Brand Activation Maximizer using Ruby on Rails | OnGraph


Presentation Description

Our client runs a successful brand activation business and BAM, being one of the most affected platforms, has successfully activated.


Presentation Transcript

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Brand Activation Maximizer Team Size 4 People Project Manager Developers Project Lead QA About Client Works as a Brand Activation Maximize that helps to connect events organizers with Sponsors INDUSTRY: BPO INDUSTRY Timeline Ongoing Project IN WEEKS OR MONTHS 01. Our client developed a platform that enables brands to reach consumers effectively and help events organizers to broaden their

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scope by acquiring sponsorships . Our client runs a successful brand activation business and BAM being one of the most affected platforms has successfully activated more than 200 million sponsorships featuring around 80 clients 300 events and 100+ retail partners. The application helps event organizers to acquire sponsorships easily and at the same time provides promotional opportunities to the brands via a hassle-free registration process. OnGraph is proud of having been selected as a partner by BAM to develop a B2B application that brings together events and sponsors. Our team organized the website in a way that it was easier for event organizers to find sponsors for attendees to search events and related information including event details venue time location on the website itself. We used different technologies such as Postgres to handle the workload and Solr Search to make users’ search filter criteria faster easier and efficient. Go To Website 02. Goals Objectives Allow Event Organizers and Sponsors to Collaborate with Each Other in a Hassle-free Way

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03. Technology Tools 04. Business Challanges We had to go through several challenges such as slow search filter inaccurate event locator finder and inconsistent records and information related to branded stores. It was not only daunting to resolve such issues but also required a lot of research and analysis in order to meet tight deadlines.

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Slow Search Filter Users were finding difficulty in searching the specific events due to the humongous information available on the website. The search filter was extremely slow and less responsive while rendering the required event details. Different Applications Required to Scan Data Fetching event information from other websites and maintaining their accuracy in terms of venue location and so on was difficult as we had to filter the event- related information fetched through the database using an entirely different application every time.

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Inaccurate Event Location Finder Finding an event locator that is not only cost-effective but also accurate when it comes to searching and locating events was extremely difficult. Inconsistent Store Records Maintaining the stores’ data on a regular basis was difficult due to frequent data invalidation due to reasons such as stores getting closed or moved to a different location. 05. Visuals

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06. The Solution

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Solr Search Integration for Search Filter Instead of SQL query we used Solr search that enables hit highlighting full-text search dynamic clustering database integration and real-time indexing. We added filters based on location city/country and the name of the event/sponsor to simplify the search and make it more efficient. Tomtom API for Locating Events Tomtom API integration was used instead of Google place API to locate the events as well as the brand stores. The integration was cost-effective and provided us with accurate results. Scripting for Filtering Events and Store-related Data To filter out data related to events and nearby stores we used crawler a script an automated program to scan through the web pages and create an index which in turn was used to maintain accurate events data. Moreover it was leveraged to determine customer and market trends in a given geography. Used Geolocation for Store Locations We used geolocation to track the exact location of the stores. Geolocation simply tracks which IP range is used by which area using an IP-to-location database. This way we were able to easily manage the bulk data related to store locations.

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