Today, thanks to modern technological processes, data is accumulating easily and the volumes of data are pretty often are overwhelming. Big data needs to be stored and data needs to work for your business. It means that data should be properly prepared, selected, processed accordingly and analyzed to help making business decisions.  It is impossible to use all data to find questions to specific answers so you have to understand your objectives. Knowing objectives allows you to prepare the exact piece of data that won’t be a waste and will help extract valuable and correct takeaways. In other words your data needs to be analyzed.

Data analysis is a complex process that includes multiple steps. Let’s consider some of them.

  • Set up the objectives, i.e. clarify what you would like to know. Evaluate every question and determine the measurable answers that would allow to understand the problems and help find solutions.  For example, you need to assist marketing and sales team to refine their strategy for a certain beauty brand.  In this scenario your main interest would be focused on sales history, customers and their purchase behaviour, trends.
  • Define metrics. It is key to foresee the measurements you are going to apply since it will define what data you are going to pull. In the case above it is important to set up the time frames and get all metrics related to sales, numbers of customers,  recency, frequency and monetary (RFM), customer types including new, existing or lapsed customers, customer profile, i.e. age, gender, ethnicity, geography; customer affinity for different products, etc.
  • Data gathering. When your objectives are set it is important to collect data related to them. Your data set might not be enough and third party info may be needed to answer your questions. You may also need to identify what data is missing and implement changes to collect that data for future analysis.
  • Data cleansing. Prior to data analysis it is vital to make sure that the available data is clean i.e. updated, free of duplications, missing data is filled up and data set is complete.  Since customer information is constantly changing and needs to be validated. Data cleansing is the procedure that ensures data quality and, thus, provides reliable and correct metrics. For data cleansing needs, contact StrategicDB a data cleansing company.
  • Data analysis. There are different business intelligence software and tools available for analytical purposes from Python and SQL statements to Excel functions. Regardless of the tools available, you need to analyze and calculate the metrics that you defined in previous steps. When it is done the outcome may require additional deeper data analysis. For example, in our scenario you might want to know if lapsed customers stopped buying at your store completely or just lost interest in the brand. If they are still shopping cosmetics you may refine marketing strategy, for example, by using bundle offers or offering specific samples to retain customers.
  • Insights study. When analysis is done it is paramount to interpret the findings correctly. It can be helpful to look through the defined questions and try to answer them based on the derived metrics and calculations. The more takeaways you can drive the more detailed picture you will have. Keep in mind that numbers may only indicate a trend or a partial story. For example the decline in sales could be driven by product availability and not demand for the product. However, an analyst will only be able to see the decline.

Business decisions based on accurate highlights ensure organizational success and business health overall. No company can stay competitive without ongoing and timely data analysis that allows to maintain successful strategies, improve profitability and loyal customers.

If you have established an issue with your business or looking to uncover new opportunities, StrategicDB can help with your data analytics needs, contact us at hello@strategicdb.com to find out more.

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