Target to Grow
“By accurately identifying which cohorts had the best conversion potential, our remarketing improved significantly.”
Data Science
To segment Sapphire’s website visitors based on how likely they are to buy, Alavi used its AI to study and evaluate the brand’s website analytics data.
Insight & Solution
Identifying similarities, Alavi grouped website visitors into distinct cohorts and presented ones that had conversion rates that were significantly higher than the site average.
Actions Taken
Sapphire targeted the cohorts through its remarketing platforms, which led to higher revenues, lower acquisition costs and better returns on their ad spends.
Expand & Save
“Understanding the types of visitors that actually convert allowed us to expand our remarketing and lower costs.”
Data Science
Alavi connected to PureVPN’s website analytics data to study the behavior of visitors with high conversion rates. Using its proprietary data models Alavi identified their common
characteristics.
Insight & Solution
Using its findings, Alavi developed a detailed audience profile, which the client could use to better focus their remarketing
campaigns.
Actions Taken
Through platforms like Google and Facebook, PureVPN was able to use Alavi’s audience profile to narrow their targeting, which allowed them to significantly increase returns as well as reduce costs.
Engage More Efficiently
“By knowing who would most likely buy again, we tailored our messaging and increased engagement.”
Data Science
To segment all existing
customers based on the
probability of them making
another purchase as well as how quickly they would do it, Alavi used its machine learning capabilities to analyze historical data from the client’s CRM.
Insight & Solution
Alavi delivered a useable list of existing customers who had a very high probability of making a purchase within 30 days.
Actions Taken
To optimize its campaigns, Takas.lk uploaded the list of customers Alavi identified to its marketing platforms. This resulted in much stronger returns and profitable campaigns.
Increase Retention Profitably
“Predicting returning customers’ spends improved our targeting and helped us retain clients for longer.”
Data Science
Using its data models, Alavi
processed the client’s analytics data and calculated how much each customer might spend the next time they made a purchase. The entire customer database was then divided into cohorts
according to the results.
Insight & Solution
Based on the price and nature of the products Spring & Summer wanted to promote, Alavi
recommended the company
only target its biggest spenders.
Actions Taken
Alavi provided the details of customers to target, which Spring & Summer used on different marketing platforms to run highly focused campaigns and reignite growth.
