Morphl是一家国外提供AI解决方案的公司(PS:这家公司,web UI挺好看的~):
网址:https://morphl.io/products/morphl-cloud.html
MorphL社区版
MorphL Community Edition使用大数据和机器学习来预测数字产品和服务中的用户行为,其目标是通过个性化来提高KPI(点击率,转换率等),主要涵盖的模型包括:
在morphl理论体系里面,预算分配包含两个步骤:
第一步 预算/收入预测函数
f(Cost) = f(Cost(t) | Cost(t-1), Revenue(t-1), ... Cost(t0), Revenue(t0)) = Revenue function
根据历史的预算/收入数据,进行预测
第二步 预算最优化问题
在有了每个活动预算/收入预测函数之后,就可以开始解决预算最优化,以下有三种情况:
黄线是预算/投入金额累计线;
蓝线是预算/投入效率曲线(原文表示:The blue line is the relation between the budget and the returning sum.
)
曲线的顶点就是最佳的budge范围,可以帮助进行预算分配
github地址:Morphl-AI/Ecommerce-Marketing-Spend-Optimization
来看github放开的两个数据源格式:
其中的几个案例,介绍了几种他们常用的方法:
这里其实是非常简单的几种方法
bucket index
概念,还没特别看懂,猜测是一个合理的活动间隔期,类似sessionLet a bucket be: C o s t B = [ 0 , 0 , 50 , 20 , 0 , 15 ] Cost_B=[0, 0, 50, 20, 0, 15] CostB=[0,0,50,20,0,15], R e v e n u e B = [ 30 , 100 ] Revenue_B=[30, 100] RevenueB=[30,100].
This means that the first revenue (30) was generated by the first two costs alone,
so we merged the next bucket as well.
We’ll sum them, getting C Σ B = 85 C_{\Sigma B}=85 CΣB=85 and R Σ B = 130 R_{\Sigma B}=130 RΣB=130. Then, the bucket constant is: α B = 130 / 85 = 1.529 \alpha_B=130/85=1.529 αB=130/85=1.529.
Then, our pseudo-revenues will be: P s e u d o − R e v e n u e B = [ 0 ∗ α B , 0 ∗ α B , 50 ∗ α B , 20 ∗ α B , 0 ∗ α B , 15 ∗ α B ] = [ 0 , 0 , 76.45 , 30.58 , 0 , 22.935 ] Pseudo-Revenue_{B} = [0*\alpha_B, 0*\alpha_B, 50*\alpha_B, 20*\alpha_B, 0*\alpha_B, 15*\alpha_B] = [0, 0, 76.45, 30.58, 0, 22.935] Pseudo−RevenueB=[0∗αB,0∗αB,50∗αB,20∗αB,0∗αB,15∗αB]=[0,0,76.45,30.58,0,22.935].
借助上述例子,猜测,
bucket constant
第四案例,可能是间断式的活动,那么第五个案例,可能是一个长期的案例,
所以这里的bucket时间间隔是固定的1周,以此进行计算。
github地址:Morphl-AI/Ecommerce-Marketing-Spend-Optimization
来看github放开的两个数据源格式:
对应jupyter - 2. Budget optimization - basic statistical model
就是直接 => R e v / C o s t Rev / Cost Rev/Cost
import pandas as pd ''' 模型一:直接算个总的ROI Directly modeling f(Cost) = Revenue ''' class StatisticalModel: def __init__(self): # This model has just a single parameter, computed as the count between targets and inputs self.param = np.nan def fit(self, x, t): assert self.param != self.param self.param = t.sum() / x.sum() # 核心,非常简单的算一个ROI,作为系数进行计算 def predict(self, x): assert self.param == self.param return x * self.param def errorL1(y, t): return np.abs(y - t).mean() def plot(model, valData, xKey, tKey): validCampaigns = list(valData.keys()) ax = plt.subplots(len(validCampaigns), figsize=(5, 30))[1] for i, k in enumerate(validCampaigns): x = valData[k][xKey] t = valData[k][tKey] y = model[k].predict(x) ax[i].scatter(x, y, label="%s Predicted" % (tKey)) ax[i].scatter(x, t) ax[i].set_title(k) ax[i].legend() # 数据读入 conversion_data = pd.read_csv('Datasets/conversion_data.csv') # marketing_spend_data = pd.read_csv('Datasets/marketing_spend_data.csv') model_cost_revenue = {} predictions_cost_revenue = {} errors_cost_revenue = {} displayDf = pd.DataFrame() res_cost_revenue = [] campaigns = set(conversion_data['xyz_campaign_id']) # from sklearn.model_selection import train_test_split # X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.3,random_state=0) trainData = {} valData = {} for k in campaigns: data = conversion_data[conversion_data['xyz_campaign_id'] == k] num = int(len(data)*0.8) trainData[k] = data[:num] valData[k] = data[num:] # Cost_col = 'Cost' # Revenue_col = 'Revenue' Cost_col = 'Spent' # 投入 Revenue_col = 'Total_Conversion' # 产出 for k in campaigns: