{"id":368,"date":"2023-08-30T17:21:57","date_gmt":"2023-08-30T15:21:57","guid":{"rendered":"http:\/\/james@data-cubed.co.uk"},"modified":"2023-08-30T17:21:57","modified_gmt":"2023-08-30T15:21:57","slug":"how-to-turn-your-data-into-a-money-maker-3-2","status":"publish","type":"post","link":"https:\/\/data-cubed.eu\/blog\/how-to-turn-your-data-into-a-money-maker-3-2\/","title":{"rendered":"How to turn your data into a money maker 3"},"content":{"rendered":"\n
Guide three: analysing your data<\/em><\/p>\n\n\n\n So you\u2019ve set strong business objectives and wrangled with your data to make sure it\u2019s accurate and relevant. Good stuff. Now comes the super important part\u200a\u2014\u200athe analysis of your data. To do it right, you\u2019ll need to know the different ways you can slice and dice your data. Whichever method you choose, think back to your business use case and what you\u2019re hoping to get from your data.<\/p>\n\n\n\n In many ways the most basic form of analysis, descriptive analytics looks at how your business has performed in the past based on simple variables. For example, how many customers bought a certain type of product. Or which customers bought the least or most number of products. It\u2019s basic stuff, but it can still be powerful.<\/p>\n\n\n\n To get the most from descriptive analytics, you can mix one variable with another to tell a story. For example, if your business use case is to generate 100 leads in Q1, look at two things. Look at the number of leads you generated after your recent Christmas campaign. Then look at how many leads you generated after you launched a new series of banner ads. By comparing the two variables, your data begins to paint a picture of what\u2019s working and what\u2019s not.<\/p>\n\n\n\n
\n\n\n\nDescriptive analytics\u200a\u2014\u200ainsight into the past<\/strong><\/h3>\n\n\n\n