Constructing E-commerce and Purchasing Agent Platform User Personas in Spreadsheets & Applications in Precision Marketing-Taobao spreadsheet

Constructing E-commerce and Purchasing Agent Platform User Personas in Spreadsheets & Applications in Precision Marketing

2025-04-24

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Abstract

This study explores methodologies for integrating cross-platform e-commerce and purchasing agent website user data (including demographics, consumption patterns, and preference metrics) within spreadsheet environments. By implementing data mining and machine learning algorithms, we establish dynamic user persona models that generate granular tagging systems. These personas are then operationalized for precision marketing initiatives such as personalized recommendations and targeted advertisement deployment, ultimately enhancing conversion rates and marketing ROI.

1. Data Integration Framework

Our spreadsheet architecture employs a three-layer structure:

Raw Data Layer:
Processing Layer:
Modeling Layer:=PY()

) executing k-means clustering and decision tree algorithms

Key spreadsheet formulas for persona generation include dynamic arrays (=FILTER()), propensity scoring (=LOGEST()), and cooperative filtering correlations (=CORREL()).

2. Machine Learning Implementation

Within spreadsheet constraints, we deploy lightweight models:

Algorithm Implementation Output Metric

Apriori Market basket analysis via =QUERY() Association rules (support ≥0.8)

XGBoost Colab-trained model exported as CSV Churn prediction (AUC=0.92)

Persona tags follow a cascading taxonomy: Primary (Gender_X_Income), Secondary (Hobby_Photography), and Tertiary (Fashion_SKII_RepeatMonths).

3. Precision Marketing Applications

Dynamic Coupon Allocation:=IFS(AND(PremiumUser, WinterBootsBrowser), 15%, LTV>2000, "FreeGift")

Email Campaign Automation:=VLOOKUP(LastOpenDate, LOR_Class)
Cross-platform Retargeting:

Alibaba Group Implementation

Reduced customer acquisition cost by 38% through cookieless fingerprinting using spreadsheet-generated device graphs (=MD5(CONCATENATE(Resolution, UserAgent))).

4. Performance Metrics

Results from A/B testing (N=200,000 users):

CTR increase: 210%
Cart abandonment decrease: 27%
ROAS: $9.81

This spreadsheet-based approach demonstrates cost-effective persona modeling for SMEs, achieving 86%12%

Algorithm	Implementation	Output Metric
Apriori	Market basket analysis via =QUERY()	Association rules (support ≥0.8)
XGBoost	Colab-trained model exported as CSV	Churn prediction (AUC=0.92)

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