
Four Ways Brands Overlook the Potential of Data Analytics
Without undermining the importance of using data KPIs to evaluate past marketing performance, there is often a lot of untapped value in re-engineering some of your data
By
CIO Applications Europe | Thursday, February 10, 2022

Despite the widespread use of analytics dashboards and data-driven KPIs across the C-suite, most senior marketing teams use data primarily for backward-looking performance analysis rather than building analytics dashboards that drive future initiatives and planning
Fremont, CA: Without undermining the importance of using data KPIs to evaluate past marketing performance, there is often a lot of untapped value in re-engineering some of your data points and what you can learn from them to understand your target audience better and optimize a campaign that speaks more effectively to their needs.
Understanding Your Target Audience
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It's challenging to deliver relevant campaigns to everyone when we have a large audience. As a result, one of the first steps toward better campaign results is to understand your target customer better and segment the audience in a meaningful and actionable way so that you can serve the immediate needs of each small audience cluster.
Combining design thinking methods and data science is an effective way to perform audience segmentation and targeting. The framework enables you to gain a comprehensive understanding of various consumer profiles with varying behaviors and needs, allowing you to communicate the right message to the right audience and ensure that products and services are displayed to meet their needs.
Reducing Acquisition Costs by Predicting Customer Lifetime Value
Marketers are constantly working with a limited budget. As a result, it's critical to optimize spending to get the most out of their campaign budgets. Data analysis and machine learning can be powerful tools for improving and lowering customer acquisition costs. For example, data can be used to estimate the customer acquisition cost (CAC) and the customer lifetime value (CLV), which tells the company how much money it can expect to make with each customer throughout their lifetime, beginning with the first purchase or contract and ending with the moment of churn.
Creating an Efficient Propensity Model
Even though marketers constantly emphasize the importance of sending the right messages to the right people at the right time, they continue to use one-size-fits-all approaches to engage leads. Data analytics can address this challenge by modeling consumer behavior with propensity models, resulting in greater personalization and business results. In addition, proper application of these models aids in predicting the likelihood of leads and consumers performing specific actions, such as making a purchase or converting to the next step of the funnel.
Consumer Sentiment Monitoring and Action
Customers share a lot of information about their needs and relationships with brands and products in today's digital world. Acquiring and analyzing this data is very strategic for businesses because it allows them to measure user satisfaction and loyalty. In addition, companies can understand customers' emotions throughout their journey with products and services and their reactions to campaigns by leveraging approaches such as ethnography and social listening. This assists in identifying areas for improvement to increase the frequency of use and consumption, increase brand love, and build loyalty.
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