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Three Key Ways Brands Underutilize Data Analytics
Natural language processing (NLP) allows for analyzing text data based on customer opinions and complaints posted on social media.
FREMONT, CA: Despite the extensive use of analytics dashboards and data-driven KPIs throughout the C-suite, most senior marketing teams primarily use backward-looking analysis to assess success rather than creating analytics dashboards to drive future initiatives and planning. As a result, the significant majority of companies underutilize data that is already available to them.
Without lowering the value of using data KPIs to evaluate past marketing performance, re-engineering some of one's data points and what you can learn from them for better understanding one target audience and optimizing a campaign speaking more effectively to their needs can often yield vast untapped value. Companies have discovered that by doing so, marketing campaigns may become more effective at converting new consumers and result in customers that are more engaged with the brand and have a greater lifetime value.
Knowing Your Target Audience
Data science allows for the study of vast amounts of data using advanced statistical techniques, allowing for discovering trends among customers. Demographics, regional information, product usage, and behavioral traits, for example, may all be utilized to evaluate and categorize customers. On the other hand, design thinking techniques enable us to conduct in-depth analyses of customers and, as a result, discover the essential characteristics that can be helpful to segment them according to their requirements and build personas. When both are useful together, it is possible to detect patterns of similarity and dissimilarity, taking into account essential elements and comprehending the relevant features that differentiate and characterize them, allowing for the construction of more effective campaigns.
Optimizing Acquisition Cost by Predicting Customer Lifetime Value
Marketers are usually working with a limited budget. As a result, it's critical to optimize expenditure to get the most out of their campaign resources. Data analysis and machine learning may help enhance client acquisition procedures while also lowering expenses. For example, data may be helpful to estimate the customer acquisition cost (CAC) and the customer lifetime value (CLV), which shows how much money a firm can anticipate to make from each client throughout their lifetime, beginning with the initial purchase or contract and ending with churn.
Monitoring and Acting On Consumer Sentiments
Natural language processing (NLP) allows for analyzing enormous amounts of text data based on customer opinions and complaints posted on social media. Machine learning techniques can perform sentiment classification using text as input data, and the results can help understand customer opinion about a specific product, facility, or campaign; users' feelings, pains, and desires when using service's in context from their point of view (POV); and the relationship between different customer segments. In addition, these analysis findings may give insight into improving the customer experience (CX) as a whole.