When you are working on a product or a feature, you cannot push it to all the customers/users at once. You need to test its performance first. 𝗕𝘂𝘁 𝗵𝗼𝘄 𝘄𝗶𝗹𝗹 𝘆𝗼𝘂 𝗱𝗼 𝘁𝗵𝗮𝘁? Here comes A/B Testing. 𝗟𝗲𝘁 𝗺𝗲 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗶𝘁 𝗶𝗻 𝗲𝗮𝘀𝗶𝗲𝘀𝘁 𝘄𝗮𝘆 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲. 𝗛𝗲𝗿𝗲, 𝘄𝗲 𝘄𝗶𝗹𝗹 𝗷𝘂𝘀𝘁 𝘁𝗼𝘂𝗰𝗵 𝘁𝗵𝗲 𝘀𝘂𝗿𝗳𝗮𝗰𝗲 𝗼𝗳 𝗔/𝗕 𝘁𝗲𝘀𝘁𝗶𝗻𝗴. • So, when you have a feature, you simply take a percentage of customers as a sample, let's say 5-10% of total customers. Also ensure that the sample is divided randomly among control and experimental sets. • Then you divide this sample into two sets - control set and experimental set. • Here, experimental set has the new feature while control group doesn't. • Then you run the test so that the data collected is statistically significant (appropriate p-value or confidence intervals). Predefine your significance level, often with a p-value < 0.05 and confidence intervals. • Then you check key metrics of both the sets such as customer engagement, conversion rate, customer retention and more. Key metrics depend upon the industry, feature and more. • Now, if you are uncertain about the result, you can perform A/B testing again with bigger sample, for ex: 25-30% of total customers as sample. This will allow you to have better statistical power if you had high margin of error in your initial test. • Now, if you find that experimental set is performing better than control set, rollout the feature for all the customers. Also, I have excluded a lot of things which might make this explanation complex like you should use power analysis to decide how much customers to select in the sample. Enjoy. Follow for more! #ABTesting #DataAnalysis #DataAnalytics #ProductTesting #DataDriven #CustomerInsights #AnalyticsCommunity #DataScience #TestingStrategies #DataMetrics #DataTesting #CustomerEngagement #AnalyticsJobs #DataProfessionals #GlobalData #TechCareers #AnalyticsHiring #StatisticalAnalysis #DataCareers #ProductAnalytics #AnalyticsExpert #BusinessIntelligence #DataExploration #DataInsights #TechCommunity #DataAnalyticsUSA #DataAnalystUSA #TechJobsUSA #USDataScience #DataCommunityUSA #AnalyticsUSA #DataAnalyticsUK #DataAnalystUK #TechJobsUK #UKDataScience #AnalyticsUK #DataCommunityUK #DataAnalyticsEurope #DataAnalystEurope #TechJobsEurope #EUDataScience #AnalyticsEurope #DataCommunityEurope #GlobalDataAnalytics #DataAnalyticsGlobal #WorldTechJobs #DataScienceWorldwide #AnalyticsWorldwide #GlobalDataAnalyst

While working as a data analyst, it is very important for you to understand the stakeholders requirements as everything depends upon it. Let's understand this in this thread #DataAnalytics #DataAnalyst #StakeholderEngagement #ClientCommunication #DataDrivenInsights #StakeholderRequirements #BusinessAnalysis #DataStrategy #DataAnalystTips #ActionableInsights #DataMeetings #EffectiveCommunication #AnalystLife #DataScience #DataConsulting #TechIndustry #DataInsights #USDataAnalytics #DataJobsUSA #USADataProfessionals #DataCareersUSA #DataScienceUSA #USBusinessAnalysis #UKDataAnalytics #DataJobsUK #UKDataScience #DataProfessionalsUK #UKTechJobs #UKBusinessAnalysis #EuropeDataAnalytics #DataJobsEurope #EUDataScience #DataProfessionalsEU #EuropeTechJobs #EUDataInsights #EuropeBusinessAnalysis

Being a data analyst, when you get a dataset or get an access to the data from a client, don't rush to decide key metrics without understanding the data in detail. Let's discuss it in this thread. #DataAnalytics #DataQuality #DataAnalystTips #DataDrivenDecisions #MetricsMatter #DataInsights #DataAnalysisProcess #DataScience #DataUnderstanding #DataPreparation #USDataAnalytics #DataJobsUSA #USADataScience #DataProfessionalsUSA #UKDataAnalytics #DataJobsUK #UKDataScience #DataProfessionalsUK #EuropeDataAnalytics #DataJobsEurope #EUDataScience #DataProfessionalsEU