digital marketing 13 Feb 2025
How does AI enhance personalization in marketing automation, and what impact does this have on customer engagement and conversion rates?
Ryan: By analyzing customer data—like behaviors, preferences, and demographics—and using that to create more relevant, timely content, AI can help personalize your marketing efforts. It allows businesses to send tailored emails with personalized subject lines, localized content, promotions, or product recommendations, making the customer experience feel more individualized.
This kind of targeted outreach leads to higher engagement, as customers are more likely to open emails and take action when they think (and feel) the content speaks to their specific needs. As a result, it often boosts conversion rates and helps businesses build stronger customer relationships.
In what ways does AI improve the process of lead nurturing through marketing automation, and how does this contribute to the sales funnel?
Ryan: Today’s AI tools can help you eliminate time-consuming tasks like automating follow-up emails, segmenting leads based on behaviors, and providing personalized content recommendations. By analyzing these past interactions, it can also predict which leads are most likely to convert, ensuring the right message reaches the right person at the right time. And, by automating these tasks, businesses can stay top of mind without overwhelming their teams. This is a more efficient nurturing process that can help you move leads smoothly through the sales funnel, increasing the likelihood of conversion.
How is predictive analytics helping businesses anticipate customer behavior and make data-driven marketing decisions?
Ryan: Predictive analytics uses historical customer data to forecast future behaviors. For instance, it can predict when a customer is likely to make a purchase or even when they might churn. With these insights, businesses can proactively adjust their marketing strategies, offering targeted promotions or re-engagement campaigns so your business stays top of mind. By taking a more data-driven approach, you can ensure you’re focusing your resources where they’re most likely to drive results, helping optimize marketing efforts, maximize ROI, and ultimately, drive sales.
How does AI enable real-time adjustments to marketing strategies, and what impact does this agility have on business growth?
Ryan: If something isn’t working, AI can suggest or implement changes—whether it’s adjusting subject lines, targeting, or content. This agility helps businesses stay relevant, respond to customer needs faster, and continually improve their strategies. Plus, it allows you to make real-time adjustments, continuously monitor campaign performance – tracking metrics like open rates and engagement – and quickly identify trends. As a result, businesses can grow by staying competitive, improving customer experiences, and maximizing their marketing impact.
What challenges do businesses face when adopting AI-driven marketing automation, and how can they overcome them?
Ryan: The biggest challenge is often the complexity of integrating AI with existing systems and processes. For many small businesses, the technology can seem intimidating and overwhelming. However, AI tools designed specifically for small businesses can simplify this, helping you fit automation into the marketing work you’re already doing.
To overcome these challenges, businesses should start with easy-to-use AI tools that automate routine tasks (like content creation and segmentation) and gradually scale as they get more comfortable. Don’t forget: Maintaining the human touch is still important and keeps your content authentic—AI should assist, not replace, personal insights.
How do businesses measure the return on investment (ROI) of AI-powered marketing automation, and what metrics are most indicative of success?
Ryan: You can start measuring your results by looking at metrics like engagement rates, conversion rates, and customer retention. For example, if AI helps you generate more relevant emails, leading to better open rates and more clicks, that directly impacts revenue. You can also track how much time and effort AI saves by automating repetitive tasks, resulting in increased efficiency. By evaluating both the financial return and time savings, businesses can gauge the full value of their AI investment.
marketing 11 Feb 2025
What are the psychological and cultural implications of removing a platform deeply embedded in users' daily lives?
The millions of users who flock to the platform daily have become dependent on this source of information. Users come to the platform to learn about current events, share their personal experiences and even foster new communities of likeminded individuals near and far. Dissipating a platform that has become a massive sociocultural communication tool could potentially create a void at the individual, community, or cultural level. The platform’s user community at large along with any sub-communities who depended on this platform may be forced to displace their feelings of solidarity and support; ultimately, dealing with the stress of sudden disconnect and the burden of fostering this once global, sociocultural connectivity elsewhere.
What strategies can marketers use to avoid "shiny object syndrome" when navigating shifts in the social media ecosystem?
I believe a solid marketing strategy should be data-driven and balance proactive and reactive approaches. To me personally, defining your brand’s core values is the foundation. You must ensure that no matter what “shiny objects” arise, your brand is recognizable, consistent, and authentic in any trends you lead or join. With this in mind, do research on your audiences, new and old. Stay true to the brand, cater to these valuable audiences, and let the performance data and measurable metrics tell you the rest. That way at any time, you can pivot your attached audiences as you redefine your strategy in accordance with ever-changing macro or micro trends.
