Data driven marketing

Data-driven marketing is a process where marketers employ a process to gain insights into consumer behavior, including purchasing patterns, advert effectiveness, and browsing habits. Contemporary methods utilize big data strategies to collect and analyze information on consumer interactions and engagements, aiming to predict future behaviors. This analysis involves evaluating existing data, acquiring new data and systematically organizing and interpreting it to improve marketing strategies. The primary objective is to better understand and address customer needs. Market research provides a detailed understanding of consumer preferences

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^[1] 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.

Contemporary data driven marketing can be traced back to the 1980s and the emergence of database marketing, which increased the ease of personalizing customer communications.^[2] In 1993, WebTrends released one of the first web analytics products when only a few hundred websites existed.^[3] 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
• 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).^[4]

There are essentially two types of data used in marketing: contact information and performance metrics.^[5] Capturing contact information allows marketers to track potential customers and target them through emails, paid social media, other digital tactics, phone calls, or direct mail like catalogs. Tracking of performance metrics – such as engagement, clicks, and page views – enables marketers to improve and refine marketing activities to more effectively reach high-value prospects.

1. Data collection – This phase ensures 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 and define several models to learn more. Based on the engagement the 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.^[6]

Analysis techniques for marketing can include:

• Web analytics: Measurement of page views, 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 and unsubscribe rate.
• Content and social media metrics: Engagement rate, follows, shares and other measurements.
• E-commerce metrics: Shopping cart abandonment rate and other activity.^[7]

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^[8]

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.^[9]