Footprint Analytics is a platform that analyzes blockchain data to facilitate its discovery and visualization. The product prioritizes user experience and offers an intuitive, drag-and-drop interface for interactive data queries. Footprint offers API and visualization tools to uncover and visualize data across the blockchain, including NFT and GameFi data. It gathers, parses, and cleans data from over 20 chains to create structured and semantic tables. It enables users to generate visualizations and dashboards without coding, utilizing a drag-and-drop interface and SQL. Although blockchain data is accessible, clients still require infrastructure maintenance and expertise to construct data pipelines. Footprint offers a practical and seamless solution, thereby removing these obstacles.

Background of Footprint Analytics

Footprint Analytics, established in June 2021, is a platform designed to uncover and display blockchain data. The organization is legally known as Vantastone Technology PTE.LTD. and is headquartered in Singapore’s Central Region. Navy Xie (Co-Founder & Chief Executive Officer), Wade Deng (Co-Founder, Chief Technology Officer & Chief Open Source Officer), Tony Zhang (Co-Founder), and Water Jiang (Vice President of Finance) are among the executive team members of Footprint.

Vanstastone comprises a team of DeFi enthusiasts with various backgrounds, including smart contract developers, consumer internet product designers, investment administrators from the banking industry, and risk modeling practitioners. Footprint Analytics has 11 investors, including Epoch Capital, Youbi Capital, SevenX Ventures, and Waterdrip Capital. Footprint Analytics has raised $4.2M in funding in two phases. On May 26, 2022, the organization concluded its most recent funding round, a seed round.

Footprint Analytics Main Features

Footprint Data Room

Footprint Analytics’ Footprint Data Room is a product that provides users access to more than 30 structured and straightforward blockchain data sets from the GameFi, NFT, and DeFi sectors. The data room allows users to access a diverse collection of abstracted datasets, thereby enabling next-generation applications to develop and facilitate uncomplicated data exploration wherever necessary. Footprint Analytics endeavors to offer a solution that is both efficient and intuitive, eliminating the challenge of developing customized solutions to maximize data utilization. The company aims to use infrastructure maintenance and expertise to construct data pipelines.

Footprint Analytics

Footprint transforms raw datasets that are challenging to comprehend into on-chain data visualizations without writing a single line of code or performing SQL operations. Users must click to add a new chart to utilize the drag-and-drop features. Footprint’s interactive drag-and-drop analysis approach enables users to use blockchain analytics rapidly. The intuitive interface offers infinite data clues, fostering creativity, curiosity, and data-driven decision-making.

Footprint Growth Analytics

Footprint Growth Analytics is another Footprint Analytics product that assists in the efficient expansion of GameFi and any Web3 initiatives. Footprint Analytics offers a comprehensive analysis to facilitate development using Footprint growth analytics. It seamlessly integrates Web3 and Web2 data with automated workflows. The Footprint Gwoth Analytics initiative aims to efficiently deliver timely data and zero-code analytics to media and analysts. The product has over 5 million consumers, 470,000 unique active wallets, and 3458 token holders.

Footprint Growthly

Growthly is a white-labeled Telegram quest tool that is customizable and requires no code, allowing Web3 initiatives to engage and expand their communities rapidly. Growthly transforms the Telegram mini-app into a dynamic center of user activity through its intuitive interface and rapid integration. Growthly offers a vast collection of customizable quest templates intended to captivate the project’s audience while maintaining the project’s distinctive brand identity. Web3 projects can effortlessly initiate quests in minutes, customize the UI to align perfectly with the brand’s aesthetic, and select from various engaging quest types with Growthly. No coding is required.

Footprint Dashboard

Footprint facilitates creating and managing users’ personalized interfaces by enabling them to fork any open analytics table on the platform with a single click. Users can locate their required dashboard by specifying their role, topic, and query type. Users can promptly replicate and discover new inspiration for their analysis by utilizing preserved visualization best practices other users have developed. Users can discern the narratives that underlie the data through diverse and comprehensive data tables. With a single click, users can perform personalized analysis and incorporate their data sources on top of the existing charts, thereby identifying trends and patterns.

Footprint API

Footprint assists projects in establishing a foundation of trust in data and individuals by enhancing the community’s culture of learning, sharing, and collaboration to develop a data-driven community that facilitates the project’s expansion. Community volunteers or project stakeholders do not have to duplicate data analysis tools.

Footprint’s Benefits

Footprint offers a plethora of datasets that foster curiosity, creativity, and data-driven decision-making, enabling anyone to begin using blockchain analytics rapidly. It also efficiently replicate and discover new inspiration with hundreds of displays created by the community. The notable benefits of the Footprint platform include the following:

Coding is not necessary

The drag-and-drop user interface of Footprint simplifies the process of examining blockchain data. There is no requirement for SQL queries or coding to investigate blockchain data; anyone can identify and communicate actionable DeFi insights.

Data-driven storytelling

Footprint solution enables users to conduct a market analysis and present their findings, regardless of their familiarity with blockchain. There are numerous chart varieties to select from with a single click, such as fork infographics. Footprint offers various data analytics templates designed to facilitate the creation and management of personalized interfaces. These templates can support forks with a single click on any open analytics table on the platform. Additionally, users can distribute their data tables and dashboards to their social media platforms or partners.

Footprints Prep

Footprint finalizes the data cleansing and integration. Community proposals enable users to acquire additional data at a quicker pace, thereby facilitating data updates.

How Does Footprint Work?

Footprint has created a model that aggregates this raw data and indexes it meaningfully. After separating the information by domain, the data model determines whether millions of transactions fall under the GameFi, NFT, DEX, or other categories. The platform then decodes this data to enable analysts to search for the required information, such as block time, TVL, and token price, and promptly present the data on a chart. Wallet addresses, chains, NFT collections, and other meaningful categories are used instead of sequences of numbers and letters that are indecipherable to the majority.

