APPEN LIMITED — AGM Information 2017

May 25, 2017

==> picture [720 x 54] intentionally omitted <==

==> picture [175 x 50] intentionally omitted <==

Appen Limited Annual General Meeting CEO Presentation 26[th] May 2017

==> picture [114 x 39] intentionally omitted <==

Machine learning – multiple techniques

Personal assistants and at-home devices use machine learning in many ways:

==> picture [222 x 178] intentionally omitted <==

• Speech recognition to ‘hear’ commands

• Conversational understanding to comprehend and clarify commands

• Speech synthesis to respond

• Various tasks: search, recommendation, prediction and many more

Speech Synthesis

==> picture [34 x 35] intentionally omitted <==

==> picture [87 x 25] intentionally omitted <==

2

5/26/17

Machine learning – multiple sectors and applications

==> picture [168 x 94] intentionally omitted <==

==> picture [167 x 95] intentionally omitted <==

==> picture [167 x 95] intentionally omitted <==

Automotive: Self-driving vehicles

eCommerce: search, recommendation and chatbots. Netflix’s recommender drives $1BN in annual revenue

Financial services: personalisation and fraud protection

Health: Image analysis and diagnosis

==> picture [34 x 35] intentionally omitted <==

Manufacturing: optimise processes, predict Speech maintenance Synthesis

==> picture [168 x 94] intentionally omitted <==

==> picture [168 x 95] intentionally omitted <==

==> picture [87 x 25] intentionally omitted <==

3

5/26/17

Machine learning – a very large market & growing fast

“Widespread adoption of cognitive systems and artificial intelligence (AI) across a broad range of industries will drive worldwide revenues from nearly $8.0 billion in 2016 to more than $47 billion in 2020.”

“45% of all work activities could be automated by current technologies; machine learning can enable 80% of those activities ”

“Cognitive technologies such as robots, artificial intelligence (AI), machine learning and automation will replace 7% of US jobs by 2025 .”

IDC, October 2016

McKinsey, December 2016

Forrester Research, June 2016

==> picture [87 x 25] intentionally omitted <==

4

5/26/17

- Appen strongly positioned to benefit

Strength

• Founded in 1996

• Trusted partner to 8 of 10 largest global tech companies

• Financially sound:

• Growing revenue

• High margins

• Strong cash flow

• No debt

Scale

• 280 staff worldwide

• Global operations

==> picture [169 x 91] intentionally omitted <==

----- Start of picture text -----

Seattle
Detroit Exeter Beijing
San
Francisco
Davao
Sydney
----- End of picture text -----

• 400,000+ on-demand global crowd

Scope

• Worked in 180 languages and 130 countries

• Proficient in speech, search, social media and ecommerce

• Experience with multiple data types: text, audio, image and video

• Delivered 1BN+ data points for one customer

==> picture [87 x 25] intentionally omitted <==

5

5/26/17

Full year highlights (A$m)

Strong growth continues:

• Revenue up 34%

• EBITDA up 24%

• NPAT up 26%

==> picture [399 x 189] intentionally omitted <==

----- Start of picture text -----

Revenue Earnings
111.0
17.2
82.7 13.8
10.5
EBITDA
8.3
NPAT
FY2015 FY2016 FY2015 FY2016
----- End of picture text -----

==> picture [87 x 25] intentionally omitted <==

6

5/26/17

High divisional growth (A$m)

Language Resources revenue up 18%

==> picture [267 x 157] intentionally omitted <==

----- Start of picture text -----

Language Resources
37.7
31.9
Revenue
14.8 EBITDA
12.5
FY2015 FY2016
----- End of picture text -----

• Ongoing demand for speech data

• Data quantity and quality drives word accuracy and improves usability

• Working with all data types

Content Relevance revenue up 44%

==> picture [262 x 156] intentionally omitted <==

----- Start of picture text -----

Content Relevance
73.2
50.7
Revenue
EBITDA
8.9 10.5
FY2015 FY2016
----- End of picture text -----

• Growing need for data for machine learning-based search, social media and ecommerce

• Working with all data types

==> picture [87 x 25] intentionally omitted <==

7

5/26/17

110.9

Resilient Revenue (A$m)

• Customers include major global technology companies, automakers and governments

• Quality and importance of Appen’s data and services drives growing and repeat revenue

Language Resources customers originating in year Content Relevance customers originating in year

==> picture [83 x 111] intentionally omitted <==

----- Start of picture text -----

82.6
----- End of picture text -----

==> picture [262 x 212] intentionally omitted <==

----- Start of picture text -----

82.6
60.5
50.9
33.3
29.4
FY2011 FY2012 FY2013 FY2014 FY2015 FY2016
----- End of picture text -----

