Amazon Expands Same-Day Drone Delivery to Phoenix, Arizona

Amazon is continuing to expand its same-day drone delivery, adding the West Valley Phoenix Metro Area to its service area.

Amazon has been working to cut the time it takes to deliver products, with drone delivery cutting that down to as little as an hour or less. The company says customers near Tolleson, Arizona will benefit from the service.

With this new location, we’re fully integrating into Amazon’s delivery network, meaning, for the first time, our new MK30 drones will deploy from facilities next to our Same-Day Delivery site in Tolleson. These smaller sites are hybrid—part fulfillment center, part delivery station. They allow us to fulfill, sort, and deliver products all from one site so we can get packages out to our customers even quicker.

Our Same-Day Delivery sites are situated close to the large metro areas they serve, which means customers get their orders faster. And with connections to the larger Amazon fulfillment centers nearby, we are able to offer Same-Day Delivery on millions of items.

Interestingly, the drone Amazon uses has been approved for Beyond Visual Line of Sight operations, significantly extending the range of deliveries.

Safety is our top priority. Our new drone, the MK30, has received FAA approval to begin operations to customers. Our approval includes the ability to fly Beyond Visual Line of Sight, using our sophisticated on-board detect and avoid system. This is an historic, first-of-its-kind approval for a new drone system and a new operating location following a rigorous FAA evaluation of the safety of our systems and processes.

This approval allows us to start making Prime Air deliveries to customers in the West Valley Phoenix Metro Area of Arizona. Customers will have access to the over 50,000 everyday essentials—including household products, beauty items, and office/tech supplies—our largest selection of items ever to be available for fast drone delivery at a service fee.

“As Amazon embarks on the national expansion of its Amazon Drone Delivery Program, we’re proud to have their innovative presence in our community. By bringing this service to new communities, they’re not just delivering goods; they’re delivering opportunities and economic growth for all,” said Juan F. Rodriguez, mayor of Tolleson, earlier this year. “Amazon’s commitment to innovation exemplifies the entrepreneurial spirit that drives our city forward.”

“This kind of delivery is the future, and it’s exciting that it will be starting in the Phoenix Metro Area,” said Kate Gallego, mayor of Phoenix, earlier this year. “The shift toward zero-emission package delivery will help us reduce local pollution and further cement our city as a hotbed for the innovative technology of tomorrow.”

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10 Proven Strategies to Maximize ROI in SaaS Marketing

Maximizing return on investment (ROI) in SaaS marketing is crucial for driving growth and retaining a competitive edge. With SaaS products often operating on subscription models, keeping customer acquisition costs low and ensuring high customer retention rates are essential. Here are ten proven strategies to maximize ROI in SaaS marketing that address each stage of the customer lifecycle.

SaaS marketing experts recommend building a strong inbound marketing strategy as a foundational approach. By creating valuable, SEO-optimized content, companies can attract organic traffic and nurture leads through content marketing, blogs, and videos that speak to target customer needs. Additionally, social media marketing amplifies this effort by expanding reach and establishing brand trust. Content-based strategies allow SaaS companies to gain long-term traction by organically pulling in leads who are already interested in solutions that meet their needs.

Another key strategy is leveraging free trials and freemium models. By offering potential customers a taste of the product without upfront costs, SaaS companies can reduce barriers to entry, allowing users to experience the value firsthand. Free trials encourage users to make quicker purchase decisions once they recognize the product’s benefits, while freemium models open up opportunities for users to upgrade as their needs evolve. Both options can be supported by product-led growth tactics, where the product’s functionality encourages user adoption, potentially leading to higher conversion rates.

Customer segmentation is a third, often underutilized, tactic. By dividing customers based on their usage patterns, needs, and behavior, SaaS companies can tailor marketing and customer service efforts for each segment. This personalization enhances user experience and can lead to higher engagement, conversions, and retention rates.

Email marketing remains one of the most cost-effective strategies for nurturing leads and retaining customers. Through segmented email campaigns, SaaS companies can target specific audiences with content that matches their lifecycle stage. Effective email campaigns keep users engaged, boost product adoption rates, and reduce churn.

