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Product Overview

Monnai is an API-based B2B solution suite that provides consumer insights for fintechs across the globe. Their primary markets are the emerging fintech markets of India, South-East Asia and LATAM, where they have a stable customer base and have currently achieved PMF with their solutions.

Monnai's API solution provides state-of-the-art, real-time intelligence powered by alternate data - we aim to bring inclusive financial coverage to all sections of the population.

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For the purpose this assignment, we will consider a flagship product offering within Monnai - Monnai Risk Engine.

What does Monnai Risk Engine do?

Monnai Risk Engine is an offering that aims to ease onboarding & credit decisioning through customizable rules & ML-driven scoring models.

The product aims to unlock access to financial instruments for all segments of the population through its tailored solution platform.

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A snapshot of the product dashboard displaying masked user information with Risk Score -

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Value Proposition(s)

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User base

Who are the users?

The primary customers of Monnai's Risk Score are fintech & lending platforms in India & Southeast Asia, with a smaller presence in LATAM, Americas & Europe.

What do they feel about the product online?

Unfortunately, being a B2B business focused on company outcomes rather than individual outcomes, online research does not reveal a lot about what Monnai customers feel about the product.

A snapshot of Google search results for the company -

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Going to the company blogs & news, we primarily see news of funding & not much direct market insights are available -

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However, by talking directly with customers through Zoom/GMeet calls, phone calls & Slack discussions - we have been able to narrow down our user base into a set of ICP's which can be chosen for the exercise.

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Ideal Customer Profile

ICP 1 - Product Leader @ Payment Fintech

Our first ICP is Shreya, a Product Lead at a Unicorn Fintech. Below are her detailed attributes -

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Key problem statement

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Jobs to be Done

• Want to identify riskiest customers at on-boarding so we can add more guardrails
• Want to identify my most trustworthy customers so we can customize better rates & offers
• Want to provide access to services for all segments of the population.

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ICP 2 - Head of Compliance @ Tier II Lending Firm

Our second ICP is Samir, Head of Compliance @ Lending firm focused in Tier II, III cities -

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Key problem statement

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Jobs to be Done

• Want to stop fraudsters from entering the customer platform
• Want to ensure good users are not affected by the fraud detection systems put in place

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ICP 3 - Growth Leader @ Identity Services Enterprise

Our third ICP is Rishab, Growth Leader @ Identity Verification Channel Partner -

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Key problem statement

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Jobs to be Done

• Want to increase customer usage of my platform by leveraging Monnai's services
• Want an explainable & attractive solution that I can evangelize to my customers

ICP Prioritization

Using the ICP Prioritization Framework, we arrive at our final ICP's to target for this asignment -

1. ICP 1 - Product Head @ Payment Unicorn
2. ICP 3 - VP of Growth @ Identity Leader Firm

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Market Analysis

Total Addressable Market

For the scope of this assignment, we will view the company's potential under the B2B2C lens in India alone. This is because Monnai earns revenue based on the number of customers/users that get on-boarded to their customer's platforms, hence indirectly tying their market to the needs of the end users.

In that regard, looking the core value that Monnai can offer to fintechs, we need to estimate the number of users who are likely to start availing online financial services over the next 5 years.

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We consider the below factors -

1. Increasing internet and smartphone penetration: India's internet penetration is expected to reach 70% by 2025, and smartphone adoption is already around 75%. This creates a large potential user base.
2. As of November 2022, UPI boasted over 300 million monthly active users in India. UPI transactions have witnessed a phenomenal CAGR of 147% in volume and 168% in value between FY 2017-18 and FY 2022-23 [Press Information Bureau, India]
3. A CAGR of 60% growth in UPI users was seen between 2023 to 2024, which is expected to continue over the coming years.
4. Extrapolating this, we add a conservative estimate of 30% CAGR over the next 5 years for users to get into digital banking, bringing the user base to 590 million. For ease of calculation, we can consider this number to be 600 million users.
5. We will add a probability of 50% of these users transitioning into other fintech & online lending platforms, bringing potential market size to roughly 300 million users.
6. These users may on average access at least 2 other platforms bringing us to a TAM of 600 million potential new user on-boardings.