How can marketers prepare for the emergence of new platforms if TikTok were to be banned?
To prepare for the emergence of new platforms, it’s important to monitor social and e-commerce trends. Research your customers; possibly even ask your loyal audiences where they are headed. Think through nuanced methods of reconnecting with your audiences as a consolidated community, elsewhere. With these insights in mind, think through a strategy that pivots well via a flexible / adaptable content strategy with a strong thread to create a loyal, core community that can locate you on any new / trending platform and help jumpstart your growth. In a nutshell, the advice boils down to “stay ready, so you don’t have to get ready.”
What lessons can marketers learn from the resilience of platforms like TikTok in the face of potential bans?
Humans are innately social beings that thrive on connectivity and content-sharing as a tool to learn, grow, and entertain. Regarding the rise and fall of any social platform, marketers should learn to LEARN; and learn to learn FASTER – it does not matter if it’s short or long-form content. New platforms, new trends, new tools, new content preferences, every day the meaning of new evolves exponentially. Stay curious, stay creative, and stay nimble… admit no day is ever the same, then grow from there!
How important is platform diversification in a marketer’s strategy to mitigate risks associated with potential bans?
Platform diversification is a must, regardless of “bans. Everybody in the USA is talking about the price of eggs recently, right? Here you go: putting all of your eggs in one basket isn’t just an added risk, it is also likely leaving money on the table. Testing and learning from other platforms can be so rewarding, and the results will surprise you. For example, if your business is only selling via TikTok Shop now, that does not mean you’re dependent on the TikTok algorithm through-and-through. Try something new while being weary of spreading thin! Echo the data-driven learnings from your TikTok success (in an adapted form) on another platform that you learn holds similar or complementary audience interests. This can lead to a trove of growth: stronger brand visibility and reach, new and untapped audiences, and potentially: a stronger Plan B to mitigate the risks of platform dependency.
What opportunities might arise for marketers in the event of TikTok’s absence from the social media space?
Endless opportunities! For those marketers who have followed TikTok’s boom but were too late to jump on in… this is your chance! Research the social trends, follow the data and jump in with creative, unique content that speaks to your brand. For marketers who were on TikTok, there is the unique opportunity to reignite the conversation about your brand elsewhere and engage with your audiences in fresh, exciting ways!
marketing 13 Jan 2025
1. How does Rocketlane ensure seamless collaboration between teams and customers during onboarding and implementation?
Seamless collaboration is all about shared accountability and transparency between teams and customers. Rocketlane acts as a single source of truth, so everyone has access to shared timelines, clear task ownership, and real-time updates. It also brings together documents, meeting notes, tasks, projects, and updates into one all-in-one portal, so customers aren’t left tracking things across siloed systems. This helps our clients avoid the silos and miscommunications that can slow down onboarding projects.
To keep everyone aligned, Rocketlane automates publishing updates to project team members at defined intervals, clearly calling out risks, delays, and next actions to eliminate confusion. These updates are delivered not only to the customer portal but also via email or Slack, ensuring they reach people where they’re already communicating.
We also recently launched our customer portal, making it easy for clients to stay up to date with minimal effort, while our vendor portal helps streamline team operations on our side.
Plus, we use AI-driven insights to predict risks like delays in task completion, low team engagement, or miscommunications, and give actionable recommendations, which helps teams tackle potential roadblocks before they become problems.
Rocketlane combines structure with flexibility and makes sure every onboarding experience is smooth and successful.
We provide standardization through project templates and playbooks, helping businesses create consistency and repeatability in their workflows. These templates can be as granular as you need them to be for different aspects you may want to standardize. They can be customized at every level—tasks, milestones, roles, and even what the customer sees—so they fit each client’s unique needs.
The magic lies in setting up templates not just for project plans and flows, but also for documents, meeting notes, communication to be sent out, governance rules, warnings, escalations, and even weekly status reports. This way, you can codify your way of working, even if the specifics of a project vary. You can also set up rules tailored to specific "types" or "sizes" of projects—for instance, enterprise projects versus velocity projects can have entirely different templates across the board.
