After a couple hundred client migrations, I can say that assumption is wrong more often than it’s right. Downtime during a migration isn’t inevitable — it’s almost always a DNS problem wearing a hosting costume. Once you understand that, you can design the migration so the DNS problem never happens in the first place.

Why Migrations Actually Go Dark

When people talk about “downtime during migration,” they’re usually describing one specific failure mode: the domain’s DNS records get pointed at the new host before the new host is fully ready, or before the change has propagated to every resolver a visitor might hit. For a window that can range from a few minutes to (worst case) 48 hours, different visitors get routed to different places — some to the old server, some to the new one, some to nothing at all if the new server isn’t serving yet.

The actual file transfer, database migration, and server configuration rarely cause outages on their own — you do that work while the live site keeps serving traffic from its current location. It’s the DNS cutover, the moment you tell the world “the site lives here now,” that’s fragile. So the fix isn’t a faster migration. It’s decoupling the migration work from the DNS cutover entirely.

The Fix: Put Cloudflare Between the Old Site and the Cutover

The technique is simple to describe and only slightly more involved to execute:

1. Point the domain’s DNS to Cloudflare first, before touching hosting at all. Cloudflare’s nameservers become authoritative, but the A/CNAME records inside Cloudflare still point at the old host. Nothing changes for visitors — the old site keeps serving exactly as before, now just proxied through Cloudflare.
2. Do the actual migration in the background. Move files, import the database, configure the new server, test it on its raw IP address or a temporary hostname. The live site, still resolving through Cloudflare to the old host, is completely unaffected by any of this.
3. Verify the new server independently before it ever touches production DNS. Load the site by IP, check the database connected correctly, confirm forms and dynamic features work, and load-test if the site has any meaningful traffic.
4. Flip the A/CNAME record inside Cloudflare from the old host’s IP to the new host’s IP — not the nameservers, just the record. Because Cloudflare is already the authoritative resolver every visitor is hitting, this change propagates through Cloudflare’s edge network almost immediately, instead of waiting on the slow, inconsistent propagation of a full nameserver change across the wider internet.

That last point is the actual trick. Traditional DNS propagation is slow and unpredictable because you’re waiting on thousands of independent resolvers around the world to notice a nameserver change and update their caches, each with its own TTL. Changing a record inside a DNS provider you’re already using is a completely different, much faster operation — you’re not waiting on the internet to notice, you’re just updating one system that’s already in the loop.

The old site never goes dark because it never stops resolving to something valid. There’s no window where a visitor’s browser tries to reach a server that hasn’t finished being set up.

The One Step That Still Makes You Wait

This process isn’t completely frictionless, and I’d rather be upfront about the part that still takes real time: SSL certificate reissuance.

Once the DNS record flips to the new server, that server needs a valid SSL certificate for the domain, and Let’s Encrypt (the free, automated certificate authority most hosts use) has to validate domain ownership before issuing one. That validation depends on the DNS change having taken effect — which means there’s a genuine waiting period, usually a handful of minutes but occasionally longer, between the cutover and having a fully valid certificate live on the new server.

Everything else in this process can be automated, scripted, or done well ahead of time. This step can’t — it’s the one place where I still sit and manually confirm the certificate has actually issued and is serving correctly before considering the migration finished. If you skip that check, you risk a browser security warning for visitors hitting the new server before the cert catches up, which is its own kind of “downtime” even if the site is technically reachable.

A Practical Checklist Before You Flip the Record

• New server is reachable and fully functional by IP or temporary hostname
• Database migrated and verified — spot-check real content, not just that tables exist
• Any forms, logins, or dynamic features tested end-to-end on the new environment
• Email routing (MX records) planned separately — don’t assume web hosting migration and email migration happen on the same timeline
• SSL/TLS is set to “Full” (not “Flexible”) in Cloudflare once the new server has its own valid certificate, to avoid redirect loops
• A rollback plan: if something’s wrong post-cutover, you can point the record back at the old host just as fast as you pointed it forward

Why This Matters Beyond “It Just Works”

The value of this approach isn’t only the absence of downtime — it’s what that absence means for how you can plan the migration. You’re no longer racing a propagation window, which means you’re no longer tempted to do the migration overnight, rush the testing, or skip verification steps to fit inside a shrinking safe period. The migration can happen in normal business hours, get tested properly, and get cut over calmly, because the live site was never at risk in the first place.

For a small business, that difference matters more than it might sound like on paper. A rushed 2 a.m. migration with a live outage clock ticking is where mistakes get made — skipped backups, unverified database imports, DNS changes made in a hurry. Removing the clock removes most of the reasons those mistakes happen.

None of this requires enterprise infrastructure. Cloudflare’s free tier is enough to run this process, which is part of why it scales down to small business migrations as easily as it scales up. The technique isn’t exotic — it’s just rarely the first thing migration guides reach for, because most of them are still solving for “how do I get this done fast” instead of “how do I make the DNS cutover a non-event.”

The post How to Migrate a Website With Zero Downtime (The Technique Most Guides Skip) appeared first on SiteProNews.

