qEngage :: Concept :: The problem and the Solution
This post deals with the core features we’ve envisaged for qEngage and the interface that we’ll be building as a part of building this framework. We also include details about our revenue model. If you’d like a very short summary of what we are — see this post.
The Problem:-
We’re tackling the problem of driving quality engagement and helping the growth of digital communities.
With ad fraud prevalent and spammy posts all over groups as they become large, it is very hard for admins to manually maintain the quality of a group while growing it. It is equally hard to grow a community in an organic way, without spending a large amount on marketting budget. We’re solving both of these problems.
Our Solution:-
Our solution to these problems is a SaaS platform in the form of a growth bot framework. We tackle both of the problems mentioned above in the following ways.
(a) This framework can be used to drive quality engagement in groups, and encourage users to produce quality content by utilising bounty distribution amongst quality content creators.
The way this works in a nutshell, is there is a certain bounty set by the group admin for each periodic interval, with tokens being distributed to each member of the group for tipping quality content. Members cannot accumulate these tokens, so it’s best they spend them. Quality content creators can receive these tokens from other members as they upvote and tip their posts. At the end of the periodic interval, members redeem these tokens in exchange for Fiat or other rewards, and are hence incentivised to earn these tokens. The exchange rate of token v/s fiat is determined by the bounty set by the admin, which allows for dynamic reward setting and content creation encouragement depending on the budget of the group admin (or owning business)
(b) Our solution also encourages organic growth of groups by utilising a referral bounty mechanism, wherein the group admin sets a particular bounty for the growth of group, distributed on the basis of successful referrals. The way this differs from traditional referral programs is that the earned quantity of tokens (from referrals), is based on the quality of the referrals, which is determined on the basis of how much quality content these referrals create. Hence it tackles the problem of unnecessary spammy growth in groups by utilising self governing smart contracts.
Our platform is governed by smart contracts and ERC20 tokens, that make payment integration seamless and allows for permission less execution of distribution of rewards when redeemed. In our case, we believe we want the code to be the law.
We will make money by (a) charging businesses for integrating our solution into their channels and(or) (b) taking a transactional cut of the bounty distribution and redemption.
The way our framework works is best illustrated with the study of an example. Let’s take a telegram group with 100 poets. Explained below is the functionality of our platform within this group.
Core Functions
Driving Engagement
Once our bot is added to the group, it will serve the following functions.
1. Allow for tipping other users and content
2. Allow for redemption of tokens by users at a specified date
3. Allow admin of group to distribute tokens to users everyday
4. Allow admin to set an exchange value between tokens and real value, called the bounty. (can be USD)
The BOT also does two things. It deducts any unspent tokens from user’s accounts at the end of the day (from those that were awarded). And it allows for accumulation of tokens gained from the user’s content being up voted or the user being tipped.
Example
Let’s say our group is called Poets, and users can up vote poems posted by other users. It has 100 members. Every day, the BOT will deposit 25 tokens to each of the 100 users in the group, distributing 25 * 100= 2500 tokens in all. The users can spend 25 tokens in a day on up voting content and other users. Any unspent tokens out of these 25 tokens will be deducted at the end of each day, making the net tokens received from admin to 0. Users can accumulate tokens based only on the tokens they receive from being up voted or tipped by other users, and can redeem them at the end of the month. The bounty in this case, is set to $1000 USD
Now, let’s say Alice posts a poem to the group. Bob is someone who likes the poem, so Bob tips the poem 5 tokens. Bob’s new balance for the day is 20 tokens now. Let’s assume Bob doesn’t like any more content, and does not spend any tokens for the rest of the day. The BOT will then take away 20 tokens at the end of the day, leaving Bob with 0 tokens. Bob thus has an incentive to spend it all, as the number of unspent tokens is always going to go to 0 at the end of the day. This way, Bob is incentivised to tip and up vote valuable content, and not accumulate tokens. If you think about it, it’s kind of like time. Each day we are given 86,400 seconds, and we lose them all at the end of the day. What we spend them on depends on us.
