Epicentr
#Logistic #Retail Android App iOS App UI Design UX Design
The product is running
link epicentrk.ua

We did:

Product concept UI Design UX Design Android application IOS Application API Design AWS Face Rekognition implementation Analytics

Problem

Epicenter, as a leading retailer in a leading market position, set itself the goal of helping users improve their experience with the loyalty program.

Problem analysis and search for the solution

  1. The analysis of the product solutions of leading retailers — Walmart, Target, HomeDepot, etc. was carried out
  2. Design Sprint was held which involved all the main directions of the customer- online, loyalty program, shopping center.
  3. On the basis of Design Sprint, a prototype of the solution was formed, and a number of functions were generated.
  4. Customer Development was conducted with the users, and and key insights were identified during two stages of prototyping.

Key features

01

1. Look and pay

Shopping without a purse in the shopping center. The user can add his/her photo and payment card in the mobile application and pay for purchases in the shopping center just looking at the camera at the self-service checkout.
1. Look and pay
02

Scan and Go

The user scans the barcodes of goods during a visit to the shopping center and pays for them at the final step. The user can then leave the shopping center through the self-service checkout area.
Scan and Go
03

Super cashback

Choosing cashback categories in the app and receiving bonuses for their purchase.
Super cashback
04

Electronic Loyalty Card "Benefit"

Full information on current points balance and electronic version of loyalty card in the mobile application. The service allows to deduct or earn points easily.
Electronic Loyalty Card
05

Personal offers

Unique personal offers that are relevant to every user. The forecast algorithm allows to provide only up-to-date product offers based on the purchase history.
Personal offers
06

Online shopping

Online catalog of the whole range of goods of the chain that allows to make an order and pay in mobile application.
Online shopping
07

Shopping history

Full transaction history. All the information about purchased items, received discounts and accumulated points.
Shopping history
08

Notifications

The user receives personalized notifications about products and promotions that may be specifically interesting for him/her based on their consumer profile. A proven channel of communication with a user which is not intrusive.
Notifications
09

Surveys

Help the company to collect feedback on the quality of the shopping experience and service process. Also, business gets the opportunity to fill its data center with information about the user, which will help to make the best personalized offers.
Surveys
10

Shopping Center and Services

One of the problems was low awareness of the services available at the shopping center. Now users have the opportunity to learn about them, and and in the long term to interact with interactive services.
Shopping Center and Services

Main challenges during the project

  1. To unite all the users within a single ecosystem — mobile application, loyalty program and epicentrk.ua
  2. To implement an effective system of personal offers for the user in relation to his/her consumer group
  3. To build Look and Pay service using AWS Face Recognition with the correct user identification by the camera at the self-service checkout
  4. To provide necessary security when implementing Scan and Go service for purchasing a product using barcode scanning
  5. To configure analytics system within a mobile application to measure product and growth metrics
  6. Creating a data informed project that is focused on numbers, not just on user feedback

Results

  1. The MVP of the mobile application is launched on both platforms in time, with following gradual changes.
  2. More than 50,000 ad-free mobile app downloads in 2 months.
  3. N-Day Retention of the product at the level of main indicators of the industry is 4%. Rolling (Unbounded) Retention (more suitable for this type of product) is 20.8%.
  4. DAU (Daily Active Users) — more than 5,800 users.
  5. Average session time
  6. The initial product feedback was collected and product roadmap and product strategy are adjusted.
  7. All key product metrics are measured using analytics tools.
  8. There is a gradual launch of new service and iterative changes in the product

Technologies and tools involved

Amazon Rekognition
Amazon Rekognition
Amplitude
Amplitude
Kotlin
Kotlin
Swift
Swift
Google analytics
Google analytics

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