Once a person connects to the hacker’s Wi-Fi network, the hacker may be able to see what the victim is doing online and what data they transfer. However, since most websites have HTTPS/TLS encryption, much of what the user does, even on the rogue network, is private.

The risk here is that the hacker may attempt to redirect the victim to a phishing website – for instance, in this case, it may have been a fake Delta login page asking for personal data like name, email, address, etc. Or, the hacker may even go further and provide fake login pages for banks, social media, and try to extract login details from the victims.”

Are the people who connected to the network at risk?

“Connecting to such a network comes with some risk in itself. Connecting to a network controlled by a threat actor allows them to probe your device for potential vulnerabilities and maliciously redirect your internet traffic to their own servers. 

If a person entered credentials into a Wi-Fi login page, noticed security warnings popping up after visiting a website, downloaded something, or entered payment information into an unfamiliar page, then they may have had their data stolen. In that case, the victim should immediately change any passwords that were transmitted, do a thorough scan of their device for malware, and if bank details were transmitted, freeze the bank account until new credentials are received.

However, if a user just connected and disconnected to the Wi-Fi without entering any details or clicking suspicious links, they should be fine.”

The post What risks do fake Wi-Fi networks pose? What is an evil twin attack? appeared first on SiteProNews.

Google Assistant was first launched in 2016 to compete with Apple’s Siri and Amazon’s Alexa. Over the last 10 years Google Assistant has become a leader in the smartphone, smart home, and wearable space.

What Changes for Users?

While the transition will take weeks, once the update hits a user’s device, Google Assistant will no longer be available. This change will effect Android smartphones and tablets, smartwatches, and connected headphones.

There are a few exceptions, for now, smart speakers, Google TV devices, and vehicles with Android Automotive will retain Google Assistant support. Migration plans for these hardware devices is expected in the future.

The Shift to AI

Gemini will automatically take over standard voice triggers, such as the “Hey Google” and holding the power button. Google has spent the past year working on Gemini to handle regular mobile tasks, smart home commands, and answer complex questions so the transition should be a positive one.

The post Google’s Gemini Will Start Replacing Google Assistant appeared first on SiteProNews.

New Billo data shows Meta ad competition rising about 40% and the cost to reach 1,000 impressions climbing roughly 18% between August and the November Black Friday peak. Brands that wait until fall to plan their campaigns enter an auction that is already crowded and expensive, with the outcome largely shaped by creative built weeks earlier. Black Friday and Cyber Monday together make up the single largest ecommerce advertising event of the year, and the brands that prepare earliest tend to capture a disproportionate share of that spending.

Billo, which connects brands with creators to produce social video ads for platforms including TikTok, Meta and YouTube, has tracked the same pattern in its own client data. Donatas Smailys, co-founder and CEO of Billo, said brands that enter Black Friday without tested creative end up paying more for weaker results, regardless of how much they spend.

“Everyone treats Black Friday as a fourth-quarter problem, so everyone ends up competing for the same ad space at the same time,” Smailys said. “Brands that create their ads early and test them in August get better results than brands that wait until October or November. By the time November comes, the brands that tested early already know what works, while the brands that waited are launching untested ads during the most expensive weeks of the year.”

Why August Is the Right Time to Start

September and October are when brands learn which ads actually convert, and that testing takes weeks. August gives brands exactly enough time to brief creators, film several options, and get results back before Black Friday begins.

“You don’t need a media budget to find out if an ad works,” Smailys said. “In August, we tell our clients to post a few short variants organically, each with a different hook and a different creator. A small, specific audience responds within days, and that tells you which version actually lands. The trick is picking the right audience for that test: people who already care about the product. Once you know what they respond to, you know exactly what to put the budget behind in November.”

Brands that follow this approach typically produce three to five short variants per product, each testing a different angle: a different opening line, a different creator, or a different pain point. The variants that perform best organically become the ads brands scale with paid budget once Q4 begins.

Methodology

Figures are based on Billo client Meta ad data, tracked monthly from June through December 2025. Comparisons reflect August 2025 against the November 2025 peak, and where noted, December. Cost per 1,000 impressions reached is a derived figure, calculated as total spend divided by total impressions, multiplied by 1,000; it is not a reported column in the underlying data.

The post Smart Brands Prepare for Black Friday in August Before Ad Costs Climb 18% and Competition Jumps 40% appeared first on SiteProNews.

Startups.co.uk’s latest survey reveals that 90% of AI-using SMEs are worried that their company data is being used to train the AI tool.

This is a sensible apprehension, as company data entered into a public AI model can’t be recalled, deleted, or localized. 

Company security traded for productivity 

According to Startups.co.uk‘s findings, a staggering 50% of SMEs admitted to leaking company data into free public AI chatbots in order to complete work tasks.

Over a quarter of SMEs (28%) are leaking company data into AI chatbots at least once a week, and another 22% are doing so multiple times a month. 

These businesses are effectively trading their long-term corporate security for short-term productivity.