Optimizing onboarding processes is equally essential. A smooth and engaging onboarding process ensures that customers understand the product and can use it effectively. By guiding new users through product features, SaaS companies can prevent early churn, making onboarding a crucial element in achieving a high ROI.

Using data-driven insights from customer analytics can identify high-value actions that improve customer retention. By analyzing user behavior, SaaS companies can identify engagement trends, optimize product features, and predict churn. This allows for proactive customer outreach to boost user satisfaction and minimize cancellations.

Investing in customer success initiatives is another way to maximize ROI. By offering resources like tutorials, webinars, and support, companies show users how to maximize value from the product, which can drive customer loyalty and reduce churn.

Referral programs are also effective in driving new customers. By incentivizing existing users to refer new customers, SaaS companies can reach new audiences at a lower acquisition cost.

Lastly, paid advertising channels, such as pay-per-click (PPC) and retargeting ads, remain powerful when targeting specific customer profiles. When used strategically, these ads can yield high-quality leads while complementing organic strategies.

Using these proven strategies together can drive sustainable, high-impact ROI in SaaS marketing, allowing companies to grow efficiently and retain satisfied customers over the long term.

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The Best GPT Apps and How Businesses are Leveraging Custom GPTs to Automate Tasks and Enhance Productivity

As AI technology continues to evolve, businesses are increasingly harnessing the power of Generative Pretrained Transformers (GPTs) to automate tasks, streamline processes, and boost productivity.. Here’s a look at some of the best GPT apps available today and how businesses are leveraging custom GPT store top apps within broader AI platforms to maximize efficiency.

Best GPT Apps Transforming Business Operations

1. Copy.ai

   Copy.ai is one of the most popular GPT-based writing tools designed to generate high-quality content at scale. It enables businesses to automate content creation tasks such as blog posts, social media captions, and email marketing. Copy.ai’s user-friendly interface and vast library of templates allow users to customize their content needs efficiently, significantly reducing the time required for marketing and communication tasks.

2. Jasper AI

   Jasper AI, formerly known as Jarvis, is a robust GPT app that focuses on creating long-form content for businesses, including blogs, ads, website copy, and product descriptions. What sets Jasper apart is its ability to integrate with Surfer SEO, making it a favorite for content marketers looking to optimize articles for search engines while ensuring high readability and engagement.

3. Notion AI

   Notion AI is a versatile productivity tool that integrates GPT into the popular workspace app, Notion. With this integration, businesses can automate the creation of meeting notes, summaries, to-do lists, and more, helping teams stay organized and focused on higher-priority tasks. Custom GPTs can do the trick.

4. ChatGPT for Business

   OpenAI’s ChatGPT offers businesses a flexible and highly customizable platform for automating customer service, generating content, and developing specialized tools. With advanced API integration options, ChatGPT can be embedded into customer support systems, HR functions, or internal knowledge bases, allowing businesses to reduce repetitive tasks and improve employee efficiency.

Custom GPTs for Tailored Solutions

Beyond pre-built apps, businesses are increasingly turning to **custom GPTs** to address their specific needs. Custom GPTs allow organizations to fine-tune language models for their own unique datasets, workflows, and industry-specific jargon. This enables businesses to build AI solutions that cater directly to their customers or internal operations, delivering more accurate and contextually relevant outputs.

How Businesses Leverage Custom GPTs to Automate Tasks

1. Customer Support Automation

   One of the most impactful uses of custom GPTs models is in customer support. By integrating custom GPTs into customer service platforms, businesses can automate responses to common queries, freeing up human agents to handle more complex issues. Custom GPTs can be trained on specific customer service scripts or knowledge bases, enabling them to provide personalized responses that enhance the customer experience.

2. Content Personalization and Marketing Automation

   Marketing teams are using GPT-based tools to generate tailored content for their audience at scale. With custom GPTs, businesses can develop models that produce highly personalized email campaigns, product recommendations, and social media content that resonates with specific target demographics. These tools not only reduce the time spent on manual content creation but also boost engagement by providing more relevant content to customers. AI platforms do this well.

