Decentralized potential customer generation service with AI-powered scoring

[GENERAL] Name and surname of main applicant

[GENERAL] Are you delivering this project as an individual or as an entity (whether formally incorporated or not)

[GENERAL] Co-proposers and additional applicants

[GENERAL] Please specify how many months you expect your project to last (from 2-12 months)

[GENERAL] Please indicate if your proposal has been auto-translated into English from another language

[GENERAL] Summarize your solution to the problem (200-character limit including spaces)

Create a decentralized platform that directly connects buyers with business startups. Use AI-powered lead scoring to identify potential buyers in a way that is simple, transparent and cost-effective

[GENERAL] Does your project have any dependencies on other organizations, technical or otherwise?

[GENERAL] If YES, please describe what the dependency is and why you believe it is essential for your project’s delivery. If NO, please write “No dependencies.”

No dependencies.

[GENERAL] Will your project’s output/s be fully open source?

[GENERAL] Please provide here more information on the open source status of your project outputs

Yes, it works under the GNU General Public License (GPL) v3.0 License.

[SOLUTION] Please describe your proposed solution

1) Introduction and Problems

Sales is a key part of any business, helping companies grow while building a strong customer base. Both B2B and B2C salespersons follow the same general selling process to connect with prospective clients and build a strong customer base. The sales pipeline and sales funnel are both vital ways to visualize two distinctly different functions within the selling process. The sales pipeline visualizes the buyer/s journey from a salesperson’s perspective, tracking open deals from start to close. Meanwhile, the sales funnel visualizes the buyer’s journey from the prospect’s perspective.

The selling process starts with Lead generation, by approaching potential buyers through different channels (e.g., email, website, social media, campaigns), to attract them to visit the business’s landing page (website) and encourage them to interact with it. With enough interest, these potential buyers will enter the sales process as new leads. Lead qualification assesses whether a potential buyer is a good fit for a business. Finally, the lead is approached by a professional sales agent who will assist the lead in making the purchasing decision via a sales meeting & proposal to close the deal. The following figure shows the reality of the sales funnel. According to Gleanster Research, Bridge Group study, and Forrester Research, only 25% of leads are legitimate and should advance to sales, that means that 75% of your leads aren't being sold the correct thing, or worse, aren't ready to buy when sales agents start pushing offers. Only 17% of leads convert into opportunities and 83% of leads are dead.

Let's quantify that. If you have 100 leads, only 25 of those are right for the product. Of those 25, approximately 21 ( 17% of 25) them will not buy. Which leaves you with a meager 4 of 100 leads. The lead conversion rate is 4%.

The problem to be solved is increasing the lead conversion rate or boosting sales by generating more lead conversion into sales with AI assistance. Aligning Marketing ( Lead generation & qualification) and Sales is the answer.

But somewhere in between, there is a process of understanding the valuable potential buyers (landing page visitors) for the business. Since the selling process has a high cost (both in time and money), it should focus on nurturing only the most engaged and fitted leads to improve and maintain a profitable “Return on Investment” (ROI). This process is called lead scoring.

By scoring leads, businesses can ensure that their marketing and sales efforts are being focused on the right leads. A lot of money can be wasted chasing leads that aren’t going to convert. Predictive lead scoring helps the sales team by providing them with qualified potential buyers (prospects) who are more likely to do business with them. For example, a sales team may choose to focus their efforts on leads with a high score, while leads with a low score may be contacted less frequently or not at all.

Within the lead scoring process, AI ( Machine Learning) can also provide more accurate results than traditional (manual) methods. This is because AI takes into account a more significant number of data points (website behavior, social media engagement, demographic information, and more) and can identify patterns that humans are likely to miss.

Existing Web 2 lead-capturing platforms such as Landingi or ConvertFlow offer convenience and accessibility, but they come with limitations:

Data Ownership and Privacy Concerns:

• Data collection and usage: Platforms often collect user data and may use it for targeted advertising or other purposes without explicit consent. This can raise concerns about privacy and transparency.
• Control and access to data: Users may have limited control over their data and may struggle to access or delete it upon request.

Limited Design Flexibility:

• Pre-built templates: Many platforms offer limited design flexibility, relying on pre-built templates that can feel restrictive and generic. Users may struggle to achieve a truly unique and brand-aligned landing page design.

Cost and Value:

• Subscription fees: While some platforms offer free plans with limited features, most require a monthly or annual subscription fee to unlock essential features and functionality.
• Hidden costs: Additional costs may arise for premium templates, add-ons, and custom features, potentially exceeding budget expectations.

Vendor Lock-in:

• Data portability: Exporting landing page data or switching to another platform can be difficult or impossible, leading to vendor lock-in and dependence on the platform provider.
• Limited control over future changes: Platforms may implement changes or updates that negatively impact existing landing pages, leaving users with little control over their design and functionality.

