E-commerce website

Problem statement

There is a significant drop-off post a customer searches or selects a product on the Network. In the Platform world, there are personalized recommendations for the customers to expedite their decision process.

1. In an unbundled environment as well, while each Buyer NP can provide a similar experience to the Customer, we need a mechanism to facilitate the Customers to get personalized recommendations on products, discounts/offers, etc basis their attributes on the Network.

2. The Sellers on the Network should also be able to relay their personalized offers to the customers on the Network.

Rationale of choosing the problem statement-

1. The problem statement was chosen because it aligns with our company's interests and goals. Our company specializes in providing personalized recommendations and optimizing the customer decision-making process. By addressing the significant drop-off observed after customers search or select a product on the network, we aim to enhance the user experience and increase engagement on our platform.

2. Additionally, enabling sellers to relay personalized offers to customers aligns with our objective of fostering strong relationships between buyers and sellers on our network. Overall, tackling this problem presents an opportunity for us to leverage our expertise and technology to drive value for both customers and sellers, ultimately contributing to the growth and success of our platform.

Solution Deep Dive (1/3) - Research & Insight validation

Interacted with stakeholders: Product managers, data scientists, engineers, and sellers.

Conducted interviews and workshops to understand requirements and challenges.

Analyzed market trends and existing e-commerce recommendation solutions.

ICP (Ideal Customer Profile):

We focus on e-commerce platforms serving all kinds of shoppers.

Our priority is customers who love getting personalized recommendations and deals that change based on their interests.

We're also looking for sellers who want to connect better with customers and sell more.

Key Insights & Learnings:

Utilize advanced analytics for customer segmentation and trend forecasting.

Implement recommendation engine with hybrid techniques.

Establish seamless seller communication and incorporate feedback.

Integrate dynamic pricing algorithms for personalized discounts.

Ensure data security through encryption and anonymization techniques.

Solution Deep Dive (2/3) - Solution approach validation

Our solution not only leverages ML-based analytics techniques and a recommendation engine but also prioritizes maintaining competition norms and privacy standards.

We ensure fair competition practices by sharing data with each seller and buyer in a privacy-preserving manner. Data is stored and analyzed in a peer-to-peer manner, making sure, we abide by the smart contracts protocol in a typical blockchain network.

User data is masked and transmitted in aggregated groups, safeguarding individual privacy while still allowing for meaningful analysis and recommendation generation. This approach upholds the integrity of the marketplace while providing valuable insights to sellers and personalized recommendations to customers.

Key Value Proposition for the Customer:

Personalized Recommendations: Tailored product suggestions based on individual preferences.

Dynamic Pricing and Offers: Personalized discounts and offers based on customer behavior.

Enhanced Shopping Experience: Seamless communication with sellers and fair competition practices foster trust and engagement.

Handling of Data Security and Privacy in Peer-to-peer Communication

Ensuring reliability and security in ONDC’s Blockchain:

Consensus Mechanism: Implement a robust consensus mechanism such as Proof of Work (PoW), Proof of Stake (PoS), or Practical Byzantine Fault Tolerance (PBFT) to ensure agreement on the validity of transactions and prevent malicious actors from compromising the network.

Encryption: Utilize strong encryption techniques to secure data both at rest and in transit. Employ cryptographic algorithms such as AES (Advanced Encryption Standard) for data encryption and SSL/TLS for secure communication between network nodes.

Ensuring privacy policy of Data:

The data shared is Encrypted properly to hide the customer details.

Ensuring competition norms of NPs:

We have created rules and regulations to share the data.

Dynamic change of Threshold value, makes it easy to share the reasonable amount of data.