3. Internal Knowledge Management

   For large organizations with vast amounts of internal documentation, custom GPTs can help in organizing, summarizing, and retrieving information efficiently. Employees can use GPT-powered tools to ask questions and receive instant answers from company manuals, policies, or product catalogs, improving productivity and decision-making.

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Majority of Employees Say Their CEOs Are ‘Digitally lliterate’

Employees in many companies have an issue with leadership, saying “digital illiteracy” is making CEOs slow to adopt game-changing technology.

AI is in the process of revolutionizing countless industries, with many companies using it to automate repetitive tasks and free up employees to focus on more important things. Unfortunately, according to a report by SThree, many professionals are losing hours of productivity because their companies have yet to make the jump.

STEM professionals are losing nearly six hours each week due to insufficient AI support. This productivity gap isn’t just a minor hiccup; it’s a significant barrier to growth and innovation.

Many employees place the blame squarely on CEOs, with 63% blaming their bosses “digital illiteracy,” saying it holds up adoption of AI technology that could improve their workflow.

63% of respondents who advocate for tech upgrades believe these aren’t adopted due to leadership’s digital illiteracy, and 48% say leadership fails to grasp the productivity benefits.

Unsurprisingly, employees fear their companies are falling behind, with only a very small minority seeing their ideas and recommendations for new technology being adopted.

A startling 49% of respondents feel their companies are trailing their peers in AI implementation. Even more concerning, only 11% of employees who proposed new technologies saw their ideas come to fruition.

“Amid rapid technological advancements, companies must embrace transformation to stay competitive,” said SThree CEO Timo Lehne. “Our latest How the STEM world works study reveals that embracing AI and fostering a trusting work environment are key to unlocking productivity and innovation. Let’s lead with understanding and support for our workforce in this ongoing journey. Read our How the STEM world works study for more insights.”

SThree’s report is worth a read, and sheds insight into the gap that exists between CEOs, as well as other executives, and the employees who stand to benefit from a forward-thinking approach.

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How to Become a Data Analyst in 2024: Embracing AI and Core Skills

In an era marked by rapid technological advancements, the role of a data analyst is evolving at an unprecedented pace. Luke Barousse, a seasoned data analyst, and YouTube content creator, offers a comprehensive guide on how to become a data analyst in 2024. Drawing from his diverse experiences in corporate America and working for top-tier influencers like Mr. Beast, Barousse shares invaluable insights into the tools and skills necessary for this dynamic field.

Core Skills for Aspiring Data Analysts

Before diving into the latest AI tools, Barousse emphasizes the importance of mastering core skills that remain essential in the data analytics landscape. “SQL, or SQL as many call it, tops the list,” he notes. This programming language is crucial for communicating with databases, a fundamental aspect of data analysis. According to Barousse, SQL is mentioned in almost half of all job postings for data analysts, underscoring its significance.

Excel, the ubiquitous spreadsheet software, follows closely. Despite its intended use for ad-hoc analysis, many companies rely heavily on Excel for complex data tasks. “Excel is in about a third of all job postings, which speaks to its continued relevance,” Barousse adds.

When it comes to programming languages, Python and R are prominent. Barousse highlights Python’s versatility, making it suitable for tasks ranging from advanced analytics to machine learning. “Python is nearly as popular as Excel, appearing in almost a third of job postings,” he points out. R, while more specialized, remains a valuable tool for statistical analysis, though it’s less commonly required than Python.

Visualization tools such as Tableau and Power BI are also critical. These tools enable data analysts to create interactive dashboards and visualizations, aiding non-technical stakeholders in understanding complex data insights. “I’ve spent weeks building dashboards that help my colleagues make data-driven decisions,” Barousse shares.

AI Revolution: Transforming Data Analysis

The landscape of data analysis is being reshaped by AI, lowering the barrier to entry and enhancing efficiency. Barousse reflects on his experience building a data analyst portfolio without writing a single line of code, thanks to advancements in AI tools. “The barrier to entry to become a data analyst and actually analyze data is getting lower and lower,” he asserts.

One significant development is the integration of AI into SQL workflows. Barousse uses GitHub Copilot, an AI coding assistant, to speed up query writing and improve efficiency. “Copilot can autocomplete queries and answer questions about SQL syntax, but I’m exploring other tools that might offer even more capabilities,” he says.

