Introduction

Staffing Co is a totally fictitious company that operates in the hospitality industry using a 2-sided marketplace business model. They provide serving staff, chefs and other personnel to restaurants, hotels and other venues. The analysis below is for the demand (restaurants, hotels and venues) side of this marketplace.

The data provided is from self-reported data upload for the period from 02-Dec-2016 through to 03-May-2020. There are no obvious inconsistencies, errors or apparent tampering with this data.

Revenue has been converted from the billing currency to USD using the day-of currency conversion as provided by the World Bank. Revenue recognition has been performed on Quarterly, Yearly and Multi-Year contracts using Proportional Monthly revenue recognition per ASC 606 / IFRS 15

NOTE: This is an entirely fictional company, with fictional users and fictional data, that is presented to illustrate the type of report our AI generates.

This report does not represent or reflect any prior or existing company. Users, Transactions and subsequent Data have all been generated in a quasi-random nature to be reflective of the typical Data from a company of this type. A total of 18,000 transactions are presented over a 4-year period - reflecting a moderately sized marketplace company

Areas Identified for Support

Staffing Co needs help in the following areas: -

  • User retention
  • Product-Market Fit
  • Sales targeting to focus on ideal customer

Metrics

Staffing Co. has seen good growth at a reasonable CAC:LTV ratio. However, the low User Quick Ratio suggests that growth is unsustainable, whilst Product-Market Fit is low for most User cohorts, resulting in rapid churn

Growth

Staffing Co. has seen good reasonable of around 4% monthly User growth and around 4% monthly Revenue growth, with growth slowing through 2019, and some indications of seasonality.

Staffing Co. had around 4.2% monthly User growth and around 4.7% monthly Revenue growth through 2017 and 2018, which is reasonable. Growth has been more variable in 2019, dropping to an around 1.2% monthly User growth and 4.1% monthly Revenue growth, with strong seasonality.

It is unclear why User growth has slowed in 2019 whilst Revenue growth has continued relatively consistently. Potential reasons include fewer but more valuable Users signing up in 2019, or revenue expansion of existing Users.

Breakdown of Revenue

Revenue growth is driven by geographic expansions in Montreal , Ottawa and Vancouver, combined with strong retained revenue from retained Users

Revenue growth has been driven by expansion of new cities, but growth has plateaued in each city after an initial growth period. Toronto revenue grew rapidly to around $40k MRR in Q1-2018, but has been consistent since. Overall revenue was then driven expansion in Ottawa, then Montreal and Vancouver. Flattening of revenue growth in each location is unusual, and suggests that attention may have moved on to other locations

Revenue retention by signup cohort is excellent. Around 90% of the 2018 signup cohort's Revenue is retained after 2 years, indicating that retained Users continue to spend consistently

Sustainability of Growth

User Quick Ratio has averaged 1.8 in the last year, which is poor-to-moderate, whilst the Revenue Quick Ratio has averaged around 2.9 in the last year, which is good. A quick ratio of over 3 is excellent, whilst a quick ratio below 2 is poor and indicative of unsustainable growth.

User Quick Ratio has been in the range of 0.9 to 2.5 over 2018 and 2019, with significant variation (potentially due to seasonality). User Quick Ratio has averaged 1.8 over the last year, which is poor-to-moderate and indicative of User growth that is unsustainable. Growth is driven by new Users being added to the platform, but rapidly churning.

Revenue Quick Ratio has been in the range of 1.4 to 15 over 2018 and 2019, with relative consistency. Revenue Quick Ratio has averaged 2.9 over the last year, which is good. This indicates that revenue is retained from Users that are retained on the platform, and suggests that retained Users have high long-term value.

Product-Market Fit and User Retention

User churn is high, averaging around 13% of Users churning each month through 2018 and 2019, whilst Product Market Fit is low in almost all cohorts

User churn is high for marketplace companies at around 13% monthly User churn, compared to an average of 7.5% monthly User churn for comparable marketplace businesses. User churn has decreased significantly from Q3-2019 onwards, with a drop to negative churn in Jul-2019 (indicative of Lost User returning to the product, or efforts to win back Lost Users), followed by a period of relatively consistent 8% monthly User churn. This may indicate a new product offering or service that retains Users better, or may be indicative of better User targeting.

Product Market Fit is low in almost all cohorts, as indicated by User Retention curves that rapidly slew off, although there are indication that a certain small subset of Users have PMF as they are retained long-term. An initial 60-70% of Users churn over the first 6mths of all cohorts. After this point, Users are better retained. This may indicate a Sales / User targeting issue, with poor PMF for a given group of Users, and potentially high PMF for a smaller subset.

LTV

LTV is approximately $1101 as calculated by the LTV equation, based upon a 15% take rate, $955 average monthly spend per User, and an 13% monthly churn rate

LTV varies tremendously by User cohort, type of User and Location of User. Furthermore, revenue spend trends (and therefore likely LTV) vary tremendously by cohort

Cumulative Revenue Retention of average User, and therefore LTV, has decreased over time. The LTV of an average User that signed up in 2016 and 2017 is around $1450 and $1600+ respectively (based upon a 15% take rate), but this has decreased to around $750 and $380 for Users that signed up in 2018 and 2019 (extrapolating to end of life). This suggests that more recently acquired Users find the product less valuable, and could indicate an expansion into Users that are not core Users

LTV varies significantly by both location and business. The average User in Ottawa has an LTV of around $1650, compared to $1650 for the average Toronto User, and $290 for the average Montreal User. This suggests that the company should focus resources on Ottawa and Toronto. Ottawa Users have slower, but more consistent spend over time, which results in longer life and greater LTV. By comparison, Toronto Users spend faster, but churn faster, resulting in a slightly lower LTV.

Hotels are around 40% more valuable that Bars, whilst Venues are around 28% more valuable than Bars. This suggests that efforts should be focused on Hotels and Venues, assuming that CAC is consistent for each User type