2015-04 eBay Statistics
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A very short overview of eBay and an explanation of why statistics are important. Intended for a high school AP Statistics presentation.
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2015-04 eBay Statistics
- 1. Welcome To Director of Engineering Search Science Recall & Spam April 23, 2015 BRIAN JOHNSON With more than 100 million active users globally, eBay is the world's largest online marketplace, where practically anyone can buy and sell practically anything. Founded in 1995, eBay connects a diverse and passionate community of individual buyers and sellers, as well as small businesses. Their collective impact on ecommerce is staggering: In 2014, the total value of goods sold on eBay was $82 billion -- more than $2,500 every second.
- 7. DATA DRIVEN DECISIONS METRICS & TESTING
- 8. We Are Data
- 9. Fish Sticks Demand Category 42% Business & Industrial > Electrical & Test Equipment > Electrical Equipment & Tools > Electrical Tools > Cable Pullers 10% Business & Industrial > Construction > Building Materials & Supplies > Electrical 9% Home & Garden > Tools > Other 7% Pet Supplies > Fish & Aquariums > Fish Pond Supplies 6% Business & Industrial > Light Equipment & Tools > Air Tools > Staplers
- 10. Fishing Sticker Demand Category 41% eBay Motors > Parts & Accessories > Car & Truck Parts > Decals/Emblems/License Frames > Decals & Stickers > Graphics Decals 28%Home & Garden > Home Decor > Decals, Stickers & Vinyl Art 12%Sporting Goods > Fishing > Novelties & Gifts 11%Sporting Goods > Fishing > Fishing Equipment > Decals, Stickers & Patches 3%Collectibles > Transportation > Automobilia > Decals & Stickers
- 11. Fish Stickers Demand Category 53%Home & Garden > Home Decor > Decals, Stickers & Vinyl Art 24%Crafts > Scrapbooking & Paper Crafts > Embellishments > Stickers 16% eBay Motors > Parts & Accessories > Car & Truck Parts > Decals/Emblems/License Frames > Decals & Stickers > Graphics Decals 3%Sporting Goods > Fishing > Novelties & Gifts 1%Sporting Goods > Fishing > Fishing Equipment > Decals, Stickers & Patches
- 12. Why? www.wallpapertimes.com $’s per year in incremental revenue
- 13. Measuring Differences - Control vs. Treatment Tests Test 1 Test 2 Control Treatment1 Control Treatment2 10.0 10.0 9.9 10.1 10.0 10.0 10.0 10.2 10.0 10.0 10.2 9.8 10.0 10.0 9.9 10.2 10.0 10.0 9.9 10.0 10.0 10.0 10.0 10.1 10.0 10.0 9.8 10.1 10.0 10.0 9.9 10.2 10.0 10.0 9.8 10.0 11.0 100.0 10.0 9.8 Mean 10.1 19.0 9.9 10.1 Median 10.0 10.0 9.9 10.1 StDev 0.3 27 0.11 0.14 T-Test 0.17 0.04
- 14. Experimental Variation By Day
- 15. Experimental Variation By Metric
- 16. METRICS •What should we optimize –Page Views –Time on Site –Click Through Rate –Normalized Discounted Cumulative Gain –Purchases per User per Session/Day/Week –Revenue per User per Session/Day/Week –Net Promoter Score •How likely would you be to recommend …?
- 17. REVENUE Every business focuses on and measures revenue. Every business focuses on profit and loss. And they should.
- 18. Be Careful What You Optimize For… Sales Revenue ($) Time
- 20. Measuring Similarity - Words in Common
- 21. Jaccard Similarity We see two sets A and B . There are three elements in their intersection and a total of eight elements that appear in A or B or both. Thus, JaccardSimilarity(A, B) = 2/5. I Love Statistics Am Learning A B
- 22. Similar Item Titles 9 words overlap 4 words different 13 words total Jaccard Similarity is 9/13 or 0.69
- 26. Compound/Decompound Expansions Add “acidwash” > “acid wash” Before After
- 27. Context Matters Uncontexted expansions: cowboys cowboy cowboys hats ≠ cowboy hats Plastic toy cowboys = plastic toy cowboy
- 28. Features: Mutual Information • Rationale: The goal of this metric is determine if the co-occurrence of the candidates in the description is significantly more than the random chance of them co-occurring.
- 29. Features: Neighborhood Similarity • Rationale: Two synonym candidates A and B, will tend to have similar neighbors (viz keywords) surrounding them. Intersection ( Neighbours(A) , Neighbours(b) ) Min (Neighbours(a), Neighbours(b)) Neighborhood similarity =
- 30. Features: KL divergence • Rationale: Two synonym candidates will have similar price and category distributions of their inventory.
Editor's Notes
- Why What How
- http://www.ebayinc.com/who
- You are in business to make money How do you know if changes you make, make money You HAVE to test You can’t manage what you don’t measure Testing is crucial Image http://www.wallpapertimes.com/files/q/Yf/4j/qYf4jp9q86379020_800x600.jpg
- http://www.inc.com/jeff-haden/4-business-metrics-you-cant-afford-to-ignore.html
- (It was very hard to find a good example of this that brought in obviously wrong data above the fold: these issues are generally more subtle, showing up in deterministic sorts and in slower processing time. If you come up with another good example to include, that would be great.) There are many entity names, including many brands, which are identical to (Cowboys) or share components with (e.g. Red Bull) common terms that describe our inventory. By identifying entities and by using whole query context, we can provide expansions only when appropriate (e.g. no Redder Bull or Crimson Bull). We can also decide the confidence of an expansion compared to the original (e.g. as is usually done in spell check). For the cowboy(s) hats, Cowboys seems to mainly refer to the football team; there are a few cowboy hats where someone used “cowboys” instead of the possessive, but not many. For the toys, the plural form is definitely more common but the singular is also used in titles even in sets (bottom row of pictures has the singular; top row the plural); so, we want to use both forms to get the maximum inventory for this.