Known as “the make-up of make-up artists”, Max Factor is a make-up brand founded in 1909 by Mr. Max Factor, the father of modern make-up. The brand is originated from its ultimate pursuit of perfect make-up skills, and has developed to be a well-known brand with a history of more than 100 years. In terms of SCRM marketing and creative marketing, Max Factor shares a long-term in-depth cooperation with Social Touch.
The major problem of Max Factor lies in its low activity and big loss of physical store members. It faced great difficulty in improving the new members’ second purchase rate and the regular members’ repeat purchase rate. Besides, Max Factor’s sporadic data made it impossible to create multi-dimensional user portraits.
With the integration of SCRM and big data, Social Touch made great efforts in surveying Max Factor user purchase process, WeChat menu design, membership system planning, user tagging system establishment, and user communication strategy design. Based on detailed user insight and knowledge of the comprehensive Max Factor product life cycle, Social Touch provided strong data support for the business and marketing decisions of Max Factor business personnel.
Integrate cross-channel user data and improve the 360-degree user portraits to serve as a data base for personalized communication with customers.
Targeting at the pain points of core businesses, design several marketing automation scenarios and real-time personalized engines to enhance member repeat purchase, boost cross-selling, as well as recall the lost users, aiming at improving operational efficiency and sales conversion rates.
Set different communication strategies for Max Factor fans, members and buyers, push information of product discounts and benefits at different phases to improve the activity of fans, members and buyers; keep promoting the buyers to become upper level consumers and enhance their brand loyalty.
Build real-time personalized data model, constantly optimize the personalized recommendation algorithm engine through machine learning, to enhance the conversion effect.
Create individual and group user portraits to learn users effectively. To achieve free combination of label dimensions, customize user group filtering, to support learning and understanding of user portraits of segmented groups and reach different groups precisely.
The monthly growth rate of mobile clients improved by 3 times.
The second purchase rate of members increased by 20% to 30%.
The repeat purchase rate improved by 15%.