The dangers of leaking company data into AI chatbots:

• Fines & operational disruption

IBM’s 2025 Cost of a Data Breach Report shows that a third (32%) of businesses who reported a data breach had to pay a fine and nearly all organisations suffered operational disruption.

Among organisations that reported recovery, most took more than 100 days on average to do so.

• Competitors could access your data

Proprietary workflows, pitch decks, and internal numbers may already be sitting in a competitor’s future AI prompt response.

Despite widespread concern for company security, startups continue to upload data in order to keep up with daily workloads.

Manual AI security checks waste company time

Startups.co.uk found that businesses are manually preparing data before using AI tools, to help mitigate security risks.

Almost a quarter (23%) of businesses say they spend a ‘significant’ amount of time manually preparing data, meaning it often takes longer to clean the data by hand than AI takes to process it. 

Additionally, over half (52%) of leaders who use AI spend a moderate amount of time manually preparing or anonymising data to ensure privacy and security. 

This need for manual preparation and rework is a clear downside of public AI adoption. Those who do not use public AI face far less friction: only 11% report significant manual rework compared to 22% of public AI users. 

Editor of Startups.co.uk, Zohra Huda, comments:

“Our data shows how startups are caught in a tough productivity trap. Half of all SMEs leak data into public chatbots just to keep up with daily workloads, yet 90% are privately terrified of where that data will end up. UK founders know that blindly feeding proprietary data into a public model means their pitch decks or internal numbers could easily end up in a competitor’s future AI prompt but they feel they don’t have a choice. Is the price of innovation gambling with your company’s intellectual property?”

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This strategy is meant to combat “choice fatigue”. Many subscribers feel overwhelmed by the endless library of TV shows and movies to select from. For some subscribers it can take longer to select a movie to watch than the actual length of the movie itself. Offering pre-programmed, 24/7 feeds would create a frictionless viewing experience. Subscribers overwhelmed by choice fatigue could simply tune in and let a channel run, which would ironically mimic traditional cable TV.

This change would also allow Netflix to compete to with free ad-supported services like Tubi, Pluto TV, and The Roku Channel that have exploded in popularity. A 24/7 feed would also boost Netflix growing ad-supported tier with increased advertising. If implemented, this strategy may well keep viewers engaged and prove that the future of streaming looks a whole lot like traditional cable.

The post Netflix Considers Adding Live TV to Increase Engagement appeared first on SiteProNews.

The shift is a big one. Cloudflare tracks bot versus human traffic and recently found that automated traffic, bots and AI agents, has overtaken human traffic on the web for the first time, crossing 57% of requests to web pages. As WordPress VIP’s CTO described the shift, websites now have two audiences, humans AND AI agents.

It’s a shift that matters a lot more for small businesses than it might sound like at first. When a potential customer queries an AI assistant for “a good web host for a small online store,” or “the best accountant near me,” the AI provides a short and confident answer pieced together through a handful of sources. Your business is either included in that answer or it’s completely invisible.

There is no in between, no page two. There’s no long results page and scrolling. And, most importantly, the analytics dashboard with its traffic, rankings, and pageviews tells almost nothing about what is actually happening. Small businesses that have not figured this out are flying blind!

Old Dashboard Built For One Audience

The old analytics assumed that a person would be typing into a search box and clicking on the results. None of it captures the reality of a new group of visitors that acts nothing like we humans do.

The metrics that most businesses have come to rely on were designed for a human-only web. Metrics like bounce rate, time on page, and session duration can reveal how humans behave on a site, but AI models do not browse that way. They don’t bounce; they don’t spend 90 seconds on a pricing page, for example. The AI reads the content, decides whether it trusts and understands it, and then, either includes you in its answer or moves on, often in just a fraction of a second, and without ever registering as a normal visit.

This is a wake-up call. A business can be doing everything that the old dashboard rewarded and still be losing the game in this new reality. The best SEO no longer defines the winners. Instead, those that win the game are now the ones that AI systems can most easily read and trust, a different skill that requires different measurements.

Three Things Worth Measuring Now

There is, however, some good news in all of this. A costly new analytics stack isn’t needed in order to serve this new world. Simple visibility of three key things will provide an edge over what the old dashboard ignores.

1. AI Visibility & Citations

Starting at zero with the most basic question is critical. When people ask AI tools about your category do you show up and is what AI says about you accurate? Consider this as the AI-era equivalent of a Google ranking with the exception that almost no one is checking it.

Site owners can do this easily in as little as ten minutes. Once a month, query ChatGPT, Claude, Perplexity, Gemini, and Google’s AI-powered search results, asking the questions that your customers might actually ask. Questions like “the best [your category] for [your type of customer],” and “is [your business] any good,” as well as “who are the alternatives to [a competitor].”

Note whether or not you appear in the results as well as the AI’s accuracy. Such a simple, manual check serves as a baseline that most competitors still don’t have.

2. Conversion Events, Not Just Clicks

The second shift comes in recalibrating what gets counted as success. The goal has been getting more people to the site, boosting traffic. But AI is changing what a visit now looks like. The raw number of visits is less important than what those visitors actually did on the site.

