Using peekd data to predict the US

retail eCommerce market

October 2019

Felix van Litsenburg

October 2019

Description of the data sources

US Census Bureau data

This paper uses US Census Bureau unadjusted data from August 20191. This is an estimate of total

retail eCommerce sales across the United States, based on responses to the US Census Bureau’s

Monthly Retail Trade Survey. These estimates are released on a quarterly basis.

peekd data

peekd analysed sales data from over 400,000 online shops. We captured $52 BN of eCommerce

sales in Q2 2019, roughly 38% of the total market as estimated by the US Census bureau. We

capture the data daily, at source, and at a transaction level. Our data does not rely on sampling or

surveys.

1 https://www.census.gov/retail/index.html#ecommerce

Summary

This white paper describes how peekd data can be used to predict the US Census Bureau’s

estimate of the United States’ retail e-commerce market. This information in turn lends itself for

further analysis of the United States economy and consumer sentiment.

For Q2 2019, the Census Bureau reported $140 BN of eCommerce sales1.

We predict this will be $144 BN for Q3 2019 and $165 BN for Q4 2019.

peekd’s data can be used as a macro input for more frequent updates on US retail eCommerce

performance, or to predict its future performance. Moreover, it can be broken down into

different product categories, from fashion to furniture, down to a very granular level (e.g.

Fashion Sneakers). This can guide for example investment decisions for specific product

categories.

October 2019

Historical correlation of the data sources

The correlation between the eCommerce sales data tracked by peekd and the estimates of US

Census Bureau is 0.9, and gives an R2 of 0.8. This means peekd’s data mimics the rates of change

present in the US data very closely.

We see an increase in the share of the US eCommerce market that our data captures, because more

shops were added to our sample over time. In Q1 2016, peekd data represented 24% of the US

eCommerce market. In Q2 2019, this number was 38%.

Using peekd data to forecast the US retail eCommerce market

peekd data is available at a daily frequency, and correlates very highly with US Census Bureau data.

This suggests two use cases:

1. Provide more frequent updates on the state of US retail eCommerce

2. Forecast the US retail eCommerce market

For case 1, we exploit the historical 3:1 ratio of US Census Bureau data to peekd data to estimate the

US retail eCommerce market size. This would suggest a Q3 2019 market size of $144 BN.

For case 2, we forecast peekd’s data and then apply the ratio above to obtain a total market

forecast. We used a decomposable time series model2 to forecast our time series, using an additive

model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.

2 Taylor SJ, Letham B. 2017. Forecasting at scale. PeerJ Preprints 5:e3190v2

0

50

100

150

2016

Q1

2016

Q2

2016

Q3

2016

Q4

2017

Q1

2017

Q2

2017

Q3

2017

Q4

2018

Q1

2018

Q2

2018

Q3

2018

Q4

2019

Q1

2019

Q2

US retail eCommerce$bn

Total US retail eCommerce (US Census Bureau)

Tracked by peekd

October 2019

15

16

17

18

19

20Ja

n/20

19

Feb/

2019

Mar

/201

9

Apr/

2019

May

/201

9

Jun/

2019

Jul/2

019

Aug/

2019

Sep/

2019

peekd tracked data$bn

$54BN tracked by peekd for Q3 2019. We estimate the US Census Bureau’s Q3 2019 figure will be 2.6x higher at $144 BN.

October 2019

This predicts the following US retail eCommerce numbers for 2019 (in BN):

Month Point

Estimate

Upper

Bound

Lower

Bound

July $17.3 Actual

August $17.3 Actual

September $19.5 $19.7 $18.9

October $19.1 $19.7 $18.5

November $23.9 $24.4 $23.3

December $19.3 $19.6 $18.7

Using the observed scaling ratio, we can then forecast the US Census Bureau’s numbers. This

forecasts, in BN:

Quarter Point

Estimate

Upper

Bound

Lower

Bound

Q3 2019 $144 $145 $142

Q4 2019 $165 $170 $161

Interpreting the data

peekd data allows for more up-to-date insights into the United States’ retail eCommerce

environment than the US Census Bureau’s data. This makes it a more agile tool to understand

consumer sentiment and the broader economic climate.

Due to its frequency, it can produce very accurate forecasts. Our forecast indicates that US retail

eCommerce will continue to expand throughout 2019, reaching a seasonal peak in the last quarter.

peekd’s data can be broken down further into more granular categories. This will allow industry

experts to make better informed decisions about their industry within retail eCommerce.

peekd c/o Cross Platform Solutions

125 Reichenberg Strasse 10999 Berlin, Germany

Managing Director: Moritz Thoma

About peekd

peekd is a Berlin-based data science and eCommerce start-up. We have developed a

proprietary online point-of-sales database on which we employ cutting-edge Big

Data and machine learning technologies to derive insights. We capture these in our

Online Retail Intelligence tool.

peekd

c/o Cross Platform Solutions GmbH

125 Reichenberger Strasse

Berlin, Germany

Managing Director: Moritz Thoma info@peekd.ai