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Thanks Facebook, but we’ll take it from here

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Laurence Cresswell, Paid Media Product Manager at Summit Media, challenges the notion that Facebook is the ‘one stop shop’ to success in paid social – based off the findings of the company’s recent ‘Voice of the Customer’ survey.

If I had a pound for every time I’ve heard a Facebook rep recommend increasing budget in a dynamic product ad to improve performance, I wouldn’t have to work for a living.

I get it, their job is to get clients to spend more money on Facebook and increasing DPA spend is normally a good way of rinsing a few extra pennies out of customers at the bottom of the funnel. My fear comes when clients start taking the word according to Facebook as gospel.

A private meeting here, a tour of Facebook London there, and suddenly brands start to lose sight of the bigger picture – they start to believe the Facebook preachers when they say the only option for success is to spend more money on Facebook. 

It’s safe to say there’s been a lot of controversy about performance metrics on Facebook. Yet brands are happy to spend hundreds of thousands of pounds for a Facebook lift study.

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Since when did we allow students to mark their own homework? Top marks all round, Facebook take the win and your paid social manager gets a standing ovation as they enter the office. There is no denying the power of Facebook as a marketing tool, with great access to highly engaged audiences.

But as soon as a marketer trusts blindly the tool they are using, they themselves become the tool.

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Juddering to a halt

Even if a brand uses third-party measurement and attribution to get a clearer idea of the impact Facebook has on their business, they still run the risk of juddering to a halt at the mere suggestion of expanding beyond the Facebook ecosystem. The myth that “if a customer isn’t on Facebook, they’re on Instagram” is an easy win for marketers too lazy to think about how potential customers use social media. 

Summit’s recent Voice of the Customer Survey looked at social media users in the UK as they went through a purchase journey. 70% of participants regularly used Facebook, 67% used Instagram – but a Facebook user was more likely to use YouTube than Instagram, and an Instagram user was only 6% more likely to use Facebook than Snapchat or TikTok. Social media users aren’t betrothed to just one platform – in fact, on average they use 3.8 different platforms regularly. 

An Analytics Partners study is often quoted when discussing multichannel marketing, and for good reason. It highlights that the highest ROI comes from having a combined approach and that there is a need to move away from channel strategies back to marketing strategies.

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The same logic can be applied in the social ecosystem. If you use Facebook alone, you run the risk of only reaching 59% of users under the age of 34 (according to the Voice of the Customer Survey), and this missed opportunity can become even wider when brands consider their audience make-up.

The need to go beyond just Facebook is clear. Segmenting why a shopper might use each platform further reinforces the point.

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Facebook and Instagram were the social platforms 42% of customers used to complete a purchase but, 47% more participants used Pinterest to get inspiration before a purchase than Facebook, despite a much smaller regular userbase.  21% more participants used YouTube to research a product they were considering buying than Instagram – increasing to 48% when you look at Facebook vs. YouTube – and users who already had TikTok were just as likely to make a purchase from that platform as users with Instagram or Facebook were to purchase directly from them.

We as marketers have a duty to think beyond just one channel, but this is often easier said than done. Directly comparing social platforms is hard, and requires a deep understanding of how customers use social, but it is also unique to each retailer.

WHSmiths do not have the same social challenges as Game, yet they both sell Xboxes online. Facebook ‘wins’ any direct comparison if marketers focus on scale and use Facebook’s own measurement metrics. As soon as you take a step back, consider your audience and your business objectives, you can go beyond cobbling together a quick Facebook campaign because you saw a competitor’s ad, or setting every objective as sales because “revenue is all that matters”.

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While Facebook might drive the most social sales for a retailer, wider-encompassing metrics such as store visits, cost per new customer acquisition and share of voice are brilliant in holding Facebook to account against other social platforms. 

So why is Facebook still the first social port of call for many retailers? It often comes down to ease and scale. It is too easy to run a Facebook campaign, the platform is master of convenience – from the little boost button under a page post to Budget Optimiser, the ad platform has been made to cut time.

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There’s also no denying a large number of potential customers are on Facebook, so reaching an engaged audience on Facebook can sometimes feel like shooting fish in a barrel. But the easiest option is not always the best. 

As marketers trying to support our clients, we need to go back to putting the customer at the heart of planning. Budget fluidity should not be reliant on the contracts you have with certain platforms or the kickbacks you get. A fancy dinner should not be all it costs for you to look the other way as platforms like Facebook lie to your clients and you should never take an advertising platforms word on the performance of a campaign.

No more repeating last year’s plan, no more ad sets without audiences, and no more using Facebook just because it’s the easy option.

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Summit is hosting a webinar later this month titled ‘Are you too social distanced from your customers?’ Learn more here.

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Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

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Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

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Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

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Updated July 18: Developers and advertising partners may be required to share information on their app’s privacy practices in third party app stores, such as Google Play and the Apple App Store, including the functionality of SDKs provided by Meta. To help make it easier for you to complete these requirements, we have consolidated information that explains our data collection practices for the Facebook and Audience Network SDKs.