On top of that, Rocketlane can standardize how you conduct reviews, manage risk, staff projects, and more. Features like custom fields, dynamic task allocation, and role-based access give teams the ability to fine-tune workflows however they need.
Rocketlane also integrates with tools like CRMs and Slack, seamlessly fitting into each customer’s existing ecosystem without disrupting their current workflow.
Success in customer collaboration comes down to alignment, engagement, and outcomes. Rocketlane tracks metrics—like task completion and CSAT scores—and gathers feedback through surveys and reviews.
We use automation and intelligent systems to help us spot risks early, like low engagement or communication delays. But ultimately, success means the customer feels supported, aligned with the team, and achieves their goals.
One good example is GoCardless. Before using Rocketlane, they were dealing with a lot of fragmented workflows and delays. Once they started using our platform, things started to improve. They started automating tasks and timelines which helped them manage projects more efficiently. They also got better at allocating resources and gave clients real-time visibility through the customer portal. This helped build trust and engagement, leading to faster results and higher satisfaction scores.
Another example is Chargebee, who had some complex onboarding workflows. Rocketlane helped them streamline processes, improving resource visibility and making it easier for customers to collaborate. This ultimately helped them reduce project turnaround times and improve stakeholder satisfaction.
At Rocketlane, we’ve built scalability and adaptability right into the platform. Businesses can start with what they need and easily grow as things get more complex. Our modular design lets customers scale up without disrupting anything, so it’s seamless.
As teams expand, role-based access ensures everyone sees only what’s relevant to them—keeping things clutter-free. Our platform integrates effortlessly with the tools they’re already using, so there’s no need to overhaul their workflow.
We also use AI to spot potential issues before they happen, helping teams stay ahead of the game. For larger businesses, we offer features like SSO and multi-project management to keep everything running smoothly. We stay in touch with customers through ongoing feedback to make sure we’re continually adapting to their needs.
We remain scalable and adaptable by always learning from how our most mature customers customize and use Rocketlane. We use those insights to guide others and even productize the patterns we find useful so everyone can benefit from them.
marketing 18 Dec 2024
1.With only 9% of organizations currently implementing real-time personalisation, what challenges do you foresee in adopting these new technologies within your team?
The true customer value of real-time personalization is all about the context. Today, we live in two parallel worlds:
A: Expectation economy: Customers now expect to be treated as individuals and it does not matter which company or organization that interacts with them. The bar has been raised.
B: The Attention economy: We are now being exposed to more digital messages across various channels than ever before. As humans, we can´t process all of them in which leaves our brain to focus on whatever information that is most relevant and timely important.
The best chance of interacting with your customers would be when they are open for a dialogue, and that usually means that you need to be able to gather information from one channel, and instantly in real-time use that intelligence to continue the dialogue in another channel.
For this to happen, we need to better understand what part of the customer journey we can impact, for what reason and in what channel. The challenges we see implementing real-time personalization is both to understand what data you should collect from various channels (customer support, in-store, online) and how you could swiftly use that information to continue the dialogue in another channel, leading to the following main challenges:
2.What role will machine learning models play in helping you optimize real-time user interactions based on behavioral data?
Machine learning has already played an instrumental part in increasing both efficiency and effectiveness of personalized marketing, far most in the shape of recommendation engines. Looking back, it has provided companies the tools to draw better conclusions from previous transactions and interactions and by that scaling personalization to provide better recommendations to customers.
Looking ahead, machine learning will take the next step by not only providing recommendations based on what has happened in the past, but also predicting what will happen next. By gathering more data points, predictions will provide more accurate recommendations down to an individualized level in context leading to personalization is not just delivered to the right person in the right channel, but also at the right time with more granular precision.
3. How will the collaboration tool streamline the management of personalisation efforts, especially for large-scale marketing programs across multiple teams?
When speaking about personalization, most companies refer to data. The ability to collect, connect and segment data from different sources. But to deliver a great personalized experience, you also need content that speaks to the different segments or groups of people (or even individuals) you want to engage with. Personalization sits in the intersection between content and data.
For personalization not to become fragmented, you need an overview of the customer journey and the different personalization and you also need a content engine to deliver personalized content and messaging at scale.
A collaboration provides the different capabilities you need to:
4. As personalisation continues to grow in importance, how do you intend to collaborate with data and marketing teams to maintain a customer-centric approach at all times?
