The Case for Semantic-Based Approaches to Product Discovery

1. The Case for Semantic- Based Approaches to Product Discovery Guest Speakers: Scott Compton, Senior Analyst, Forrester Peter Curran, GM of Digital Commerce Lucidworks

2. Guest Speakers SCOTT COMPTON SENIOR ANALYST PETER CURRAN GM, DIGITAL COMMERCE

3. January 2021 The Case for Semantic-Based Approaches to Product Discovery Scott Compton Senior Analyst

5. Base: 435 to 765 US online adults; Source: Forrester’s August 2020 US COVID-19 Retail Consumer Survey One-third of online US adults note that items were out of stock and that shipping times were longer than expected.

6. 24% percent of US online adults who started using curbside pickup during the pandemic expect to continue after the pandemic is over. Base: 435-765 US Online Adults Source: Forrester’s Retail COVID-19 August 2020 Consumer Ad Hoc Survey

7. Digital Commerce Search Best Practices Matching customer intent with product information.

8. 8 © 2018 FORRESTER. REPRODUCTION PROHIBITED. Visuals expedite the search and reduce incorrect selections. Search and navigation: Visual product cues Source: https://www.costco.com + Costco’s predictive search results provide product information and images in the search result. This may help with product recognition, allowing the customer to bypass the search result page with confidence.

9. 9 © 2018 FORRESTER. REPRODUCTION PROHIBITED. Predictive search provides product images and a call-to-action – reducing steps, speeding checkout. Search and navigation: Shopping functionality Source: https://www.ralphs.com + Ralphs provides a quick-link to add to cart directly from the search menu where the customer is even able to adjust the quantity. For the product- focused consumer, this reduces steps and expedites check-out.

10. © 2020 Forrester. Reproduction Prohibited. Findability: Search filters, results Search filters and results highlight price, product ratings, fulfillment options. + Fulfilment options + Detailed product filters + Ratings and reviews + Strong product details including price per unit

12. © 2020 Forrester. Reproduction Prohibited. Findability: Content search + Searchable recipes + Searchable ingredients Content search helps customers find ideas and products. Don’t forget customer service content!

13. © 2020 Forrester. Reproduction Prohibited. Merchandising moments: Rich content Deliver rich, meaningful content to help customers more deeply explore products. + Amazon has extensive content on the product detail pages, including product benefits and video + Recipe/event content helps to expose multiple product uses

14. © 2020 Forrester. Reproduction Prohibited. Merchandising moments: Comparison shopping content Comparison functions as part of the eCom suite can be employed as well as comparison content in the product descriptions. + Amazon uses comparison content on the product detail page. Convenience-focused customers need comparison shopping content to find the right product.

15. © 2020 Forrester. Reproduction Prohibited. Merchandising moments: Search “badging” Use badging in navigation (including search filters), product results pages, and product detail pages. + Visual badges highlight the items in weekly circular + Content support for badging definitions + Lifestyle badging used in nav and search filters

17. Behind the scenes of effective search programs Evolving the retailer’s search discipline to serve the best results.

18. © 2020 Forrester. Reproduction Prohibited. What does ‘tuning’ search look like? Start at global settings and work your way down to keyword / product settings Field Mapping & Weightings Synonyms Boosting Products Redirects

19. © 2020 Forrester. Reproduction Prohibited. Cautions of using static rules It is likely your tunings that are creating the customer experience hiccups Static rules in a dynamic environment have their limitations. Hard-coding products into the results doesn’t work very well with OOS situations. Static rules can sometimes overlap creating search traps that are difficult to unravel. Beware of multiple synonyms meanings – Example Khaki. Beware of two-way synonyms – Red / Ruby.

20. © 2020 Forrester. Reproduction Prohibited. Using search insights throughout the organization Progressive retailers are using search data in new ways Use keyword insights to drive SEO pages for converting products. Customer Service B2B Customer Retargeting SEO Historical searches can be attached to the account records. Audiences can be built from search activity. If you have discovered intent why not empower your CS reps?

