Data driven marketing

Data-driven marketing is a process used by marketers to gain insights and identify trends about consumers and how they behave — what they buy, the effectiveness of ads, and how they browse. Modern solutions rely on big data strategies and collect information about consumer interactions and engagements to generate predictions about future behaviors. This kind of analysis involves understanding the data that is already present, the data that can be acquired, and how to organize, analyze, and apply that data to better marketing efforts. The intended goal is generally to enhance and personalize the customer experience. The market research allows for a comprehensive study of preferences.

History of data driven marketing

Some marketing decisions have always been made on the basis of data, defined in the general sense as information. Audience targeting and segmentation strategies provide many examples. Since 1950, the Nielsen ratings have provided information to media buyers about television program audiences. Business-to-business marketers often target advertising to specialized trade publications and their digital channels.

Data driven marketing in the contemporary sense can be traced back to the 1980s and the emergence of database marketing, which increased the ease of personalizing customer communications. In 1993, Web Trends released one of the first web analytics products when only a few hundred websites existed. In the twenty-first century, social media and mobile technology have contributed to an explosion in the amount of data and its availability. Today, marketers use tools such as:

• Google Analytics and Google Search Console
• Customer relationship management (CRM) and marketing automation platforms
• Social media analytics
• Pay-per-click (PPC) and search engine marketing (SEM) analytics
• Heat maps or web optimization tools (A/B testing data).

Types of data driven marketing

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Data-driven marketing leverages data to make informed decisions about marketing strategies and tactics. Here are several key types of data-driven marketing:

1. Customer Data Analytics

• Behavioral Data: Analysis of customer behavior, such as purchase history, website interactions, and engagement with marketing campaigns. This helps in understanding customer preferences and predicting future behavior.
• Demographic Data: Information about customers' age, gender, income, education, etc., to tailor marketing messages and target specific segments.
• Psychographic Data: Insights into customers' values, interests, and lifestyles to create more personalized and relevant marketing campaigns.

2. Predictive Analytics

• Uses historical data and statistical algorithms to predict future customer behavior, such as likelihood to purchase, churn rates, and response to marketing campaigns.
• Helps in identifying high-value customers and tailoring marketing efforts to maximize ROI.

3. Personalization and Segmentation

• Personalized Marketing: Delivering individualized content and offers based on customer data. For example, personalized email campaigns that address customers by name and recommend products based on past purchases.
• Customer Segmentation: Dividing the customer base into distinct groups based on specific criteria (e.g., demographics, buying behavior) to target them more effectively with tailored marketing strategies.

4. Multi-channel Campaigns

• Utilizing data to coordinate and optimize marketing efforts across multiple channels (e.g., email, social media, SMS, and in-app notifications).
• Ensures a consistent and cohesive customer experience across all touchpoints.

5. Real-time Marketing

• Leveraging real-time data to deliver timely and relevant marketing messages. This can include responding to current events, customer actions, or market trends as they happen.
• Examples include dynamic website content that changes based on user behavior or real-time offers pushed to customers via mobile apps.

6. Customer Journey Mapping

• Analyzing data to understand the steps customers take from initial contact to purchase and beyond.
• Helps in identifying touchpoints and optimizing each stage of the customer journey to improve conversion rates and customer satisfaction.

7. A/B Testing and Optimization

• Running experiments by comparing two versions of a marketing element (e.g., email subject lines, website layouts) to determine which performs better.
• Continuous optimization of marketing strategies based on data-driven insights to improve performance.

8. Attribution Modeling

• Analyzing data to determine which marketing channels and touchpoints contribute most to conversions.
• Helps in understanding the customer path to purchase and allocating marketing budgets more effectively.

9. Social Media Analytics

• Monitoring and analyzing data from social media platforms to understand audience engagement, sentiment, and the impact of social media campaigns.
• Utilizes tools to track metrics such as likes, shares, comments, and follower growth to refine social media strategies.

10. Market Basket Analysis

• Analyzing transactional data to understand which products are frequently bought together.
• Used to optimize cross-selling and upselling strategies by recommending complementary products to customers.

11. Customer Feedback and Sentiment Analysis

• Collecting and analyzing customer feedback from surveys, reviews, and social media to gauge customer satisfaction and sentiment.
• Helps in identifying areas for improvement and tailoring marketing messages to address customer concerns.

12. Lifecycle Marketing

• Using data to engage customers at different stages of their lifecycle (e.g., acquisition, retention, re-engagement).
• Strategies include onboarding campaigns for new customers, loyalty programs for repeat customers, and win-back campaigns for lapsed customers.

Phases

1. Data collection – This phase ensures customer/consumer data is collected from various source systems to create a 'Complete Customer Profile'
2. Data activation – This phase focuses on 'personalized marketing'. Based on the data collected, marketing strategy can be planned and focused. Activation can be across multiple channels (email marketing, SMS marketing, social marketing, digital ads etc.). Marketers can target their audience with relevant messaging that can be personalized – i.e.., different communication based on phase of customer life cycle.
3. Analytics and Insights – Marketers can collect information on their consumers/customers and define several models to learn more. Based on the engagement the customer/consumer has with the brand, the models can help refine the target audience and predictions, thus ensuring focused effort of marketers to acquire new customers or retain existing customers.
   • Analytic tools allow for targeted and personalized marketing to the customer. Companies use customer reviews and customer support conversations to extract data for planning the marketing strategy. Approaching an audience with a targeted campaign increases the chances of their conversion. Marketers can now understand customer behavior and make informed decisions based on the data, thus allowing for relevant targeting.

Data analysis techniques

Analysis techniques for marketing can include:

• Web analytics: Measurement of page views, events, traffic by device and other activity.
• Metrics for "lead magnets" or content offers: Simple measurements such as call-to-action (CTA) click-through rates and more complex data such as the ratio of generated leads to marketing-qualified leads (MQL).
• Email marketing metrics: Including open rate, click rate and unsubscribe rate.
• Content and social media metrics: Engagement rate, follows, shares and other measurements.
• E-commerce metrics: Shopping cart add to carts, purchases and abandonment rate and other activity.

Advanced marketing analytics uses complex models to provide intelligence such as:

1. Customer lifetime value
2. Marketing attribution: evaluate the effectiveness of the campaign, attribute success or failure to channels and presentation
3. Clustering: group customers based on personal characteristics
4. Conversion prediction: list users who are likely to turn into customers
5. Anomaly detection
6. Forecasting

Examples of data driven marketing

E-commerce retailers use data driven marketing extensively to ensure the best customer experience and increase sales. One example cited in the Harvard Business Review is Vineyard Vines, a fashion brand with brick-and-mortar stores and an online product catalog. The company has used an artificial intelligence (AI) platform to gain insights about its customers from actions taken or not taken on the e-commerce site. Email or social media communications are automatically triggered at certain points, such as cart abandonment. Insights are also used to refine search engine marketing.

In business-to-business marketing, where inbound leads must be captured and nurtured, tactics are more likely to be aimed at long-term retention of the prospect rather than urging them to buy. Content marketing is frequently used. Prospects may be offered a white paper or other high-value information resources in exchange for their email address. Marketing automation tools support continuing activity along the customer journey.