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How these women entrepreneurs leveraged the Facebook family of apps to sustain their …

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The rapid spread of COVID-19 and the consequent lockdowns caused massive disruptions for many businesses in the country.

A Facebook Future of Business study, conducted with the World Bank and the OECD, showed that women-owned small-medium businesses (SMBs) were more likely to report closed due to COVID-19, even when considering the business size, sector, and geography.

The study also that women are disproportionately bearing the burden of domestic responsibilities. Access to finance has also been a critical challenge for the MSME sector, and the same report revealed that almost a third of small businesses in India expect cash flow to be a challenge in the coming months.

At the same time, the studies also found that women business leaders showed a greater degree of flexibility in their business models in response to COVID-19 and were more likely to make more than 50 percent of sales through digital channels.

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Keeping these studies in mind, Facebook sprang into action and enhanced its focus on business skilling initiatives during this period.

Leveraging social media

In an interview with HerStory, Archana Vohra, Director – Small and Medium Businesses, Facebook India, outlined the different programmes initiated to help businesses cope and sustain during this period.

She explains, “Our Managed Partners Program helps mid-tier businesses scale and grow through account management support from our third-party partners. This is available free of cost to customers, and we have enabled 16X more businesses since March 2020.

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“Another flagship programme, the Advertiser Bootcamp, offers deep business learning support through masterclasses and custom content in Hindi and English, and has reached out to 15 million customers on the platform. Our VC Brand Incubator Program, which works with venture capital funds to scale young brands, just finished two years during which time it has tied up with nine VC funds and scaled more than 200 early funded businesses.” 

Archana says the inability to secure timely credit has been a massive impediment to the growth of small businesses.

“The pandemic has been challenging for small businesses, especially women-owned ones. At the same time, insights from our apps indicated that women were showing tremendous resilience, leadership, and optimism during these times. In 2020, women created twice as many fundraisers on Facebook as men did and made twice as many donations, with 64 percent of total funds raised from women.

“Women have also led the way in growing communities and rallying resources, creating 2.7X more COVID-19-related groups than men, with four times more members. And most importantly, despite challenges and uncertainties, women continued to start their businesses – 20 percent of Instagram Business profiles created since November 2020 have the words ‘female/women owned’,” she adds.

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Increasing sales and building communities

Kanika Gupta Shori of Square Yards, Vasavi Polimer of Studio Shreshtha, and Anju Srivastava of Wingreens Farms

Women entrepreneurs did not have it easy, but found different ways to pivot, increase sales, build a strong community of users on Instagram and Facebook, and find new customers.

Anju Srivastava, Founder, Wingreens Farms, a brand that offers sauces, mayo, chips, herbs and seasonings and other products, saw the focus shift from sampling in stores to utilising social media to have a direct line of communication with customers.

Anju explains that the product mix needed to be diversified to empower consumers to cook gourmet meals at home. “Since the pandemic struck, Wingreens has launched over 30 new products spanning five different categories. All of these products/categories have been curated keeping in mind not only ‘taste’ but also ‘easy gourmet cooking at home’.”

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“Facebook and Instagram became our primary channels for brand awareness. These platforms not only allowed us to keep in touch with our customers and generate awareness for our new products but to create a tight-knit community in which the entire Wingreens family can share and grow together,” she adds.

Kanika Gupta Shori, Co-founder and COO of real estate and mortgage platform Square Yards, explains that the first step was to immediately utilise their energies and tech capacities to build an integrated online platform to enable a home buyer to search, view selected projects, and even book and make payments for the desired unit from the comfort of their homes.

“Within days of the lockdown, we were able to pull off a digital platform and evolve our business model in a way that the April to June 2020 quarter ended on a high. As opposed to an expected slump in business, Square Yards ended up capturing 20 percent of the total market share during the quarter, making it one of the best quarterly performances to date,” she says.  

For Vasavi Polimera, Founder, Studio Shreshtha, an Instagram-based women’s ethnic wear label, the business was already doing well online before the pandemic struck.

