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Algonomy DeepRecs vs. Constructor.io Search

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Algonomy DeepRecs

    N/A
    Enterprise companies (1,001+ employees)
    DeepRecs makes recommendations for 'Similar Products' and ‘Complete the Look’ using product images and without manual merchandising. It leverages convolutional neural networks to detect and extract feature vectors and graph visual similarities between products. Further, DeepRecs helps shoppers discover new, seasonal, niche, and long-tail products—that otherwise remain buried due to lack of historical data—using NLP algorithms that leverage catalog descriptions and other textual data.N/A

    Constructor.io Search

    Score7.1 out of 10
    Enterprise companies (1,001+ employees)
    Constructor Search promises to improve conversions and revenue from onsite and in-app search, using search science and artificial intelligence, Constructor's cloud-based search-as-a-service solution uses natural language processing, machine learning-enhanced results ranking, collaborative personalization, and merchant controls to power enterprise-grade onsite and in-app search. Whether search results are optimized for relevance, revenue, conversions, conversations — or all of the…N/A
    Pricing
    Algonomy DeepRecsConstructor.io Search
    Editions & Modules
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    Offerings
    Pricing Offerings
    Algonomy DeepRecsConstructor.io Search
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
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    Algonomy DeepRecsConstructor.io Search
    Small Businesses
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    Medium-sized Companies
    Dynamic Yield
    Score8.2 out of 10
    Dynamic Yield
    Score8.2 out of 10
    Enterprises
    Dynamic Yield
    Score8.2 out of 10
    Dynamic Yield
    Score8.2 out of 10
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    User Ratings
    Algonomy DeepRecsConstructor.io Search
    Likelihood to Recommend
    -
    (0 ratings)
    9.0
    (1 ratings)
    User Testimonials
    Algonomy DeepRecsConstructor.io Search
    Likelihood to Recommend
    Algonomy (Manthan-RichRelevance)
    No answers on this topic
    Constructor.io Corporation
    Constructor.io Search takes all of the guesswork out of maintaining a search engine. As merchandisers or product teams, we have educated guesses at how search relevance should work but the customer is always king. We can't always predict the ways in which consumers will search or what their intent is. That's why the behavioral-driven approach that Constructor.io employs works so well. It means that merchandisers can focus on their sales and promotional responsibilities, instead of wasting time and bandwidth on base-level relevance questions.
    Incentivized
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    Pros
    Algonomy (Manthan-RichRelevance)
    No answers on this topic
    Constructor.io Corporation
    • Optimizes for business KPIs, not string matches, so merchandisers can focus on strategy instead of maintaining a dictionary.
    • Gives merchandisers control to curate the right customer experience per their expert understanding.
    • Customer/technical support during and after implementation.
    Incentivized
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    Cons
    Algonomy (Manthan-RichRelevance)
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    Constructor.io Corporation
    • Visibility of the personalization algorithm.
    • Exposing detailed analytics within the customer-facing dashboard.
    • "Top searches" available via API.
    Incentivized
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