Analytics and Insights for Marketers

Analytics & Insights for Marketers is the third in a series of our Marketers’ Guide to Data Management and Analytics. In this series, we cover major terms, acronyms, and technologies you might encounter as you seek to take control of your data, improve your analytics, and get more value from your MarTech investments.

In case you missed them, you can access part one here and part two here.

In this post, we’ll explore:

  • Business intelligence (BI)
  • Real-time analytics
  • Embedded analytics
  • Artificial intelligence (AI)
  • Machine learning

Business Intelligence

Business intelligence refers to the process in which data is prepared and analyzed to provide actionable insights and help users make informed decisions. It often encompasses various forms of visualizations in dashboards and reports that answer key business questions.

Why It Matters for Marketers:

With an increasing number of marketing channels comes an increasing amount of marketing data. Marketers who put BI tools to use gain essential insights faster, more accurately define key demographics, and make marketing dollars last.

Marketers without access to a BI tool spend a disproportionate amount of time preparing, rather than analyzing, their data. With the right dashboards in place, you can visualize observations about customer and demographic behaviors in the form of KPIs, graphs, and trend charts that inform meaningful and strategic campaigns.

Real-World Examples:

Your BI dashboards can help answer common questions about more routine marketing metrics without spending hours preparing the data. In a way, they take the pulse of your marketing initiatives. Which channels bring in the most sales? Which campaigns generate the most leads? How do your retention rate and ROI compare over time? Access to these metrics and other reports can shape the big picture of your campaigns. They help you make a measurable impact on your customer lifetime value, marketing RPI, and other capabilities.

Analytics & Insights: BI

Real-Time Analytics

Real-time analytics utilizes a live data stream and frequent data refreshes to enable immediate analysis as soon as data becomes available.

Why It Matters for Marketers:

Real-time analytics enhances your powers of perception by providing up-to-the-minute understanding of buyers’ motivations. A real-time analytics solution allows you to track clicks, web traffic, order confirmations, social media posts, and other events as they happen, enabling you to deliver seamless responses.

Real-World Examples:

Real-time analytics can be used to reduce cart abandonment online. Data shows that customers abandon 69.57% of online transactions before they are completed. Implementing a real-time analytics solution can enable your marketing team to capture these lost sales.

By automatically evaluating a combination of live data (e.g., abandonment rates, real-time web interactions, basket analysis, etc.) and historical data (e.g., customer preferences, demographic groups, customer click maps, etc.), you can match specific customers to targeted messaging, right after they leave your site.

Analytics & Insights: Real-time analytics

Embedded Analytics

Embedded analytics is the inclusion of a business intelligence functionality (think graphs, charts, and other visualizations) within a larger application (like your CRM, POS, etc.)

Why It Matters for Marketers:

The beauty of embedded analytics is that you do not need to open up a different interface to visualize data or run reports. Integrated BI functionality enables you to review customer data, sales history, or conversion rates along with relevant reports that enhance your decision-making. This enables you to reduce time-to-insight and empower your team to make data-driven decisions without leaving the applications they use daily.

Real-World Examples:

A member of your marketing team is reviewing individual customers in your CRM to analyze their customer lifetime value. Rather than exporting the data into a different analytics platform, you can run reports directly in your CRM – and even incorporate data from external sources.

In doing so, you can identify different insights that improve campaign effectiveness such as which type of content best engages your customers, how to re-engage detractors, or when customers expect personalized content.

Artificial Intelligence

AI is the ability for computer programs or machines to learn, analyze data, and make autonomous decisions without any major contributions from humans.

Why It Matters for Marketers:

Implementing AI can provide a better understanding of your business as it detects forward-looking data patterns that employees would struggle to find – and in a fraction of the time. Additionally, marketers can improve customer service through a data-driven understanding of customer behavior and with new AI-enabled services like chatbots.

Real-World Examples:

Customizing email messaging used to be a laborious process. You’d need to manually create a number of campaigns. Even then, you could only tailor your messages to segments, not to a specific customer. Online lingerie brand Adore Me pursued AI to mine existing customer information and histories to create personalized messages across omnichannel communications. As a result, monthly revenue increased by 15% and the average order amount increased by 22%.

AI chatbots are also making waves, and Sephora is a great example. The beauty brand launched a messaging bot through Kik as a way of engaging with their teenage customers preparing for prom. The bot provided them with tailored makeup tutorials, style guides, and other related video content. During the campaign, Sephora had more than 600,000 interactions and received 1,500 questions that they answered on Facebook Live.

Machine Learning

Machine learning is a method of data analysis in which statistical models are built and updated in an automated process.

Why It Matters for Marketers:

Marketers have access to a growing volume and variety of complex data that doesn’t always provide intuitive insight at first glance. Machine learning algorithms not only accelerate your ability to analyze data and find patterns, but they can identify unforeseeable connections that a human user might have missed. Through machine learning, you can enhance the accuracy of your analyses and dig deeper into customer behavior.

Real-World Examples:

One Chicago retailer used a centralized data platform and machine learning to identify patterns and resolve questions about customer lifetime value. In an increasingly competitive landscape, their conventional reporting solution wasn’t cutting it.

By combining data from various sources and then performing deeper, automated analysis, they were able to anticipate customer behavior in unprecedented ways. Machine learning enabled them to identify which types of customers would lead to the highest lifetime value, which customers had the lowest probability of churn, and which were the cheapest to acquire. This led to more accurate targeting of profitable customers in the market.

That’s only the beginning: a robust machine learning algorithm could even help predict spending habits or gather a customer sentiment analysis based on social media activity. Machine learning processes data much faster than humans and is able to catch nuances and patterns that are undetectable to the naked eye.

We hope you gained a deeper understanding into the various ways to analyze your data to receive business insights. Feel free to contact us with any questions or to learn more about what analytics solution would work best for your organizational needs.