Understanding what data-driven marketing is begins by looking at the many ways consumer behavior has changed in recent years and the technologies that make it possible to collect and analyze massive amounts of data about customers, markets, and industries. Data-driven marketing applies the latest data analytics capabilities to pinpoint the most productive media buys and to craft creative, personalized awareness about products.
New marketing and advertising technologies make it possible to personalize every aspect of marketing. Prime examples of personalized marketing are the product recommendations that Amazon customers find each time they log in to the online shopping service.
Digital marketing firm Stirista outlines the steps required to implement a personalized marketing campaign
· Identify the demographics of the campaign’s target audience, such as women ages 18 to 34 who spend more than $50 a month on beauty products.
· Determine the products and services that generate the most revenue in that target category.
· Use data analytics tools to gain insight into the campaign’s target audience, and convert those insights into personalized experiences for those customers.
· Apply A/B testing and other variations to learn which message and medium delivers the highest level of customer engagement.
Social Media Today describes hyper personalized digital marketing as the combination of internet and mobile technologies to harness “all forms of data being used in unison across all marketing channels and customer journey stages.” The goal is to know what the customer wants before the customer does and to move customers from “top of funnel awareness to post-purchase happiness in record time through higher and more effective engagement at every stage.”
Customer value analytics is designed to help firms identify which customers are most profitable and which are least profitable.
These are among the metrics that customer value analytics applies to determine a customer’s value to the business:
· Historical value measures the value of a customer over time and compares it with other periods and other customers for the same periods.
· Current value analyzes a customer’s activity in a shorter time period to compare recent activity with past values to determine the impact of marketing campaigns, new offers, and changed prices.
· Lifetime value applies the analytics over a longer period by multiplying a customer’s average order by purchase frequency to show how the customer’s value has changed over time.
· Cost to serve compares the profit a customer generates to the cost of serving the customer’s support requirements to identify “service drain” customers: customers who buy few or low-margin products but require high sales administration and delivery expenses.
A challenge for marketing departments planning to implement a data-driven strategy is integrating the variety of data they receive from diverse internal and external sources. Much of the data must be cleaned and conditioned before it can be used, as MarTech Series explains.
The customer engagement tech formula allows marketers to assess available technologies in three categories:
· Decision engineering inverts the traditional marketing model by identifying decision opportunities first, and then running the data analysis. This allows marketers to focus on goals rather than on the analysis itself.
· Advanced analytics applies smart algorithms and other innovative analytics techniques to segment customers based on their specific lifestyles rather than on demographics. This improves the accuracy and effectiveness of customer profiles.
· Cutting-edge technology includes machine learning and other AI technologies, such as chatbots, that engage customers and manage their interaction with the brand. Chatbots have become successful because they’re convenient for marketers and widely accepted by customers.
Omnichannel Marketing Strategies A company’s marketing message must stay consistent as it spans platforms and devices. MarTech Advisor describes the four components of a successful omnichannel marketing effort: · Identify the channels that customers are using most frequently, and increase their presence on those channels. · Make sure that the marketing message is consistent across channels in terms of presence, communication, customer experience, and processes used. · Customize the message at the most opportune moment. Personalization enhances engagement and brand loyalty. · Measure the impact of marketing activities across channels, and continually optimize processes and messages to improve results.
A Harvard Business Review survey sponsored by SAS, Intel, and Accenture identifies the ability to deliver a unique, real-time customer experience across all touch points as the best way for companies to distinguish themselves from the competition. The study describes three interrelated capabilities that allow businesses to apply analytics and insights to create effective customer experiences: · Unified customer data platforms combine customer data from online and offline sources to extract insights that shape the customer experience. · Artificial intelligence-based proactive analytics provide data collection and analysis functions that convert information about customers, marketing programs, and other business processes into actionable intelligence. · Contextual interactions apply real-time insights to identify where customers are on their journey, such as browsing product reviews or visiting a brick-and-mortar store, and to coax them into taking the next steps toward the company’s desired outcome.
Converting big data into insight requires combining the science of analytics with the art of communicating the resulting insights into actionable intelligence. TechGenyz reports that for every dollar spent on analytics and business intelligence solutions, companies realize an average return of $13.01, which represents an ROI of 1,301%. TechGenyz describes five ways data science is applied to increase the ROI of marketing campaigns: · Break down departmental silos to promote the free flow of data throughout the organization. In addition, companies must ensure that the data is easy to integrate with other systems and share with internal and external sources, such as sharing social media demographics with affiliate marketers and internal search engine optimization (SEO) teams. · Ensure that data streams are updated in real-time to promote fast action based on timely and accurate information. Include data “trails” in the stream to allow marketers to compare past performance of campaigns with current campaigns. Streaming analytics helps marketers identify new business models, product enhancements, and revenue sources. · Apply visualization tools that simplify complex data and communicate the results of analytics in a way that’s easier for nonmathematicians to grasp. Visualization also helps data scientists and marketers discuss the results of the analyses and their implications for future campaigns. · Conduct smart business experiments based on variations of marketing approaches to gain insight and discover alternatives. Even simple business experiments can provide keys to rapid revenue growth opportunities. · Base marketing decisions on past customer data by using data-based tools to assign values to unknowns, forecast the potential for obstacles, and determine the best ways to avoid and mitigate risks.
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