So now the metric to keep closer attention to moves downstream, from clicks to actions. Things like form submissions, phone-number taps, bookings, quote requests, actual purchases are what we should be paying closer attention to.

Today, the AI-assisted visitor who arrives, reads a few pages, and then books a call is worth far more than a hundred casual clicks. If a dashboard is still centered around traffic volume, the old game is still being measured. Measure the actions that mean money, and you will make better decisions about where your site is actually working.

3. Performance Under Bot Load

The third metric happens to be the one that most businesses miss entirely, and it is where this whole conversation connects back to your web host.

With bots and AI agents now accounting for more than half of all web traffic, websites are being hit far more often, and differently than even just a year ago. AI crawlers are not polite browsers, looking at one page at a time. They tend to request many pages rapidly, hammering parts of your site a human would never touch. That can generate load patterns your setup was never tuned for.

Consider this. When an AI crawler comes to your site to answer a potential customer’s question and the site is slow, timing out, or throwing out errors under the load, you won’t get cited. The AI will move to a competitor whose site answered more cleanly and didn’t time out. The key is being reachable and fast when a bot comes calling; this has become a new ranking factor in the AI era. A site that buckles under the pressure of automated traffic will be invisible.

Uptime and server response time are not abstracts. It’s how your host handles sudden bursts of automated requests – the unglamorous infrastructure layer that most small business owners never think about until it costs them.

What To Actually Do About It

While this new world might feel overwhelming, there’s no reason for panic. None of it means abandoning what already works. Good SEO is still important. Human visitors still matter. Don’t toss the old dashboard, simply stop treating it as the whole picture.

Start with the things that don’t cost anything. Run the manual AI visibility check this month and note where you stand. Check the facts, make sure your information is clear, consistent, and easy to find. AI crawlers reward a site that plainly states who you are, what you do, and who you serve. Shift attention from raw traffic to visitor actions that actually convert. Check with your host and ask about site performance when automated traffic spikes, are you optimized for when a crawler comes calling?

Thriving in this new world means noticing early on that the web now has two audiences and to start measuring for both. Those that keep watching only the old dashboard won’t necessarily fail, but they will slowly become invisible to more than half of the web that is now deciding who gets recommended. The opportunity is ripe as not many are paying attention to this yet, which means, for now, that simply looking is an advantage!

The post Your Website Now Has Two Audiences. Only One of Them Shows Up in Your Analytics appeared first on SiteProNews.

Starting at $28,350 ($29,945 after transportation fees), the Fathom is reasonably priced when compared gas power alternatives. Pre-orders will begin in early 2027 and deliveries are expected later in the year.

The mid-sized Fathom truck features include:

• Seating for five adults, with a nice sized cargo bed, and a front trunk.
• The Fathom also comes with BlueCruise hands-free highway driving assistant, Android Auto compatibility, Apple Maps integration, and Apple CarPlay.

By combining an affordable price with an eco-friendly EV, Ford is positioning the Fathom to be a real winner.

The post Ford’s New Fathom Will Be A Game-Changer appeared first on SiteProNews.

When implemented it delivers the best value to a business and especially when it is customized properly. Hence, choosing the right partner matters for a business

This article shows what a business must look while selecting a Salesforce partner, what to ask and how to take informed decisions for a business.

Why Does Choosing the Right Salesforce Development Company Matter?

When it comes to Salesforce implementation, then it is a long-term investment. By selecting the right partner helps to meet the business goals and make the process fit into the software. As per the state of sales report it shows that sales teams are making use of AI automation & CRM platforms. In the future it is going to create millions of jobs over $2 trillion in business revenues worldwide by 2028.

However, working with a Salesforce development company can help with –

• Reduce implementation risks
• Improve CRM adoption across teams
• Automate repetitive task
• Build scalable Salesforce applications
• Increase productivity
• Maximize ROI from Salesforce investments

It’s not just about installing software, however an experienced partner can help develop a CRM that helps to grow the business.

What Services Should a Salesforce Development Company Offer?

A business must hire a reliable partner that can provide end-to-end salesforce development services beyond basic implementation.

Look for partners who offers with –

• Salesforce consulting services
• CRM strategy and planning
• Salesforce implementation
• Salesforce customization
• Salesforce CRM development
• Salesforce integration services
• App development
• Data migration
• Testing and quality assurance
• User training
• Ongoing support and maintenance

These all services provided smoother execution of processes at one roof with long-term support.

Difference Between a Salesforce Consultant and a Salesforce Implementation Partner

Businesses become confused between Salesforce Consultant and a Salesforce Implementation Partner. A salesforce consulting company is a firm that helps businesses develop CRM strategies, analyze business processes & suggest salesforce products. Whereas in terms of salesforce implementation partner, its all about handling the technical stuffs like –

• System setup
• Configuration
• Custom development
• Data migration
• Integrations
• Testing
• Deployment
• Post-launch support

An experienced & skilled provider offers both salesforce consulting services with strategic guidance to the businesses.

