Tag Archives: marketing

Zelle Is Using My Name and Voice without My Consent

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/01/zelle-is-using-my-name-and-voice-without-my-consent.html

Okay, so this is weird. Zelle has been using my name, and my voice, in audio podcast ads—without my permission. At least, I think it is without my permission. It’s possible that I gave some sort of blanket permission when speaking at an event. It’s not likely, but it is possible.

I wrote to Zelle about it. Or, at least, I wrote to a company called Early Warning that owns Zelle about it. They asked me where the ads appeared. This seems odd to me. Podcast distribution networks drop ads in podcasts depending on the listener—like personalized ads on webpages—so the actual podcast doesn’t matter. And shouldn’t they know their own ads? Annoyingly, it seems time to get attorneys involved.

What would help is to have a copy of the actual ad. (Or ads, I’m assuming there’s only one.) So, has anyone else heard me in a Zelle ad? Does anyone happen to have an audio recording? Please email me.

And I will update this post if I learn anything more. Or if there is some actual legal action. (And if this post ever disappears, you’ll know I was required to take it down for some reason.)

Facial Scanning by Burger King in Brazil

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/01/facial-scanning-by-burger-king-in-brazil.html

In 2000, I wrote: “If McDonald’s offered three free Big Macs for a DNA sample, there would be lines around the block.”

Burger King in Brazil is almost there, offering discounts in exchange for a facial scan. From a marketing video:

“At the end of the year, it’s Friday every day, and the hangover kicks in,” a vaguely robotic voice says as images of cheeseburgers glitch in and out over fake computer code. “BK presents Hangover Whopper, a technology that scans your hangover level and offers a discount on the ideal combo to help combat it.” The stunt runs until January 2nd.

Scaling marketing for merchants with targeted and intelligent promos

Post Syndicated from Grab Tech original https://engineering.grab.com/scaling-marketing-for-merchants

Introduction

A promotional campaign is a marketing effort that aims to increase sales, customer engagement, or brand awareness for a product, service, or company. The target is to have more orders and sales by assigning promos to consumers within a given budget during the campaign period.

Figure 1 – Merchant feedback on marketing

From our research, we found that merchants have specific goals for the promos they are willing to offer. They want a simple and cost-effective way to achieve their specific business goals by providing well-designed offers to target the correct customers. From Grab’s perspective, we want to help merchants set up and run campaigns efficiently, and help them achieve their specific business goals.

Problem statement

One of Grab’s platform offerings for merchants is the ability to create promotional campaigns. With the emergence of AI technologies, we found that there are opportunities for us to further optimise the platform. The following are the gaps and opportunities we identified:

  • Globally assigned promos without smart targeting: The earlier method targeted every customer, so everyone could redeem until the promo reached the redemption limits. However, this method did not accurately meet business goals or optimise promo spending. The promotional campaign should intelligently target the best promo for each customer to increase sales and better utilise promo spending.
  • No customised promos for every merchant: To better optimise sales for each merchant, merchants should offer customised promos based on their historical consumer trends, not just a general offer set. For example, for a specific merchant, a 27% discount may be the appropriate offer to uplift revenue and sales based on user bookings. However, merchants do not always have the expertise to decide which offer to select to increase profit.
  • No AI-driven optimisation: Without AI models, it was harder for merchants to assign the right promos at scale to each consumer and optimise their business goals.

As shown in the following figure, AI-driven promotional campaigns are expected to bring higher sales with more promo spend than heuristic ones. Hence, at Grab we looked to introduce an automated, AI-driven tool that helps merchants intelligently target consumers with appropriate promos, while optimising sales and promo spending. That’s where Bullseye comes in.

Figure 2 – Graph showing the sales expectations for AI-driven pomotional campaigns

Solution

Bullseye is an automated, AI-driven promo assignment system that leverages the following capabilities:

  • Automated user segmentation: Enables merchants to target new, churned, and active users or all users.
  • Automatic promo design: Enables a merchant-level promo design framework to customise promos for each merchant or merchant group according to their business goals.
  • Assign each user the optimal promo: Users will receive promos selected from an array of available promos based on the merchant’s business objective.
  • Achieve different Grab and merchant objectives: Examples of objectives are to increase merchant sales and decrease Grab promo spend.
  • Flexibility to optimise for an individual merchant brand or group of merchant brands: For promotional campaigns, targeting and optimisation can be performed for a single or group of merchants (e.g. enabling GrabFood to run cuisine-oriented promo campaigns).

