TAMBe is a solution to all your woes of remembering and visiting numerous sites for your favorite products when you want to buy them. Here, you can collate your wishlist items from various sites in one place. So you can effortlessly manage your personalized collection of stores and items for buying, sharing, and collecting.

TAMBE

About Us

Data collection is crucial for becoming aware of trends, audience preferences, and rising competition. Extracting, analyzing, and interpreting this large volume of data helps in marketing strategies & knowing the competition. One such method, website scarping helps in data organization for easy access of the information for various purposes and enhances efficiency while saving time.

Applying this idea in our scraping product TAMBe, we bring a new angle to e-commerce data management. Understanding the hassles of saving multiple items on different sites and then forgetting about them is not going to be an issue anymore. TAMBe is your personal digital wardrobe that you can assemble from various e-commerce sites. Here, you can create multiple wishlists and stores for your items for organized access. And further share it for private or public usage with your friends.

Focusing on the online customer experience, this app simplifies the compilation process for a seamless user experience. The easy scraping of product details via URLs enables the customer to verify and compare fashion items at convenience. Additionally, it doubles as a platform suitable for e-commerce data collection for study and analysis.

Challenges

Combining two variant concepts had its challenges to deal with. Involving many technical complexities, it had to be user-friendly without compromising on the seamless UX. Being one-of-its-kind, TAMBe had to set an example of a modern digital wardrobe organization. Some of the challenges that came along the way are:

01

Customizing the product as per the industry and client requirements.

02

Going about scraping the secured websites for information from the client-listed sites.

03

Going about scraping the secured websites for information from the client-listed sites.

04

Going about scraping the secured websites for information from the client-listed sites.

05

Customizing the product as per the industry and client requirements.

06

Going about scraping the secured websites for information from the client-listed sites.

07

Going about scraping the secured websites for information from the client-listed sites.

08

Going about scraping the secured websites for information from the client-listed sites.

05

Customizing the product as per the industry and client requirements.

Platform

App and Website

Platform

App and Website

Solution

Overcoming a long list of obstacles, our adroit developers managed to make a breakthrough in structuring a product that stands out as an absolute personal Fashion Management App.

It began with the combined efforts of the developers and the analytic teams to learn about the project scrapping difficulties and brainstorm over them. As a result, they managed to identify the websites that allowed clean scrapping without blocking and the type of information it permitted to be scrapped. Based on that, they put their coding skills at work to tweak and produce idea data scraping technique that unanimously allows scraping data effortlessly. Significantly, they developed a method that would prove to be trustworthy and fair.

Besides that, the other difficulties were taken care of with a more subjective approach with the solutions like,

 

  • Enabling efficient, concurrent data scraping from multiple websites.
  • Designed specific import templates with appropriate file configurations for various websites.
  • Single page usage for consistent navigation.
  • Enhanced loading speed for high performance.
  • Add store and import products via links.
  • Wishlist for products that can be filtered based on categories.

Key App

Features

Along with efficiently handling the challenges, we also made it a point to add significant features that TAMBe flaunts for high user retention.

 

  • Tambe brings in on-demand data from various sites to your easily generated store.
  • Can explore limited features of the application without user login.
  • Conveniently update the user’s profile for personalization.
  • Preserve their data and progress as well as communicate with one another through the app.
  • The wishlist can be made Private, invisible to other users, or Public, accessible to other users by sharing the link.
  • Immediate product upload with compatible import format.
  • Search pre-existing stores and items created by other platform users.
  • Favorite store listing.
  • Add and compare similar products in the same wishlist.
  • Efficiently manage your added products for various purposes.

Results

The results of our efforts gave us TAMBe, a fashion platform for moderating your online favorite items. Here, the users can use the app to construct a virtual shopping list of items they want to buy. Also, they can simplify & keep track of their online purchasing activities in a smart way along with getting suggestions and tips from like-minded fashion enthusiasts.

Besides, it is a self-monitoring product for tracking changes and modifying them as needed.

So you get the actual status of the product, its availability, price, offers, and sales in a progressive manner. And it brings in all the data that is validated and can be easily understood. Ultimately, it creates an active community of shopping lovers for a holistic

Technology

Stack

Flutter
react-logo
React
MongoDB
MongoDB

Admin panel

The website version of TAMBe comprises of Admin Panel for back-end operations of the App. Here, the app facilitators manage the overall functions of the products, stores, and their categories. Especially, adding, editing, and deleting operations.

The highlights of the admin panel boast the below features:

  • Efficient Store Management
  • Add or Modify Products of the Users
  • Creation of Categories
  • Organizing Platform Users
  • Editable Website content
  • Data Analytics