Personalization is a topic that is moving up the C-Suite. It needs to become a management topic, since personalization rely on different dependencies like data, content, analytics and omnichannel execution. Removing silos is a first step, but also focusing on customer-centric KPIs to move towards relationship building dialogues in opposite of mass communication and a shorter term campaign focused mindset.
5.Given the advancements in personalisation technology, how do you plan to balance automating personal experiences with maintaining human oversight and creativity?
Successful personalization is complex, and you would need to run multiple experiments to not only deliver a personalized experience, but a meaningful personalized experience. Automation and scale can only be the results of personalization that matters, that is delivering value for the customer while also having a positive impact of the business KPIs.
marketing 18 Dec 2024
What led to Instreamatic’s focus on AI-driven contextual video and audio ads, and how does this approach differ from more traditional advertising methods?
Our focus on AI-driven contextual advertising (which we began offering before the industry’s broader AI boom of the past couple of years) came from recognizing 1) a fundamental shift in consumer expectations for more contextualized campaigns, and 2) a disconnect in how brands and agencies were able to deliver on those expectations.
Back when we first started Instreamatic as a programmatic audio ad platform in 2015, we saw that brands were struggling with the traditional ‘one-size-fits-all’ approach to campaigns. But marketers that tried to create more individualized ad variations—manually—were spending enormous resources doing so. And even then, they were never going to achieve true personalization at scale. It would take weeks of production time and significant budget just to create a few variations of an ad.
Our AI-driven approach fundamentally transforms this process by enabling brands to generate hundreds of personalized video, audio, or CTV ad variations from a single creative within minutes. What really sets our approach apart is that we’re not just creating random variations—we’re using AI to analyze context and data to ensure each variation resonates with its intended audience. The numbers support the strategy: our work this year with one of the largest technology companies in the world showed that even simple contextual relevance in ads drove an 18 percentage point increase in purchase intent compared to standard ads. We’re essentially enabling brands to have meaningful conversations with their audiences at scale, rather than broadcasting the same message to everyone. This shift from mass messaging to personalized communication represents the future of advertising, and we’re proud to be at the forefront of this transformation.
How can marketers use AI to enhance storytelling in video and audio ads, making them more impactful and relevant?
AI is revolutionizing storytelling by enabling dynamic narrative adaptation in real-time. Rather than creating one linear story, we can now craft flexible narratives with multiple elements that can be personalized while maintaining the core brand message. Our platform uses AI to analyze what storytelling elements resonate most with different audience segments—whether it’s adjusting the pacing, music, voice-over style, or even visual sequences in video ads. For instance, we might find that morning commuters respond better to energetic, quick-paced narratives, while evening audiences engage more with relaxed, thoughtful approaches.
The key is that we’re not just making superficial changes or changes for the sake of changes; rather, AI helps us identify deeper patterns in how different audiences connect with storytelling elements. This allows brands to maintain their authentic voice while adapting how they tell their story based on context. What’s particularly exciting is how AI can help brands extend the lifespan of their creative assets by continuously finding new ways to make existing content relevant to different audiences.
What challenges do brands typically face in ad personalization, and how does Instreamatic address these?
The biggest challenge brands face with personalization is scale. Most brands understand the value of personalization. But traditional methods make it prohibitively expensive and time-consuming to create enough variations to truly personalize at scale. A typical video ad campaign might need hundreds of variations to account for different locations, times of day, audience segments, and other contextual factors. Creating these manually could take months and cost hundreds of thousands of dollars. Our platform addresses this by automating the personalization process, allowing brands to generate these variations in minutes rather than months.
We’re also solving the quality control challenge—our solution ensures that each variation maintains brand consistency while still optimizing for relevance. It’s not just about making more versions; it’s about making the right versions for each context.
What key metrics are used to measure the success of personalized ad campaigns for the industry?
We look beyond traditional metrics to understand the full impact of personalization. As we detailed in our recent report “2025: AI in Creative Production,” the industry needs to expand its measurement framework to capture the nuanced effects of contextual relevance. Our platform analyzes the performance delta between personalized and non-personalized versions, measuring not just if an ad worked, but why it worked. For example, in this campaign case study, we saw a 22 percentage point increase in brand favorability with personalized ads. But more importantly, we could attribute which personalization elements drove that improvement. Even seemingly minimal personalization can deliver impressive results, prompting customers to lean into a more tailored experience. As additional contextual elements are then layered into ads—like time of day, location, or audience behavior—the outcomes only become more impactful.