Architecture

Figure 3 – Bullseye architecture

The Bullseye architecture consists of a user interface (UI) and a backend service to handle requests. To use Bullseye, our operations team inputs merchant information into the Bullseye UI. The backend service will then interact with APIs to process the information using the AI model. As we work with a large customer population, data is stored in S3 and the API service triggering Chimera Spark job is used to run the prediction model and generate promo assignments. During the assignment, the Spark job parses the input parameters, pre-validates the input, makes some predictions, and then returns the promo assignment results to the backend service.

Implementation

The key components in Bullseye are shown in the following figure:

Figure 4 – Key components of Bullseye
  • Eater Segments Identifier: Identifies each user as active, churned, or new based on their historical orders from target merchants.
  • Promo Designer: We constructed a promo variation design framework to adaptively design promo variations for each campaign request as shown in the diagram below.
    • Offer Content Candidate Generation: Generates variant settings of promos based on the promo usage history.
    • Campaign Impact Simulator: Predicts business metrics such as revenue, sales, and cost based on the user and merchant profiles and offer features.

    • Optimal Promo Selection: Selects the optimal offer based on the predicted impact and the given campaign objective. The optimal would be based on how you define optimal. For example, if the goal is to maximise merchant sales, the model selects the top candidate which can bring the highest revenue. Finally, with the promo selection, the service returns the promo set to be used in the target campaign.

      Figure 5 – Optimal Promo Selection

  • Customer Response Model: Predicts customer responses such as order value, redemption, and take-up rate if assigning a specific promo. Bullseye captures various user attributes and compares it with an offer’s attributes. Examples of attributes are cuisine type, food spiciness, and discount amount. When there is a high similarity in the attributes, there is a higher probability that the user will take up the offer.

    Figure 6 – Customer Response Model

  • Hyper-parameter Selection: Optimises toward multiple business goals. Tuning of hyper-parameters allows the AI assignment model to learn how to meet success criteria such as cost per merchant sales (cpSales) uplift and sales uplift. The success criteria is the achieving of business goals. For example, the merchant wants the sales uplift after assigning promo, but cpSales uplift cannot be higher than 10%. With tuning, the optimiser can find optimal points to meet business goals and use AI models to search for better settings with high efficiency compared to manual specification. We need to constantly tune and iterate models and hyper-parameters to adapt to ever-evolving business goals and the local landscape.

    As shown in the image below, AI assignments without hyper-parameter tuning (HPT) leads to a high cpSales uplift but low sales uplift (red dot). So the hyper-parameters would help to fine-tune the assignment result to be in the optimal space such as the blue dot, which may have lower sales than the red dot but meet the success criteria.

    Figure 7 – Graph showing the impact of using AI assignments with HPT

Impact

We started using Bullseye in 2021. From its use we found that:

  • Hyper-parameters tuning and auto promo design can increase sales and reduce promo spend for food campaigns.
  • Promo Designer optimises budget utilisation and increases the number of promo redemptions for food campaigns.
  • The Customer Response Model reduced promo spending for Mart promotional campaigns.

Conclusion

We have seen positive results with the implementation of Bullseye such as reduced promo spending and maximised budget spending returns. In our efforts to serve our merchants better and help them achieve their business goals, we will continue to improve Bullseye. In the next phase, we plan to implement a more intelligent service, enabling reinforcement learning, and online assignment. We also aim to scale AI adoption by onboarding regional promotional campaigns as much as possible.

Special thanks to William Wu, Rui Tan, Rahadyan Pramudita, Krishna Murthy, and Jiesin Chia for making this project a success.

Join us

Grab is the leading superapp platform in Southeast Asia, providing everyday services that matter to consumers. More than just a ride-hailing and food delivery app, Grab offers a wide range of on-demand services in the region, including mobility, food, package and grocery delivery services, mobile payments, and financial services across 428 cities in eight countries.

Powered by technology and driven by heart, our mission is to drive Southeast Asia forward by creating economic empowerment for everyone. If this mission speaks to you, join our team today!

Stepping up marketing for advertisers: Scalable lookalike audience

Post Syndicated from Grab Tech original https://engineering.grab.com/scalable-lookalike-audiences

The advertising industry is constantly evolving, driven by advancements in technology and changes in consumer behaviour. One of the key challenges in this industry is reaching the right audience, reaching people who are most likely to be interested in your product or service. This is where the concept of a lookalike audience comes into play. By identifying and targeting individuals who share similar characteristics with an existing customer base, businesses can significantly improve the effectiveness of their advertising campaigns.

