Welcome to our new blog!

Here we will talk about the key topics that are making and shaking the funding ecosystem! This blog will not only spotlight the innovation, but also dive deep into challenges that we are facing towards our vision of enabling entrepreneurs and their businesses, and making capital deployment more streamlined and informed for funders!

Disclaimer! Level is a “built by operators” software product, thus I am writing about first-hand experiences both as an operator providing capital to African SMEs, as well as a software-as-a-service provider. For more details, see our “origin story” linked here.

We have a long list of topics we want to talk about, as you can see below. Feel free to write to us directly about which topics you would like to hear first!

For the first blog post, I would like to start with the basics, namely “The four key outcomes of operational excellence when setting up a funding process”:

• Consistency

• Learning

• Quality

• Efficiency

For most of you this will be your daily bread and butter, and hopefully, you can relate. This is meant to be a building block for my future blogs, diving deep into underlying challenges, with the aim of sharing insights and how we are addressing these solutions with Level.

Why are these so important?

Consistency

Having consistent processes (i.e. SOPs, Weekly calls, Quarterly OKS, Investment Committees, etc) reduces risk and allows you to systematically improve. But let’s be honest, who has ever started an operation with all SOPs in place and running smoothly? I would bet no one! Every funding operation has a set of variables that makes it unique. Also, you often don’t know your blind spots before you get surprised by them! Therefore, building a consistent operation requires a customized approach to your workflows.

Example: Payment reconciliation

At Uncap, one example of consistency challenges surfaced from our attempt to lower the barriers for entrepreneurs to make repayments, in the context of our RBF funding instrument. When we added payment gateways in addition to traditional bank transfers, including mobile wallets, we failed in immediately establishing accounting SOPs to reconcile repayments coming in through different channels. This, in turn, caused us a lot of manual work to reconcile these payments, as well as to understand the position of each portfolio company and their outstanding amounts. On the other hand, establishing the respective SOPs also gave our tech team a blueprint to build automation to reconcile repayments in real-time, providing the portfolio management team with actionable insights.

Learning

Whether our goal is creating jobs, climate impact or just making responsible investments, we can only learn if we capture data and knowledge, turn them into insights, and progressively translate them into strategy and operations improvements. Making successful “gut decisions” is only helpful if you can repeat them. Thus, utilizing data as a base layer on which you make decisions allows you to retrospectively understand them, and constantly learn.

Example: Clustered Data Repositories

When we first started, we were collecting data embedded in various tools, such as Microsoft Excel, Airtable, Google Forms, SohoCRM, and many more. Each tool was specialized in one task, but building a streamlined process was fraught with data entry, integration, exports, imports, and general compatibility challenges. Instead of a “Frankenstein” tool-stack for our unique workflow, we built a customisable end-to-end workflow software bringing all datasets into one consolidated infrastructure. This made our data more secure, manageable, and accessible, now our cornerstone of data-driven decision-making.

Quality

Deploying capital is surprisingly easy. The difficult part (putting fundraising aside for now!) is delivering capital in a form where it is being put to good use, and which can be serviced, meaning building business value, making loan repayments, and/or delivering on impact metrics. However, too often this servicing is done at the sacrifice of the funded business. This is frequently due to a lack of “product-market fit” of the financial instrument and the targeted businesses. Quality, in that case, means delivering a robust financial product well-suited to the business it is funding. This requires extensive knowledge of the needs of businesses, and how capital affects them. Collecting and verifying specific data is therefore key to iterating on your financial instrument and its deployment measures, as well as managing and maintaining it.

Example: Site visits

From the start, our thesis was quite radical - fully remote, fully digital, no-touch! We wanted to push the envelope and see what would happen if having never met the entrepreneurs face-to-face. In an attempt to redefine funding, we wanted to strip everything down to the bone and discover what really makes a difference. Since then, we obviously had a steep learning curve, and have identified some process steps which are not quite ready, and some that will never be digitized. One process step we have now regressed on is for our investment team to do a site visit as part of the due diligence. A key lesson was that improving quality does not always require a high-tech solution. However, the most crucial realization was that we needed to identify the right product-market fit for our mission and redesign our capital instrument accordingly to fit the right target group. The process involved clearly defining the eligibility criteria, collecting verifiable data points, and matching the best-fit entrepreneurs and businesses to a well-designed financial product.

Efficiency

The most crucial aspects of funding efficiency are Cost and Time. Everyone knows the term “time is money”! Of course, you could probably make everything a function of cost. But specially in our context, these two are key barriers to success for most funders.

Cost, on the one hand, pushes funders to increase their ticket sizes in order to forcefully reduce their cost margin. As a result, this ends up limiting the amount of funding products deployed to SMEs, as instruments catering to their needs are just too expensive relative to the perceived risk they bare. So how can funders reduce costs without increasing risk? Often the cost reducing effect of technology lies on places that are not obvious, or rather on totally replacing certain processes that are deemed crucial. Some tech solutions imply that they can reduce cost while reducing risk, albeit requiring to drastically change systemic beliefs and power dynamics.

On the other hand, turnaround time in funding decisions is usually dependent on multiple aspects, namely sourcing and screening, data collection and data qualification cycles, decision cycles and hierarchies, fund capital cycles, scheduling with key stakeholders, legal and marketing… just to mention a few! The effect of these dependencies on the beneficiaries could be detrimental, often leading to teams having to repeat the processes, as too much time has passed and the data has lost representativeness. Moreover, the instrument and fund economics might also be affected, given that the faster the capital gets serviced the better it performs to the stakeholders. Intuitively, dedicated workflow management could solve many of these challenges - but how to select the tool that best caters to the specific needs of your unique operations?

Example: Pitchdecks/Business Plans

At the start, when we opened our “call for applications for funding” back in 2019, an immediate constraint on efficiency was screening and selection. Given the fact that demand for funding grossly outnumbered the supply of funding, we were immediately inundated with entrepreneurs pitching their businesses for our funding “offer”. Reviewing pitchdecks, business plans, cover letters, and endlessly long emails, was an immediate bottleneck and it raised a concerning question. We wanted to be as unbiased as possible, and be fair, especially towards the disadvantaged, most importantly the female entrepreneurs. To be objective, we needed conformity of data, so that the evaluation of applicants could be made on the same data points. We didn’t want the decision of funding to be influenced by how good the founder was at pitching their business, but instead on the objective merit of the founder and their business.

We challenged the “pitch deck culture” and developed an application process every entrepreneur had to complete, collecting all the data needed for the evaluation. Now we can automatically screen thousands of applications based on eligibility criteria and automatically evaluate their application on an equal basis. Besides that, we even have the option to manually grade open text questions, or have AI grade them, as we identified inconsistencies and biases in manually grading so many applications. More about that in a deep dive! 😄

Here is an outlook of upcoming topics/deep dives:

• Reducing Unconscious Bias in Funding

• AI Application Grading

• Optimizing your Funding Workflow for all stakeholders

• How to build a successful funnel for deals

• Streamlining communications and data collection with beneficiaries

• Feature-set for deployment of funds in Sub-Saharan Africa

Feel free to write to us about which topics you would like to hear about first!

Thanks for reading, contributing, and sharing!

Johannes