When a user visits your Internet resource for the first time, you don't know anything about it. You don't have the data to do personalization for it. At the same time, good personalization will allow you to reduce Customer Acquisition Cost and increase Conversion Rate to purchase.
Enrich your databases with external data with a key IP-address. Use it when you get user's IP address. Based on this data, you will be able to personalise your products for users, which will lead to new sales and increasing retention rate.
Upgini could enrich your DWH with a rich customer profile by IP address. Use this data for Cold start personalisation.
Obtain a train dataset with customer IP-address and label. Label is a product id that was clicked by user.
Search for new relevant features for your task: launch FIT method of Upgini and get a list of relevant features from connected data sources.
Make a test enrichment: launch TRANSFORM method of Upgini and enrich your dataset with only new relevant features. This dataset could consists any phone number or any IP-address.
Use Upgini in production: Upgini pushes relevant features for the all IP-addresses to your DWH. You may use this data directly in your DWH.
Personalized search refers to web search experiences that are tailored specifically to an individual's interests by incorporating information about the individual beyond the specific query provided.
Search personalization in online stores helps increase sales by improving the user experience, increasing customer engagement and satisfaction, enhancing business competitiveness and growth.
Upgini could provide you new relevant information about the individual. Upgini has proven track record of using connected data source for search personalisation.
Obtain train dataset with customer IP-address and target label. Add another customer IDs such phone number, email if you have it. Target label may be a search result id that was clicked or not.
Search for new relevant features for your task: launch FIT method of Upgini and get a list of relevant features from connected data sources.
Make a test enrichment: launch TRANSFORM method of Upgini and enrich any dataset with only new relevant features that was found on FIT step.
Use Upgini in production: Upgini will transfer relevant features for the all IP-addresses, Phone numbers, Emails to your DWH. You may use this data directly in your DWH.
Personalization can significantly impact conversion rates. By tailoring product recommendations, offers, and messaging based on clients' behavioral profile, you can increase the probability of users making a purchase or taking a desired action.
You probably already have a personalization system. To increase the conversion rate from visiting your site to making a purchase, you may to improve the quality of your personalization system and add new relevant data
Enrich your DWH with new relevant external data that could improve a quality of your recommendation system.
Upgini could enrich your DWH with a rich customer profile using IP address, email, or phone number. Use this data to personalize content on your site.
Improving your payment options can increase your sales and customer satisfaction. Do not show your clients payment options like BNPL or POS-credit if their bank declines them.
Use an approval prediction machine learning application to score your customers before checkout.
Search for new relevant features that could help you predict the approval:
You can't determine a location for a customer who enters your site for the first time. Therefore, you can't personalize your site automatically, and the user could see items that are not available to them.
Get the postal code of the user by IP from Upgini. Or enrich your systems with a huge GEO-IP vector and try to determine a user's location by yourself.
Upgini provides out-of-the-box postal code determination by IP-address.
Upgini provides a huge GEO-IP vector for your DWH.
Don't show your client a payment option like BNPL or POS-credit if the bank declines them. It will increase your sales and customer satisfaction.
Use a machine learning model to predict approval by the bank or BNPL service before checkout.
Search for new relevant features that could help you predict the approval:
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We are ready to help you with these cases.