understanding consumer sector using transaction data doug ... · 2018. 18. deal announced. rolling...

Understanding ConsumerSector Using Transaction Data

How to swim with sharks

DecaData: Transaction Data With Daily Delivery.

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Manufacturers

InvestmentManagement

Retailers

DecaData Transaction

Data

Why Alternative Data? Informational Surprise.

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Obvious Features

Non-Obvious Features

Two Case Studies

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Public company B acquires public company C

Private Company IPO

Data Sources: Odyssey 2 Daily Transaction Data,Atlas Forward Looking Promotional Data

Case Study #1 Case Study #2

Sales growth is hard.● Dynamic Equilibrium: If it was easy, all would grow.

● Finite space: (shelf space or warehouse space).

● Behavioral Economics: Consumers tend to be habitual, hard to get them to try new things.

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Firms have to incentivize behavior change.

Obvious Features

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Increasing Distribution

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Increasing SKU counts

More products, more stores, higher TAM are obvious data features.

2017-02-10 2017-11-17 2018-08-26 2019-06-03

2017-02-10 2017-11-17 2018-08-26 2019-06-03

# SKU Listed In Any Store

# Stores Featuring Any Product

Source: Odyssey 2

Source: Odyssey 2

Case study #1: Pre-Post IPO $ Sales.

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IPO

Q1 Q3

1. Prior to IPO Q2 sales gap down.

Daily $ Sales

1

Source: Odyssey 2Q2

2019-01-01 2019-07-01

Case study #1: $ Sales hint at features.

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Q1 Earnings

Q2 Earnings

IPO

Q1 Q3

1. Prior to IPO Q2 sales gap down.

2. Q1 earnings: price rises.3. Q2 earnings: price tumbles.

Daily $ Sales

Share Price

1

2

3

Source: Odyssey 2Q2

Non-Obvious Features

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$2.50

$4.00

$5.50

$7.00

2016-01-01 2016-07-01 2016-12-31 2017-07-01 2017-12-31 2018-07-01 2019-01-01 2019-07-02

Case Study #1: Changing behavior through sustained price promotion.

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More Products Listed In Store, Sales Mix Changes AIP Moves

Price Promotion

Extended Promotion

Average Item Price

Source: Odyssey 2

2019-01-01 2019-01-31 2019-03-02 2019-04-01 2019-05-01 2019-05-31 2019-06-30 2019-07-30Q3

Case Study #1:Overlaying Average Item price.

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Share Price

Q2 Earnings

Q1 Q2

Source: Odyssey 2

Q1 Earnings

2

3

IPO

$5.29

$5.79

$6.29

2019-01-01 2019-01-31 2019-03-02 2019-04-01 2019-05-01 2019-05-31 2019-06-30 2019-07-30Q3

Case Study #1: 12% drop in Average Pricealters economics, at expense of share price.

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Average item Price

Share Price

Q2 Earnings

Q1 Q2

Source: Odyssey 2

Q1 Earnings

1

2

3

IPO

Case Study #1 Average item Price, a non-obvious indicative feature.

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2014 2015 2016 2017 2018 20192013

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Rolling 4 Weekly $

Case Study #2: 5 year sales ramp-up prior to M&A news. Sales falls post announcement.

Source: Odyssey 2

Deal Announced

Earnings Call

wk1

wk14

wk27

wk40

wk53

wk13

wk26

wk39

wk52

wk12

wk25

wk38

wk51

wk11

wk24

wk37

wk50

wk10

wk23

wk36

wk49

wk9

wk22

wk35

wk48

wk8

wk21

Case Study #2: Sales ramp-up linked to price promotions increasing.

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Number of Products On Promotion Per Week

Deal Announced

2013 2014 2015 2016 2017 2018 2019

Source: Odyssey 2 / Atlas

4-wkly $ Sales

Earnings Call

2018

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Rolling 4 Weekly $Deal Announced

Price promos cause H1 margin hit (H1 prices -7% vs H2) & H2 sales hit as price corrected.

2019

Source: Odyssey 2 / Atlas

H1 $2.69 H2 $2.902018 Average Item Price

CEO described these deals as “highly inefficient…acquired firm was “chasing volume over value”

Earnings Call

Case Study #2 Weekly Promotions, non-obvious indicative features.

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Indicators of informational surprise in consumer TX data:

• Sustained Price Changes.

• Increased Store Distribution.

• Better Quality Distribution.

• Increased UPC count.

• Reduced shelf voids / Out-of-stocks.

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Setting up the radar.

Thank You