Which Experience Should You Look for in Salesforce Developers?

It is not necessary that all developers have the same level of experience. However an experience Salesforce developer must know about – 

• Apex programming
• Lightning Web Components (LWC)
• Visualforce
• Salesforce APIs
• Flow Automation
• Experience Cloud
• Sales Cloud
• Service Cloud
• Marketing Cloud
• Data security
• Integration architecture

Rather than focusing on coding they must understand the actual business process. They must possess experience in working with different industries like healthcare, finance, manufacturing, retail etc.

Why Are Salesforce Consulting Services Important Before Development?

In order to avoid unnecessary costs and complicated CRM systems it is important to go with salesforce consulting services. This helps businesses –

• Identify current challenges
• Define project goals
• Prioritize features
• Improve workflows
• Reduce customization costs
• Choose the right Salesforce products

By doing this it ensures that the development is meeting the business goal & objective rather than just creating assumptions. This helps to save expensive rework as well.

What Should You Ask Before Hiring a Salesforce Development Company?

While hiring a salesforce development company there are many questions that strike a business. However, some of the important questions to ask are –

• How many Salesforce projects have you completed?
• Do you have certified Salesforce professionals?
• Which industries have you worked with?
• Can you share client case studies?
• How do you manage project communication?
• What is your development methodology?
• How do you handle data migration?
• What happens after project deployment?
• Do you provide long-term support?

The answers will help to know the experience and reliability of the company.

Why Is Salesforce Customization Important?

Different businesses have their unique workflows. It is not important that each feature may support the process. Hence, for this reason salesforce customization is important.

Customization includes:

• Custom objects
• Custom workflows
• Approval processes
• Dashboards
• Reports
• Role-based access
• Industry-specific applications
• Automated notifications

This makes the operation simple, avoiding unnecessary complexities. An experienced salesforce development partner can help to add value to the business.

The goal is to simplify operations without making the platform unnecessarily complex.

How Do Salesforce Integration Services Improve Business Operations?

Nowadays, businesses make use of multiple software applications. This includes –

• ERP systems
• Accounting software
• HR platforms
• Marketing automation tools
• Payment gateways
• E-commerce platforms
• Customer support software

This requires proper integration otherwise it leads to wastage of time in switching systems and adding the data manually. Hence, professional salesforce integration services help connect with applications into the business ecosystem.

• Data accuracy
• Operational efficiency
• Customer experience
• Reporting
• Decision-making

Once integrated well into the salesforce environment it helps to get real-time data across all departments.

Which Signs Indicate a Reliable Salesforce Consulting Company?

Following are some of the indicators to look for hiring a reliable salesforce consulting company

Certified Salesforce Professionals

Salesforce certifications show their technical expertise experience.

Strong Client Portfolio

Case studies and testimonials represents their success stories they work for different industries

Transparent Communication

A good partner needs to represent project timelines, costs, and project challenges.

Agile Development Process

They must follow an agile approach that can get continuous feedback, faster improvements, and better project flexibility.

Post-Implementation Support

Even after deployment, Salesforce requires continuous support. Select a partner that provides post-launch support like user support, platform update and other enhancement.

Conclusion

Selecting the right Salesforce development company is like getting the right technical expertise. They must be able to understand goals, challenges and support with long-term business plans. A right partner must provide Salesforce consulting services, Salesforce Development Services, Salesforce CRM development, Salesforce customization, Salesforce integration services, and ongoing support as well.

Before selecting, analyze the company experience, expertise, certifications and most importantly methodology & testimonials. This is because choosing the right one can help improve efficiency and get long-term ROI with tailored salesforce solutions. Invest with the right expertise and develop a strong customer relationship in the coming years.

The post How to Choose the Best Salesforce Development Company appeared first on SiteProNews.

Browser Reality

Atlas was marketed as a revolutionary. The Atlas browser included ChatGPT built right into the sidebar and also offer a premium “agent” that was capable of navigating websites, comparing products, and completing tasks like making restaurant reservations.

The browser faced intense competition and drew scrutiny from the cybersecurity community. Cybersecurity research firms were concerned with severe vulnerabilities embedded in the AI agents, warning that a malicious website could prompt the browser to execute data leaks or perform unauthorized actions.

Returning Focus

OpenAI decision to end Atlas reflects a return to it’s core business. The company is to pivoting away from expensive side projects. This refocus has also led to the cancellation Sora, OpenAI’s AI video generator in April.

Rather trying to pull market share from entrenched competitors like Google Chrome or Microsoft’s Edge, OpenAI has decided to focus on integrating AI into existing daily workflows, rather than forcing users to adopt new platforms.

The post OpenAI Says Goodbye to Atlas appeared first on SiteProNews.

Digital Sovereignty

Digital sovereignty has been gaining momentum over the past few years, driven by stricter data protection laws and growing geopolitical tensions. Governments and companies alike are increasingly concerned about storing sensitive information in data centers that subject to foreign rules and regulations. The European Union’s GDPR, along with national cybersecurity laws in Germany, France, Canada, Australia, and more have reinforced the need for cloud environments that guarantee data residency.

