{"id":5663,"date":"2024-11-28T10:52:55","date_gmt":"2024-11-28T10:52:55","guid":{"rendered":"https:\/\/www.odbms.org\/blog\/?p=5663"},"modified":"2024-12-06T10:01:19","modified_gmt":"2024-12-06T10:01:19","slug":"on-generative-ai-and-consumer-marketing-interview-with-ziv-fridfertig-and-madhukar-kumar","status":"publish","type":"post","link":"https:\/\/www.odbms.org\/blog\/2024\/11\/on-generative-ai-and-consumer-marketing-interview-with-ziv-fridfertig-and-madhukar-kumar\/","title":{"rendered":"On Generative AI and Consumer Marketing. Interview with Ziv Fridfertig and\u00a0Madhukar Kumar.\u00a0"},"content":{"rendered":"\n
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 \u201cIn many ways the Large Language Models (LLMs) have become commoditized, and the true differentiator is now the substrate of intelligence \u2013 which is data. Companies that take advantage of their data to drive richer and better experiences for their customers with AI are going to be the clear winners. \u201c<\/p>\n<\/blockquote>\n\n\n\n


Q1. How can generative AI boost consumer marketing? <\/strong><\/p>\n\n\n\n

MK: <\/strong>Gen AI helps marketers be more agile in fast-changing markets and do more with fewer resources. It\u2019s about scaling your creativity, personalizing the customer experience, and optimizing campaign strategy and execution. It\u2019s also about automating repetitive tasks \u2013 getting more of the grunt work done extremely fast. With gen AI, everything from ads to social media to email campaigns can also be thoroughly personalized and targeted. AI-guided chatbots and virtual assistants can learn from customer preferences and behavior patterns to make better recommendations and ultimately improve the customer experience. And by analyzing trends and making predictions, gen AI can play a role in audience segmentation and strategy \u2013 even modifying campaigns dynamically based on real-time feedback and data. Finally, AI tools can help marketers make data-driven decisions by analyzing data without getting an entire team of data analysts involved.
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Q2.<\/strong> A recent <\/strong>McKinsey report<\/strong><\/a> estimates that gen AI could contribute up to $4.4 trillion in annual global productivity. What is your take on this?<\/strong><\/p>\n\n\n\n

MK:  <\/strong>I believe it. Using consumer marketing as an example, the cost savings and  productivity gains are real. Gen AI is already automating some of consumer marketing\u2019s more routine and time-intensive tasks, giving creative teams more time to focus on strategy. For example, gen AI can do scalable A\/B testing to test what resonates best, design concepts and layouts, or localize content for different regions and languages. In all of these ways, gen AI is a powerful tool for consumer marketers who seek to do more with less. Similar gains are happening in every industry, so you can easily see how McKinsey has reached such bullish conclusions.<\/p>\n\n\n\n

Q3. What are the ways consumer companies can create value with gen AI?<\/strong><\/p>\n\n\n\n

MK: <\/strong>In many ways the Large Language Models (LLMs)<\/a> have become commoditized, and the true differentiator is now the substrate of intelligence \u2013 which is data. Companies that take advantage of their data to drive richer and better experiences for their customers with AI are going to be the clear winners. <\/p>\n\n\n\n

One great example of a company realizing these benefits is Skai. Skai\u2019s AI-powered platform <\/a>offers consumer and market insights and omni-channel media activation services. As with so many of our martech and adtech customers, Skai’s integration of gen AI helps them deliver more personalized, efficient, and innovative marketing solutions. <\/p>\n\n\n\n

Skai was one of SingleStore\u2019s first customers that built a multi-tenant service on SingleStore<\/a>. Multi-tenant is now the most common deployment among all of our martech\/adtech customers. Our partners in this deployment are AWS <\/a>and also Twingo<\/a>, who helped Skai deploy our solution and push the technology to the limits for optimal performance.<\/p>\n\n\n\n

Q4. How does Skai use gen AI? And how does Skai use SingleStore? <\/strong><\/p>\n\n\n\n

ZF: <\/strong>We use gen AI in many of the ways Madhukar described above. It\u2019s about eliminating the friction inherent in \u201cwalled garden media\u201d \u2013 digital advertising and media ecosystems where the platform owner controls and restricts access to the data, content, and audience interactions within their environment. Our machine-learning algorithms and proprietary NLP break down those barriers, enabling companies to listen, predict and keep pace with the consumer journey.<\/p>\n\n\n\n

We use SingleStore as our foundational database for real-time analytics and we use SingleStore Pipelines to ingest the massive amount of data that we require. SingleStore has saved our developers a lot of time while getting our users the real-time insights they need. Though we started with an on-prem deployment, we recently moved to the cloud with AWS, which has enabled us to more efficiently utilize our hardware and maximize our performance. <\/p>\n\n\n\n

