Mava’s mission is to accelerate the world’s transition to web3.
What are some of the barriers currently holding back web3 mass adoption? User interfaces that are clunky and hard to use, coupled with a lack of customer support. Have an issue? Good luck reaching out to the relevant team member and getting a quick and accurate response. For support teams too, the flood of incoming messages from different platforms combined with dozens of spam messages and DMs can be overwhelming and it is hard to keep track of what’s going on. To solve these problems, Mava has built the first web3 customer support platform.
Omni-channel customer support
Users expect to be able to reach support from wherever they are and studies have shown that companies with strong omni-channel strategies have a significantly higher retention rate. However, allowing users to contact customer support across multiple different channels (e.g. Twitter, Discord, Website, Telegram) comes with its challenges: it’s hard to keep track of issues, productivity goes down because of constant context switching, and organizations cannot get access to any customer support metrics as none of the sources are connected.
Mava connects channels such as Discord, Telegram, Twitter and web in one powerful and secure dashboard. Support agents can answer questions from one place, assign statuses, tags and categories and collaborate with their team members. Community and customer support managers can get access to relevant analytics, such as how their team is doing and what topics are asked about most.
Creating a safe environment and reducing scams
Security is a primary concern for many in the web3 industry. One way scammers attack users is via pretending to be a support agent and getting personal details that way. Web3 communities that don’t use dedicated support tools often handle most questions in a public support channel, which opens up an avenue for scammers to “help” and message unknowing community members. Similarly, if it’s hard for users to find out how to reach support, they might resort to publicly tweeting their complaints or questions, which usually results in them getting targeted with comments and DMs from fake support agent.
Losing money and trust is a surefire way to lose customers and the reputational damage can be hard to bounce back from. Using dedicated support tools lets communities set up an official and trusted channel where users can reach the team. This helps build trust and a more secure experience - which is especially important for newcomers.
Personalize the customer support experience
A user with little experience with decentralized finance (DeFi) might struggle to set up a wallet or connect to the right network or chain. On-chain integrations and insights enable web3 organizations to target users with relevant messages to help them onboard and get started. For instance, think of messaging a user via a chat widget the first time they enter your website and haven’t yet connected a wallet.
On-chain integrations will also help to resolve an issue if users reach out and ask about, for example, a problem they experienced with a recent transaction. Today the flow might be something like this: a support agent asks a user for their wallet address, waits for an answer and checks, manually reminds them again if they haven’t replied, they then check it out in a block explorer and help the user, losing time with each action. Mava is building on-chain integrations, so web3 support teams can access all relevant data at their fingertips, enabling a faster and more efficient support experience.
Implementing a support system is a no-brainer for organizations looking to take their customer experience to the next level. It enables teams to automate and work more efficiently, stay on top of issues and get insights to improve their support. While generic customer support systems will help in certain areas, using a web3 specific tool, such as Mava, ensures that you can provide consistent and secure support across all social channels used by web3 communities and view on-chain data to help answer support questions with the relevant data at hand, while remaining privacy conscious way.
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