Enterprise SaaS Product Launch Promo Video
Description
Vocal Characteristics
Language
EnglishVoice Age
Young Adult (18-35)Accents
North American (General)Transcript
Note: Transcripts are generated using speech recognition software and may contain errors.
target is a general merchandise retailer with stores in all 50 U. S. States. Their online shopping has outgrown in store purchases and their mobile app has been used by over 27 million customers since its launch in 2013. During Covid target began to rely more on their mobile app to facilitate customers in store purchases, having purchases pulled from the shelves and ready to go upon their arrival for curbside pickup there. S VP of digital Julia leads the company's marketing media and creative strategy as well as its loyalty program target circle. She starts every monday morning monitoring the impact of all her marketing campaigns and experiments across targets official metrics. Julia can also monitor campaign attribution conversion rates by funnel stage or drill into any official metrics and all other outcomes. Her team cares about the most Julia is particularly interested in a retargeting campaigns, impact on missed revenue. The campaign shows a slight positive trend but because it's integrated with product data, she is able to see that there is an overall decrease in repeat usage and decides to investigate further. She drags and drops region as an object onto the retention chart which clearly shows a dip in the California stores unclear of the root cause Julia is able to quickly dive in and watch a session replay of a customer from that cohort among the possible causes for this increased wait time. One particular piece stands out targets new store in store concept that was launched recently in California digging into it a little more. The team learns that there is an increase in foot traffic inside these stores around lunchtime with people lining up to get a taste of the impossible bacon burger. Oh wow, that would mean staff is getting redirected away from contact less pickup. As she starts wondering about what can be done to help the situation. Julia gets reminded that they're about to roll this out to another 500 stores within this quarter. It's going to get much worse and have a bigger impact on their metrics. Julia wants to make sure that any customers that have or will be impacted by this, receive 20% off their next purchase. Julia can create a trigger event right from the journey analysis screen and create a continuous audience, sink to her braids account, emailing all impacted customers a coupon in real time Julia feels that target could see improvements to their metrics if they improve how customers demand is managed during times of high foot traffic. So she adds two more hypotheses all automatically configured for collision detection stat Sig one by sending a push notification to alert customers that wait times have increased since the order was placed and another showing customers nearby stores that have shorter wait times once it's ready. She turns the experiment on and lets it run for a few days, showing customers a nearby store with a shorter wait time, has the highest conversion rate and the least amount of impact on returning users Julia then rolls this experience out to all customers Julia opens her dashboard the following day and sees her retention rates bouncing back up. Her team has also added a delivery efficiency official metric to monitor for future rollouts. Julia and her customers are happy, and so is target.