Q5. What are the main benefits for Skai\u2019s clients? What are the main benefits of moving to what you call an <\/strong>omnichannel<\/em><\/strong>?<\/strong><\/p>\n\n\n\n

ZF: <\/strong>Our omnichannel advertising platform is the ultimate outcome of us listening to our client needs. We\u2019ve been around for a while and learned that clients have their own systems and methods for marketing operations and deal differently with publishers, but there was a common ground \u2013 it\u2019s expensive, confusing and exhausting with the fast pace of change. <\/p>\n\n\n\n

Today our clients see the value of working with a single platform over multiple retailers.
It allows them much more visibility and control. The language gap is reduced. They can get insights faster, quickly identify and react to low-performing campaigns, save time with bulk operations and maximize their budget.

The platform includes smart customized widgets, real-time analysis, advanced targeting and automated actions. With this toolset, campaign managers can easily oversee their campaigns across 100 publishers. Our AI-based features inform a strategic dashboard that gives marketing directors with actionable insights on forecasting and trends.  <\/strong><\/p>\n\n\n\n

Q6. What data challenges do you face at Skai?<\/strong><\/p>\n\n\n\n

ZF:<\/strong> Skai has quite a unique setup, with data ingestion by 30,000 Kafka<\/a> pipelines across tens of clusters, and we must cater to a high concurrency of client requests, made of 80 tables joined in a single query. That tends to push database technology to its limits. <\/p>\n\n\n\n

And huge amounts of fast-moving data also creates hardware challenges. Hardware is a big investment so it can be difficult to keep pace with evolving data demands and processing needs. And administering thousands of servers isn\u2019t cheap either.

<\/em>Moving this system to AWS cloud improved our infrastructure in a few matters.
With EBS performance we migrated most of our in-memory data to storage, the outcome was reduction of hardware demands by half and improving DB performance.
With EC2 Multi-AZ we built an advanced setup to endure hardware failures without downtime, and managed to make our daily backups on S3 redundant.
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Q7. You have built a multi-tenant service on SingleStore. Could you please tell us a bit about it? Why did you choose a multi-tenant service?<\/strong><\/p>\n\n\n\n

ZF:<\/strong> In a multi-tenant architecture, a single instance of a software application or platform is shared among multiple customers or users, known as tenants. Each tenant’s data is isolated and remains invisible to others, but they share the same infrastructure, resources, and core application services.  <\/p>\n\n\n\n

With SingleStore, we managed to achieve unmatched query performance which is impossible to get with single-tenant or inefficiently too expensive. It is cost effective, easier to manage, it is more resilient, data scale becomes a non-issue and we minimized our maintenance downtimes.  <\/em><\/p>\n\n\n\n

Ultimately, being multi-tenant makes a bigger impact for our clients. Deployments are easier, faster, and give us more horsepower for peak or ad-hoc loads, which keeps our user interface consistently responsive.<\/p>\n\n\n\n

Q8. Why did you choose SingleStore? What are the main benefits?<\/strong><\/p>\n\n\n\n

ZF:<\/strong> Speed, scale and simplicity. SingleStore had the best query performance for our platform\u2019s main use case. In just milliseconds, it can aggregate millions of metadata rows with billions of performance events. It can handle more than 80 tables in a single query with high concurrency. We love that the data lands in near real-time from Kafka to SingleStore, it was a hard requirement at the beginning of our journey 6 years ago.

Scalability was also a key. Big clients have big data and we need to grow along with them, with amazing query performance and zero downtime.

For simplicity, I would note the single dialect. MySQL compliance means that no special treatment was needed for building queries. In SingleStore, we don\u2019t have any issues with inconsistency in querying. <\/p>\n\n\n\n

Qx. Anything else you wish to add?<\/strong><\/p>\n\n\n\n

ZF: <\/strong>On a personal note, I project a good future for SingleStore and Skai. Both companies are attentive to client needs and success and share a vision for a centralized eco-system for data processing. With this great partnership, I’m excited for the upcoming capabilities in the cloud.
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Madhukar Kumar<\/strong>, <\/em>chief marketing officer,  SingleStore <\/p>\n\n\n\n

Madhukar is a developer turned growth marketer and an expert in product-led growth (PLG) with more than 18 years of experience leading product management and marketing teams. He has successfully implemented PLG at Nutanix, Redis and DevRev, and is a guest lecturer at Duke University on PLG and new product development.<\/p>\n\n\n\n

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Ziv Fridfertig,<\/strong> Data Infra Manager, Skai<\/p>\n\n\n\n

Ziv is a Data Infrastructure Manager with extensive experience in the ad-tech industry. His work focuses on managing and optimizing complex data solutions, building scalable data pipelines, and implementing real-time streaming systems that support high-performance applications.<\/p>\n\n\n\n

Core Expertise:<\/p>\n\n\n\n