model_cost_revenue[k] = StatisticalModel() model_cost_revenue[k].fit(trainData[k][Cost_col], trainData[k][Revenue_col]) predictions_cost_revenue[k] = model_cost_revenue[k].predict(valData[k][Cost_col]) errors_cost_revenue[k] = errorL1(predictions_cost_revenue[k], valData[k][Revenue_col]) res_cost_revenue.append([k, trainData[k][Cost_col].sum(), trainData[k][Revenue_col].sum(), \ model_cost_revenue[k].param, errors_cost_revenue[k]]) displayDf = pd.DataFrame(res_cost_revenue, columns=["Campaign", Cost_col, Revenue_col, "Fit", "Error (L1)"]) display(displayDf) print("Mean error:", displayDf["Error (L1)"].mean()) plot(model_cost_revenue, valData, Cost_col, Revenue_col)
只是一个范例,
类似:cost -> 曝光 -> 收入
Cost x Revenue ~= Cost x Sessions + Sessions x Revenue 曝光 = a1 * cost 收入 = a2 * 曝光
分两步走,主要截取的也是2. Budget optimization - basic statistical model
# 随机设定一个session session_col = 'Impressions' # 曝光 Cost_col = 'Spent' # 投入 Revenue_col = 'Total_Conversion' # 产出 # 第一步:曝光 = a1 * cost model_cost_sessions = {} predictions_cost_sessions = {} errors_cost_sessions = {} displayDf = pd.DataFrame() res_cost_sessions = [] for k in campaigns: model_cost_sessions[k] = StatisticalModel() model_cost_sessions[k].fit(trainData[k][Cost_col], trainData[k][session_col]) predictions_cost_sessions[k] = model_cost_sessions[k].predict(valData[k][Cost_col]) errors_cost_sessions[k] = errorL1(predictions_cost_sessions[k], valData[k][session_col]) res_cost_sessions.append([k, trainData[k][Cost_col].sum(), trainData[k][session_col].sum(), \ model_cost_sessions[k].param, errors_cost_sessions[k]]) displayDf = pd.DataFrame(res_cost_sessions, columns=["Campaign", Cost_col, session_col, "Fit", "Error (L1)"]) display(displayDf) print("Mean error:", displayDf["Error (L1)"].mean()) plot(model_cost_sessions, valData, Cost_col, session_col) # 第二步:收入 = a2 * 曝光 model_sessions_revenue = {} predictions_sessions_revenue = {} errors_sessions_revenue = {} displayDf = pd.DataFrame() res_sessions_revenue = [] for k in campaigns: model_sessions_revenue[k] = StatisticalModel() model_sessions_revenue[k].fit(trainData[k][session_col], trainData[k][Revenue_col]) predictions_sessions_revenue[k] = model_sessions_revenue[k].predict(valData[k][session_col]) errors_sessions_revenue[k] = errorL1(predictions_sessions_revenue[k], valData[k][Revenue_col]) res_sessions_revenue.append([k, trainData[k][session_col].sum(), trainData[k][Revenue_col].sum(), \ model_sessions_revenue[k].param, errors_sessions_revenue[k]]) displayDf = pd.DataFrame(res_sessions_revenue, columns=["Campaign", session_col, Revenue_col, "Fit", "Error (L1)"]) display(displayDf) print("Mean error:", displayDf["Error (L1)"].mean()) plot(model_sessions_revenue, valData, session_col, Revenue_col) # 第三步:合并 displayDf = pd.DataFrame() errors_cost_revenue = {} res_cost_revenue_combined = [] class TwoModel(object): def __init__(self, modelA, modelB): self.modelA = modelA self.modelB = modelB def predict(self, x): return self.modelA.predict(self.modelB.predict(x)) models_cost_revenue = {k : TwoModel(model_cost_sessions[k], model_sessions_revenue[k]) for k in valData} for k in campaigns: predictions_cost_revenue[k] = models_cost_revenue[k].predict(valData[k][Cost_col]) errors_cost_revenue[k] = errorL1(predictions_cost_revenue[k], valData[k][Revenue_col]) res_cost_revenue_combined.append([k, errors_cost_revenue[k]]) displayDf = pd.DataFrame(res_cost_revenue_combined, columns=["Campaign", "Error (L1)"]) display(displayDf) print("Mean error:", displayDf["Error (L1)"].mean()) plot(models_cost_revenue, valData, Cost_col, Revenue_col)