Before we configure your new flow, let's talk a bit about your requirements and how the framework functions.
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By Lydia Graflund
Published 30 December 2019
It's hard to believe that 2019 almost already is over!
Over the past 12 months we've created a new framework to break new ground for AI-driven personalization, added dozens of improvements to increase overall conversion rates and grown our team of conversion specialists.
We could not have done any of this without you, so a huge thanks to all of you who joined our journey towards better and more enjoyable sign-ups!
Hope you'll have a wonderful upcoming holiday, and that you'll go easy on the Glögg!
We're happy to hear that Regily Dedicate was much appreciated by the partners that managed to get a spot. The Regily Dedicate concept, ensuring 20 hours of dedicated work per week for the partner, enables specific requests to be met each month - leading to much more being done.
We have successfully run traffic through the automated system, and the first results are being analyzed. We can't share anything yet, but the conversion team tells us that early numbers look promising - so stay tuned!
As written in the previous update, each flow in Flowbrain is personalised for the end user based on behavioural data. Focus now is on developing an extensive library of high converting modules for automated learning and testing, in order to enable an endless set of personalized flows catering the end users different behaviours.
We have built two modules, one focusing on dark mode being a setting on the end users' device, and the other one has human readable questions for each data set we collect. These are two of many different ways to get information from the users completing a flow, and Flowbrain configures the flow in real time to get the most optimal one for the end consumer.
We're running A/B-tests to measure which method of collecting address with help of geo-location is the best way to help the user type their address.
This version eases the process for entering address. It utilises the user's geolocation to suggest address based on current location.
Full address is being pre-filled with possibility for the user to confirm location with one click only. If suggested address is incorrect, users can continue flow with standard address flow, which is market specific.
Improvements of address flow by suggesting street address and postcode based on users geolocation. Suggested postcodes are being presented below input field for postcodes on postcode screen. Suggested street addresses are listed on top of the list of addresses. User confirm postcode or street address with one click only.
We constantly add new markets to the framework, and lately we have been focusing on market localization in South America and India. If you have any specific requests on markets you want to enter (we probably have most of them already), we're happy to help!
Based on local research, we have enabled support for address lookups in Mexico and Brazil, where users in most cases are only asked to enter postcode and house number. For users don’t remembering their post code, it will be automatically set in callbacks (reverse lookup), based on other address details. This improves address quality and decreases cognitive load for the user, leading to a higher likeability of them completing the flow.
Product has enabled support for address lookups in Argentina, where we provide an one-line-address solution. Suggestions are shown while user is typing and takes users geolocation into account.This reduces the cognitive load for the user and decreases the risk of user entering incorrect addresses.
To make it easier for the user to enter address as well as minimise the risk for entering a faulty address, we've improved support in the Indian market when manually entering address. When users manually enters the address, we use a new API to set state and division based on pin code. If users don’t remember the pin code, pin code is set without users input, based on state and district entered (reverse lookup). The list of districts are filtered from the entered state.
Hope you enjoyed the read, feel free to reach out if you have any questions, feedback or input - we're always happy to help!