Security and Reliability:

• Centralized vulnerabilities: The centralized nature of these platforms makes them susceptible to security breaches and data leaks.

2) Proposed solution

We propose and develop an MVP of decentralized lead capturing and scoring service that helps users (businesses, SMEs..) save time & boost sales with minimum of investment.

The proposed service can roughly cover two categories in the Sales pipeline: lead generation and lead qualification.

• Lead generation: Users can design or buy landing pages ( from individual designers) without requiring any technical knowledge. It allows users to design their landing pages with simple drag-and-drop tools and templates, without knowing any programming language. After the landing page is created, users can publish it with their domain name and run marketing campaigns on any social platforms ( Facebook, TikTok, Twitter..) to capture their potential buyers.
• Lead qualification: This is a marketing technique that helps decision-makers identify the more profitable potential buyers among the generated leads. As a result, the sales agent will not waste time randomly contacting all prospects and will only concentrate their efforts on more likely converted ones.

a) Architecture

he proposed Web3 lead generation service is built on Cardano infrastructure, meaning it is not controlled by any single entity. This allows participants as designers to retain more control over their pre-designed landing page templates and earn a fairer share of the revenue. Leads captured data are stored on decentralized storage as IPFS so businesses such as SMEs and individual households have ownership and control over it.

Here's a general overview of the architecture:

1. User Interface:

• Users ( a single person who belongs to a business or an individual) interact with the platform through a web browser.
• They can explore landing page templates, create & edit landing pages, and purchase them.

2. Decentralized:

• The platform uses blockchain (Cardano & decentralized storage as IPFS) to store data about templates & captured leads ownership, designer royalties, and user consuming history.
• This ensures that the data is secure and tamper-proof.

3. Smart Contracts:

• Smart contracts automate the payment of royalties to designers.
• When a user purchases a landing page template or consumes the service to capture leads, a smart contract automatically distributes a portion of the revenue to the designers.

4. Storage:

• Landing page templates and data of potential customers are stored on a decentralized storage network, such as IPFS.
• This ensures that the landing page is accessible to everyone, even if the platform goes offline.

5. Tokenization:

• Landing page template NFTs can be purchased and traded using cryptocurrency tokens.
• This creates a new market for designing and allows fans to support their favorite designer directly.

6. Oracles:

• Oracles are used to provide external data to smart contracts.
• For example, oracles can be used to verify the authenticity of landing page template NFTs or to track the price of cryptocurrencies.

b) Business model

The Web3 lead generation service can generate revenue in several ways, including:

• The business startups only pay as they use in ADA by smart contracts. No up-front.
• Template sales: Designers can sell their templates as NFTs, through traditional subscriptions, or pay as the user goes.
• In-app purchases: Users can purchase features such as exclusive templates or designs.
• Token sales: Platforms can sell tokens to raise capital or to give users access to exclusive features.

Benefits:

• Business startups face less financial pressure when they begin identifying potential customers.
• Fairer compensation for designers: Web3 lead-capturing platforms can help designers earn a fairer share of the revenue from their designed templates.
• More control for designers: Designers have more control over their templates and can choose how it is distributed and monetized.
• Direct connection between designers and businesses: Businesses can directly support their favorite designers by purchasing their templates or NFTs.
• New market for landing page design: Web3 designing platforms create a new market for design, which can help designers reach a wider audience and generate more revenue.
• Decentralization: Decentralization helps to reduce the risk of censorship and ensures that designers and businesses have more control over their data.

c) AI-powered lead scoring

The main idea is to assign scores to all leads based on how their characteristics match the pre-established profile of a converted buyer. The leads that score above a specific threshold are considered ideal buyers. The bottleneck of such an approach is the determination of profile-relevant attributes with their respective weights. A decentralized AI-powered landing page generator is an attractive topic but we will propose it in the next round. In this proposal, we will build a decentralized landing page generator and focus on AI-powered lead scoring.

In Traditional (i.e., Manual) lead scoring, the task of finding the most relevant customer traits and points assignation is handled either by a marketing expert or senior sales department manager. The manager’s experience can help to determine the most optimal key attributes among thousands of possible features. Traditionally, scores are calculated based on how well the lead fits the company’s buyer portrait (demographic details) and their commitment (behavioral details). The above figure presents the architecture of a traditional lead-scoring system. Due to human nature, the sales manager cannot always define precisely what features are more important, his behavior can be biased or based on prior prejudices, he also tends to trust acquired knowledge and rarely updates the selection process.

