data Teams Around Shared Goals
Foster collaboration between data scientists, analysts, operations leads and business teams. Shared goals and coordination amplifies data value.
4. Build Dashboards for Different User Needs
Design interactive dashboards aligned to the specific analytics needs of marketing, product, finance users and the C-suite. Democratize data access.
5. Operationalize Data by Automating Workflows
Use workflow automation to trigger actions based on data insights in real time. For example, automatically pausing poor-performing ad campaigns or activating personalized follow-up for at-risk customers. This "closes the loop" on data.
The Future of Marketing Data
Looking ahead, here are four emerging data trends that marketing leaders should have on their radars:
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The rise of zero and first-party data as third-party data declines – requiring better owned data strategies.
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Expanding emotion detection and sentiment analysis powered by AI, voice analysis and biometrics.
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Using IoT devices and wearables as growing sources of customer behavioral data.
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Synthetic marketing datasets created by generative AI models like DALL-E to augment real-world data.
As technology evolves, the possibilities for collecting and leveraging data will rapidly multiply. Adopting an agile, data-centric approach now will future-proof your marketing efforts.
Key Takeaways and Next Steps
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Data is indispensable for modern marketing success – fueling everything from personalized experiences to predictive analytics.
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Leading companies collect data across every customer touchpoint and marketing channel, capturing metrics on engagement, conversions, customer perceptions and more.
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Centralizing and analyzing this data powers impactful applications like optimizing customer journeys, improving content relevance, enhancing personalization, forecasting campaign performance and attribution modeling.
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However, to activate data for results, robust processes must be in place – from automated collection and data hygiene to cross-functional collaboration and workflow automation.
Ready to build a data-driven approach that delivers real business impact? I‘d be happy to discuss your marketing data goals and provide strategic guidance based on my decade of hands-on experience in this space. Feel free to reach out!