Many cloud service providers have introduced sovereign cloud tailored for specific regions. Microsoft, Google Cloud, Amazon Web Services (AWS), and Oracle have all partnered with governments around the world to deliver cloud platforms meet local requirements.

Worldwide Growth

Europe is a leader in sovereign cloud adoption. Governments and regulated industries such as healthcare, banking, and defense are investing heavily in sovereign cloud capabilities to protect critical data but maintaining access to cloud services. Collaborative efforts between European technology providers and American big tech strike a balance between innovation and regulatory compliance.

The Asia-Pacific region is also growing quickly. Countries including India, Japan, Singapore, and Australia are implementing regulations that require local data storage and local cloud infrastructure. As cyber threats evolve, sovereign cloud solutions are becoming essential for protecting critical data and national infrastructure.

Sovereign cloud demand is growing in North America among public sector organizations and industries like banking that handling highly sensitive information. While the United States has always required secure government cloud platforms, Canada is increasingly prioritizing data sovereignty for public services and regulated sectors. This prioritization reflects Canada’s concerns about data privacy, cybersecurity, and cross-border data access.

The Future

Implementing sovereign cloud solutions presents a number of challenges. Building a localized infrastructure requires significant investment and careful consideration of sovereignty requirements for scalability, and cost efficiencies. Maintaining interoperability across international borders may also become an issue.

The sovereign cloud is expected to become a foundational component of the global digital transformation. Artificial intelligence, critical infrastructure, and public services will increasingly rely on cloud technologies. Both businesses and governments will continue seeking secure, compliant, and local platforms. Sovereign cloud is the evolution of cloud services with in an increasingly data-driven and geopolitically tense world.

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Streaming services, including Disney Plus late last year, have been increasing prices regularly for years and consumers are starting to feel the pinch. Free services such as YouTube, Tubi, and Pluto TV are growing quickly, capturing almost 20% of all U.S. television watch time. Disney likely hopes to capture a piece of this advertising only market and reduce subscriber churn rate.

A free version of Disney Plus would likely only provide access to a curated selection of older films and classic television shows. This strategy would allow Disney to encourage viewers to upgrade to it’s premium service with access to the latest movies and TV shows while earn advertising income from the free version viewership.

The main competitors for Disney Plus like Netflix currently have no free option. If Disney Plus were to push ahead with this idea it would add millions of viewers to it’s platform and give it a leg up on it’s competition.

The post Subscription Fatigue causing Disney Plus to consider a Free Streaming Option? appeared first on SiteProNews.

ROI Benefits of Good Design

When a company invests in it’s own UX, it removes friction and bottlenecks from the user journey and the financial benefits can be substantial:

• Higher Conversion Rates: A seamless checkout prevents cart abandonment and increases the number of paying customers.
• Lower Support Costs: If a product or service is easy to use, customers do not need to contact support as often. This can significantly lower the costs of providing customer service.
• Increased Retention: It has always been cheaper to keep an existing customer than to acquire a new one.

Business owners recognize that every dollar invested in UX yields a ROI, transforming design teams into revenue generators for the company.

Evolving Customer Expectations

Consumers interact daily with large tech companies like Amazon, Apple, and Spotify. These companies that have set standard for UX and as a result, user expectations are for this level of UX.

If an app is slow or an e-commerce site is hard to navigate, users will not hesitate move to a different product or service. Websites and apps must work quickly as customer patience is at an all-time low. A superior user experience is often what separates a company in a crowded marketplace.

Brand Perception

For modern businesses a poorly designed website or app can damage your brand credibility. Users will equate a frustrating UX with a company being untrustworthy. A thoughtful and aesthetically pleasing UX builds trust and fosters a positive experience with the brand. It signals that the company values its customers.

Innovation Through User Experience

UX is deeply rooted in user research and testing. By prioritizing the user experience, businesses can ensure they are genuinely solving real human problems before committing massive development resources. This mitigates business risk and take executive assumptions out of the equation allowing business to focus on products or service improves that users actually want.

Final Thoughts

Treating UX as a business strategy means that what is important to your users are also important for your business. It is the realization that you cannot have business growth without providing a good user experience. When you are completing with the world online, a great UX is not optional, it’s required.

The post Why Good UX Is Necessary For Your Business appeared first on SiteProNews.

The World Cup final took place on Sunday, closing out the most expensive advertising period in years. New data from creator marketing platform Billo shows advertisers’ price of a single video ad rose 17%, compared with the average from July through September 2025. There was an increase in 13 of the 15 e-commerce categories tracked.

Billo, a marketplace connecting brands with video creators, analyzed more than 13,000 ads across 15 categories on Meta, TikTok, and YouTube Shorts. The data points to what small businesses should do now that the tournament premium is lifting: spend on relevance, not volume.

The analysis showed that, unsurprisingly, the category that benefited from the World Cup the most was Sporting Goods. Their relevance helped them to achieve a 28.9% hook rate, making it the highest of 15 tracked categories. Purchases tied to those ads rose 26% over the 2025 monthly average, while click-through rates climbed about 31%.

