1. What criteria are most critical for your enterprise when considering a transition to a new ad management solution (e.g., automation capabilities, cross-platform integration, AI-driven optimization)?

With the rise of machine learning and AI-driven ad campaigns, like Google's Performance Max and Meta’s Advantage+ shopping campaigns, much of the campaign optimization is handed over to algorithms. As such, marketers and advertisers may lose direct control over individual ad placements, and targeting decisions, with the platforms recommending or even automating many key aspects.

As a result, many marketers feel like they’re working within a “black box” because they don't have full transparency into how these platforms make decisions about ad delivery, budget allocation, audience targeting, and performance measurement—limiting their ability to make fully informed optimizations. 

That is an important reason why advertisers should consider using a digital advertising platform - as these platforms can offer more transparency.

That is the major premise behind Shirofune, a company I founded in 2014. Shirofune, offers a transformative approach to automated advertising management, blending traditional campaigns with AI-driven ones – providing a far more comprehensive perspective, not to mention transparency, allowing marketers to analyze and understand how different channels contribute to overall performance.

For example, I programmed Shirofune to automatically handle budgeting, and bidding, and even to provide suggestions and explanations for why performance is either getting better or worse, just like an actual human expert.


The Shirofune platform is designed to function like a professional agency ad manager, automating key operational tasks while delivering improved campaign performance. Its AI-powered optimization engine dynamically adjusts budgets, bids, and strategies across platforms, giving marketers more time to focus on strategy rather than execution.

What customers are saying about Shirofune:

One of our clients, a major brand, told us they wanted to have a more programmatic approach to their advertising.

For example, when it came to budgeting, our customer told us that they used to have to create extra side reports to see if budgets were on or off target – and they just didn't have a holistic approach to navigating the spending.

Rather than relying on manual adjustments, Shirofune continuously analyzed performance across multiple data points — including ROAS, CPC, CVR, and volume — to identify statistically relevant trends. Now, underperforming ads or targeting settings can be automatically deprioritized within days, not weeks. Budget and bid modifiers are adjusted in real-time based on actual results, allowing the team to test, learn, and shift strategies with speed and precision — all without the delay of manual reporting cycles. 

Shirofune has also empowered clients like Yamazen Corporation and Newegg Commerce to achieve measurable business growth. For Yamazen, Shirofune's automation capabilities resulted in a 30% increase in ad-driven sales and an 18% improvement in ROAS within two months. Similarly, Newegg Commerce saw an 18% increase in clicks and a 10% improvement in ROAS, demonstrating the platform's ability to dynamically optimize campaigns during high-converting periods.

For Dentsu, the platform reduced bid and budget management tasks by 45% and client reporting workloads by 62%, enabling the agency to scale operations while maintaining quality. These outcomes showcase Shirofune's ability to drive innovation, improve operational efficiency, and deliver tangible business value.

2. How important is automation in your digital advertising strategy, particularly features like automated budget allocation, bid adjustments, and target ROAS optimization?

In the digital advertising world, the be-all and end-all metric is ROAS. However, manual budget allocation makes optimizing ROAS nearly impossible: there are simply too many factors to stay on top of. Digital ad platforms, by contrast, make it quite easy.

ROAS is just one of the many objectives. The Shirofune platform makes testing against different objective optimizations significantly easier, so advertisers can better understand how changes in campaign goals can impact performance.

Shirofune is designed to handle cross-channel ad budget allocation using both data and AI — making real-time adjustments that human teams simply can't replicate at scale. 

○     Here is what the Shirofune platform can accomplish:

1.    Automated Budget Allocation:
Automatically adjusts daily ad spending based on performance data across all platforms. If Meta underperforms one day and Google is converting better, your budget shifts accordingly — no guesswork, no delays.

2.    Unified Strategy Layer:
Shirofune offers only one interface that manages, compares, and optimizes campaigns across multiple channels. This leads to faster decision-making and holistic budget planning.

3.    Continuous Optimization with AI:
The role of AI is paramount. AI doesn’t just track metrics — it learns from performance patterns over time and adjusts spend, bidding, and targeting dynamically.