Microsoft Excel has also seen transformative updates. The introduction of Microsoft 365 Copilot, which leverages OpenAI’s technology, allows users to ask questions about their data and receive insights directly within Excel. Another major feature is the integration of Python, enabling advanced calculations and analysis within the familiar Excel environment. “These updates make Excel more powerful than ever, bridging the gap between traditional spreadsheets and modern data analysis tools,” Barousse explains.

The Importance of Learning Python

For those starting their journey as data analysts, Barousse recommends Python as the go-to programming language. “Python is a multipurpose language that can handle a wide range of tasks, from data scraping to building web applications,” he says. He also notes that AI coding assistants like GitHub Copilot and Google’s Duet AI can help learners quickly grasp Python by providing real-time feedback and code suggestions.

Visualization Tools: Power BI vs. Tableau

When it comes to visualization tools, Barousse has a preference for Power BI due to its integration with Power Query and DAX functionality. “Power BI makes it easier to clean and analyze data, though Tableau excels in community support and sharing capabilities,” he explains. Both tools have received AI enhancements, with Power BI incorporating a basic version of Copilot and Tableau developing its own AI features under Salesforce’s Einstein Analytics.

AI Assistants and Job Security

A common concern among data analysts is whether AI will replace their jobs. Barousse addresses this by citing a KPMG survey, which found that over half of business leaders expect AI to expand their workforce rather than shrink it. “AI is designed to assist, not replace, data analysts. It enhances productivity and allows us to focus on more complex, value-added tasks,” he emphasizes.

Supporting this view, a Harvard study revealed that consultants using AI were significantly more productive and produced higher quality results compared to those who didn’t use AI. “The data is clear: AI is here to improve our jobs, not take them away,” Barousse concludes.

As Barousse navigates the transformative landscape of data analysis, he remains optimistic about the future. With AI tools streamlining workflows and enhancing capabilities, the role of a data analyst is more dynamic and exciting than ever. For those entering the field, embracing these advancements while mastering core skills is key to thriving in this evolving profession.

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Intel Kills Off One of Its Most Promising Chip Designs

Intel is backtracking on one of its most promising designs, with CEO Pat Gelsinger saying Lunar Lake was a “one-off” that the company has no intention of repeating.

Lunar Lake was a break from Intel’s traditional designs in the way it handled RAM. Rather than being a separate component, Lunar Lake integrated the memory directly on the processor, much like Apple’s M-series chips. In exchange for no ability to upgrade the RAM post-purchase, Lunar Lake offered significant performance and battery savings boosts.

Unfortunately, users hoping that Lunar Lake would become the default path for Intel moving forward are in for a disappointment. According to comments by Gelsinger at the Q3 2024 earnings call, via VideoCardz, Lunar Lake proved too expensive for the company to continue down that path.

That’s at a volume product and a volume industry like the PC industry, you don’t want to have volume memory going through that channel [memory on package]. It’s not a good way to run the business. So it really is, for us, a one-off with Lunar Lake. That will not be the case with Panther Lake, Nova Lake and its successors as well. We’ll build it in a more traditional way iwth memory off package in the CPU, GPU, NPU and I/O capabilities in the package. But volume memory will be off package in the roadmap going forward.

Unfortunately, it seems Lunar Lake’s advances aren’t the only thing Intel may be abandoning. Gelsinger went on to indicate that the company’s discrete graphics cards may also be on the chopping block.

Similarly, in the client product area, simplifying the roadmap, fewer SKUs to cover it. How are we handling graphics and how that is increasingly becoming a large integrated graphics capabilities. So less need for discrete graphics in the market going forward.

The news is sure to disappoint many users. Intel has struggled to compete with Apple M-series, as well as other Arm-based chips, in terms of energy efficiency. Lunar Lake was an important step forward, only for Intel to now be taking two steps back.

The issue is the latest in a long line of challenges Intel is facing as Gelsinger struggles to turn the company around. The situation has become dire enough that lawmakers are reportedly considering more bailout options for the troubled chipmaker.