Our proposed AI approach: Predictive Lead Scoring with AI assistance

The concept of predictive lead scoring is based on a statistical approach called

propensity modeling. This technique attempts to predict the chances that a potential buyer (visitor) will perform certain actions (purchase, reservation, etc.). By combining machine learning and data mining, it tries to forecast the behavior of the targeted audiences and the likelihood of successive conversions. Machine Learning (ML) algorithms can detect automatically the relations and hidden rules in historical sales data to pick relevant attributes and discover useful patterns that indicate a lead’s propensity to conversion. The constructed model is trained and evaluated to give the lowest possible false-positive predictions. If an error/anomaly is perceived in a prediction, it can be annotated and re-added to the training data which allows the model to adjust itself and stay relevant, especially in growing businesses. The below figure presents the architecture of a Lead Scoring System with AI assistance.

In simple, ML can assist with lead scoring in two ways:

• It can be used to improve the accuracy of the scores generated by AI.
• It can “teach” AI how to score leads.

To improve the accuracy of AI-generated scores, machine learning can be used to identify patterns in data that are not easily detected by humans.

• For example, a lead who regularly visits your website/landing page during work hours and reads articles about your product is likely to be further along in the buying process than a lead who only visited your site once. AI can take into account these types of behaviors to provide a more accurate score.

At the start, we use a public dataset, that has been manually scored by humans, and widely used in the lead's prediction process. The AI system will then use this data to learn how to generate scores that are similar to those created by humans. As more data is fed into the system, the AI will continue to learn and improve its ability to generate accurate scores. Second, dataset understanding is an important step in selecting relevant data points. The dataset contains several variables that cover the following aspects:

• The outcome of the Lead (Converted or not).
• Visitor actions on the website (e.g., pages visited, time spent).
• Pieces of information are collected through website forms (e.g., contact, newsletter).
• Lead source (search engine, referrer, direct).

The Dataset contains 9240 data points with 37 features. These features are properly stored for each prospect to describe its characteristics. Some features have numerical values (6 features), such as the website’s visit duration and visit frequency, others are categorical such as search keywords, lead source, and contact preferences.

Lastly, accurate prediction is our priority, so we select the machine-learning algorithm entitled gradient-boosted trees modeling based on its impact on sales performance.

[IMPACT] Please define the positive impact your project will have on the wider Cardano community

1) Positive impact in Cardano's branding

This project will not only have a positive impact on Cardano but also on the startup community, which is known for its dynamism and open-mindedness. This will lead to more users joining Cardano and accelerate its adoption.

1. This is the first decentralized lead generation service with AI-powered lead-scoring on Cardano.
2. We offer a solution to a common problem faced daily by both businesses and individuals – improving lead conversion rates in sales. It aims to simplify the process of creating effective landing pages and identifying potential buyers through AI-powered lead scoring at very low costs (compared with existing enterprise solutions such as Microsoft Dynamics 365, Oracle CX,..). They pay as they are consumed by ADA, not a yearly/quarterly plan as the existing centralized platform. It is expected to be used by thousands of SMEs and individuals so it will create massive utilization of the ADA token. It will accelerate the adoption of Cardano to enterprises.
3. Designers can sell their designed landing page templates directly to users to earn ADA and reward platform-minted tokens.

2) Positive impact in performance - the successfully of the project will be measured as following

• Onboard at least 5,000 non-crypto users through startups community ( BNI Vietnam).
• More than 20 startups will be onboarded and will lock 200,000 ADA into the platform's smart contracts then stake into VILAI pool -> Contribute to the network security and Total Value Locked.

[CAPABILITY & FEASIBILITY] What is your capability to deliver your project with high levels of trust and accountability? How do you intend to validate if your approach is feasible?

1). The Core team: The strong team possesses expertise and experience, along with the necessary skills, in both Cardano blockchain and the sales/marketing industry. Proven experience with Cardano projects.

1. Project Leader: Project Leader: Tuan Le (Ph.D.) brings 20+ years of experience in Digital Transformation, leading projects at Microsoft, Nokia, Vincom Retail, and TMS Group. He was CIO of Vincom Retail, the largest shopping mall chain in Vietnam with 80 brick-and-mortar malls.
2. AI Researcher: Dat Pham, he is a full-time Ph.D. candidate in the Institute of Information Technology, Vietnam Academy of Science and Technology. He has 20 years of experience in AI and Big Data.

2). Partnership

• To address the "bootstrapping" challenge, we're partnering with BNI Vietnam, a prominent local network of 9,479 SMEs and startups. This project presents a significant opportunity for both Cardano and BNI Vietnam if it's successful.

[PROJECT MILESTONES] What are the key milestones you need to achieve in order to complete your project successfully?