We also measure adaptation efficiency—how quickly our AI can optimize ad variations based on real-time performance data. This helps brands understand not just the end result, but the ongoing optimization process that got them there. The findings in that new report linked to above reinforce what we’ve seen across campaigns: successful personalization requires looking at both immediate performance metrics and longer-term brand impact indicators.
Can you explain how your platform optimizes the delivery and performance of contextual ads at scale?
Our platform’s optimization process works on three levels, simultaneously. First, we use AI to analyze the initial creative assets and identify elements that can be modified without compromising brand integrity. This could include everything from background music to visual transitions in video ads. Second, our system creates variations based on contextual parameters—time, location, user behavior, and platform-specific requirements.
But what makes our approach especially unique is the third level: real-time performance optimization. As these variations are deployed, our AI continuously monitors performance data to refine the personalization rules. For example, if we notice certain elements performing better in specific contexts, the solution automatically adjusts the distribution to favor those combinations. This creates a virtuous cycle where each ad served helps improve the performance of future ads. We’re essentially building a self-improving system that gets smarter with every interaction, ensuring brands can maintain peak performance at scale. Our platform also integrates with existing DSPs and SSPs, so marketers can incorporate our enhanced personalization into current workflows without additional costs or infrastructure changes.
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marketing 5 Dec 2024
marketing 18 Nov 2024
1.Why should brands reconsider their advertising strategies in light of recent industry trials and disruptions?
The recent antitrust trial against Google has revealed significant scrutiny over their practices in search and programmatic advertising. The trial's findings have prompted many brands to reconsider their dependency on Google’s ecosystem, and the risks at hand should encourage advertisers to use a platform that enable them to navigate all these channels on a level footing. This presents an opportunity for brands to diversify their ad tech strategies, adopting a more omnichannel approach that reduces reliance on a single ecosystem. By leveraging alternative channels like Connected TV (CTV), Digital Out of Home (DOOH), and audio, brands can ensure that they are not overly exposed to changes in any one platform, thereby fostering a more balanced and resilient advertising strategy.
2.What are some alternative data sources that brands can explore to replace third-party data?
With third-party data deprecation already impacted the majority of browsers and new channels, brands are looking to bolster their first-party data strategies and exploring other privacy-compliant data sources. Some effective alternatives include:
3.What are the benefits of audience curation and more focused targeting for brands in the current ad tech environment?
Audience curation allows brands to refine their target groups based on well-defined characteristics and behaviours, leading to higher engagement and improved ROI. In the current landscape, where data privacy is paramount, focusing on specific audience segments rather than broad-based targeting helps brands achieve more relevance and reduce wasted ad spend. Additionally, curating audiences based on first-party and contextual data allows brands to retain more control over their messaging while aligning with consumer privacy expectations. This curated targeting approach mitigates against a reliance on third-party cookies and aligns well with omnichannel strategies that emphasise cohesive customer experiences across multiple platforms.
4.How has the deprecation of third-party cookies impacted the digital advertising ecosystem?
The phase-out of third-party cookies has fundamentally shifted how advertisers approach data-driven marketing. Without cookies, advertisers face challenges in tracking user behaviour across sites and personalising ads at scale. This shift has led to a greater emphasis on building first-party data infrastructures and adopting privacy-first advertising models. Many brands and platforms are exploring cookieless tracking methods, like browser APIs (e.g., Google’s Privacy Sandbox) contextual advertising, not to mention probabilistic and deterministic cookieless soloutions including ID5, RampID and Ftrack. The cookie deprecation is also accelerating innovation in data privacy technologies, including AI-powered audience segmentation, which offer advertisers new ways to achieve effective targeting without compromising user privacy.
5.What are the long-term implications of current ad tech trials for the broader digital advertising ecosystem?