Let’s take a client we have worked with for the past year. The ability to allocate appropriate budgets to each campaign and dynamically adjust target ROAS as per budgets can lead to a 10%+ increase in ROAS, including an 18% increase in clicks while reducing ad spend. By using Shirofune, the client was able to get a more accurate assessment of Google Ads and Microsoft Advertising performance.

3. What challenges have you faced in achieving seamless cross-platform budget allocation and performance analysis, and how are you addressing them?

We have helped many of our clients rely on Shirofune to automate their budget allocation based on performance data. Our system constantly compares campaigns and placements, identifying where each dollar delivered the strongest return. Budgets were shifted daily — sometimes hourly — toward top performers, and bid modifiers were adjusted across devices, times of day, and audiences. The platform’s automation ensured that spending followed results, not assumptions, which meant more budget went to what was working and none was wasted on what wasn’t.

4. What role does LTV-based optimization play in your advertising strategy, and how are you implementing it across campaigns?

Return on ad spend (ROAS) has traditionally been considered the gold standard for measuring the financial return of ad campaigns. Its primary focus is on short-term revenue, however, it doesn't consider either customer lifetime value (LTV) or new user acquisition, while indiscriminately mixing both new and existing customer conversions in its ROAS calculations. This narrow focus may please clients with immediate results but can overlook opportunities.

In contrast, Lifetime ROAS is emerging as a more holistic metric that assesses the value of acquiring new users as well as the long-term engagement and profitability of customers. By optimizing campaigns to identify and target customers with higher LTV, businesses can enhance their overall profitability and achieve sustainable growth.  

This information is continuously evaluated by the system and used to make real-time decisions that actually optimize ROI. These decisions are undergirded not just by primary data but by deep, AI-augmented analyses of performance patterns over time. In other words, these ad platforms are not merely reactive but predictive.

Instead of rapidly cycling between spreadsheets, advertisers can get an at-a-glance view of cross-platform campaign performance on a unified interface. Instead of groping in the dark to build monthly or quarterly ad budgets out of guesswork, they can undertake long-range planning with the kind of confidence that only AI-enhanced data can provide.

○     Data Accessibility: LTV-based ad management requires more robust data from a breadth of sources. For example, Google knows when people buy products on e-commerce platforms through its advertisements. Shopify, on the other hand, can identify whether customers are new or existing and can track if customers come in organically and when they make return purchases. Google does not have access to this range of data, so both of these sources would better enable LTV. Capturing and analyzing customer data benefits from integrating different elements, from customer relationship management (CRM) software to analytics tools, in order to better track and measure buyer behavior.

○     Predictive Modeling: Recent advances in artificial intelligence have increased predictive modeling capabilities. Predicting customer LTV accurately is crucial for making informed advertising decisions. By leveraging machine learning algorithms and historical customer data, businesses can build better predictive models to estimate LTV and allocate resources strategically.

○     Testing and Optimization: No matter how advanced technology tools become, humans will always need to fine-tune systems and course correct as needed. LTV-based ad management requires continuous testing and optimization. Experimenting with different ad creatives, platforms and targeting strategies is essential to identify the best-performing combinations that maximize LTV.

5. As your organization expands globally, how are you ensuring that your advertising management platforms can support diverse markets and regional requirements?

Shirofune is built on a philosophy of automating what experienced professionals desperately need—replicating human insight and decision-making at scale. Marketers and agencies require continuity, automation, and trusted support, and Shirofune’s goal is to offer advertisers an exciting new way forward for a complete digital marketing program.

Moreover, Shirofune continues to adapt to emerging channels such as programmatic and retail media, which are expected to grow significantly. As advertising complexity increases, Shirofune not only keeps pace with industry changes—it anticipates them, earning the trust of brands and agencies alike.” 

6. What strategies are in place to adapt your advertising campaigns to different cultural and market dynamics while maintaining brand consistency?

