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User Experience (UX) is often treated as an afterthought, but good UX is a critical business strategy that drives growth and provides real ROI.

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.

As ad prices rose during the World Cup, small businesses struggled to compete with global brands. Now when the prices will get back to normal, Billo expert says the answer isn’t spending more; it’s creating authentic, creative ads that connect with the right audiences.

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.

The post World Cup Drove Ad Costs Up 17% – Now the Cheapest Window in Months Has Opened appeared first on SiteProNews.

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.

The post Saugat Nayak on How AI Is Helping Lenders Find Creditworthy Businesses Traditional Models Miss appeared first on SiteProNews.

A new social media contender has emerged from Europe. After a soft launch at the World Economic Forum in Davos earlier in the year, “W” (or W Social) has started its beta phase in June. Led by CEO Anna Zeiter, a Swiss data protection expert and former eBay executive, W is positioning itself as Europe’s alternative to American platforms like X. The reason? To reclaim digital sovereignty from American social media platforms and provide an exclusive space for human conversation.

European Digital Sovereignty

European regulators and privacy advocates have grappled with the overwhelming dominance of American tech giants for years. W seeks to change that by ensuring that its entire infrastructure is hosted in Europe, governed by European law, and funded by European shareholders. Zeiter emphasized this during the platform’s launch in Brussels. She also highlighted that Europeans have been providing non-Europeans companies revenue and their personal data.

W aims to keep these resources within the borders of the European Union. This commitment aligns with broader EU initiatives that are designed to reduce dependence on American and Chinese tech giants. There is a strong political movement towards autonomy with European leaders such as Ursula von der Leyen, Antonio Costa, and Christine Lagarde joining the homegrown platform.

Combating AI and Bots

To aggressively combat the flood of bots, AI-generated spam, and disinformation online, W requires users to verify their identity. This is done through a separate app called W Identity, where users must submit a photo of an official passport or national ID card, along with a real-time selfie, before they can post or interact. Users who do not verify themselves can only read content but cannot post anything.

Many believe this is a necessary step to clean up social media and creating a significantly safer online environment. However critics warn that without anonymity people will not be able to freely express themselves, especially for vulnerable individuals who rely on pen names to avoid harassment or political reprisal.

Built on Open Source Technology

W operates on the AT-Protocol, the exact same decentralized, open-source technology that powers Bluesky. This allows W to leverage tried and true social networking technology while customizing it’s ecosystem to fit strict European regulatory standards.

The Future

Taking on behemoths like X, Meta, and TikTok will be a huge challenge. The verification process on W and it’s focus on data privacy will help it but the platform must prove it can scale it’s business beyond tech enthusiasts. Whether or not W becomes the default social media platform for Europe, its launch marks a shift in how Europe and it’s people view the future of the internet.

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