Analyzing Lovegobuy User Purchase Preferences in Spreadsheets & Building a Personalized Recommendation System

2025-04-28
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Introduction

Lovegobuy, as a cross-border shopping agent, serves diverse user needs by purchasing goods from platforms like Taobao on behalf of international customers. To enhance user experience and drive higher conversion rates, analyzing purchase preference data—such as product style, brand affinities, and price ranges—is essential. By leveraging data mining algorithms and machine learning models within Spreadsheets, we can build a robust personalized recommendation system

Data Collection & Preprocessing

The first step involves aggregating historical purchasing data from Lovegobuy users. Relevant fields for analysis include:

  • User ID: Unique identifier for cross-referencing behavior.
  • Product Category
  • Brand Preference: Frequency of specific brands purchased.
  • Price Range: To identify budget patterns.
  • Purchase Frequency & Timestamps: For tracking trends.

Using spreadsheet tools (Google Sheets or Excel), raw data is cleaned (removing duplicates, formatting inconsistencies) and enriched via categorization (e.g., =IF

Exploratory Data Analysis (EDA)

EDA reveals insights into user segments. Formulas and pivot tables help visualize:

  • Top Brands: =SORT(UNIQUE())
  • Popular Styles: Tag clouds created via text analysis add-ons.
  • Price Sensitivity: Histograms of =AVERAGEIF()

For example, clustering users into "Budget-Conscious" (≤$50)"Luxury Seekers" ($200+)

Machine Learning Integration

Though Spreadsheets lack native ML capabilities, integrations bridge this gap:

  1. Collaborative Filtering: Using Apps ScriptXLSTAT) to identify "Users who bought X also liked Y."
  2. Regression Models: Predicting preferred price points via =LINEST()
  3. K-Means Clustering: Grouping users by behavior patterns (add-ons like AbleBits

Example: Recommendation Score Formula

=IF(
    AND(UserBrand="Nike", RecentPurchase="Sneakers"),
    VLOOKUP("Nike_AirMax", ProductDatabase, 2, FALSE),
    "Explore similar styles from Adidas"
)

System Implementation & Benefits

The recommendation engine delivers outputs via:

  • Automated Email Campaigns: Triggered by purchasing cycles (=QUERY()
  • Dashboard Widgets: Real-time suggestions powered by =IMPORTRANGE()

Benefits include:

Metric Improvement
Conversion Rate ↑ 20-35% (personalized suggestions)
User Retention ↑ 15% (reduced search fatigue)

Conclusion

Analyzing Lovegobuy’s purchase data in Spreadsheets reveals actionable patterns, while lightweight ML integrations enable scalable personalization. By harnessing existing workflows with minimal coding, businesses can deploy efficient recommendation systems that delight users and boost revenue.

Keywords: Data Mining, Recommendation System, Google Sheets, User Behavior Analysis, Lovegobuy

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