Servicable Addressable Market

In conversations with customers, most of the banking & fintech industry is currently not leveraging the alternate data insights as a major factor in their on-boardings.

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Considering the available market of 600 million new user on-boardings, Monnai will only be able to capture a proportion of these due to -

1. Roughly 30-40% of the TAM users who have likely already on-boarded on to different platforms, bringing down the available whitespace by at least 30%.
2. Financial services which have built comprehensive on-boarding models already which will see no major overhaul in the next 4-5 years (<5%)
3. Financial services which choose not to use alternate data for on-boarding - We estimate this number to also be <5%, due to the vast & rich nature of the data & its effectiveness in credit decisioning.
4. Financial services which choose only specific cohorts of on-boarding users to go through Monnai - This could cause between a 10-15% dip in volumes, considering the larger pie of available users.

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Considering these limitations, we will take a reasonable ~50% of the market, bringing a SAM of 300 million new user on-boardings.

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Serviceable Obtainable Market

The SOM for this product will depend on the market-fit between value prop offered by Monnai & the customer need within the near future, given the available competitive landscape.

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Competitive Landscape

Monnai's competition comes from three different types of companies -

1. Traditional credit bureaus - The likes of Experian, CRIF & CIBIL already provide coverage to a large section of the population. However, these scores are unavailable for new-to-credit & unreliable for limited credit consumers.
2. Direct competitors - Companies such as Bureau.ID, Karza & Signzy operate in the decisioning space, offering similar or complementary solutions to banks & fintechs.
3. Data providers - There are a number of alternate data providers in the market, who provide specific data points that companies can directly integrate with. However, Monnai's intelligence & aggregation layer acts as a USP that is tough to replicate.

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Based on current user feedback & inputs from sellers, we find that at least 8 out of 10 accounts reached (customers) are open to services from Monnai's suite, helping us validate a need for the service.

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Based on this, we will take 60% of the SAM to arrive at a SOM of ~180 million new user on-boardings.

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Monnai generates a revenue of anywhere between $0.1 to $0.4 for each of these user's on-boarding transactions, with a potential to generate further revenue during the user's lifetime with the company. For this case, we will consider the on-boarding revenue alone.

Attributing an average revenue of $0.2 per transaction, we get -

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Serviceable Obtainable Market** = $36M

**Considering Indian market alone for current analysis

Core Value Proposition(s)

Based on the ICP's and the available value propositions for the product, we can narrow down on the below Core Value Propositions that will go into the Acquisition channel messaging -

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Acquisition Channels

Channel Decision Framework

Product Stage - Early Scaling

​Target ICP's - ICP 1 & ICP 3

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Organic

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For the product nature (B2B API-based solution) and target ICP's (Product & Growth leaders), Organic is a low-impact channel.

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Paid ads/Sponsorships​

This is a strategy that Monnai has not invested in so far, but is generally a high impact & ROI channel observed amongst competitors.

Targeted paid ads & sponsorships are a good strategy to identify & target our ICP's as they solve for -

1. Reach - A good number of top executives can be targeted via this channel
2. Flexibility - The targeting mechanism can be as small/big as required, and used as required
3. Targeting - Targeting of our high-profile ICP's is a big challenge that ads & sponsorships within relevant channels can solve for.

​Partner Program​

Partner programs is a strategy that Monnai has already adopted with a few different partners -

1. Channel partners - Sumsub, Yubi etc
2. Bureaus - CIBI (Philippines), CTOS (Malaysia)

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While this is a proven strategy, it has not been designed to take the fullest advantage of Monnai's offerings & the complete synergies have not been explored. We will see how the same can be achieved.

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​Product Integration​

Product Integrations as an acquisition channel has not been explored by Monnai very deeply so far. The nature of the product ensures that integrations within customer's user flows are already available - however, using this as an acquisition channel does not help target our chosen ICP's.