However, as the scale of Grab advertisements grows, there are several optimisations needed to maintain the efficacy of creating lookalike audiences such as high service level agreement (SLA), high cost of audience creation, and unstable data ingestion.

The need for an even more efficient and scalable solution for creating lookalike audiences was the motivation behind the development of the scalable lookalike audience platform. By developing a high-performance in-memory lookalike audience retrieval service and embedding-based lookalike audience creation and updating pipelines, t​his improved platform builds on the existing system and provides an even more effective tool for advertisers to reach their target audience.

Constant optimisation for greater precision

In the dynamic world of digital advertising, the ability to quickly and efficiently reach the right audience is paramount and a key strategy is targeted advertising. As such, we have to constantly find ways to improve our current approach to creating lookalike audiences that impacts both advertisers and users. Some of the gaps we identified included:

  • Long SLA for audience creation. Earlier, the platform stored results on Segmentation Platform (SegP) and it took two working days to generate a lookalike audience list. This is because inserting a single audience into SegP took three times longer than generating the audience. Extended creation times impacted the effectiveness of advertising campaigns, as it limited the ability of advertisers to respond quickly to changing market dynamics.

  • Low scalability. As the number of onboarded merchant-partners increased, the time and cost of generating lookalike audiences also increased proportionally. This limited the availability of lookalike audience generation for all advertisers, particularly those with large customer bases or rapidly changing audience profiles.

  • Low updating frequency of lookalike audiences. With automated updates only occurring on a weekly basis, this increased the likelihood that audiences may become outdated and ineffective. This meant there was scope to further improve to help advertisers more effectively reach their campaign goals, by targeting individuals who fit the desired audience profile.

  • High cost of creation. The cost of producing one segment can add up quickly for advertisers who need to generate multiple audiences. This could impact scalability for advertisers as they could hesitate to effectively use multiple lookalike audiences in their campaigns.

Solution

To efficiently identify the top N lookalike audiences for each Grab user from our pool of millions of users, we developed a solution that leverages user and audience representations in the form of embeddings. Embeddings are vector representations of data that utilise linear distances to capture structure from the original datasets. With embeddings, large sets of data are compressed and easily processed without affecting data integrity. This approach ensures high accuracy, low latency, and low cost in retrieving the most relevant audiences.

Our solution takes into account the fact that representation drift varies among entities as data is added. For instance, merchant-partner embeddings are more stable than passenger embeddings. By acknowledging this reality, we optimised our process to minimise cost while maintaining a desirable level of accuracy. Furthermore, we believe that having a strong representation learning strategy in the early stages reduced the need for complex models in the following stages.

Our solution comprises two main components:

  1. Real-time lookalike audience retrieving: We developed an in-memory high-performance retrieving service that stores passenger embeddings, audience embeddings, and audience score thresholds. To further reduce cost, we designed a passenger embedding compression algorithm that reduces the memory needs of passenger embeddings by around 90%.

  2. Embedding-based audience creation and updating: The output of this part of the project is an online retrieving model that includes passenger embeddings, audience embeddings, and thresholds. To minimise costs, we leverage the passenger embeddings that are also utilised by other projects within Grab, beyond advertising, thus sharing the cost. The audience embeddings and thresholds are produced with a low-cost small neural network.

In summary, our approach to creating scalable lookalike audiences is designed to be cost-effective, accurate, and efficient, leveraging the power of embeddings and smart computational strategies to deliver the best possible audiences for our advertisers.

Solution architecture

  • The advertiser creates a campaign with a custom audience, which triggers the audience creation process. During this process, the audience service stores the audience metadata provided by advertisers in a message queue.
  • A scheduled Data Science (DS) job then retrieves the pending audience metadata, creates the audience, and updates the TensorFlow Serving (TFS) model.
  • During the serving period, the Backend (BE) service calls the DS service to retrieve all audiences that include the target user. Ads that are targeting these audiences are then selected by the Click-Through Rate (CTR) model to be displayed to the user.

Implementation

To ensure the efficiency of the lookalike audience retrieval model and minimise the costs associated with audience creation and serving, we’ve trained the user embedding model using billions of user actions. This extensive training allows us to employ straightforward methods for audience creation and serving, while still maintaining high levels of accuracy.