Donatas Smailys, CEO of Billo, says rising costs and global campaigns leave small businesses with few good options.

“The World Cup creates a battle for attention that goes far beyond the matches themselves,” said Smailys. “Big brands can run campaigns across TV, social media, sponsorships, and creators all at once. Small businesses are chasing the same customers, but they can’t respond by matching that budget.”

Billo isn’t the only one tracking rising ad costs. According to Common Thread Collective, Meta’s cost per thousand impressions, what it costs to show an ad to 1,000 people, hit a four-year high this summer, above $17, up from roughly $12 in summer 2024. WARC expects the World Cup to add $10.5 billion to global ad spending this quarter.

“When advertising gets this competitive, a business really has three choices,” Donatas Smailys added. “Spend more, reach fewer people, or find a more relevant way to get noticed. The third option is what we saw play out in Sporting Goods.”

Sporting Goods Beat Reach with Relevance

Relevance is exactly what saved sporting goods, the category most connected to the tournament. Billo’s data shows sporting goods ads had a hook rate of 28.9%, the highest of all 15 categories tracked, meaning more viewers kept watching past the first few seconds instead of scrolling past. Purchase numbers tied to those ads rose 26% compared with the category’s 2025 monthly average, and click-through rates climbed about 31%.

Sporting goods advertisers also got roughly 15% more revenue per dollar than the overall average, while spending about 16% less per ad than average in other categories.

However the ads for this category were still more expensive than last year. Spend per ad in the category still rose, about 37% above if we compare it to the sporting goods 2025 benchmark.

“Sporting goods matched what people were already watching and talking about. However, the lesson for smaller brands isn’t to bolt a football reference onto every ad,” said Smailys. “It’s finding a real connection between the product and the moment – getting ready for a match, hosting friends, whatever people are already doing.”

The Post-Tournament Playbook

Billo’s advice for the weeks ahead: don’t put everything behind one big ad. Build several shorter versions with different hooks and different faces, test them, then move budget to whichever performs. Target communities that already care about a sport or a team rather than the whole tournament audience.

“Prices drop once the final’s over, and that’s your chance to make back some of what this month cost,” Smailys said. “Post a few ideas organically first and let the audience tell you what’s landing. That signal is free. Give creators a clear brief and let them run with it, then put money behind the one that’s already working.”

Methodology

Billo’s analysis compares advertising performance in June 2026 with the average monthly performance from July–September 2025, the most recent consistent dataset available. The 2026 dataset covers more than 13,000 creator-made video ads across 15 e-commerce categories, compared with 79,347 ads analyzed across the three-month 2025 comparison period. Spend-per-ad figures reflect media costs on advertising platforms, not Billo’s fees. Sporting goods changes were measured against the category’s 2025 monthly average; cross-category comparisons were made within June 2026.

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Artificial intelligence is becoming a competitive tool for lenders seeking to grow small-business portfolios, improve risk selection, and reduce the cost of fraud and manual underwriting. The opportunity is especially significant in segments where traditional credit models may overlook viable businesses because they rely too heavily on limited financial histories, collateral, or conventional borrowing patterns.

Saugat Nayak is a data science and risk analytics specialist with more than 15 years of experience across financial technology, telecommunications, and consulting. His work focuses on machine learning-driven credit risk, real-time fraud detection, behavioral analytics, and AI decision systems deployed in production environments. He has helped examine how alternative data and dynamic behavioral signals can give lenders a more current view of business performance while supporting faster, more consistent decisions.

In this interview, Nayak discusses how financial institutions can use AI to identify creditworthy small businesses that legacy models may miss, reduce false positives in fraud detection, and move risk models successfully from research into day-to-day operations. He also explains why data quality, explainability, model monitoring, and cross-functional adoption are essential for institutions that want to expand lending responsibly while improving efficiency and capturing underserved market opportunities.

Traditional credit scoring has been the foundation of lending for decades. Why do you believe these models often fall short when evaluating small and minority-owned businesses?

Traditional credit scoring was designed in a different era, for a different borrower profile. It relies heavily on credit history, debt-to-income ratios, and collateral, basically metrics that favor established businesses with long financial track records. The problem is that many small and minority-owned businesses simply don’t fit that mold, not because they’re poor credit risks, but because they’ve historically operated outside the systems that generate those signals.

A business owner who bootstrapped their operation, reinvested cash flow instead of taking on debt, or built their customer base through community networks won’t show up well in a FICO-based model. That doesn’t mean they’re not creditworthy but It means the model isn’t asking the right questions.

You’ve spent much of your career developing AI-powered risk models. How are machine learning and behavioral analytics changing the way lenders assess creditworthiness?

The shift I’ve seen is from static snapshots to dynamic patterns. Traditional models look at where a borrower has been. Machine learning lets us look at how they behave and that behavioral signal is often far more predictive than a credit score.

