User portrait based marketing method and system
A marketing system and user technology, applied in marketing, data processing applications, commerce, etc., can solve problems such as failure to improve order conversion, revenue loss, etc., to maximize revenue, save costs, and improve efficacy.
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Embodiment 1
[0057] Such as figure 1 Shown is a flow chart of the marketing method based on user portraits in this embodiment.
[0058] The marketing method based on user portraits in this embodiment includes:
[0059] S101. Obtain user data;
[0060] S102. Determine whether the user has installed a competing product and whether the user is a lost user or an active user. When it is determined that the user has not installed a competing product and is a lost user, process the user data according to the decision tree algorithm, and establish an order probability prediction for offline users Model;
[0061] Wherein, the user data for establishing the offline user order probability prediction model includes at least one of user historical browsing data, user historical order data, and user portrait data.
[0062] The user profile data includes at least one of user sensitivity information to coupons, user preference information, user consumption ability information, user generosity information...
Embodiment 2
[0071] Such as figure 2 As shown, the marketing method based on user portraits in this embodiment is different from that in Embodiment 1 in that:
[0072] The marketing method based on user portraits in this embodiment includes:
[0073] S201. Obtain user data;
[0074] S202. Determine whether the user has installed a competing product and is a lost user or an active user. When it is determined that the user has installed a competing product and is a lost user, process the user data according to the decision tree algorithm, and establish an offline user order probability prediction model ;
[0075] S203. Predict the user's order probability according to the user order probability prediction model;
[0076] S204. Judging whether the order placing probability of the user is lower than a set threshold, and sending marketing content to the user if the judgment is yes.
[0077] In this embodiment, aiming at the situation that users who install competing products and are lost u...
Embodiment 3
[0079] Such as image 3 As shown, the marketing method based on user portraits in this embodiment is different from that in Embodiment 1 in that:
[0080] The marketing method based on user portraits in this embodiment includes:
[0081] S301. Obtain user data;
[0082] S302. Determine whether the user has installed a competing product and is a lost user or an active user. When it is judged that the user has installed a competing product and is an active user, process the user data according to the XGBoost algorithm, and establish an offline user order probability prediction model;
[0083] Wherein, the user data for establishing the online user order probability prediction model includes the user historical browsing data, the user historical order data, the user portrait data, user real-time browsing data, user real-time order data, user At least one of the historical data of hotel stays, real-time data of users’ hotel stays, real-time data of third parties and competing pr...
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