preference mapping for automated recommendation of product attributes for designing marketing...
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Preference Mapping for Automated Recommendation of Product Attributes for Designing Marketing ContentMoumita Sinha, Rishiraj Saha Roy
Adobe Research Labs
© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Overview
Introduction
Method
Dataset
Results
Conclusions
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Introduction
© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Introduction
Marketers need to highlight attributes of their products in campaigns
Preference Mapping is an approach to identify customer preferences based on surveys of product attributes
Using these preferences, recommendations of product attributes can be provided to marketers to design their campaigns
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Workflow
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Method
© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Method
Consider 𝑘𝑘 products each with 𝑝𝑝 attributes
Example 𝑘𝑘 = 4 products with each product having 𝑝𝑝 = 13 attributes
One of these is the product for which campaign is being designed
Customers go to the product or retailer website and write textual reviews
These reviews are accompanied with positive, neutral or negative sentiments about the various attributes of the products
A preference mapping is then performed with the customer averaged scores of each of the various attributes for the different products
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Preference Mapping
Matrix of Reviewer Averaged Scores for 𝑝𝑝 attributes:
𝑋𝑋 = 𝑋𝑋1,𝑋𝑋2, …𝑋𝑋𝑝𝑝𝑇𝑇
𝑋𝑋𝑖𝑖 is a vector with elements 𝑋𝑋𝑖𝑖𝑗𝑗 Reviewer averaged sentiment score for attribute 𝑖𝑖 and product 𝑗𝑗
Principal Component transformation of feature vector
𝑌𝑌 = Γ𝑇𝑇 𝑋𝑋 − 𝜇𝜇 such that 𝜇𝜇 = 𝐸𝐸 𝑋𝑋 Γ ΛΓ𝑇𝑇 = 𝑉𝑉𝑉𝑉 𝑟𝑟 𝑋𝑋
This transformation is such that 𝑉𝑉𝑉𝑉 𝑟𝑟 𝑌𝑌 is maximized
𝜆𝜆1 ≥ 𝜆𝜆2 ≥ … .≥ 𝜆𝜆𝑝𝑝 where 𝜆𝜆𝑗𝑗 = 𝑉𝑉𝑉𝑉𝑟𝑟(𝑌𝑌𝑗𝑗)
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Preference Mapping
𝜆𝜆𝑗𝑗 ’s are eigenvalues (Components of Λ)
Corresponding eigenvectors are: 𝛾𝛾1, 𝛾𝛾2 … 𝛾𝛾𝑝𝑝 (Components of Γ)
Thus 𝑖𝑖𝑡𝑡𝑡 Principal Component (PCi) Score for each product is the weighted sum of the score of the attributes for the
product and the weight being 𝑖𝑖𝑡𝑡𝑡 eigenvector: Y𝑗𝑗 = ∑𝑚𝑚=1𝑝𝑝 𝛾𝛾𝑖𝑖𝑚𝑚𝑋𝑋𝑚𝑚𝑗𝑗
A biplot graph from PC1 and PC2 provides easily interpretable visualization
Shows how products compare among each other
Relative proximity of each attribute to their respective products
Marketing contents can be designed based on the recommendation of this multivariate approach and its visualization
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Dataset
© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Dataset
Primary Dataset
Products: 4 cameras
Attributes for each Camera: 13 E.g.: Flash, zoom, battery, quality of automatic mode, photo quality
These attributes are mentioned 583 times in the review data collected
Expert Ratings (To compare results)
Collected from http://www.dcresource.com and http://www.imaging-resource.com
For the same thirteen attributes in 4 cameras
Comments with “exceptional”, “excellent” and “good” for an attribute Score 2
Comments with “weak” and “worst” for an attribute Score 1
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Results
© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Averaged Sentiment Scores for the Four Cameras
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Preference Mapping
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Correlation: Preference Mapping Scores Vs Rank of attributes Based on Average Sentiment Scores
Camera Kendall-Tau P-Value
Canon G3 0.564 0.007
Canon S100 0.615 0.003
Canon Powershot SD 500
0.641 0.002
Nikon Coolpix 4300 0.294 0.172
Preference mapping has high correlation with intuitive understanding of importance of attributes
Provides further refinement : Provides multivariate relation between products with respect to each attribute
No direct relation between expert ratings and preference mapping score was observed
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Conclusions
© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Conclusions
Preference mapping technique recommends valuable attributes of products to marketers for highlighting in a marketing campaign
By focusing on attributes that are known to have received positive sentiments of customers, the risk in the campaign is minimized
Can potentially increase response rates
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Conclusions
The proposed technology does not require large amounts of customer preference data to be available internally with the advertiser
Reviews can be collected from any publicly available review site
The comparison with the experts' comments suggests:
What customers value more may be different from what experts consider of high quality in a product
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Future Work
© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Future Work
Multiple factor analysis (MFA) instead of PCA if some or all scores are of categorical nature
Cluster products using attribute sentiment scores as features
Observe the correlation of the clustering output to the representation produced by preference mapping
Quality of reviews can be improved by choosing active users only
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© 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
For further questions, the authors can be contacted at:
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