In my work building risk models for SME lending, I’ve seen how variables like cash flow consistency, transaction velocity, seasonal patterns, and even supplier payment behavior can tell a much richer story about a business’s health than a balance sheet alone. Behavioral analytics adds another layer and it captures how borrowers interact with financial products over time, flagging anomalies that indicate risk or, equally important, patterns that indicate reliability that traditional scoring would miss entirely.

What excites me most is that these models get better with data. The more diverse the borrower population I train on, the more nuanced and fairer the model becomes.

Many small businesses struggle to access financing despite having healthy operations. How can alternative data help create a more complete picture of a borrower’s financial health?

Alternative data fills in the gaps that traditional underwriting leaves blank. When I work on credit risk models for small business lending, I look at signals like point-of-sale transaction data, payroll consistency, inventory turnover, and even online review sentiment as proxies for business health. These aren’t exotic data sources but they’re traces that every operating business leaves behind naturally.

For a restaurant, consistent weekend revenue spikes and steady supplier payments tell me far more about viability than a two-year-old tax return. The challenge is building models that can synthesize these signals responsibly, making sure the data is relevant, not just correlated.

AI has the potential to improve lending decisions, but it also raises concerns about fairness and bias. What steps should financial institutions take to ensure these systems remain transparent and equitable?

This is something I think about deeply in my work, especially given the populations these models most directly affect. The first step is acknowledging that bias in AI doesn’t appear out of nowhere but it’s inherited from biased historical data. If my training data reflects decades of discriminatory lending, my model will learn those patterns unless I actively intervene.

Practically, that means building fairness constraints directly into the model development process, not treating them as an afterthought. It means testing model outputs across demographic segments before deployment, not just overall accuracy metrics. And it means investing in explainability, lenders need to be able to tell a borrower why they were declined in plain language, which is both a regulatory expectation and a basic matter of fairness.

Explainable AI isn’t just a compliance checkbox; it’s what makes these systems trustworthy enough to actually expand access rather than entrench exclusion.

Fraud prevention has become increasingly important as financial services continue to digitize. How can AI help organizations detect fraud while maintaining a positive customer experience?

The tension between fraud detection and customer experience is real, and it’s one I’ve worked on directly. The traditional approach, flagging anything that looks unusual and putting it through a manual review queue creates friction that legitimate customers feel acutely, while sophisticated fraudsters find workarounds.

What AI enables is much more precise targeting. By building behavioral baseline profiles for individual users, models can distinguish between a customer who is traveling and making unusual purchases versus a fraudster who has compromised an account. That precision means fewer false positives, which means fewer good customers getting blocked at the worst possible moment. The key is layering real-time scoring with contextual signals like device fingerprinting, geolocation consistency, session behavior, so the system is making decisions based on a full picture, not a single anomaly.

As financial institutions adopt more sophisticated AI systems, what are some of the biggest implementation challenges you’ve seen when moving these models from research into real-world production environments?

The gap between a model that works in a notebook and a model that works in production is where most AI initiatives quietly fail. I’ve seen this pattern repeatedly. In research, data is clean, latency doesn’t matter, and edge cases can be manually reviewed. In production, none of those luxuries exist.

The challenges I see most often are data pipeline fragility, model drift, and organizational readiness. A model trained on last year’s data starts degrading the moment the market shifts and in SME lending, economic conditions can shift fast. Building monitoring systems that catch drift early, and retraining pipelines that can respond quickly, is as important as the model itself.

The other challenge is people getting underwriters, compliance teams, and product managers to actually trust and act on model outputs requires investment in communication and education that most ML teams underestimate.

Looking ahead, How do you see AI reshaping access to capital for entrepreneurs and underserved communities over the next five to ten years?

I’m genuinely optimistic about this, with some important caveats. The next decade will see alternative data and real-time underwriting become standard practice rather than competitive differentiators. That structural shift will meaningfully expand the pool of bankable small businesses, particularly in communities that traditional lending has chronically underserved.

What gives me confidence is that the economic incentive aligns with the social one. There are hundreds of billions of dollars in untapped lending opportunity in the SME segment. Lenders who build models sophisticated enough to identify creditworthy borrowers that legacy scoring misses aren’t just doing good. They’re accessing a market their competitors are leaving behind. That alignment of profit and purpose tends to drive real, sustained change.

What advice would you give financial institutions that want to leverage AI not only to improve operational efficiency but also to expand responsible lending and better serve small business owners?

Start with the problem you’re actually trying to solve, not the technology. I’ve seen institutions invest heavily in AI infrastructure before they’ve clearly defined what success looks like for their specific borrower population and risk appetite. That leads to models that are technically impressive but operationally irrelevant.

The institutions doing this well are the ones treating AI as a continuous capability, not a one-time project. They invest in data quality upstream, they monitor model performance downstream, and they build cross-functional teams where data scientists work alongside credit officers and compliance leads from day one. Responsible lending isn’t a constraint on AI, rather it’s a design principle. The best models I’ve built have been the ones where fairness, explainability, and accuracy were treated as equally important from the very first line of code.

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