Getting Started on UA Automation

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6

Warren Woodward (CGO at Upptic) has extensive experience in automating UA processes and shares how to get started and what he learned.

Source:
Getting Started on UA Automation
(no direct link to watch/listen)
(direct link to watch/listen)
Type:
Presentation
Publication date:
February 28, 2020
Added to the Vault on:
March 5, 2020
These insights were shared through the free Growth Gems newsletter.
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💎 #
1

An intermediary step before going with full automation can be to download the rules of a redundant task as a .csv, run a Python script and get the output that you can upload to the ad network. It can already save up 80% of the time.

07:54
💎 #
2

Getting started with automation:
1. Identify a need and what's eating up your time,
2. Identify the infrastructure you need (data format you need),
3. Figure out if it's buy or build

11:45
💎 #
3

If you are currently doing something day-to-day in a spreadsheet, it is probably a good candidate for automation.

13:15
💎 #
4

Start with clean data then figure out how to implement the system.

16:57
💎 #
5

Be ok with a "working solution" if it already helps you save half of your time. It doesn't have to be perfect and you can refine later.

18:15
💎 #
6

When it comes to creatives, machines might not create ideas but can be amazing loops for feedback and iteration.

19:35
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💎 #
1

An intermediary step before going with full automation can be to download the rules of a redundant task as a .csv, run a Python script and get the output that you can upload to the ad network. It can already save up 80% of the time.

07:54
💎 #
2

Getting started with automation:
1. Identify a need and what's eating up your time,
2. Identify the infrastructure you need (data format you need),
3. Figure out if it's buy or build

11:45
💎 #
3

If you are currently doing something day-to-day in a spreadsheet, it is probably a good candidate for automation.

13:15
💎 #
4

Start with clean data then figure out how to implement the system.

16:57
💎 #
5

Be ok with a "working solution" if it already helps you save half of your time. It doesn't have to be perfect and you can refine later.

18:15
💎 #
6

When it comes to creatives, machines might not create ideas but can be amazing loops for feedback and iteration.

19:35
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💎 #
1

An intermediary step before going with full automation can be to download the rules of a redundant task as a .csv, run a Python script and get the output that you can upload to the ad network. It can already save up 80% of the time.

07:54
💎 #
2

Getting started with automation:
1. Identify a need and what's eating up your time,
2. Identify the infrastructure you need (data format you need),
3. Figure out if it's buy or build

11:45
💎 #
3

If you are currently doing something day-to-day in a spreadsheet, it is probably a good candidate for automation.

13:15
💎 #
4

Start with clean data then figure out how to implement the system.

16:57
💎 #
5

Be ok with a "working solution" if it already helps you save half of your time. It doesn't have to be perfect and you can refine later.

18:15
💎 #
6

When it comes to creatives, machines might not create ideas but can be amazing loops for feedback and iteration.

19:35
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Risks of scaling a campaign (w/ a small team)

Start by thinking of "what's really stupid?" that you do on a day-to-day basis: something that time consuming.


What is UA Automation?

Automation doesn't have to be a "blackbox". It can be as simple as automated reports, or simple UA rules for an ad network.


Why build in-house?

Some partners/ad networks offer automation rules. But the goal of that partner is most likely to sell its inventory.


Building in-house gives you ownership of the system.


Getting started with UA automation

Have the UA manager write what he does to automate campaigns:

  1. Write that out as if he UA manager was training a new employee. Exemples: blocking publishers with bad retention, bidding up placement where ROI greater than goal, etc.
  2. Write that down in a set of rules in a spreadsheet and make these rules accessible to all the team,
  3. Start thinking how to automate the rules. Partner with someone in the BI team or an engineer to build out a simple workflow to execute this thing.


[💎@07:54] Intermediary step before full automation:  download the rules as a .csv, run a Python script and get the output that you can upload to the ad network. Can already save up 80% of the time.


The most important is to identify the need, then you can identify a provider you can work with in the space.

Identify the goal, then figure out if it's buy or build.


Before automation things were much more stressful because UA never sleeps. Automation allows you to refocus your team to work more closely with other teams in your company.

[💎@11:45]

  1. Identify a need and what's eating up your time,
  2. Identify the infrastructure you need (which data you need and how to get it in a useable format),
  3. Figure out the means of execution. Either license a product that can do this for you with your dataset, or build something (which can be as simple as a spreadsheet).


Main areas to start with automation

[💎@13:15] If you are currently doing something day-to-day in a spreadsheet, it is probably a good candidate for automation.

For UA, automation is typically used to automate the most redundant task on:

  1. Reporting side
  2. Optimization side


How to validate

It starts with verifying that the data is clean.

An area often overlooked: are you comparing what the App Store paid you with what you actually got paid.

You need a "first line of defense" of basic data governance (someone checking the data).

[💎@16:57] Start with clean data then figure out how to implement the system.


Demystifying

You do not need an engineering team to get started: "what's the most simple version of this".

[💎@18:15] Be ok with a "working solution" if it already helps you save half of your time. It doesn't have to be perfect and you can refine later.


Wishlist

Not many valid solutions to automate App Store Optimization (what Upptic does): split-testing process, combining elements, etc.

[💎@19:35] When it comes to creatives, machines might not create ideas but can be amazing loops for feedback and iteration.


