Data-Driven Learning and Firm Performance: Evidence from Kenya

2025-07-15

2025-10-31

Primary Outcomes:
o App Usage – Primary outcomes: (i) adoption of app (binary decision and continued usage), (ii) app log-in frequency, (iii) duration of time spent on app, (iiv) number of entries made in the app.
o Business Practices – Primary outcomes: (i) record-keeping for sales and inventories, (ii) business operation hours and timing, (iii) frequency of orders to suppliers, (iv) number of distinct suppliers, (v) discounts offered, (vi) credit extended to clients. Secondary outcomes: (i) time spent by entrepreneur at business premise, (ii) business specialization (e.g., share of consumer retail in total revenue)
o Business Performance – Primary outcomes: (i) business survival, (ii) value of inventory, (iii) number of unique products in inventory, (iv) stock-out frequency, (v) sales revenue, (vi) profit, (vii) number of customers, (viii) number of employees, (ix) additional businesses,
o Business Awareness – Primary outcomes: (i) knowledge about inventory, (ii) knowledge about most and least popular items, (iii) business sales and profits in previous months

Secondary outcomes: (i) clicks on specific functions in the app, (ii) perceived usefulness of keeping records of inventories, sales, and financial transactions (iii) perceived usefulness of POS app

This field experiment evaluates whether SMEs in Kenya underinvest in record-keeping because of challenges in turning raw data into actionable knowledge. We will test whether integrating analytics into a smartphone-based POS system (“mSpark”) encourages consistent record-keeping and improves business practices and outcomes. Participants comprises of retail-focused SMEs (e.g., small supermarkets, pharmacies, salons, and mom‑and‑pop stores) recruited via Technoserve’s network.

The key intervention is implemented in two stages. In the first stage, we promote mSpark app among 3,300 SMEs in our sample pool and encourage them to adopt the app. All participating businesses who decide to install the app receive the standard mSpark app, which enables sales and inventory tracking, low-stock alerts, and viewing transaction histories. In the second stage, one month after the initial onboarding, we roll out the main treatment to a random subset of the users of the app. To be specific, we introduce them to an advanced version of the app with analytical features.

At the stage of the randomization – which we intend to do in mid-July 2025 – we expect to have a sample of around 2,000 consistent users of the app. We will introduce the advanced mSpark app to random half of the consistent users we will have at this point. We will follow a clustered randomization approach, where we will use location of the businesses as randomization clusters.

Additionally, to be able to speak towards the effect of the basic mSpark app, we set aside a random subsample of our initial sample pool (a total of 300 SMEs) to serve as a pure control group. This randomization is done before the first stage of app promotion commences. These businesses do not receive information about the mSpark app but we conduct a complete baseline survey of their businesses.

We collect data through at least three rounds of surveys, including one listing, one baseline and at least one follow-up survey. If funding allows, we intend to conduct a final round of survey around 12 months after the treatment roll-out.

Not available

Randomization done using Stata software.

Business location

Yes

500-600 location clusters.

2,300-2,500 businesses.

Around 100 clusters in pure Control arm, 250 clusters for basic app, and 250 clusters for analytic app arms.