==> picture [23 x 21] intentionally omitted <==

==> picture [23 x 21] intentionally omitted <==

==> picture [23 x 21] intentionally omitted <==

==> picture [23 x 21] intentionally omitted <==

==> picture [22 x 21] intentionally omitted <==

==> picture [22 x 21] intentionally omitted <==

==> picture [23 x 22] intentionally omitted <==

==> picture [23 x 22] intentionally omitted <==

==> picture [23 x 22] intentionally omitted <==

==> picture [23 x 22] intentionally omitted <==

==> picture [22 x 22] intentionally omitted <==

==> picture [22 x 22] intentionally omitted <==

==> picture [87 x 25] intentionally omitted <==

8

5/26/17

Margin Improvement

• Volume discounts for major customers dampened margins in 2H

• Margins improving:

• Productivity improvements from updated processes and systems

• Cost control through sourcing crowd workers at different rates

• Expect further improvement through 2017

==> picture [87 x 25] intentionally omitted <==

9

5/26/17

High Growth (A$m)

	FY2016	FY2015	% change	% change constant currency
Statutory Results
Language Resources	37.7	31.9	18%
Content Relevance	73.2	50.7	44%
Total Revenue	111.0	82.7	34%	33%
EBITDA	17.2	13.8	24%	22%
EBITDA Margin	15.5%	16.7%
NPAT	10.5	8.3	26%	23%
Underlying Results**
EBITDA	17.3	14.0	23%	21%
NPAT	10.6	8.5	25%	24%

• Revenue up 34% on FY2015

• Growth from current and new projects with existing customers plus new customer acquisition

• EBITDA* up 24%

• Volume discounts dampened margins in 2H. Productivity measures and cost control delivering improvements in Q4 16 and Q1 17

• NPAT* up 26%

*Refers to Statutory Results

**Underlying results exclude one-off expenses associated with the IPO and transaction costs

==> picture [87 x 25] intentionally omitted <==

10

5/26/17

Strong Balance Sheet (A$m)

	FY2016	FY2015
Cash	16.5	12.7
Receivables	21.9	17.3
Other Current Assets	0.4	0.3
Non-Current Assets	15.2	11.7
Total Assets	54.0	42.0
Current Liabilities	15.4	11.5
Non-current Liabilities	3.2	1.8
Total Liabilities	18.6	13.3
Net Assets	35.4	28.7
Total Equity	35.4	28.7

• Strong balance sheet. No debt.

• Increase in cash reserves and receivables related to increase in revenue volumes

• Final dividend of 3.0 cents per share fully franked, in line with prior year.

• Total dividend for FY2016 of 5.0 cents up 19% on prior year.

==> picture [87 x 25] intentionally omitted <==

11

5/26/17

Strong Cash Conversion (A$m)

	FY2016	FY2015
Receipts	106.8	72.5
Payments and other	(90.1)	(67.6)
Cash flow from operations before interest and tax	16.7	4.9
Taxes	(4.0)	(0.8)
Total Cashflow from Operations	12.7	4.1
Cashflows - Investment Activities	(4.6)	(0.6)
Cashflows - Financing Activities	(4.4)	(0.6)
Net Cashflows for the period	3.7	2.9
Opening cash balances	12.7	8.6
FX Impact	0.1	1.2
Closing cash balances	16.5	12.7

• Cash balance increased by $3.7m over FY2015

• Cash flow from operations significantly improved due to change in payable cycle and better timing of customer receipts

Cash flow conversion:	FY2016	FY2015
EBITDA	17.2	13.8
Working capital	(0.4)	(6.2)
FX impact	(0.1)	(1.2)
Other	-	(0.2)
Payment of IPO costs not going through P&L	-	(1.3)
Cash flow from operations before interest and tax	16.7	4.9

==> picture [87 x 25] intentionally omitted <==

12

5/26/17

Currency Impact (A$m)

• Most revenue derived offshore in USD

• Negligible currency impact in 2016

• Strong underlying growth

==> picture [422 x 171] intentionally omitted <==

----- Start of picture text -----

Revenue EBITDA
27.5 0.8 111.0 17.2
3.0 0.4
82.7 13.8
FY2015 Currency Currency FY2016 FY2015 Currency Currency FY2016
Neutral Impact Neutral Impact
Growth Growth
----- End of picture text -----

==> picture [87 x 25] intentionally omitted <==

13

5/26/17

Outlook

• YTD revenue plus orders in hand for delivery in FY2017 ~$100m at end April 2017

• Full year outlook for EBITDA growth at 40% to 50% on prior year

Outlook susceptible to upside or downside from factors including timing of work from major customers and Australian dollar fluctuations (outlook at A$1 = US$0.76)

==> picture [87 x 25] intentionally omitted <==

14

5/26/17

==> picture [720 x 54] intentionally omitted <==

==> picture [128 x 37] intentionally omitted <==

Thank you

Mark Brayan, CEO, mbrayan@appen.com Kevin Levine, CFO, klevine@appen.com Leanne Ralph, Company Secretary, leanne.ralph@boardworx.net.au

==> picture [114 x 39] intentionally omitted <==

appen.com