22. Thank You. Scott Compton Senior Analyst 2

23. Semantic Vector Search A deep-learning dense-vector based solution to low-performing queries Peter Curran GM, Digital Commerce Lucidworks Inc. January 21, 2021

24. 24 COVID Date Night! My real life search experience last Saturday night

25. 25 Check out Palm Springs!

26. 26 I love horror movies, and this is my fave! The VVitch, 2015, Robert Eggers, A24

27. 27 What to watch next?

28. 28 You can’t always get what you want! But sometimes …. (you know the rest)

29. 29 Remember half marathons?!?!? Suppose you had a great running top and you need a larger size a few years later.

30. 30 What happens when we fix this? Utilization of search grew Search utilization surge from 14-16% baseline to 31-34%. Semantic vector search utilization dropped from 16% to 14%. Semantic results convert at 2x to 3x true null results. A few findings from a top-20 US retailer running Fusion & semantic vector search over Cyber 5 2020 Massive growth over cyber 5 last year Clickthrough rates up 20% Search-influenced orders up 30% Reduction of nulls by 91%

31. 31 Let’s visualize semantic vector space

32. 32 A better visualization of vector space

33. 33 Frequency Head: skirt, laptop, toilet paper Torso: black merino wool cardigan, 13” laptop on sale Tail: warm carbon shirt dress, white gold rose cut ruby ring Lexical Search Prevails Vector Search Prevails Lexical vs. semantic vector search Vector search isn’t a panacea. But it solves your hardest problem at scale. Percentage of Total Query Volume 20-40% 20-40% 20-60%

34. 34 TRAINED ON POSITIVE RESULTS What do people add to cart from search results? TRAINED ON ZERO RESULTS What do people add to cart after zero results? Two encoders, two opinions Two separate vector spaces give us different opinions on the same query at runtime. In this case, we trained purely on Adobe Analytics Clickstream data.

35. 35 IN STOCK / OUT OF STOCK Train on a combination of what people cart in both situations. Favor non-zero results encoder (opinion 2). PRODUCTS NOT CARRIED Rely on the persistence of loyalists. What do they cart after zero results? Favor zero results encoder (opinion 1). VOCABULARY Rely on the persistence of loyalists. What do they cart after zero results? Favor zero results encoder (opinion 1). MISSPELLING Semantic similarity. Favor non-zero results encoder (opinion 2). Four use case categories Solved with absolutely zero curation (no synonyms) and existing training data from Analytics platform.

36. 36 Brands & collections not carried

37. 37 Expanding to new business problems Superseded parts ”Kit” results for projects Improvement of the existing solution Improved embedding management Training “forward” That’s cool! What else can it do? Solution areas we’re working on now

38. 38 38 ORGANIC LEMONADE A very precise result Could we do more? IMPROVE SEARCH RESULTS VALUE

39. 39 39 ORGANIC LEMONADE A less precise result But a better experience IMPROVE SEARCH RESULTS VALUE

40. 40 40 ORGANIC LEMONADE A more precise result Higher potential order value FUTURE: COMBINE LEXICAL & VECTOR SEARCH

41. 41 41 THE BALLAD OF SONGBIRDS & SNAKES A very precise query An uninspiring result. IMPROVE SEARCH RESULTS VALUE

42. 42 42 THE BALLAD OF SONGBIRDS & SNAKES Less precise, but better inspiration The user has a goal, but they may not be as satisfied with a literal match as a semantically & thematically useful match. MERCHANDISING SEARCH RESULTS

43. 43 Fusion for Commerce & Digital ML-based product & content discovery. Operable at massive scale for key use cases. Search Type Ahead Guided Navigation Semantic LPQ/ZRP Browse Listing Landing Pages Classify & Enrich Finders & Configurators Personalization Recommendations Chatbots Self-Service Traffic SEO: Sitemap Expansion SEM: Auction Buys

44. 44 Questions? GUEST SPEAKER: SCOTT COMPTON SENIOR ANALYST PETER CURRAN GM, DIGITAL COMMERCE

45. 45 THANK YOU

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