Often, the information that can help answer certain specific questions (churn, cross &-upselling and employee flow) already exist within the company, hiding in plain sight.
It is often a challenge leveraging data about your customers or employees for insights to answer questions. If companies could improve their data practices at five important stages, they could become much more effective in solving some of the most pressing problems they face. We think the following steps are important.
Step 1: Improve data quality. Before you can use your data to get answers, data quality has to be improved. For example design a more rigorous survey with better measures or find new data sources. Consider what kind of data will be useful to collect, and then collect it — systematically. Have regular conversations with people throughout the company to identify what questions are pressing and what kind of data you may need to answer those questions.
Step 2: Link different data. To answer a question like “Why are my customers leaving?” you need to compare customers who’ve stayed with customers who’ve moved on. To do this, you need to link the data from different sources throughout your organization. Find out what kind of data is being collected in the organization. Design processes that make it easier to connect the dots between individuals to get as many data points on each customer as possible.
Step 3: Analyze your data. Simply put, data analysis requires data processing abilities. To decide what kind of data analysis techniques to use, and more important, to conduct the analyses, you need skilled data analysts capable of using advanced data processing software, such as R. The third step to leveraging your data, then, is to be competent in your data analysis. Consider what kind of analysing techniques are most suitable, given your type of data.
Step 4: Infuse your data with theory. Although many problems might seem pressing, you are not the only one facing them. Always check differente existing theories with regard to the specific problem. During the last few decades, a lot of attention has been paid to investigate the predictors of employee performance, turnover, and creativity. Academic researchers have documented what relationships exist and developed a wealth of theory that explains why they do. The fourth step to leveraging your data is to infuse your data with theory. Investigate past research that has attempted to provide answers to similar questions you may be asking yourself.
Step 5: Implement changes and keep track of outcomes. You have done it all: You increased your data quality, connected disparate data sets throughout the company, consulted strong data analysts, and consulted the research on relevant theory. You have a working model of why your customers are leaving the organization. Now that you have this insight, you need to turn it into an intervention. This is a crucial step of the process: testing whether what you have learned can provide actionable insights that improve your organization. Often A/B testing is used to assess and evaluate. The fifth and final step is therefore to implement changes and keep track of relevant outcomes. The intervention may require several attempts to have the intended outcome. Some interventions might not work at all, and others may even backfire. But the best way to find out whether the insights you have gained are accurate is to put them to the test.
Sherlok Machine Learning with the use Nebu Data Hub can help you with performing those steps. Want to know more details - request a call by submiting the form (on the right),
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