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Customer Satisfaction & User Experience Optimization

  • Résumé
    Whether it’s for marketing campaigns, sales, or customer services, bring together and apply methods of predictive analytics to your lead, customer, contact, touchpoint, channel data to optimize your Customer Relationship Management strategy with behavior patterns, customer relationship metrics... Discover Dataiku's use case with PagesJaunes.fr
    Citation : Dataiku. (Mai 2018). Customer Satisfaction & User Experience Optimization. Management et Datascience, 2(2). https://management-datascience.org/articles/4087/.
    L'auteur : 
    • Dataiku
      - (Pas d'affiliation)
    Copyright : © 2018 l'auteur. Publication sous licence Creative Commons CC BY-ND.
    Liens d'intérêts : 
    Financement : 
    Texte complet

    Compagny

    PagesJaunes.fr is the French equivalent of the YellowPages. PagesJaunes.fr is the French
    leader in local advertising and information on web, mobile, and print, with annual revenue of over $1Billion.

    Challenge

    Improve User Experience Without Increasing Workload

    More than 80% of the French rely on PagesJaunes.fr to get information or promote
    their activity, generating hundreds of millions of queries each year. The quality and
    relevance of results is a top priority for PagesJaunes. Category managers are
    responsible for maintaining the quality and relevance of the directory by creating the
    pertinent associations between terms and categories.
    PagesJaunes wanted a solution that would help them:

    • Measure and improve customer satisfaction,
    • Help Category Managers automatically detect and correct problematic queries,
    • Optimize the quality of results to improve customer satisfaction.

    Solution

    Score Customer Satisfaction & Automate Detection of False
    Queries

    With Dataiku Data Science Studio (DSS), PagesJaunes built an app that scores customer
    satisfaction. First, the app gathers search engine data (query lists, navigation logs,
    clicks, page visit rankings, etc.) and analyses them in order to isolate unsuccessful
    queries.
    Thanks to the creation of an algorithm, a score is computed for every query and is then
    used to predict which queries will provide unsatisfactory results for users. The
    algorithm is fed with qualified and enriched usage data.
    The technology eventually targets the engine’s failures enabling category managers to
    focus their operations on those failures.
    Image1

    Results

    30% Boost in Category Manager Productivity & Optimized Customer Experience

    PagesJaunes now has the capacity to explore and correlate heterogeneous and multiple data sources to deduce rules and models that can create value and be fed back into the app. PagesJaunes is constantly able to improve customer satisfaction all the while improving Category Manager’s productivity by:

    • Closely monitoring and managing unsuccessful searches,
    • Automatically detecting the most critical signals & applying the most relevant rules
      when interpreting a query,
    • Targeting and fixing false query results.

    “With DSS, we built an app in only 3 months that enabled a 30% boost in our
    team’s productivity. We were able to adopt Hadoop and Machine Learning faster
    than we had anticipated.” Erwan Pigneul, Project Manager – PagesJaunes

    Since the project started, more than ten PagesJaunes’ collaborators have been trained
    to Big Data technologies (Hadoop, machine learning, statistics) with DSS.

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