The ad tech trials, particularly those focusing on Google, are likely to reshape the entire digital advertising landscape by pushing the industry towards greater transparency and reduced customer lock-in. Regulatory actions, like the Digital Markets Act (DMA), underscore a move toward more equitable competition, which could amplify opportunities for independent ad tech companies and smaller players to thrive. In the long term, this will likely result in a more diverse ecosystem with multiple viable options for advertisers, reducing reliance on a few dominant platforms. For brands, this means a shift toward more diversified, omnichannel strategies that emphasise flexibility and independence from any single ecosystem. Additionally, the rise of AI in advertising could bring new considerations for data privacy and ethical ad targeting, shaping the future of consumer engagement across channels.
These trials indicate an industry-wide push toward a fairer marketplace that values transparency, accountability, and consumer trust —fundamentals that will define the next era of digital advertising.
marketing 4 Nov 2024
1. Given the saturation of Super Apps in Asia, how can new entrants differentiate themselves from established players like WeChat or Gojek?
“Even in saturated markets, new apps can (if perfectly executed) disrupt established Super Apps by offering a solid Unique Selling Proposition (USP) that sets them apart. This differentiation doesn’t have to be a radical change; it can be an improvement in functionality, cost, or user experience.
To differentiate effectively, new Super Apps should:
By focusing on these strategies, new apps can narrow down and master their niche and compete effectively against established players with a wider and lesser focussed product offering.”
2. How can brands leverage the data provided by Super Apps, such as user preferences across various services, to create more targeted and effective advertising campaigns?
“One of the reasons why Super Apps are so successful is because they keep all their user data primarily for themselves. This increases the value of the Super App due to the better understanding of their users (in various all-day life situations). This data is made accessible to marketers by tapping into the native advertising possibilities offered by these Super Apps. With all that data, you can set up very targeted and specific ads, targeting various user types as well as user segments and use-cases for which the Super App is used in the first place.”
3. In a market where users are turning to specialized apps for media consumption, how should media companies balance diversification with focused user experiences to stay competitive?
“The app industry is among the most competitive and dynamic sectors around. Consumer preferences are in a constant state of flux and there are literally thousands of new apps launched every single day. This means that even very successful apps can’t stay still - they need to be constantly reevaluating both their offering and their marketing initiatives. What is important to remember is that the average consumer does not think about using a Super App or a specialized app as a binary choice. Most wouldn’t even recognise an app like Uber as a Super App. The critical factor is that every aspect of the app works as well as it can because if it doesn’t there are plenty of competitors that a user can switch to. Key to ensuring the best user experience is responding to change. This means knowing what new innovations can enhance your app or how it is marketed, what your competitors are doing and how market conditions and consumer demands are changing. If you stay ahead of the curve, you will be able to stay competitive.”
4. What strategies can developers adopt to tap into the growing appetite for Super Apps in Western markets, particularly with the rise of Uber and Revolut?
“Although we often talk about Asia or Western markets as a homogeneous block, the reality is that there are huge variations between each country. What may work well in France, might not appeal to an audience in Australia. Knowing the market and tailoring the app offering and how it is marketed to the demands of consumers in each country is fundamental. App developers will naturally know their home market the best, so that’s the most obvious place to start. From there, it is about identifying the next most similar market and modifying the app and how you promote it to that audience. It may seem appealing to go after the most lucrative markets first, for example, the US. However, not only will they be the most competitive, they will also likely be the most expensive places to do business. It is better to take an incremental, pragmatic approach to growth - learning lessons on the way - and build up your audience and capabilities on this journey.”
5. With growth slowing, how should investors evaluate the future of Super Apps in Asia and emerging markets? Are there specific sectors within Super Apps that still hold high-growth potential?
“Although growth has slowed, the number of new users being acquired is still considerable. User growth is also not the only metric of success, it also really matters how engaged an existing user base is as that will have the most important impact on the bottom line. In addition to this, Asian Super Apps also have the capacity to break out of the region and look to acquire substantial growth in other markets that are largely untapped. One of the virtues of Super Apps is that there are an endless configuration of services that can be created. Fintech, ecommerce and mobility services are all growing at a considerable pace in Asia, so too are AI tools - everything from companion apps to art generation. There is therefore plenty of scope for these Super Apps to add a new service from a fast growing industry and maintain their market position. I think investors will look at all of this potential and keep up the level of investment.”
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Interview Of : Greg Zakowicz
Interview Of : Ryan Burke
Interview Of : Jonathon Baggia
Interview Of : Srikrishnan Ganesan
Interview Of : Mårten Bokedal
Interview Of : Stas Tushinskiy