Creating lookalike audiences

The Audience Creation Job retrieves the audience metadata from the online audience service, pulls the passenger embeddings, and then averages these embeddings to generate the audience embedding.

We use the cosine score of a user and the audience embedding to identify the audiences the user belongs to. Hence, it’s sufficient to store only the audience embedding and score threshold. Additionally, a global target-all-pax Audience list is stored to return these audiences for each online request.

Serving lookalike audiences

The online audience service is also tasked with returning all the audiences to which the current user belongs. This is achieved by utilising the cosine score of the user embedding and audience embeddings, and filtering out all audiences that surpass the audience thresholds.

To adhere to latency requirements, we avoid querying any external feature stores like Redis and instead, store all the embeddings in memory. However, the embeddings of all users are approximately 20 GB, which could affect model loading. Therefore, we devised an embedding compression method based on hash tricks inspired by Bloom Filter.

  • We utilise hash functions to obtain the hash64 value of the paxID, which is then segmented into four 16-bit values. Each 16-bit value corresponds to a 16-dimensional embedding block, and the compressed embedding is the concatenation of these four 16-dimensional embeddings.
  • For each paxID, we have both the original user embedding and the compressed user embedding. The compressed user embeddings are learned by minimising the Mean Square Error loss.
  • We can balance the storage cost and the accuracy by altering the number of hash functions used.

Impact

  • Users can see advertisements targeting a new audience within 15 mins after the advertiser creates a campaign.
  • This new system doubled the impressions and clicks, while also improving the CTR, conversion rate, and return on investment.
  • Costs for generating lookalike audiences decreased by 98%.

Learnings/Conclusion

To evaluate the effectiveness of our new scalable system besides addressing these issues, we conducted an A/B test to compare it with the earlier system. The results revealed that this new system effectively doubled the number of impressions and clicks while also enhancing the CTR, conversion rate, and return on investment.

Over the years, we have amassed over billions of user actions, which have been instrumental in training the model and creating a comprehensive representation of user interests in the form of embeddings.

What’s next?

While this scalable system has proved its effectiveness and demonstrated impressive results in CTR, conversion rate, and return on investment, there is always room for improvement.  

In the next phase, we plan to explore more advanced algorithms, refine our feature engineering process, and conduct more extensive hyperparameter tuning. Additionally, we will continue to monitor the system’s performance and make necessary adjustments to ensure it remains robust and effective in serving our advertisers’ needs.

References

Join us

Grab is the leading superapp platform in Southeast Asia, providing everyday services that matter to consumers. More than just a ride-hailing and food delivery app, Grab offers a wide range of on-demand services in the region, including mobility, food, package and grocery delivery services, mobile payments, and financial services across 428 cities in eight countries.

Powered by technology and driven by heart, our mission is to drive Southeast Asia forward by creating economic empowerment for everyone. If this mission speaks to you, join our team today!

Hacking Food Labeling Laws

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/08/hacking-food-labeling-laws.html

This article talks about new Mexican laws about food labeling, and the lengths to which food manufacturers are going to ensure that they are not effective. There are the typical high-pressure lobbying tactics and lawsuits. But there’s also examples of companies hacking the laws:

Companies like Coca-Cola and Kraft Heinz have begun designing their products so that their packages don’t have a true front or back, but rather two nearly identical labels—except for the fact that only one side has the required warning. As a result, supermarket clerks often place the products with the warning facing inward, effectively hiding it.

[…]

Other companies have gotten creative in finding ways to keep their mascots, even without reformulating their foods, as is required by law. Bimbo, the international bread company that owns brands in the United States such as Entenmann’s and Takis, for example, technically removed its mascot from its packaging. It instead printed the mascot on the actual food product—a ready to eat pancake—and made the packaging clear, so the mascot is still visible to consumers.

UK Government to Launch PR Campaign Undermining End-to-End Encryption

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/01/uk-government-to-launch-pr-campaign-undermining-end-to-end-encryption.html

Rolling Stone is reporting that the UK government has hired the M&C Saatchi advertising agency to launch an anti-encryption advertising campaign. Presumably they’ll lean heavily on the “think of the children!” rhetoric we’re seeing in this current wave of the crypto wars. The technical eavesdropping mechanisms have shifted to client-side scanning, which won’t actually help — but since that’s not really the point, it’s not argued on its merits.

