Every marketer worth his/her salt knows the role data plays, and how important is data analysis to determine course of action. Every good marketing team has at its core an analytics platform that provides actionable insights. This is possible only when data is easily accessible so that recommendations, forecasts, and troubleshooting takes place seamlessly. However, no matter how closely you monitor things do go wrong and more often than not it can traced back to incorrect use of data.
Let us discuss about the different types of analytics and general places we can start with them. The number one reason why analytics is ignored is because data intimidates most. The only way we can confront this is by knowing and interpreting data. Knowledge eventually triumphs over fear. The more you know about analytics the less intimidated you will be.
Types of analytics
The essential goal of analytics is to give us insights we were unaware of before we looked at data. Analysis can be further divided into three parts descriptive, predictive, and prescriptive. It has been observed that marketers prefer the first type, descriptive, over the other two; thus leaving a lot on the table.
Let’s understand the three types of analysis
Descriptive analytics essentially gives us an idea of how things are proceeding. For this we look at historical data for insights. We attempt to get the context and tell a story with the data in hand. This is what most marketers do on a regular basis with web analytics. Marketers look at data and figure out how things are going, try to understand the current progress, and determine how it affects a campaign.
At the end of this analysis marketers have answers to how the campaign went, the performance that was achieved in the last three months, and the performance indicators that were affected by a site’s down time.
This type of analytics does not attempt to answer any questions, but based on data makes predictions as to what to expect in the next few months. Quarter on quarter performance or year on year performance is predicted. This type of analytics is the natural next step marketers should take after descriptive analytics, but more often than not marketers stop with descriptive analysis. This is because predictions are risky business.
This type of analysis requires experience, and is more fun to do than the first two types. In this type of analysis you use data and then apply your business knowledge, gained with experience, and suggest changes to reverse the trend. At times data sets are mined and computers or other machines used to prescribe moves that will reverse negative trends. However, the sad part is most marketers think this is the job of higher ups in the organization.
This type of analysis attempts to predict when a customer is likely to leave the organization and what steps can be taken to change that. Prescriptive analysis also attempts to determine when a customer is ripe for a second purchase and what products can be offered to the customer.
As a marketer are you doing all these things? Or do you think that your responsibility ends with descriptive analysis and that the other type of analytics is the job of your higher ups.
Ideally the entire marketing team should be engaged in all three types of analytics. No doubt data is intimidating, but at the same time data can be fun and if you make it accessible to all in the marketing team it becomes everyone’s responsibility to make inputs to improve situations.
Today a host of analytic tools are available and it will be immensely satisfying to suggest new moves that benefit client and your parent organization rather than just make descriptive analysis and not effect a change.