Further, we will achieve integrations with our targeted partners via the partner program approach, which serves Monnai's use-case well.

Conclusion​

We will hence choose the below 2 channels for our experiments -

1. Paid ads
2. Partner program

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Experiment #1

Paid advertising

In this experiment, we will run a paid ads program that generates in-bound leads & helps build publicity for the product.

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Our first step is to identify the strongest PMF & ICP for the product - this is where our right to win is.

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For this, we will choose our ICP 1 - Product @ Payment Fintech as a target. In this case, we will not further optimize for factors such as gender or hobbies, and instead focus on the keys to unlock this channel.

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Channel selection - LinkedIn Ads

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Audience - Middle & upper management executives aged 30+ in Finance, Banking & Fintech companies

This can be achieved through LinkedIn's ad targeting mechanism -

Geography - India, to start with. We can hyper-target with Bengaluru, Mumbai if required.

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Target company industry & size filters will be applied

Industry - Finance

Size - 500+

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To match with our ICP, we can choose job experience of 10+ years and mid-senior to senior levels.

Further, we can optimize on member skills to be in Product, Compliance & Business Development.

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We will not optimize for gender, but optimize for ages above 30. The remaining attributes do not need further filtering.

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The goal is also to avoid hyper-targeting in this experiment, so as to put the word out on the company & get as many leads as possible.

Ad creative sample #1

The messaging is clear and succinct - "Monnai provides a better way to on-board and know your customers, so that you can identify the safe users while eliminating risk."

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Ad creative sample #2 -

The messaging here - "Monnai helps you make better decisions for all segments of the population, ensuring we do right by both you and your customers."

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Experiment #2

Paid ads with a flavour of Product Led Growth

For this experiment, we will continue to use similar channels, but instead go for targeted Account-Based Marketing.

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On top of this ABM strategy, we will attempt to create a mini PLG motion where the user gets to experience the product before they move into the Sales cycle.

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Step 1 - Monnai will identify a set of leads & potential companies for the platform, which can span across geographies in India, Indonesia, LATAM. The idea is to identify qualified companies which have a need for the product, but are not yet engaged or working with Monnai on the same.

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Step 2 - Using LinkedIn Company Targeting, we implement an ABM mechanism that displays targeted ads across a list of prospects using a list.

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Here, since we have already shortlisted the prospect list, we only need to ensure fitment within our ICP #1, ICP #3 (chosen ICP's). For this, LinkedIn's Job Experience/Demographics filters can be applied on top -

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Step 3 - Once we have this targeted strategy ready, the task is to create a good creative that tempts the user to actually try the product directly.

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Step 4 - With a CTA on "Try now", the page will lead them to a direct sandbox portal, where Monnai will control access for users who arrive through these links only.

In this page, users will have 2 credits which they can use to check email & phone number of any user, which will give them a masked response with blurred details.

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(Mockup for reference, this implementation will need to have a lot more finesse)

Step 5 - User will ideally try to use their 2 credits & check for the risk categories & recommendations out of curiosity. On the third attempt, the page will prompt them to contact Monnai for a POC & proceed with the Sales cycle.

CAC to LTV calculations for Paid ads channel​

Ad Budget

Monnai has a current MoM run-rate of $100-150k, giving us a forecasted year-end revenue of $1.2M to $1.8M.

As a rule of thumb, we will limit the marketing budget to <5% of revenue, bringing us to a ceiling of roughly $60,000 across all channels.

Considering each channel as an experiment, we will stick to 20-25% of total budget, bringing us to $12,000 to $15,000 budget for the paid ads channel.

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LTV​

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The targeted ICP typically represents a mid to large-sized fintech/financial services platform, which typically provides an ARR of $120k to $200k.

Calculating an average retention period of at least three years, and considering the lower end of expected ARR, we can attribute LTV of $360k for any on-boarded customer.