Banning Surveillance-Based Advertising

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/06/banning-surveillance-based-advertising.html

The Norwegian Consumer Council just published a fantastic new report: “Time to Ban Surveillance-Based Advertising.” From the Introduction:

The challenges caused and entrenched by surveillance-based advertising include, but are not limited to:

  • privacy and data protection infringements
  • opaque business models
  • manipulation and discrimination at scale
  • fraud and other criminal activity
  • serious security risks

In the following chapters, we describe various aspects of these challenges and point out how today’s dominant model of online advertising is a threat to consumers, democratic societies, the media, and even to advertisers themselves. These issues are significant and serious enough that we believe that it is time to ban these detrimental practices.

A ban on surveillance-based practices should be complemented by stronger enforcement of existing legislation, including the General Data Protection Regulation, competition regulation, and the Unfair Commercial Practices Directive. However, enforcement currently consumes significant time and resources, and usually happens after the damage has already been done. Banning surveillance-based advertising in general will force structural changes to the advertising industry and alleviate a number of significant harms to consumers and to society at large.

A ban on surveillance-based advertising does not mean that one can no longer finance digital content using advertising. To illustrate this, we describe some possible ways forward for advertising-funded digital content, and point to alternative advertising technologies that may contribute to a safer and healthier digital economy for both consumers and businesses.

Press release. Press coverage.

I signed their open letter.

How Grab Leveraged Performance Marketing Automation to Improve Conversion Rates by 30%

Post Syndicated from Grab Tech original https://engineering.grab.com/learn-how-grab-leveraged-performance-marketing-automation

Grab, Southeast Asia’s leading superapp, is a hyperlocal three-sided marketplace that operates across hundreds of cities in Southeast Asia. Grab started out as a taxi hailing company back in 2012 and in less than a decade, the business has evolved tremendously and now offers a diverse range of services for consumers’ everyday needs.

To fuel our business growth in newer service offerings such as GrabFood, GrabMart and GrabExpress, user acquisition efforts play a pivotal role in ensuring we create a sustainable Grab ecosystem that balances the marketplace dynamics between our consumers, driver-partners and merchant-partners.

Part of our user growth strategy is centred around our efforts in running direct-response app campaigns to increase trials on our superapp offerings. Executing these campaigns brings about a set of unique challenges against the diverse cultural backdrop present in Southeast Asia, challenging the team to stay hyperlocal in our strategies while driving user volumes at scale. To address these unique challenges, Grab’s performance marketing team is constantly seeking ways to leverage automation and innovate on our operations, improving our marketing efficiency and effectiveness.

Managing Grab’s Ever-expanding Business, Geographical Coverage and New User Acquisition

Grab’s ever-expanding services, extensive geographical coverage and hyperlocal strategies result in an extremely dynamic, yet complex ad account structure. This also means that whenever there is a new business vertical launch or hyperlocal campaign, the team would spend valuable hours rolling out a large volume of new ads across our accounts in the region.

Sample Google Ads account structure
A sample of our Google Ads account structure.

The granular structure of our Google Ads account provided us with flexibility to execute hyperlocal strategies, but this also resulted in thousands of ad groups that had to be individually maintained.

In 2019, Grab’s growth was simply outpacing our team’s resources and we finally hit a bottleneck. This challenged the team to take a step back and make the decision to pursue a fully automated solution built on the following principles for long term sustainability:

  • Building ad-tech solutions in-house instead of acquiring off-the-shelf solutions

    Grab’s unique business model calls for several tailor-made features, none of which the existing ad tech solutions were able to provide.

  • Shifting our mindset to focus on the infinite game

    In order to sustain the exponential volume in the ads we run, we had to seek the path of automation.

For our very first automation project, we decided to look into automating creative refresh and upload for our Google Ads account. With thousands of ad groups running multiple creatives each, this had become a growing problem for the team. Overtime, manually monitoring these creatives and refreshing them on a regular basis had become impossible.

The Automation Fundamentals

Grab’s superapp nature means that any automation solution fundamentally needs to be robust:

  • Performance-driven – to maintain and improve conversion efficiency over time
  • Flexibility –  to fit needs across business verticals and hyperlocal execution
  • Inclusivity – to account for future service launches and marketing tech (e.g. product feeds and more)
  • Scalability – to account for future geography/campaign coverage

With these in mind, we incorporated them in our requirements for the custom creative automation tool we planned to build.

