Improving Conversion Rates with Data Analysis in Ecommerce

– The data analysis shows a 49.3% subscription to conversion rate for an underwear brand.
– The company plans to use this data to optimize their Facebook advertising campaigns and target the audience more selectively.
– They also aim to address gaps in the customer journey and focus on repeat purchasers to improve overall conversion rates.Target the audience that is most likely to convert based on the data provided and use predictive analytics to identify similar segments for better conversion rates.There’s a data combination for our underwear brand that shows a 49.3% subscription to conversion rate. Our overall subscription to conversion rate for this period was 26.38%. No, the data doesn’t relate to a specific product. Yes, now that we know this, we’ll plug it into our Facebook reporting to see if the ads are returning this data model at different levels by campaign, ad set, and ad. We’ll look for the leading indicator at 1 data point, then expand to data point combinations 2 and 3 to understand the quality of the audience being driven and their statistical likelihood of conversion. Now that we know who is most likely to convert, we can target that remaining 50% more selectively and aggressively in order to improve overall subscription to conversion rates based on answers provided and gaps. This should raise our conversion rate higher than the 6%+ it currently sits at. Maybe even weave some social proof into a special Welcome series for them addressing the gaps from the data they provided. We can create large buckets for them and automate most of the journey. With this newly established model for ads, we can train our algorithms to look for these data matches in ads to show quality of audience as well, making media decisions a breeze. We can also look to better address the gaps in the customer journey not only on our landing pages but also on our product pages to fully optimize the customer’s questions. The last step is to look at the repeat purchaser data markers and combinations to make sure we’re focused on those not only most likely to convert but also those most likely to purchase multiple times. This is what predictive analytics for ecommerce looks like. If you knew that one segment of your email signups were going to convert at near 50%, I bet you’d be looking to find more people like them. It sure beats the average of 26%. This is how you change the game for brands. You build repeatable models on intent markers and apply those intent markers to business decisions. #data #ecommerce #strategy

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