Facebook SDK

To provide functionality within the Facebook SDK, we may receive and process certain contact, location, identifier, and device information associated with Facebook users and their use of your application. The information we receive depends on what SDK features 3rd party applications use and we have structured the document below according to these features.

App Ads, Facebook Analytics, & App Events

Facebook App Events allow you to measure the performance of your app using Facebook Analytics, measure conversions associated with Facebook ads, and build audiences to acquire new users as well as re-engage existing users. There are a number of different ways your app can use app events to keep track of when people take specific actions such as installing your app or completing a purchase.

With Facebook SDK, there are app events that are automatically logged (app installs, app launches, and in-app purchases) and collected for Facebook Analytics unless you disable automatic event logging. Developers determine what events to send to Facebook from a list of standard events, or via a custom event.

When developers send Facebook custom events, these events could include data types outside of standard events. Developers control sending these events to Facebook either directly via application code or in Events Manager for codeless app events. Developers can review their code and Events Manager to determine which data types they are sending to Facebook. It’s the developer’s responsibility to ensure this is reflected in their application’s privacy policy.

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Advanced Matching

Developers may also send us additional user contact information in code, or via the Events Manager. Advanced matching functionality may use the following data, if sent:

  • email address, name, phone number, physical address (city, state or province, zip or postal code and country), gender, and date of birth.
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Facebook Login

There are two scenarios for applications that use Facebook Login via the Facebook SDK: Authenticated Sign Up or Sign In, and User Data Access via Permissions. For authentication, a unique, app-specific identifier tied to a user’s Facebook Account enables the user to sign in to your app. For Data Access, a user must explicitly grant your app permission to access data.

Note: Since Facebook Login is part of the Facebook SDK, we may collect other information referenced here when you use Facebook Login, depending on your settings.

Device Information

We may also receive and process the following information if your app is integrated with the Facebook SDK:

  • Device identifiers;
  • Device attributes, such as device model and screen dimensions, CPU core, storage size, SDK version, OS and app versions, and app package name; and
  • Networking information, such as the name of the mobile operator or ISP, language, time zone, and IP address.

Audience Network SDK

We may receive and process the following information when you use the Audience Network SDK to integrate Audience Network ads in your app:

  • Device identifiers;
  • Device attributes, such as device model and screen dimensions, operating system, mediation platform and SDK versions; and
  • Ad performance information, such as impressions, clicks, placement, and viewability.

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Enabling Faster Python Authoring With Wasabi

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This article was written by Omer Dunay, Kun Jiang, Nachi Nagappan, Matt Bridges and Karim Nakad.


Motivation

At Meta, Python is one of the most used programming languages in terms of both lines of code and number of users. Everyday, we have thousands of developers working with Python to launch new features, fix bugs and develop the most sophisticated machine learning models. As such, it is important to ensure that our Python developers are productive and efficient by giving them state-of-the-art tools.

Introducing Wasabi

Today we introduce Wasabi, a Python language service that implements the language server protocol (LSP) and is designed to help our developers use Python easier and faster. Wasabi assists our developers to write Python code with a series of advanced features, including:

  • Lints and diagnostics: These are available as the user types.
  • Auto import quick fix: This is available for undefined-variable lint.
  • Global symbols autocomplete: When a user types a prefix, all symbols (e.g. function names, class names) that are defined in other files and start with that prefix will appear in the autocomplete suggestion automatically.
  • Organize Imports + Remove unused: A quick fix that removes all unused imports and reformats the import section according to pep8 rules. This feature is powered by other tools that are built inside Meta such as libCST that helps with safe code refactoring.
  • Python snippets: Snippet suggestions are available as the user types for common code patterns.

Additionally, Wasabi is a surface-agnostic service that can be deployed into multiple code repositories and various development environments (e.g., VSCode, Bento Notebook). Since its debut, Wasabi has been adopted by tens of thousands of Python users at Meta across Facebook, Instagram, Infrastructure teams and many more.

Figure 1: Example for global symbols autocomplete, one of Wasabi’s features

Language Services at Meta Scale

A major design requirement for language services is low latency / user responsiveness. Autocomplete suggestions, lints and quickFixes should appear to the developer immediately as they type.

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At Meta, code is organized in a monorepo, meaning that developers have access to all python files as they develop. This approach has major advantages for the developer workflow including better discoverability, transparency, easier to share libraries and increased collaboration between teams. It also introduces unique challenges for building developer tools such as language services that need to handle hundreds of thousands of files.

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The scaling problem is one of the reasons that we tried to avoid using off-the-shelf language services available in the industry (e.g., pyright, jedi) to perform those operations. Most of those tools were built in the mindset of a relatively small to medium workspace of projects, maybe with the assumptions of thousands of files for large projects for operations that require o(repo) information.

For example, consider the “auto import” quick fix for undefined variables. In order to suggest all available symbols the language server needs to read all source files, the quick fix parses them and keeps an in-memory cache of all parsed symbols in order to respond to requests.