Requirements Gathering & Design - ( 1 month / 9,000 ADA)

Main tasks:

• All elements of the scope will be clarified and we will gather the data required to start the design and build. Preparing developer environments & infrastructures for implementation. This will include processes, data requirements, and much more.
• Getting the look and feel right ensures the most important information is where we need it

Deliverables/outputs/outcomes:

1. Project management: ADA 5,000

2. Project documentation: ADA 4,000

• Business Requirements Document
• Software Requirement Specification Document
• Function Requirements Specification Document
• UI/UX Mockup Document
• Use Cases Document
• Data Flow Document
• User Stories Document
• Marketing Action Plan Document

Build - ( 2 months / 25,000 ADA )

Main tasks:

• This is the largest part of the project getting everything built and deployed.

Deliverables/outputs/outcomes:

1. Build and deploy the Web3 landing page designing platform: 10,000 ADA

• 50+ pre-designed landing page templates for the retail & blockchain industry
• Drag-and-drop landing page builder website
• Blockchain-based smart contracts build & integration (Cardano).

2. Build and deploy AI-powered lead scoring: 10,000 ADA

• Dataset collecting and cleaning
• Train, Build, and Deploy the selected AI model

3. Other: 5,000 ADA

• Reports & Dashboards
• Open source code on GitHub
• Demo

QA/UAT Testing & Go live - ( 1 month / 5,000 ADA )

Main tasks:

• System testing & bug fixing
• Getting users's feedback/input and making required changes
• User guide build

Deliverables/outputs/outcomes:

1. QA and UAT Testing: 4,000 ADA

• Testing and bug-fixing reports
• Collecting community feedback via CSAT survey & report
• Video training on how to use
• User guide document with step-by-step instructions on how to use it as well as screenshots to help explain the concepts. 

2 . Completion report: Video and document: 1,000 ADA

[RESOURCES] Who is in the project team and what are their roles?

• Tuan completed his Ph.D in Software Engineering at Sejong University in Seoul, South Korea with many publications, was a Postdoctoral Researcher in Politecnico di Torino, Italy. He was with Nokia, and Microsoft, and now is Vietnam Managing Director for MediaMonks, a world-leading digital-first marketing, advertising & technology services company. Telegram: https://t.me/tuanle0810, Email: tuan.le@koet.com.vn Linkedin: https://www.linkedin.com/in/minh-tuan-le-9210448/
• Role: Developer & PM

• Minh Tran is an expert in UI/UX Design with more than 10 years of experience. This is his portfolio for reference.
• Role: UI/UX and Landing page designer

• MSc. Pham Thanh Dat received a B.S. degree in Computer Science from Hanoi National University, Viet Nam in 1995, and an M.S. degree in IT & Web from University Lyon 1, France in 2013. Now he is a Ph.D. candidate in the Institute of Information Technology, Vietnam Academy of Science and Technology. He has 20 years of experience in AI and Big Data.
• Role: AI researcher and developer

[BUDGET & COSTS] Please provide a cost breakdown of the proposed work and resources

• Requirements Gathering & Design ( 1 month)

Deliverables/outputs/outcomes:

1. Project management: 5,000 ADA

2. Project documentation: 4,000 ADA

• Business Requirements Document
• Software Requirement Specification Document
• Function Requirements Specification Document
• UI/UX Mockup Document
• Use Cases Document
• Data Flow Document
• User Stories Document
• Marketing Action Plan Document

• Build ( 2 months)
• Deliverables/outputs/outcomes:

1. Build and deploy the Web3 landing page designing platform: 10,000 ADA

• 50+ pre-designed landing page templates for the retail & blockchain industry
• Drag-and-drop landing page builder website
• Blockchain-based smart contracts build & integration (Cardano).

2. Build and deploy AI-powered lead scoring: 10,000 ADA

• Dataset collecting and cleaning
• Train, Build, and Deploy the selected AI model

3. Other: 5,000 ADA

• Reports & Dashboards
• Open source code on GitHub
• Demo

• Final - QA/UAT Testing & Go live - ( 1 month)
• Deliverables/outputs/outcomes:

1. QA and UAT Testing: 4,000 ADA

• Testing and bug-fixing reports
• Collecting community feedback via CSAT survey & report
• Video training on how to use
• User guide document with step-by-step instructions on how to use it as well as screenshots to help explain the concepts. 

2 . Completion report: Video and document: 1,000 ADA

[VALUE FOR MONEY] How does the cost of the project represent value for money for the Cardano ecosystem?

One of our core team members is pursuing a PhD course in AI and Web3 domain so we do not requested funds for Project Manager, AI researcher, developer and hardware ( servers for AI models), cloud infrastructure ( Microsoft Azure).

Below is our proposed rate card for local resources.

• 40$/hour for backend & database developer
• 45$/ hour for a mobile application developer
• 45$/hour for a smart contracts developer
• 30$/hour for QA/QC, UI, UX designer
• 50$/hour for solution architecture
• 40$/hour for Project Manager

1 ADA = 0.45$ ( Mat 13, 2024, Coinmarketcap).