“While the pandemic brought in challenges, there was also a silver lining. Since physical marketplaces were shut, we were under the impression that this would lead to a manifold increase in online shopping. What we didn’t consider was that the pandemic would also affect people’s purchasing power, and that would, in turn, affect our business,” she explains.

This translated into the business losing sales significantly – the pandemic had started a chain reaction. Vasavi says while some customers wanted to shop occasionally, they were unable to buy from the store, as its Instagram handle could not aid purchases.

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“This is where WhatsApp Business came to our rescue. We adopted WhatsApp Business to ensure that if our customers could not reach us, we reached out to them. The catalogue feature ensured the smooth launch of new product offerings and showcased existing clothing lines in a concise display of offerings. This drove traction, further generating sales due to ease of purchase,” she adds.

Growth during a pandemic

The shift in marketing and social media strategies seems to have paid off for these entrepreneurs.

“Our overall business has grown beyond what it was during pre-COVID times, and the ecommerce revenue has grown more than 30X of what it was pre-COVID, largely due to advertising on Facebook and Instagram,” Anju says.

According to Kanika, Square Yards’ market grew 3-4X. She claims transactions outperformed the industry by 80 percent and the company’s global ambitions were on point, with international businesses contributing to almost one-third of the total revenue.

“The complete online re-modelling of our business meant that our traffic on www.squareyards.com jumped 4X+ to 3 million-plus monthly visitors in 2020. At a time when the economy was badly hit, and business in the real estate sector had suffered losses to the tune of 30-40 percent, we recorded double-digit growth with a double-digit EBITDA margin,” she says.

Though shopping via online channels was already on the rise, the adoption and onboarding of technology sped up post-pandemic, Vasavi says. “This influenced a rise in revenue for us. As time progressed and the pandemic came under control, it translated the shift in consumer behaviour to sales. Being on WhatsApp Business helped us scale up the brand reach and ease the shopping experience for our customers.”

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Optimistic about the future

Anju has big dreams and ambitions for Wingreens and on top of the D2C brand’s list is product expansion.

“You can come to us for Wingreens dips and sauces, RAW juices, almond milk, and protein shakes, Appitas pita chips and other healthy snacking products, The Impatient Baker ready mixes, Spice Rack international herbs, seasonings and Indian spice mixes, breakfast muesli – we will soon have a full range of organic products under Organic Country, fruit yoghurts, cheese, oat milk, regular milk, bread, tea, coffee…the list is endless!”

Square Yards kicked off FY22 by clocking a 50 percent year-on-year growth in revenue at Rs 100.8 crore, with a positive EBITDA run rate, while its Gross Transactional Value (GTV) was up 80 percent at Rs 2,197 crore, as compared to Q1 FY21. Its property transactions also witnessed an 80 percent on-year jump at 3,916 deals during the quarter ending June.

“Despite continuous investments in ramping up distribution capacity and building blocks of new business segments, corporate profitability for Square Yards also held up strongly at 32 percent with positive EBITDA margins. Most importantly, our marketplace has continued to gain momentum with 4.5 million monthly traffic run rate and 50k+ active agents,” Kanika says.

Vasavi is in the process of revamping Studio Shreshta’s website to make it easier for customers to shop through its WhatsApp catalogues that can be accessed directly from its website.

Archana emphasises that Facebook is committed to supporting women-led businesses in India.

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Apart from its Small Business Loans Initiative, Facebook, in collaboration with The Nudge Centre for Social Innovation, incubates and accelerates early-stage women-led nonprofits. In its second phase, the initiative awards six grants of up to Rs 50 lakh for each non-profit to scale its work.  

Created in 2016, Facebook’s #SheMeansBusiness Initiative supports women’s economic empowerment through training in digital skills and providing avenues to expand their business connections and networks. 

“Last year, together with the CSC Academy, we upskilled 2.5 lakh rural entrepreneurs on digital tools related to digital marketing and online safety across 25,594 villages in 12 states. At least 70 percent of the people trained were first-time internet users. The average income of women village level entrepreneurs increased by ~20 percent quarterly,” Archana says.

Edited by Teja Lele Desai

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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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