We need to change to -  what specific steps have you taken so that US agencies understand and appreciate Shirofune?:

The Shirofune ad platform has a 91% market share in Japan because it automates most of the work, cutting the time needed to manage online advertising by at least 50%. We want to replicate our success in Japan in North America, giving American marketers and agencies the same savings in time and the most optimized performance in ad spending. Shirofune streamlines and standardizes ad operations, is incredibly easy to use and delivers superior results and intelligence across all digital ad channels.

In order to bring Shirofune to the United States and other markets around the world, we have broadened our reach by joining more associations and organizations such as the Association of National Advertisers. 

We also are constantly updating the platform.  We recently announced the integration with Google’s DV360 (Display & Video 360), an enterprise-level demand-side platform (DSP) for programmatic ad campaigns.

Additionally, we integrated LinkedIn Ads into our digital advertising automation management platform. The integration offers advertisers and agencies the ability to manage their LinkedIn ad campaigns alongside other major platforms such as Google Ads, Facebook, Instagram, Amazon Ads, Microsoft Advertising, Pinterest, TikTok, BigCommerce, and Shopify.

Shirofunealso recently integrated with BigCommerce to predict and optimize ad campaigns based on customer lifetime value. The integration allows advertisers to connect ad performance data with customer lifecycle insights, including whether customers are new or returning, their purchasing behaviors, and predicted lifetime value.

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1. With prices correcting and inventory rising, how should prospective buyers adjust their strategy when entering the market now?

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1. What best practices should publishers follow to streamline their ads.txt approach without sacrificing revenue opportunities?

The best approach to ads.txt is grounded in quality over quantity. Publishers should conduct regular audits to remove outdated or inactive sellers and focus on maintaining partnerships that consistently perform. A clean, accurate file makes it easier for buyers to identify trusted paths and prioritize inclusion in curated deals. This contributes directly to supply path optimization, reducing waste while improving monetization. In a tighter economic climate, clarity in ads.txt also helps buyers allocate budgets with confidence, especially when paired with exclusive inventory access.

2. What are the biggest misconceptions about reducing intermediaries in ads.txt, and how should publishers approach this strategically?

The biggest misconception is that all intermediaries are inefficient or unnecessary. In reality, some resellers provide real value by facilitating performance, offering proprietary demand, or enhancing yield through optimization technology. The goal should not be indiscriminate removal but smart curation. Publishers should assess partners based on financial outcomes, not just labels.

It’s a bit like buying a rare classic car. Just because you’re purchasing it through a trusted dealer instead of the original owner doesn’t mean it’s any less valuable. The same logic applies to reseller paths that connect directly to premium inventory. Strategic pruning reduces clutter while keeping the partners that contribute to scale, efficiency, and even sustainability by minimizing duplicative auction activity and tech strain.

3. What key factors should advertisers and publishers consider when evaluating reseller relationships?

Performance, transparency, and efficiency should be the benchmarks. Advertisers and publishers need to evaluate whether a reseller provides access to exclusive inventory, enhances campaign delivery, or brings in demand that improves fill rates and yield. Curation is key here. In a market impacted by inflation, tariffs, and tighter margins, every intermediary must help stretch budgets and reduce complexity. The best partners are those who drive measurable results while lowering the total cost of acquisition and delivery.

4. How does an optimized programmatic supply chain contribute to reducing ad fraud and improving trust in digital advertising?

An optimized supply chain simplifies the buyer-to-publisher path, which inherently reduces opportunities for fraud and inventory spoofing. With fewer unknown entities in the mix, buyers gain a clearer view of where their ads are showing and how their dollars are being spent. This transparency builds trust and helps brands feel confident investing in programmatic, especially when coupled with curated, exclusive supply paths. It also improves sustainability by reducing unnecessary auction calls and network load.                                                                                                                                                                                                        

5. What role do resellers play in ensuring quality inventory and optimizing programmatic transactions?

Resellers often provide the connective tissue between premium publishers and buyers. Many operate curated marketplaces, manage direct publisher relationships, or offer tools that improve on-page delivery and contextual alignment. These capabilities contribute to supply path optimization by streamlining access and reducing operational bloat. When done right, resellers also support sustainability by improving efficiency and cutting down on excessive bidding activity. Their impact is especially valuable in a market where economic efficiency is non-negotiable.