While this may scale to be performed in a single process on the development machine for small-medium repositories, this approach doesn’t scale in the monorepo use case. Reading and parsing hundreds of thousands of files can take many minutes, which means slow startup times and frustrated developers. Moving to an in-memory cache might help latency, but also may not fit in a single machine’s memory.

For example, assume an average python file takes roughly 10ms to be parsed and to extract symbols in a standard error recoverable parser. This means that on 1000 files it can take 10 seconds to initialize which is a fairly reasonable startup time. Running it on 1M files would take 166 minutes which is obviously a too lengthy startup time.

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How Wasabi Works

Offline + Online Processing:

In order to support low latency in Meta scale repositories, Wasabi is powered by two phases of parsing, background processing (offline) done by an external indexers, and local processing of locally changed “dirty files” (online):

  1. A background process indexes all committed source files and maintains the parsed symbols in a special database (glean) that is designed for storing code symbol information.
  2. Wasabi, which is a local process running on the user machine, calculates the delta between the base revision, stack of diffs and uncommitted changes that the user currently has, and extracts symbols only out of those “dirty” files. Since this set of “dirty” files is relatively small, the operation is performed very fast.
  3. Upon an LSP request such as auto import, Wasabi parses the abstract syntax tree (AST) of the file, then based on the context of the cursor, creates a query for both glean and local changes symbols, merges the results and returns it to the user.
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As a result, all Wasabi features are low latency and available to the user seamlessly as they type.

Note: Wasabi currently doesn’t handle the potential delta between the revision that glean indexed (happens once every few hours) and the locally base revision that the user currently has. We plan on adding that in the future.

Figure 2: Wasabi’s high level architecture

Ranking the Results

In some cases, due to the scale of the repository, there may be many valid suggestions in the set of results. For example, consider “auto import” suggestions for the “utils” symbol. There may be many modules that define a class named “utils” across the repository, therefore we invest in ranking the results to ensure that users see the most relevant suggestions on the top.

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For example, auto import ranking is done by taking into account:

  • Locality:
    • The distance of the suggested module directory path from the directory paths of modules that are already imported in this file.
    • The distance of the suggested module directory path from the current directory path of the local file.
    • Whether the file has been locally changed (“dirty” files are ranked higher).
  • Usage: The number of occurrences the import statement was used by other files in the repository.

To measure our success, we measured the index in the suggestion list of an accepted suggestion and noted that in almost all cases the accepted suggestion was ranked in one of top 3 suggestions.

Positive feedbacks from developers

After launching Wasabi to several pilot runs inside Meta, we have received numerous positive feedbacks from our developers. Here is one example of the quote from a software engineer at Instagram:

“I’ve been using Wasabi for a couple months now, it’s been a boon to my productivity! Working in Instagram Server, especially on larger files, warnings from pyre are fairly slow. With Wasabi, they’re lightning fast 😃!”

“I use features like spelling errors and auto import several times an hour. This probably makes my development workflow 10% faster on average (rough guess, might be more, definitely not less), a pretty huge improvement!”

As noted above, Wasabi has made a meaningful change to keep our developers productive and make them feel delightful.

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The metric to measure authoring velocity

In order to quantitatively understand how much value Wasabi has delivered to our Python developers, we have considered a number of metrics to measure its impact. Ultimately, we landed on a metric that we call ‘Authoring Velocity’ to measure how fast developers write code. In essence, Authoring Velocity is the inverse function of the time taken on a specific diff (a collection of code changes) during the authoring stage. The authoring stage starts from the timestamp when a developer checks out from the source control repo to the timestamp when the diff is created. We have also normalized it against the number of lines of code changed in the diff, as a proxy for diff size, to offset any possible variance. The greater the value for ‘Authoring Velocity,’ the faster we think developers write their code.

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Figure 3: Authoring Velocity Metric Formula

The result

With the metric defined, we ran an experiment to measure the difference that Wasabi brings to our developers. Specifically, we selected ~700 developers who had never used Wasabi before, and then randomly put them into two independent groups at a 50:50 split ratio. For these developers in the test group, they were enabled with Wasabi when they wrote in Python, whereas there was no change for those in the control group. For both groups, we compare the changes in relative metric values before and after the Wasabi enablement. From our results, we find that for developers in the test group, the median value of authoring velocity has increased by 20% after they started using Wasabi. Meanwhile, we don’t see any significant change in the control group before and after, which is expected.

Figure 4: Authoring Velocity measurements for control and test groups, before and after Wasabi was rolled out to the test group.

Summary

With Python’s unprecedented growth, it is an exciting time to be working in the area to make it better and handy to use. Together with its advanced features, Wasabi has successfully improved developers’ productivity at Meta, allowing them to write Python faster and easier with a positive developer experience. We hope that our prototype and findings can benefit more people in the broader Python community.

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To learn more about Meta Open Source, visit our open source site, subscribe to our YouTube channel, or follow us on Twitter, Facebook and LinkedIn.

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