Sparking Growth in Corner Shops

Finbots for Shopkeepers Series #1

David del Ser
Finance for Life
5 min readNov 19, 2017

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Small and micro enterprises (SMEs) are the economic backbone of emerging markets. Formal small and micro enterprises contribute up to 60% of total employment in emerging markets and create 4 out of 5 new formal jobs.

Unfortunately, many of these enterprises never reach their economic potential due to challenges such as unfavorable regulation and tax regimes, and a lack of employee trust, all of which eat into managerial bandwidth. Responding to these challenges leaves little capacity for these businesses to adopt new technologies and as a result, opportunities to boost productivity are left unmet and their economic potential remains unfulfilled.

Shopkeepers of the World, Unite and Tech Over

In this series of posts I will focus on how one formula, using Finbots for shopkeepers, can enable one type of micro-enterprise, the small retailer or corner shop, to grow. By corner shops I mean those ubiquitous small businesses that sell groceries and packaged goods surrounded by a colorful chaos. They are probably the most abundant type micro business worldwide, so they should act as a good proxy for SMEs in general. For the small shopkeepers of the world, could these new technologies — utilizing smartphones, data and insights — unlock their growth?

Credit: Sokowatch

An Urgent Need for Tech

Most shops in emerging markets currently run their businesses using sheer mental power, perhaps aided by a paper notebook and a calculator. With such rudimentary technology, each process is manual and every record is mental; automation is impossible, calculations are error-prone, and decisions are unfortunately based on gut instinct instead of data and economics.

If you run a lemonade stand, this method is probably okay, but these businesses aren’t simple. As founder of Frogtek, I saw that the median corner shop in Mexico City carries over 1000 products, sells 200–300 times a day, and makes 100 purchases a month. SMEs typically keep six times more inventory than a modern retailer of a similar size. If managing that volume of transactions and inventory mentally isn’t hard enough, the family team of one or two shopkeepers also has to run marketing, accounting, operations and finance without any digital tools. They do all this without a proper high-school education. No wonder it’s so hard to stay afloat and make a decent living, let alone innovate or grow the business.

Smartphones are not your father’s PC

In the past, technology hasn’t been a viable solution because PCs were too clunky and expensive, Windows was difficult to learn and a desktop was not suited to shopkeepers on the move. Plus, this kind of software wasn’t designed as a micro-businesses tool.

Fortunately, the post-PC era holds more promise. Smartphones are becoming so affordable that every adult is buying one for personal purposes. They are easy to learn and one can expand the phone’s functionality by downloading apps. Smartphones not only fit into your pocket, but also pack a computational punch, giving everyone superpowers.

A smart use of sensors

And here’s the thing: a smartphone has multiple sensors like an accelerometer, light sensor, GPS, compass, proximity sensor, pressure sensor and gyroscope, all of which are constantly capturing data. In the first part of our shop growth formula, we consider the smartphone as a device for collecting new, rich data about consumers and operations. Some of this data is hugely applicable to how a shopkeeper can better run her business. For instance, it’s easy to imagine a smartphone app that reads barcodes and tracks sales quickly and efficiently.

AI for shopkeeper business intelligence

In the next post, we explore how, through machine learning, this data can be processed to derive valuable information to back business decisions. The gut instinct that shopkeepers develop over years of experience can be unpacked into powerful and dynamic statistical algorithms. Machine learning can unravel inventory stock-outs, customer buying patterns, cross-selling, and customer credit profiles — and these are just some of the variables that shopkeepers can optimize to be more efficient, do more business, and manage risk.

Image Credit: FIBR

Productivity from Insights

In some cases, these algorithms will generate evidence of what shopkeepers already know intuitively, but in many other instances, automated and predictive insights can accelerate good business sense even further. With reliable forecasts, business owners can take action to improve how they run their businesses to be productive drivers of growth, instead of behaving as passive reactors to circumstances. For example, insights such as sales forecasts can help shopkeepers buy the correct amount of inventory to preserve cash flow and minimize trips to suppliers, while simultaneously keeping customers satisfied with the consistency of product stock.

The Rise of the Finbot: A New Formula for Growth

In the final post we will consider how to marry a smartphone app with machine learning, teach it finance 101, and craft a conversational interface to create a Finbot: an AI chatbot that acts as your CFO and answers financial questions on your phone. Sounds too good to be true? Imagine a Penny for micro-retailers, a personal coach financial app that shopkeepers can use to gather data, transform the raw data into information, and create insights to increase productivity and grow their businesses.

In the following three posts of this series, we will examine each part of the formula for a Finbot for shopkeepers. First, we look closely at how smartphone sensors can capture terabytes of relevant data with minimal effort. Next, we explore how AI techniques can produce information from the raw data. Finally, we consider how to translate this data into valuable business insights, predictions, and automated processes to fuel shop growth.

Next in the series, first published on Machine Learnings

FIBR stands for Financial Inclusion on Business Runways and aims to learn how to transform emerging business data about low-income individuals and link them to inclusive financial services to deepen financial inclusion and its impact. FIBR is a project of BFA in partnership with Mastercard Foundation.

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