6. How does transparency in the supply chain influence advertiser confidence and budget allocation in programmatic advertising? 

Transparency is the foundation of smarter, more efficient media buying. When advertisers can clearly trace where inventory comes from and who is involved in the transaction, it eliminates guesswork and builds confidence in the ecosystem. That trust leads to better allocation of media budgets toward curated, premium, and exclusive inventory paths that offer stronger performance and lower waste. In this economy, efficiency and accountability are just as important as reach.

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1. How is your organization exploring the integration of AI-driven commerce within content to create new monetization streams and deepen consumer engagement?

At Shopsense, we’re focused on transforming content into commerce in a way that feels seamless, scalable and deeply tied to consumer passion. That’s because fandom drives purchases; whether it’s tied to a talk show, drama or live sports, viewers are inspired by what they watch on-screen. We built our AI engine to ingest video, image and text-based content to identify contextually relevant products and automatically generate curated storefronts around that very content. In fact, we’ve already ingested over 1 billion SKUs from more than 1,250 retail partners, so our system can quickly surface product matches—including exact matches or items that are stylistically aligned—without requiring a ton of labor and manual effort. This capability is what allows us to unlock monetization opportunities wherever content lives, and gives publishers, networks and creators tools to activate their IP in new and meaningful ways.

2. How do you plan to maintain editorial integrity while embedding commerce solutions within native content experiences?

When it comes to content, we’re not the creators—we’re the enablers. Here’s one way to think about it: our technology uses existing content as a creative input and pairs it with shoppable experiences that are authentic and aligned. But of course it’s critical that the content partners maintain full control of the experience. We work closely with each partner to respect their brand, editorial and audience guidelines, which could mean weeding out certain retailers, filtering by price point, or blocking entire categories. For example, a publisher may want to exclude big-ticket luxury items from a mass-market piece, and so our models adapt accordingly. Because we’re not authoring the editorial, but rather enhancing it with intelligently surfaced commerce, we’re able to preserve integrity while still driving value.

3. What role does personalization and product relevance play in improving conversion rates and retaining audience attention in a saturated content market?

Our whole ethos is that product relevance is everything. When we’re able to surface the exact lipstick worn by a talk show host or the really cool blazer featured in a scripted series, we see conversion rates jump—as much as 4 times higher compared to more generic thematically aligned recommendations. Audiences are emotionally invested in the content they consume. If someone connects with a character or host, they want that exact same item because it’s a tangible extension of their emotional connection to the content. That’s where our AI-powered personalization engine really shines. We’re able to tap into fandom and emotional connection at scale, and bridge the gap between the screen you’re watching and the shopping that’s inspired by that screen. In fact, nearly 63% of consumers say they discover new brands or products through TV content, which shows just how powerful entertainment has become as a driver of commerce.

4. How are you rethinking your monetization model to capitalize on the convergence of storytelling and shopping, particularly among Gen Z and Millennial audiences?

This is exactly why we prioritize the second screen experience. Gen Z and Millennials are fully accustomed to consuming content with a phone in hand—it’s their default behavior. We’ve seen this play out: if a show is shoppable but the call-to-action isn’t clear or well timed, even the most eager viewers might miss it. But when we lean into that phone-in-hand behavior—surfacing real-time shoppable moments on mobile, aligning products with the content they’re already immersed in—engagement rates spike when we meet them in the moment on the device they’re already using. For younger audiences, content-integrated commerce isn’t disruptive when it’s done right; it’s an experience they expect these days. The key is to meet them in the moment without asking them to shift context.

5. As commerce integration expands across editorial, video, and social content, how are you preparing your organization to scale AI-driven commerce across multiple formats and channels?

