Data analytics, a thorough study of historical and real-time information, looks beyond the information itself and derives meaning from it. Done correctly, data analytics can give you valuable insight into what has happened and is happening with your customers. It can also predict behaviors and results of your future campaigns. Done incorrectly, data analytics can lead you wildly off course. In order to achieve accurate results, data scientists follow four main phases.
Phase One: Establish Parameters
Data Scientists look through thousands and thousands of data points all of which can offer insight. The amount of information can be overwhelming and can offer false conclusions if interpreted incorrectly. In order to make sure you’re receiving true interpretations, parameters need to be setup prior to analyzing the data.
Basically, data scientists will want to know what insights you are looking for and what questions you are hoping to have answered. You might ask which customers are buying your number one product or where you should open your new store. Knowing what questions you need answered helps data scientists narrow down the pertinent information and provides context for how they should analyze the data points.
Phase Two: Prepare the Data
Data often arrives messy, so data scientists will spend time cleaning up and preparing the information to be analyzed. This can be anything from removing duplicate records to cleaning up formatting to removing irrelevant information. Having clean data ensures you are receiving accurate results. It also helps data scientists identify patterns and provide more valuable insights.
Phase Three: Analyze the Data
Analyzing the data is where data scientists really start to have fun. This process is often complex and requires using in-depth algorithms. Thinking through your questions, they’ll decide which data points will give you the information you need and then write code to create a data model.
In this phase, automation is really important, so having a strong model is critical. Once they have the model ready, they will run all of your data through it. The more automated the analysis is the fewer chances there are of human error tainting the results.
Phase Four: Scrutinize the Results
Machines are capable of doing crazy things, including interpreting data and bringing up valuable insights. But, a data scientist knows, you need to really scrutinize the machine’s results. This phase involves creativity and problem solving for data scientists.
They ask questions like are these data points the best ones to give us this answer? What patterns did we miss? Do these results make sense? They will evaluate the results in the context of the market, the customer demographics, the parameters set, and a variety of other contexts to make sure you get the information you need to make informed decisions.
This is a very simplified look at the process of data analytics. Ultimately, it’s a complex field that combines art and science to provide valuable information you can use. Instead of guessing with your marketing campaigns, you can know exactly what did and didn’t work and how you can adjust the next one for success.
At Analytic Marketing Partners, we are passionate about helping people understand their customer demographics, reduce waste in their marketing efforts, and achieve desired results.