  • Performance-driven – while many advertising platforms, such as Google’s App Campaigns, have built-in algorithms to prevent low-performing creatives from being served, the fundamental bottleneck lies in the speed in which these low-performing creatives can be replaced with new assets to improve performance. Thus, solving this bottleneck would become the primary goal of our tool.

  • Flexibility – to accommodate our broad range of services, geographies and marketing objectives, a mapping logic would be required to make sure the right creatives are added into the right campaigns.

    To solve this, we relied on a standardised creative naming convention, using key attributes in the file name to map an asset to a specific campaign and ad group based on:

    • Market
    • City
    • Service type
    • Language
    • Creative theme
    • Asset type
    • Campaign optimisation goal

  • Inclusivity – to address coverage of future service offerings and interoperability with existing ad-tech vendors, we designed and built our tool conforming to many industry API and platform standards.

  • Scalability – to ensure full coverage of future geographies/campaigns, the in-house solution’s frontend and backend had to be robust enough to handle volume. Working hand in glove with Google, the solution was built by leveraging multiple APIs including Google Ads and Youtube to host and replace low-performing assets across our ad groups. The solution was then deployed on AWS’ serverless compute engine.

Enter CARA

CARA is an automation tool that scans for any low-performing creatives and replaces them with new assets from our creative library:

CARA Workflow
A sneak peek of how CARA works

In a controlled experimental launch, we saw nearly 2,000 underperforming assets automatically replaced across more than 8,000 active ad groups, translating to an 18-30% increase in clickthrough and conversion rates.

Subset of results from CARA experimental launch
A subset of results from CARA’s experimental launch

Through automation, Grab’s performance marketing team has been able to significantly improve clickthrough and conversion rates while saving valuable man-hours. We have also established a scalable foundation for future growth. The best part? We are just getting started.

Authored on behalf of the performance marketing team @ Grab. Special thanks to the CRM data analytics team, particularly Milhad Miah and Vaibhav Vij for making this a reality.

Join Us

Grab is the leading superapp platform in Southeast Asia, providing everyday services that matter to consumers. More than just a ride-hailing and food delivery app, Grab offers a wide range of on-demand services in the region, including mobility, food, package and grocery delivery services, mobile payments, and financial services across 428 cities in eight countries.

Powered by technology and driven by heart, our mission is to drive Southeast Asia forward by creating economic empowerment for everyone. If this mission speaks to you, join our team today!

Opt-in to the new Amazon SES console experience

Post Syndicated from Simon Poile original https://aws.amazon.com/blogs/messaging-and-targeting/amazon-ses-console-opt-in/

Amazon Web Services (AWS) is pleased to announce the launch of the newly redesigned Amazon Simple Email Service (SES) console. With its streamlined look and feel, the new console makes it even easier for customers to leverage the speed, reliability, and flexibility that Amazon SES has to offer. Customers can access the new console experience via an opt-in link on the classic console.

Amazon SES now offers a new, optimized console to provide customers with a simpler, more intuitive way to create and manage their resources, collect sending activity data, and monitor reputation health. It also has a more robust set of configuration options and new features and functionality not previously available in the classic console.

Here are a few of the improvements customers can find in the new Amazon SES console:

Verified identities

Streamlines how customers manage their sender identities in Amazon SES. This is done by replacing the classic console’s identity management section with verified identities. Verified identities are a centralized place in which customers can view, create, and configure both domain and email address identities on one page. Other notable improvements include:

  • DKIM-based verification
    DKIM-based domain verification replaces the previous verification method which was based on TXT records. DomainKeys Identified Mail (DKIM) is an email authentication mechanism that receiving mail servers use to validate email. This new verification method offers customers the added benefit of enhancing their deliverability with DKIM-compliant email providers, and helping them achieve compliance with DMARC (Domain-based Message Authentication, Reporting and Conformance).
  • Amazon SES mailbox simulator
    The new mailbox simulator makes it significantly easier for customers to test how their applications handle different email sending scenarios. From a dropdown, customers select which scenario they’d like to simulate. Scenario options include bounces, complaints, and automatic out-of-office responses. The mailbox simulator provides customers with a safe environment in which to test their email sending capabilities.

Configuration sets

The new console makes it easier for customers to experience the benefits of using configuration sets. Configuration sets enable customers to capture and publish event data for specific segments of their email sending program. It also isolates IP reputation by segment by assigning dedicated IP pools. With a wider range of configuration options, such as reputation tracking and custom suppression options, customers get even more out of this powerful feature.