Even though we launched last year with broadcasters in our sights, we built Shopsense from day one to be format-agnostic. Whether it’s text, video, stills or live content, our models can ingest and interpret it—identifying relevant products, creating storefronts and tagging shoppable moments across channels. Because our product library is already robust and our AI is contextually trained, we can easily plug into new formats with very little friction. We’ve already proven we can do this across digital articles, linear TV, CTV and social posts. So as content continues to fragment across platforms, which it inevitably will, we’re positioned to scale along with it, and to ensure commerce is embedded natively and intelligently wherever audiences choose to engage.

6. How is your company managing the balance between leveraging contextual data for commerce personalization and maintaining user trust and data privacy?

This is such a great question. We focus on contextual signals, not behavioral ones. Our approach to personalization is rooted in the content itself, rather than tracking individual users or hoovering up personal data. What that means is that we can deliver highly relevant product recommendations while keeping user trust intact. In other words, our technology is privacy-first by design, offering our partners a compliant way to unlock commerce-driven value without the thorny complexity of data collection. And for consumers, the experience feels seamless and secure and something they don’t have to second-guess. We believe the smartest personalization simply requires intelligent alignment between content and commerce.

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1. How are you positioning yourself to support clients in navigating regulatory, cultural, and market-specific complexities? 

Launching our newest office in the MENA region allows us to build a team of regionally based talent, possessing an expert understanding of the diverse regulatory landscape, cultural significance, and market-specific complexities. We are positioned as true partners to our clients, so we don’t just deliver the final product; we guide them through cultural nuances and regulatory guidelines to ensure they comprehensively understand these markets. 

2. How do you plan to tailor services such as digital health innovation, patient engagement, and multichannel marketing to meet the specific cultural and clinical needs of the MENA audience? 

Central to all our work is a core understanding of how communities across different MENA markets access, trust, and act on health information. In this region, tailoring solutions accordingly is necessary, whether it’s adjusting digital tools or incorporating cultural and social considerations. From hyper-specific messaging to leveraging trusted community figures, our engagement strategies are rooted in cultural respect and clinical relevance.

3. How can you integrate global best practices with local healthcare priorities to deliver meaningful outcomes for patients and healthcare professionals?

We benefit from a dual approach. Our work is guided by the Havas Life global mission to inspire ideas and improve lives, which ensures alignment with international best practices and standards. At the same time, we operate with deep local insights, allowing us to balance global consistency and local relevance. This dual approach empowers impactful, meaningful, and localized work.

4. How do you ensure that storytelling and campaign strategies reflect the cultural values, health literacy levels, and communication preferences of diverse populations? 

Authentic storytelling starts with audience research to understand beliefs, behaviors, and cultural contexts that shape how patients in the MENA region engage with information. This localized strategy informs the tone of voice, imagery, and how we structure information within a campaign. Values, literacy levels, and communication preferences aren’t just respected; they’re embedded in every step of the creative process, influencing how the work is conceived, created, and shared.

5. What are the biggest risks or barriers to success in this regional expansion, and how is the leadership team planning to mitigate them? 

One of the key challenges in the MENA region is the fragmented regulatory landscape. Each market requires its own approval process, which can create additional complexities for clients operating across borders. We’ve addressed this by structuring an agency with regulatory fluency, ensuring we can provide expert guidance at both local and regional levels.

We are also working to overcome the uneven pace of digital impact while the demand for healthcare communications accelerates. Some markets are highly digitized with advanced healthcare ecosystems, while others are still building digital foundations. This means we tailor digital strategies market-by-market.

6. How do you see the role of healthcare advertising evolving in the next 3–5 years, and how are you preparing for this shift?

We believe healthcare advertising is shifting from transactional to transformation. As the landscape becomes more consumer-centric, we expect advertising to take on more responsibility for building awareness and providing education, empowerment, and navigation. The most successful campaigns won’t just inform; they will inspire action, drive behavioral changes, and support long-term health outcomes. We’re building teams, acquiring tools, and seeking partnerships to set us up for early success as this shift takes shape.

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1. How critical is it for organizations to detect and address data collection activity that occurs after a user opts out? 

It's mission critical. If businesses can record a consumer opt-out of data sharing or selling but fail to stop firing tags and trackers according to that consumer’s wishes, are consumer preferences really being respected? Certainly not.