Raw signal data alone is not enough to optimize customer interactions.
In the world of transaction-oriented customer event and response analytics, volumes of customer data are generated about your customers every second. This information is a stepping-stone to making data-driven decisions and allocating finite resources to the most opportune interaction points.
However, while this raw transaction data is vast, it alone is not sufficient to provide the insights necessary to enhance customer relationships.
Maximum insight requires a transformation from raw signal data into analytics-ready intelligence.
Before any significant analysis on event data can take place, you must first transform it from its raw state into actionable information.
Unfortunately, there is no magic crystal ball that will turn real-world data into analytics-ready data. As such, practitioners need a toolkit of data transformations to gain strategic insights from customer transactions.
But what are the techniques for transforming this data? And how to separate the signal from the noise?
In this RedPaper, Corios President Robin Way leads an in-depth discussion around:
- The importance of data transformation in your analytics – Discover why an investment into data transformation needs to be made, and what to expect from the transformation process.
- How to turn raw transactional data into stories about your customers – Learn the nine stages of data transformation, in order of increasing relevance and investment, and how to convert tedium into productivity.
- How to separate useless data from useful data – Discuss cleansing transformations, and the different recording activities involved in filtering data.
- Technological innovations to speed up data storage and retrieval – Explore the technical capabilities required to accelerate your rate of transaction data refresh and processing.