Search is a key component of companies’ product discovery and customer service strategy. Much more than simply avoiding the dreaded “null results” problem, sophisticated search programs provide contextually relevant products and content in real time, based on deriving intent from the customer and using that information deep into the customer interaction to serve their needs explicitly.
Guest speaker and Forrester senior analyst, Scott Compton, is an expert in retail ecommerce and direct to consumer marketing. Scott advises companies on the strategies and technologies needed to serve the customer within today’s quickly – changing ecommerce environment. Lucidworks Digital Commerce General Manager, Peter Curran, will host a webinar on January 21 discussing:
-The many ways companies can apply digital commerce search to drive onsite conversions, grow revenue and increase customer loyalty
-How to extend the insights from your company’s search program to omnichannel customer service use cases
-From Lucidworks, how Vector Search helped some of the world’s largest ecommerce companies address consumer goals over the 2020 holiday shopping season
-How AI-powered product discovery enables retailers to understand and deliver on customer goals for higher customer satisfaction and engagement amplifying the impact on AOV and conversion
Featuring
-Scott Compton Senior Analyst Serving eBusiness & Channel Strategy Professionals, Forrester
-Peter Curran General Manager, Digital Commerce, Lucidworks
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
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
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
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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%
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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%
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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.
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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.
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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
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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
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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