  • Default configuration set
    One important feature to highlight is the introduction of the default configuration set. By assigning a default configuration set to an identity, customers ensure that the assigned configuration set is always applied to messages sent from that identity at the time of sending. This enables customers to associate a dedicated IP pool or set up event publishing for an identity without having to modify their email headers.

Account dashboard

There is also an account dashboard for the new SES console. This feature provides customers with fast access to key information about their account, including sending limits and restrictions, and overall account health. A visual representation of the customer’s daily email usage helps them ensure that they aren’t approaching their sending limits. Additionally, customers who use the Amazon SES SMTP interface to send emails can visit the account dashboard to obtain or update their SMTP credentials.

Reputation metrics

The new reputation metrics page provides customers with high-level insight into historic bounce and complaint rates. This is viewed at both the account level and the configuration set level. Bounce and complaint rates are two important metrics that Amazon SES considers when assessing a customer’s sender reputation, as well as the overall health of their account.

The redesigned Amazon SES console, with its easy-to-use workflows, will not only enhance the customers’ on-boarding experience, it will also change the paradigms used for their on-going usage. The Amazon SES team remains committed to investing on behalf of our customers and empowering them to be productive anywhere, anytime. We invite you to opt in to the new Amazon SES console experience and let us know what you think.

Should There Be Limits on Persuasive Technologies?

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/12/should-there-be-limits-on-persuasive-technologies.html

Persuasion is as old as our species. Both democracy and the market economy depend on it. Politicians persuade citizens to vote for them, or to support different policy positions. Businesses persuade consumers to buy their products or services. We all persuade our friends to accept our choice of restaurant, movie, and so on. It’s essential to society; we couldn’t get large groups of people to work together without it. But as with many things, technology is fundamentally changing the nature of persuasion. And society needs to adapt its rules of persuasion or suffer the consequences.

Democratic societies, in particular, are in dire need of a frank conversation about the role persuasion plays in them and how technologies are enabling powerful interests to target audiences. In a society where public opinion is a ruling force, there is always a risk of it being mobilized for ill purposes — ­such as provoking fear to encourage one group to hate another in a bid to win office, or targeting personal vulnerabilities to push products that might not benefit the consumer.

In this regard, the United States, already extremely polarized, sits on a precipice.

There have long been rules around persuasion. The US Federal Trade Commission enforces laws that claims about products “must be truthful, not misleading, and, when appropriate, backed by scientific evidence.” Political advertisers must identify themselves in television ads. If someone abuses a position of power to force another person into a contract, undue influence can be argued to nullify that agreement. Yet there is more to persuasion than the truth, transparency, or simply applying pressure.

Persuasion also involves psychology, and that has been far harder to regulate. Using psychology to persuade people is not new. Edward Bernays, a pioneer of public relations and nephew to Sigmund Freud, made a marketing practice of appealing to the ego. His approach was to tie consumption to a person’s sense of self. In his 1928 book Propaganda, Bernays advocated engineering events to persuade target audiences as desired. In one famous stunt, he hired women to smoke cigarettes while taking part in the 1929 New York City Easter Sunday parade, causing a scandal while linking smoking with the emancipation of women. The tobacco industry would continue to market lifestyle in selling cigarettes into the 1960s.

Emotional appeals have likewise long been a facet of political campaigns. In the 1860 US presidential election, Southern politicians and newspaper editors spread fears of what a “Black Republican” win would mean, painting horrific pictures of what the emancipation of slaves would do to the country. In the 2020 US presidential election, modern-day Republicans used Cuban Americans’ fears of socialism in ads on Spanish-language radio and messaging on social media. Because of the emotions involved, many voters believed the campaigns enough to let them influence their decisions.

The Internet has enabled new technologies of persuasion to go even further. Those seeking to influence others can collect and use data about targeted audiences to create personalized messaging. Tracking the websites a person visits, the searches they make online, and what they engage with on social media, persuasion technologies enable those who have access to such tools to better understand audiences and deliver more tailored messaging where audiences are likely to see it most. This information can be combined with data about other activities, such as offline shopping habits, the places a person visits, and the insurance they buy, to create a profile of them that can be used to develop persuasive messaging that is aimed at provoking a specific response.