The Footprint platform utilizes three data sources:

• on-chain data, including DeFi, NFTs, GameFi, and other types
• off-chain data, such as specific token prices
• the community uploaded data

Footprint Application

Charts

Footprint is a user-friendly analytics application that does not require prior knowledge of programming languages, SQL, or other technical aspects. However, there is an option for users who prefer to maintain the code, wish to generate more intricate queries, or want to perform query optimizations. Even though Footprint’s volumes are immense and necessitate a highly sophisticated technical solution to guarantee adequate performance in processing queries, Footprint is powered by a relational database. SQL is employed to retrieve data from the relational database.

Sample of No-code. Source: Footprint

For the SQL mode, Metabase serves as the foundation for Footprint’s visualization and plotting capabilities. Metabase is architecturally designed to ensure that any chart generated in an intuitive user interface is initially converted to SQL and subsequently transmitted to the server.

Sample of SQL. Source: Footprint

Dashboard

In Footprint Analytics, a dashboard is a grid-based compilation of text boxes and charts. It displays the most recent data to assist users in making well-informed decisions. A dashboard can contain a variety of chart formats. The following are some fundamental instruments that can be employed in creating a new dashboard:

• Select the “Create” icon on the screen’s upper right-hand side.

• Select “New Dashboard” from the pop-up menu that appears.

• Select the “Add chart” button to launch the Dashboard editor in a new tab.

• The right-hand side of the screen displays the “Add Chart” tab, which provides various options for rapidly adding charts.

• Click on the +Add icon next to each chart to add it to our dashboard one at a time.

Users can also include charts in the “My charts” tab. This will retrieve and exhibit any charts generated in their account.

• Users must ensure that they select the “Save” option.

Templates (forks) and Design

It may be challenging to begin designing from scratch, but concern is unnecessary. Users can modify a few settings or filters using an already-finished Footprint template (fork). They can rapidly transform the completed charts into their own, usable charts. It is effortless to implement these Dashboard Templates; all that is required is for users to modify the protocol name in the filter field and modify the template to their liking.

Sample of Dashboard Template. Source: Footprint

Sample of Updated Template. Source: Footprint

Aside from modifying existing dashboard templates, users can design a new dashboard. After the dashboard is prepared, you can begin designing a dashboard to enhance its appearance and organization. This can be achieved by adjusting the positions and sizes and adding a text box, visualization options, and a filter.

Adjusting positions and sizes. Sample: Footprint

Adding Text. Source: Footprint

Adding Visualization. Source: Footprint

Visualization

Data visualization is a highly effective method of conveying the graphic representation of data by employing a variety of charts, tables, bar charts, and pivot tables. Users can choose any visualization display for their purposes. The proper display depends on what users want to illustrate. Other Footprint data visualization choices include number and trend, line and row race, dy amic pies, sunburst, and treemaps.

Line Charts

Line charts plot sequences of data, such as time or process stages. Line charts represent time series, with the x axis showing time and the - axis showing metric values at each point.

Tables

Tables are helpful for users with several fields. Footprint Analytics’ table visualization is feature-rich. Users can add, remove, and update columns to their table to better represent data after generating it. Users can also apply conditional formatting to the produced table by adding rules to change cell colors under particular conditions. Clicking on each column’s gears symbol under the Columns tab in Visualization Setting lets users adjust its settings.

Bar Charts

Bar charts are helpful for categorizing numbers. Bar charts let users quickly compare data sets. The first axis lists the groups, while the second counts them. Users can show charts differently based on their purpose. A stacked bar chart may be implemented when users visualize data that can be partitioned into numerous categories within a single entity. Users can include a goal line that delineates the desired position of their numbers. Users may also elect to display values on data points, which show the numbers of each bar above its corresponding bar.

Pivot Tables

A pivot table is a data visualization tool that allows users to rotate (“pivot”) the columns to observe the summaries in various ways. It summarizes the rows and columns of a table. The summary rows are typically grand totals or subtotals; however, they may also be other metrics, such as summations and averages. Additionally, users can collapse and expand divisions in groups.

Footprint Products Ecosystem

The Footprint product ecosystem is intended to assist users in obtaining insights and optimizing their marketing strategies. Growth Analytics, Analytics APP, and Data API comprise the ecosystem.

Growth Analytics provides a comprehensive solution for collecting and analyzing data from various Web2 and Web3 sources. With its robust analytics capabilities, users can monitor key performance indicators (KPIs), identify trends, and gain a competitive advantage in their marketing strategies.

Analytics App is a crypto industry-specific analytics platform that eliminates the need for coding. With its drag-and-drop interface and real-time on-chain data, it is effortless to create visualizations and dashboards that exhibit innovative blockchain market insights without the need to write any code. SQL queries can also be employed to explore data further.

Data API is a unified data API compatible with all main chain ecosystems and designed for NFTs, GameFi, and DeFi. It supports raw data and statistics metrics, enabling you to effortlessly access and utilize the required data with a single line of code.

Combining these three products creates a potent ecosystem that allows users to optimize their marketing strategies, acquire a competitive edge, and make data-driven decisions.

Conclusion

Footprint is an analytics application that is user-friendly and powerful, capable of revealing and visualizing blockchain data. Footprint allows users to upload their data and effortlessly analyze the data on the chain without the need to write SQL with Footprint, even if they lack programming expertise. The platform facilitates the integration of on-chain and off-chain data analysis and cross-chain analysis. Users can rapidly comprehend the intricate data on the chain due to semantic data.