How do users benefit from this then? Well, Alice had 25 tokens in the beginning, now she has 30 tokens. The BOT takes away only unspent tokens from the 25 tokens, leaving Alice with 5 tokens at the end of the day. Alice is thus rewarded with 5 tokens. She can keep accumulating these and redeem them at the end of the month for a prize. She is thus incentivised to create good content.
Bounty Distribution
The admin of the group can set a bounty for the end of month rewards distribution. In the above example, let’s suppose that the admin has set the bounty equal to $1000 USD. And total tokens redeemed across all users in the group is 250 tokens [tokens accumulated by users]. Of these, Alice has managed to accumulate 25 tokens in a month. Now as per the bounty, the value of the token is 1000/250 = $4 per token. So , here Alice will get $4 * 25 = $100 at the end of the month.
This does two things.
(a) It allows the admin to set a spending budget according to their capacity
(b) It encourages users to post more quality content
The more the budget, the more quality content people are incentivised to produce. If you think about it, it kind of acts like advertising budgets, except much more quality oriented.
Tackling Exchange Problem
There is an issue here. What if 2 users decide to simply up vote or tip each other continuously to facilitate accumulation? Or may be 3 users form a ring? While it’s easy to spot such fraud in small groups, it might not be possible in large groups. We provide two ways to avoid this issue.
- Allow admin to restrict bounty distribution to suspected groups
- Have algorithms in place that detect such bi-directional or triangular exchanges.
This problem exists even in the real world, where we see ads being clicked by users in exchange marketplaces. We intend to solve this problem in a similar way that advertisers solve them.
Driving Growth
Growth based sample is very easy to understand. As mentioned previously, it takes the form of a referral campaign, wherein a smart contract tracks the amount of referrals a user makes.
Once this is done, each of the referrals is assigned a score. The score is computed on the basis of tokens earned by this referred user at the end of each billing cycle. The higher the tokens the referral earns, the higher is his or her score.
At the end of the billing cycle, the referrer is awarded a part of the referral bounty tokens set up by the admin, in a similar mechanism as that explained above.
To take an example, let’s assume that the Referral Bounty is RefBounty, and there is a user, Todd, who has referred 3 users, U1 , U2 and U3 to the group. These users have earned a score of S1, S2 and S3 at the end of the first billing cycle, which is directly proportional to their redeemed tokens at the end of that billing cycle. The overall score of Todd’s referrals (referred as Todd’s Referral Score) is then TRefScore = U1*S1 + U2*S2 + U3*S3 .
Similarly, let’s say Emily has referred Users U4, U5, U6, and U7 and their associated scores as S4, S5 and S6 and S7 respectively. Emily’s Referral Score is then given by ERefScore = U4*S4 + U5*S5 + U6*S6 + U7*S7
If we assume that there are only two users in the groups who have referred other users, the RefBounty is then split amongst Todd and Emily as follows.
Todd’s Referral Earnings =
(TRefScore / (TRefScore + ERefScore)) * RefBounty
Emily’s Referral Earnings =
(ERefScore / (TRefScore + ERefScore)) * RefBounty
The admin can keep the referral program running as long as he or she wants, and set the Ref Bounty according to their wish. This can also be done using the bot using simple commands such as set_ref_bounty and set_ref_bounty_period
Revenue Model
For smaller groups, we will take a cut out of the referral and reward bounties, meaning we take a cut each time a user redeems their earned tokens.
We are focusing on the SaaS business model for larger groups, charging businesses for integrating our framework into their channels. This is cost effective for large budget groups as it is a subscription fee irrespective of the size of the group (for now).
Another option, which makes sense for smaller budget groups,is us taking a cut of the bounty set by the admin, at each redemption date. So let’s say the bounty set is $1000, we’d essentially take 10% of it, and leave the other 90% for member token redemption, at each redemption date. More detailed analysis coming in an upcoming post.
Conclusion
We are hopeful that the solution we are building will be a revolutionary product in terms of growth marketing amongst groups. We intend to build across LINE, Whatsapp, Reddit etc, but have decided to go with Telegram first.
Stay tuned and follow us on twitter:- https://twitter.com/qEng_GrowthBot
