Facilitating product discovery in e-commerce inventory, The Fifth elephant, 2016


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The Fifth elephant, 2016

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Facilitating product discovery in e-commerce inventory, The Fifth elephant, 2016

  1. 1. Facilitating product discovery in e-commerce inventory @ektagrover Member Technical Staff, BloomReach Ekta Grover http://www.specommerce.com.s3.amazonaws.com/images/marketplaces-ar.png https://www.linkedin.com/in/ektagrover Img source : The Fifth Elephant Bangalore, 2016
  2. 2. Structure for this talk Beyond the store-front 2 specific problems in search Influencing product design Context & taxonomy
  3. 3. ecommerce ecosystem at 30ft Independent merchandizers Marketplaces Technology providers
  4. 4. Discoverability Engagement drive incremental revenue Img source :https://blog.optimizely.com/wp-content/uploads/2015/06/shopping-cart-crop-1.jpg
  5. 5. The visitor’s search query often differs from the product description in the catalog. Product Description How shoppers may describe it Crafted of soft 100% cotton with a herringbone weave and clean mitered seams, our exclusive teal pillow is a classic update for any seating arrangement. Pick up multiple colors to refresh your decor instantly and affordably. blue pillow blue couch cushion turquoise cushion aqua throw pillow * *not mentioned in the description Product Name: Teal Herringbone Cotton Throw Pillow And this is just the tip of the ice-berg
  6. 6. Quick taxonomy search query signals intent user has segment & intent product has purpose store front search results page
  7. 7. problem #1 : Cart Abandonments Initiate a search View products Add to cart Convert
  8. 8. Diagnosis : Cart Abandonments Well formed queries alphanumeric queries Queries with exact product_ids metric for seperation frontier branded queries non-branded queries others MECE mutually exclusive collectively exhaustive discoverability & engagement gap ..to find
  9. 9. Diagnosis : Cart Abandonments Diagnosis: viewing & adding products to cart, but not converting Cause : Most popular sizes OOS ! Inference : People use carts to bookmark
  10. 10. • Custom sizes • No standardization across category • Size map Challenges & constraints Goal : Blend the availability of SKU's/sizes to (re)rank the products Pre-cursor: Need to know the real distribution of sizes, across categories score(rank) = f(availability factor,x2,x3,x4..)
  11. 11. 321 unique sizes/150 unique leaf categories …And so we cluster sizes
  12. 12. Product design re-rank the products where the availability is factored in by size- popularity availability factor =: rate of fill of inventory [Supply] rate of depletion of inventory [Demand] Opportunity for the merchant is to align these and fill inventory
  13. 13. problem #2 : Handling Special events
  14. 14. Challenges & constraints conflicting goals : prevent starvation vs. Business performance http://sayrohan.blogspot.in/2013/06/finding-trending-topics-and-trending.html The Britney Spears Problem : Tracking who's hot and who's not presents an algorithmic challenge http://www.americanscientist.org/issues/pub/the-britney-spears-problem/1 • huge demand generation, often not in line with intent • short-lived events - too small a period to let the algorithm learn • need to separate the trend from popular events • fair bootstrapped impressions do not work
  15. 15. Solution is a mix of opportunities new products , new intents & (reverse engineering) merchandizing plan
  16. 16. New products marketplace products regular product …”related” is relative • quantify relatedness • get feedback from curated QA • borrow “scores” with decay
  17. 17. inherit from “related” products Product design QA borrowed_score(pid)= f(mlt_pid,decay,relatedness) score(rank) = f(borrowed_score,x2,x3,x4..) custom params
  18. 18. women's villanova wildcats navy blue classic arch full zip hooded sweatshirt www.we-sell-stuff.com/COLLEGE_Villanova_Wildcats_Sweatshirts_And_Fleece www.we-sell-stuff.com/prod-nm2614 women's sweatshirt www.we-sell-stuff.com/NHL_Minnesota_Wild_Mens/pg/1/ps/72/so/newest_items www.we-sell-stuff.com/NHL_Minnesota_Wild_Mens/pg/1/ps/72/so/top_sellers www.we-sell-stuff.com/NHL_Minnesota_Wild_Mens/pg/1/ps/72/so/highest_price www.we-sell-stuff.com/NHL_Minnesota_Wild_Mens/pg/1/ps/72/so/lowest_price www.we-sell-stuff.com/NHL_Minnesota_Wild_Mens/pg/1/ps/72/so/top_rated Exclusivity Vanity Quality & utility spend thrift Understanding user segments redirect product/category page www.we-sell-stuff.com/shop/wd/womens-dresses? quantity=144&evansignore=10051&filtered=true&catFilter=140325&cmp=SOC:ANF_BND_US_FBK_PRD_FMLdresses - campaigns/promotions/repeat users www.we-sell-stuff.com/webapp/wcs/stores/servlet/Search?search-field-submit=SEARCH&catalogId=10901&search- field=tops&cmp=PDS:ANF_US_BNG_BRD_General-Tops&kid=6ccb15d4-7758-f1a9-bb46-0000732cf85f&langId=-1&storeId=11203 Queries that have campaigns
  19. 19. decoding user experience • Pagination depth of users across queries - which queries are worse off? • Price sensitive vs. Brand sensitive users - re-ranking & personalization www.we-sell-stuff.com/search=hoodie+sweater&pn=2 www.we-sell-stuff.com/search=final+four&pn=4 Cluster brand facets vs. price signal facets to infer user-segments
  20. 20. reverse-engineer consumer preferences www.we-sell-stuff.com/hoody+sweater/directory_hoody%2520sweater?fids=Clothing!Hanger!_26!Accessory!Type_3A_22Clothes! Hangers_22&sr=true&sby=&min=&max= from your weblogs ..and likewise for handling search redirects, new product launches, campaigns & deals …dynamic facets
  21. 21. consumers have different taxonomy products have a purpose & positioning What we know so far match this well
  22. 22. Be metric driven Common sense math beats intense data science :) look beyond your cursory tool-kit Match intent to purpose of the product segment intimately till a separation frontier emerges Reverse engineer quantitatively and then commoditize at scale Isolate. Synthesize. Commoditize. Scale
  23. 23. Consumer behavior + technology Awesomeness Questions ? https://www.linkedin.com/in/ektagrover @ektagrover ekta1007@gmail.com