Organizations are increasingly adopting big data analytics to understand and then fix business problems. They’re learning how to extract value from multi-sourced information and then relate that information to an issue in their marketing, manufacturing, advertising, or shipping, etc.
For example, T-Mobile (and the other main carriers) consistently use big data analytics to spot the reasons for (and prevent) Customer turnover. Customer attrition is a significant expense in this industry, so firms that can best use data to improve retention and improve satisfaction will have a leg up on the competition. Big data is not just a tool for the enterprise level firms. It’s appropriate for businesses of varying sizes that want to better understand Customer behaviors and improve their marketing tactics.
Every Organization wants to use data to find actionable insights. It’s “cause and effect” on a broader scale, where there could be multiple factors working in concert that are producing a certain result. The difficulty is in generating the right data, keeping it organized, and then having the right analytics tools and staff members who know how to extract correlations. Doing this right to optimize the customer experience and boost sales requires adherence to several best practices:
Marketers working on TV campaigns now have at their disposal a number of modeling tools to help them gauge performance. They can use customer demographics, Nielsen-derived viewing data, airing size, and specific data on the actual stations utilized and the airing timeframes. Marketers can use this clean data to gauge current performance and then dynamically adjust future campaigns accordingly. There can be surprises uncovered in this process, as marketers might find for example a previously under-served demographic that is generating impressive sales in response to TV campaigns.
The “mess” in this context refers to data that is not properly structured and is essentially useless when it comes to analysis. Even the best data scientist and marketing wizard can’t pull insights from broken data. Do some work on the front end to ensure all of the data streams coming into the analytics tool are organized and clean.
