The demand generation and assortment planning are two critical components of running a retail business. Traditionally, retail companies use the historical sales data for modeling and optimization of assortment selection, and they use a marketing strategy for demand generation. However, today, most retail businesses have e-commerce sites with rapidly growing online sales. An e-commerce site typically has to maintain a large amount of digitized product data, and it also keeps a vast amount of historical customer interaction data that includes search, browse, click, purchase and many other different interactions. In this paper, we show how this digitized product data and the historical search logs can be used in understanding and quantifying the gap between the supply and the demand side of a retail market. This gap helps in making an effective strategy for both demand generation and assortment selection.

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Quantifying and Visualizing the Demand and
Supply Gap from E-commerce Search Data using
Topic Models
Anjan Goswami (Salesforce, UC Davis), Prasant Mohapatra
(UC Davis), Chengxiang Zhai (UIUC)
May 14, 2019

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Agenda of this Presentation
The Problem
Visualizations
Quantification of Supply Demand Gap
Future Work

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The problem: Economics of E-commerce Search
Search queries reflect customer demand.
Items reflects the supply side.

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The problem: Business of E-commerce Search
Sales and revenue.
Growth

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Assortments planning issues
Figure: Query: “Bengali Book”, Site: Amazon, Evaluation: Bad Result
because Amazon.com (the U.S site) does not keep Bengali books.

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Assortments planning issues
Figure: Query: “Bengali Book”, Site: Flipkart, Evaluation: Good Result
because Flipkart is a site in India and they keep Bengali language books

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Supply and demand gap in e-commerce

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The problem: Two core components
Unsold supply needs Demand generation.
Unmet demand needs new Assortment selection.

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Supply Demand Gap
Identify items that require new demand.
Identify queries that require new items.

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Visualization: Demand and Supply Gap using Query
Frequency and Item Quantity
Figure: Supply and Demand Visualization

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Visualization: Demand and Supply Gap using Word
Clusters
Figure: Search queries and Item Titles word clusters

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Visualization: Demand and Supply Gap using Word
Clusters
Figure: Search queries and Item Titles word clusters

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Visualization: Demand and Supply Gap using Word
Clusters
Figure: Search queries and Item Titles word clusters

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Visualization: Demand and Supply Gap using Word
Clusters
Figure: Search queries and Item Titles word clusters

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Visualization: Assortment keywords
Figure: Term Frequency vs Uniqueness

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Topic Model
Figure: A picture from David Blei’s paper.

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Supply and demand gap in e-commerce
Use a vocabulary from a category.
Generate topic model from queries weighing by frequency.
Generate topic model from the item titles weighing by their
quantities in inventory.
Find out the statistical distance (KL-divergence) between the
two topic distributions.
Validate using a simulation while solving for a known gap by
getting more assortments or queries through SEM.

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Challenges
How to make topics from keyword queries?
How to compare two topic models?

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Topic model from Queries
Figure: Query clustering

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Supply and demand gap in e-commerce

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Future work
Better topic model to capture the topics of query and item
titles within one model.
Using word embeddings and deep neural nets for modeling
such gaps.

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Acknowledgeents
Wei Han
Angela Jiang
WalmartLabs

23. Thank you

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References I