Our senses of self, meanwhile, are increasingly shaped by our interaction with technology. The same digital environment where we read, search, and converse with our intimates enables marketers to take that data and turn it back on us. A modern day Bernays no longer needs to ferret out the social causes that might inspire you or entice you­ — you’ve likely already shared that by your online behavior.

Some marketers posit that women feel less attractive on Mondays, particularly first thing in the morning — ­and therefore that’s the best time to advertise cosmetics to them. The New York Times once experimented by predicting the moods of readers based on article content to better target ads, enabling marketers to find audiences when they were sad or fearful. Some music streaming platforms encourage users to disclose their current moods, which helps advertisers target subscribers based on their emotional states.

The phones in our pockets provide marketers with our location in real time, helping deliver geographically relevant ads, such as propaganda to those attending a political rally. This always-on digital experience enables marketers to know what we are doing­ — and when, where, and how we might be feeling at that moment.

All of this is not intended to be alarmist. It is important not to overstate the effectiveness of persuasive technologies. But while many of them are more smoke and mirrors than reality, it is likely that they will only improve over time. The technology already exists to help predict moods of some target audiences, pinpoint their location at any given time, and deliver fairly tailored and timely messaging. How far does that ability need to go before it erodes the autonomy of those targeted to make decisions of their own free will?

Right now, there are few legal or even moral limits on persuasion­ — and few answers regarding the effectiveness of such technologies. Before it is too late, the world needs to consider what is acceptable and what is over the line.

For example, it’s been long known that people are more receptive to advertisements made with people who look like them: in race, ethnicity, age, gender. Ads have long been modified to suit the general demographic of the television show or magazine they appear in. But we can take this further. The technology exists to take your likeness and morph it with a face that is demographically similar to you. The result is a face that looks like you, but that you don’t recognize. If that turns out to be more persuasive than coarse demographic targeting, is that okay?

Another example: Instead of just advertising to you when they detect that you are vulnerable, what if advertisers craft advertisements that deliberately manipulate your mood? In some ways, being able to place ads alongside content that is likely to provoke a certain emotional response enables advertisers to do this already. The only difference is that the media outlet claims it isn’t crafting the content to deliberately achieve this. But is it acceptable to actively prime a target audience and then to deliver persuasive messaging that fits the mood?

Further, emotion-based decision-making is not the rational type of slow thinking that ought to inform important civic choices such as voting. In fact, emotional thinking threatens to undermine the very legitimacy of the system, as voters are essentially provoked to move in whatever direction someone with power and money wants. Given the pervasiveness of digital technologies, and the often instant, reactive responses people have to them, how much emotion ought to be allowed in persuasive technologies? Is there a line that shouldn’t be crossed?

Finally, for most people today, exposure to information and technology is pervasive. The average US adult spends more than eleven hours a day interacting with media. Such levels of engagement lead to huge amounts of personal data generated and aggregated about you­ — your preferences, interests, and state of mind. The more those who control persuasive technologies know about us, what we are doing, how we are feeling, when we feel it, and where we are, the better they can tailor messaging that provokes us into action. The unsuspecting target is grossly disadvantaged. Is it acceptable for the same services to both mediate our digital experience and to target us? Is there ever such thing as too much targeting?

The power dynamics of persuasive technologies are changing. Access to tools and technologies of persuasion is not egalitarian. Many require large amounts of both personal data and computation power, turning modern persuasion into an arms race where the better resourced will be better placed to influence audiences.

At the same time, the average person has very little information about how these persuasion technologies work, and is thus unlikely to understand how their beliefs and opinions might be manipulated by them. What’s more, there are few rules in place to protect people from abuse of persuasion technologies, much less even a clear articulation of what constitutes a level of manipulation so great it effectively takes agency away from those targeted. This creates a positive feedback loop that is dangerous for society.

In the 1970s, there was widespread fear about so-called subliminal messaging, which claimed that images of sex and death were hidden in the details of print advertisements, as in the curls of smoke in cigarette ads and the ice cubes of liquor ads. It was pretty much all a hoax, but that didn’t stop the Federal Trade Commission and the Federal Communications Commission from declaring it an illegal persuasive technology. That’s how worried people were about being manipulated without their knowledge and consent.

It is time to have a serious conversation about limiting the technologies of persuasion. This must begin by articulating what is permitted and what is not. If we don’t, the powerful persuaders will become even more powerful.

This essay was written with Alicia Wanless, and previously appeared in Foreign Policy.