More data is available than ever to marketers to gain insight into customer behavior. We have seen an explosion of internal and external data over the last 10 years, which creates a big challenge for marketers. As the volume and diversity of data expands, the need for data scientists who can process and analyze this vast amount of data also increases. The abundance of customer data also presents an opportunity for marketers who have access to data science expertise to improve the effectiveness of their target audiences and to improve the ROI of their marketing spend.
Data generated by digital analytics systems provide insight into how customers are interacting with brands. They indicate how frequently customers are engaging and how much time they are spending on the website or apps. First party digital engagement data combined with transactional data provides a more holistic view of customer behavior and provides insight into the purchase journey. Furthermore, there is an explosion of third-party data which can be used to complement the available first-party data. For example, some of the marketers ‘enhance’ their existing known customer base with external data attributes such as income, home value, and presence of children to gain a better understanding of their customers. Others combine their anonymous digital data sitting on DMPs with third party data to gain insight into their digital behavior such as interests in categories like sports.
In our experience, first party data is the most valuable data source as it provides insight into the actual customer behavior. Value of first party data gets amplified when enhanced with external data sources. Challenges are there since so much third-party data is available in the marketplace, and most marketers do not have the time or skill set to navigate the complexity of the data ecosystem. They get exposed to the aggressive sales teams of data providers who all talk about how great their data is. This creates a lot of confusion and often leads to sub-optimal decision making based on a gut feeling.
There are ways of using data science expertise to determine the most valuable data that will work best in a given situation. The Data Optimization Lab solution we developed at BLEND360 is an example of using an analytics approach to sort through the clutter that exists in the data ecosystem and determine the most valuable sources that should be integrated into a client’s marketing technology environment. The benefits of the Data Optimization Lab approach are increased marketing performance and reduced cost as the marketers will only pay for the data that will work for them by eliminating the waste and increasing the impact of their marketing spend.
There are 4 key dimensions that we consider in creating a scorecard of external data sources. Predictive value determines how effective the data source is in developing predictive models. Descriptive value indicates how much insight the data provides in describing and segmenting the customer base. Universe value indicates the coverage of the population, and quality value indicates if the data has accurate values and good coverage for certain attributes. A composite score that combines these dimensions gets used to create a scorecard of various data samples. The scorecard becomes the basis for ranking the data sources and determining the ‘fair’ price for each data source. As a result, marketers can make a better decision in determining the most valuable external sources and negotiating the best price on the basis of analytical findings. This leads to high performance at the lowest cost, maximizing the return on data investments.
More data is available than ever to marketers to gain insight into customer behavior. We have seen an explosion of internal and external data over the last 10 years, which creates a big challenge for marketers. As the volume and diversity of data expands, the need for data scientists who can process and analyze this vast amount of data also increases. The abundance of customer data also presents an opportunity for marketers who have access to data science expertise to improve the effectiveness of their target audiences and to improve the ROI of their marketing spend.
Data generated by digital analytics systems provide insight into how customers are interacting with brands. They indicate how frequently customers are engaging and how much time they are spending on the website or apps. First party digital engagement data combined with transactional data provides a more holistic view of customer behavior and provides insight into the purchase journey. Furthermore, there is an explosion of third-party data which can be used to complement the available first-party data. For example, some of the marketers ‘enhance’ their existing known customer base with external data attributes such as income, home value, and presence of children to gain a better understanding of their customers. Others combine their anonymous digital data sitting on DMPs with third party data to gain insight into their digital behavior such as interests in categories like sports.
In our experience, first party data is the most valuable data source as it provides insight into the actual customer behavior. Value of first party data gets amplified when enhanced with external data sources. Challenges are there since so much third-party data is available in the marketplace, and most marketers do not have the time or skill set to navigate the complexity of the data ecosystem. They get exposed to the aggressive sales teams of data providers who all talk about how great their data is. This creates a lot of confusion and often leads to sub-optimal decision making based on a gut feeling.
There are ways of using data science expertise to determine the most valuable data that will work best in a given situation. The Data Optimization Lab solution we developed at BLEND360 is an example of using an analytics approach to sort through the clutter that exists in the data ecosystem and determine the most valuable sources that should be integrated into a client’s marketing technology environment. The benefits of the Data Optimization Lab approach are increased marketing performance and reduced cost as the marketers will only pay for the data that will work for them by eliminating the waste and increasing the impact of their marketing spend.
There are 4 key dimensions that we consider in creating a scorecard of external data sources. Predictive value determines how effective the data source is in developing predictive models. Descriptive value indicates how much insight the data provides in describing and segmenting the customer base. Universe value indicates the coverage of the population, and quality value indicates if the data has accurate values and good coverage for certain attributes. A composite score that combines these dimensions gets used to create a scorecard of various data samples. The scorecard becomes the basis for ranking the data sources and determining the ‘fair’ price for each data source. As a result, marketers can make a better decision in determining the most valuable external sources and negotiating the best price on the basis of analytical findings. This leads to high performance at the lowest cost, maximizing the return on data investments.
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