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GitHub: Python the Most Popular Programming Language As Developer Numbers Surge

GitHub has released “Octoverse 2024,” revealing that Python is now the most popular programming language, and AI is boosting development, not ending careers.

JavaScript was the previous king of programming languages, used for everything from websites to applications to desktop environments. Despite its ubiquity, JavaScript’s reign has finally come to an end, with Python taking the top spot.

As GitHub points out, Python’s rise in popularity owes to its use in data science and machine learning.

In 2024, Python overtook JavaScript as the most popular language on GitHub, while Jupyter Notebooks skyrocketed—both of which underscore the surge in data science and machine learning on GitHub. We’re also seeing increased interest in AI agents and smaller models that require less computational power, reflecting a shift across the industry as more people focus on new use cases for AI.

Interestingly, Python’s rise coincides with a general rise in developers.

Our data also shows a lot more people are joining the global developer community. In the past year, more developers joined GitHub and engaged with open source and public projects (in some cases, empowered by AI). And since tools like GitHub Copilot started going mainstream in early 2023, the number of developers on GitHub has rapidly grown with significant gains in the global south. While we see signals that AI is driving interest in software development, we can’t fully explain the surge in global growth our data reflects (but we’ll keep studying it).

GitHub Octoverse 2024 Metrics – Credit GitHub

GitHub goes on to highlight three major trends in the industry.

• A surge in global generative AI activity. AI is growing and evolving fast, and developers globally are going far beyond code generation with today’s tools and models. While the United States leads in contributions to generative AI projects on GitHub, we see more absolute activity outside the United States. In 2024, there was a 59% surge in the number of contributions to generative AI projects on GitHub and a 98% increase in the number of projects overall—and many of those contributions came from places like India, Germany, Japan, and Singapore.
• A rapidly growing number of developers worldwide—especially in Africa, Latin America, and Asia. Notable growth is occurring in India, which is expected to have the world’s largest developer population on GitHub by 2028, as well as across Africa and Latin America. We also see Brazil’s developer community growing fast. Some of this is attributable to students. The GitHub Education program, for instance, has had more than 7 million verified participants. We’ve also seen 100% year-over-year growth among students, teachers, and open source maintainers adopting GitHub Copilot as part of our complimentary access program. This suggests AI isn’t just helping more people learn to write code or build software faster—it’s also attracting and helping more people become developers. First-time open source contributors continue to show wide-scale interest in AI projects. But we aren’t seeing signs that AI has hurt open source with low-quality contributions.
• Python is now the most used language on GitHub as global open source activity continues to extend beyond traditional software development. We saw Python emerge for the first time as the most used language on GitHub (more on that later). Python is used heavily across machine learning, data science, scientific computing, hobbyist, and home automation fields among others. The rise in Python usage correlates with large communities of people joining the open source community from across the STEM world rather than the traditional community of software developers. This year, we also saw a 92% spike in usage across Jupyter Notebooks. This could indicate people in data science, AI, machine learning, and academia increasingly use GitHub. Systems programming languages, like Rust, are also on the rise, even as Python, JavaScript, TypeScript, and Java remain the most widely used languages on GitHub.

GitHub’s findings are a significant data point in an industry that is in the process of evolving, thanks to AI’s impact. Many developers and industry veterans have been worried that AI would replace programmers, leading to mass firings. Already, companies are relying heavily on AI to help write code.

GitHub’s Findings Echo Statements From Industry Leaders

For example, in a recent quarterly report, Alphabet CEO Sundar Pichai said more than 25% of all Google code has been written by AI. Similarly, Google co-founder Sergey Brin highlighted just how much AI has impacted his development habits.

“I think that AI touches so many different elements of day-to-day life, and sure, search is one of them,” Brin said in an interview with All-In Podcast’s David Friedberg. “But it kind of covers everything. For example, programming itself, the way that I think about it is very different now.

“Writing code from scratch feels really hard, compared to just asking the AI to do it,” Brin added, to laughter from the audience. “I’ve written a little bit of code myself, just for kicks, just for fun. And then sometimes I’ve had the AI write the code for me, which was fun.”

Brin’s experience seems to support GitHub’s findings, that AI is enhancing development and likely leading to a surge in developer engagement.

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