The notes from this resource are only available to premium members.
↘ At this point, you know what to do ↙
Upgrade Your Plan

Risks of scaling a campaign (w/ a small team)

Start by thinking of "what's really stupid?" that you do on a day-to-day basis: something that time consuming.


What is UA Automation?

Automation doesn't have to be a "blackbox". It can be as simple as automated reports, or simple UA rules for an ad network.


Why build in-house?

Some partners/ad networks offer automation rules. But the goal of that partner is most likely to sell its inventory.


Building in-house gives you ownership of the system.


Getting started with UA automation

Have the UA manager write what he does to automate campaigns:

  1. Write that out as if he UA manager was training a new employee. Exemples: blocking publishers with bad retention, bidding up placement where ROI greater than goal, etc.
  2. Write that down in a set of rules in a spreadsheet and make these rules accessible to all the team,
  3. Start thinking how to automate the rules. Partner with someone in the BI team or an engineer to build out a simple workflow to execute this thing.


[💎@07:54] Intermediary step before full automation:  download the rules as a .csv, run a Python script and get the output that you can upload to the ad network. Can already save up 80% of the time.


The most important is to identify the need, then you can identify a provider you can work with in the space.

Identify the goal, then figure out if it's buy or build.


Before automation things were much more stressful because UA never sleeps. Automation allows you to refocus your team to work more closely with other teams in your company.

[💎@11:45]

  1. Identify a need and what's eating up your time,
  2. Identify the infrastructure you need (which data you need and how to get it in a useable format),
  3. Figure out the means of execution. Either license a product that can do this for you with your dataset, or build something (which can be as simple as a spreadsheet).


Main areas to start with automation

[💎@13:15] If you are currently doing something day-to-day in a spreadsheet, it is probably a good candidate for automation.

For UA, automation is typically used to automate the most redundant task on:

  1. Reporting side
  2. Optimization side


How to validate

It starts with verifying that the data is clean.

An area often overlooked: are you comparing what the App Store paid you with what you actually got paid.

You need a "first line of defense" of basic data governance (someone checking the data).

[💎@16:57] Start with clean data then figure out how to implement the system.


Demystifying

You do not need an engineering team to get started: "what's the most simple version of this".

[💎@18:15] Be ok with a "working solution" if it already helps you save half of your time. It doesn't have to be perfect and you can refine later.


Wishlist

Not many valid solutions to automate App Store Optimization (what Upptic does): split-testing process, combining elements, etc.

[💎@19:35] When it comes to creatives, machines might not create ideas but can be amazing loops for feedback and iteration.


The notes from this resource are only available to premium members.

The detailed notes taken for a resource are an easy way to see the gems in context to get a better understanding. They also include any relevant visuals from the source.
↘ At this point, you know what to do ↙
Request Access

Risks of scaling a campaign (w/ a small team)

Start by thinking of "what's really stupid?" that you do on a day-to-day basis: something that time consuming.


What is UA Automation?

Automation doesn't have to be a "blackbox". It can be as simple as automated reports, or simple UA rules for an ad network.


Why build in-house?

Some partners/ad networks offer automation rules. But the goal of that partner is most likely to sell its inventory.


Building in-house gives you ownership of the system.


Getting started with UA automation

Have the UA manager write what he does to automate campaigns:

  1. Write that out as if he UA manager was training a new employee. Exemples: blocking publishers with bad retention, bidding up placement where ROI greater than goal, etc.
  2. Write that down in a set of rules in a spreadsheet and make these rules accessible to all the team,
  3. Start thinking how to automate the rules. Partner with someone in the BI team or an engineer to build out a simple workflow to execute this thing.


[💎@07:54] Intermediary step before full automation:  download the rules as a .csv, run a Python script and get the output that you can upload to the ad network. Can already save up 80% of the time.


The most important is to identify the need, then you can identify a provider you can work with in the space.

Identify the goal, then figure out if it's buy or build.


Before automation things were much more stressful because UA never sleeps. Automation allows you to refocus your team to work more closely with other teams in your company.

[💎@11:45]

  1. Identify a need and what's eating up your time,
  2. Identify the infrastructure you need (which data you need and how to get it in a useable format),
  3. Figure out the means of execution. Either license a product that can do this for you with your dataset, or build something (which can be as simple as a spreadsheet).


Main areas to start with automation

[💎@13:15] If you are currently doing something day-to-day in a spreadsheet, it is probably a good candidate for automation.

For UA, automation is typically used to automate the most redundant task on:

  1. Reporting side
  2. Optimization side


How to validate

It starts with verifying that the data is clean.

An area often overlooked: are you comparing what the App Store paid you with what you actually got paid.

You need a "first line of defense" of basic data governance (someone checking the data).

[💎@16:57] Start with clean data then figure out how to implement the system.


Demystifying

You do not need an engineering team to get started: "what's the most simple version of this".

[💎@18:15] Be ok with a "working solution" if it already helps you save half of your time. It doesn't have to be perfect and you can refine later.


Wishlist

Not many valid solutions to automate App Store Optimization (what Upptic does): split-testing process, combining elements, etc.

[💎@19:35] When it comes to creatives, machines might not create ideas but can be amazing loops for feedback and iteration.