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Conversion funnel

The typical conversion rate for Monnai from lead-generation to contract looks like this -

1. Initial Response Rate on cold reach-outs (70% drop-off) - 30%
2. Move to POC Stage (up to 10% drop off) - 20% of total
3. POC Successful (up to 5% drop off) - 15% of total
4. Negotiations Stage (no drop-off) - 15% of total
5. Contract signed (up to 5% drop-off) - 10% of total

With LinkedIn ads, a conservative TOFU (Step 1 above) conversion rate of 2-5% can be expected, based on available market statistics & trends, giving us a final stage conversion of 0.5 - 1% of the targeted base.

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If Monnai targets anywhere from 1000 to 2000 executives** via the paid ads strategy with the above conversion rate, we would end up with a rough CAC of $1200 - $1500.

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CAC to LTV ratio is upwards of 300, making it a highly viable strategy if done right.

**For more broadly targeted campaigns, we can expect a lower conversion rate. However the CAC to LTV is expected to be highly favourable considering the potential deal size & LTV of acquired customers.

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Experiment #3

Partner program

Monnai currently has integrations with a set of partners, who drive usage of the product with their customers & integrators.

Some of the partners include -

1. Channel partners - Sumsub, Yubi, TrustDecision
2. Bureaus - CIBI (Philippines), CTOS (Malaysia)

However, we will aim here to craft a strategy here to partner with an Indian/International Credit Bureau to supplement their current offerings with Monnai's alternate data score.

Partner Fitment Test

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Based on these, we consider the below 3 bureaus as potential partners for this engagement -

1. Experian
2. Equifax
3. CRIF

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While Experian & Equifax have experimented with alternate data in other markets, Monnai's positioning & coverage makes us an excellent choice to work with in Indian market, with potential expansion into SEA markets.

How will the partnership work?

We will define a clear pitch based on the below CVP that aligns well with the goals of these potential partner organizations -

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User Flow​

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The below user flow will be defined for Monnai Risk Score -

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​How does the partner benefit from this?

By leveraging Monnai data & insights, these partners can enhance their coverage beyond consumers with traditional credit histories, potentially helping their customers accurately on-board up to 30% more consumers.

They can enhance their offering for the thin-file segment, while adding a completely new stream of revenue from the new-to-credit consumers, thus significantly enhancing their own value proposition to banks & financial institutions.

Further, due to the potential higher volumes of transactions that this unlocks for Monnai, the partner will get discounted rates at which they can access this data, and improve their overall value offering with minimal impact to their bottom-line.

How does Monnai benefit from this?

Monnai gets direct access to financial institutions who can make the maximum utilization of the company's core value proposition.

Further, this enhances brand value significantly, while bringing in additional revenue from across the segments that the partner unlocks for the product.

Sample outreach e-mail

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Dear Mr. Manish,

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My name is Deepak Shravan, and I lead the Risk Scoring Product & Partnerships at Monnai India. I'm excited to bring you a potential collaboration opportunity that can unlock credit for the underserved population, while bringing huge tailwinds to our combined business.

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At Monnai, our expertise is alternate data aggregation, and we have spent the last two years testing and building world-class intelligence on top, which reflects in our Scoring Capabilities for Credit Risk Assessment.

We aim to solve for credit risk in the new-to-credit and thin-file segments with a >99% data coverage for these consumers, alongside the ability to predict & separate creditworthy & delinquent consumers at a >80% accuracy in these segments.

I would love to discuss a potential opportunity with Experian where we can collaborate, explore our synergies and forge a successful bond - please let me know of a convenient time in the coming weeks for us to discuss the same.

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I look forward to your response.

Thanks & Regards,

Deepak Shravan

Product & Partnerships

Monnai

deepak@monnai.com

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Conclusion

With that, we come to the closure of Acquisition for Monnai. We have covered all facets of Acquisition, right from ICP identification to Market Analysis to Acquisition channels, where three different experiments were explored with high potential for success.

Thank you for reading!

Deepak Shravan K S

Senior Product Manager

Monnai

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