Artificial Intelligence, Big Data, Machine Learning


Artificial intelligence, machine learning, deep learning, big data are the buzz words and often are used interchangeably or incorrectly in the advertising ecosystem. Artificial intelligence is used where machine learning should be and machine learning is often confused with data mining. Machine learning is a method of developing algorithms for recognizing patterns within data. Data mining uses techniques developed in machine learning, i.e. machine-learning algorithms, and statistics.

In this blog, I will help provide some clarifications pertaining to advertising platform. Perhaps, view part of the blog as “Artificial Intelligence, Machine Learning and Big Data – 101”. In the future blogs, I hope to introduce you to #MonaLisa, DaVinci IITM‘s Intelligent Cloud Marketing Platform.

Understanding the data

With the ever growing libraries of the big data, data mining has been an ongoing task. Platforms need to have the computational power to form meaningful advertising correlation, otherwise, much of this data is of no use. Predominant dimensions of “big data” are characterized in four sectors: 

  • Volume of “big” data (while important) is not the sole principle variable; rather, it is the method in which the platforms organize this data – to create better decisions & strategies for the marketing campaigns.
  • Velocity is the measure of the data inflow capacity.  It is often high, especially for platforms like DaVinci IITM, where the inflow stream is ingesting social, mobile, and sensor based data. Aside from the efficient inflow of this data stream, some variables require real-time processing (ie: geo-location from mobile), which means data stream must be handled in timely manner. 
  • Variety is further of importance to decipher the data from structured and unstructured formats. 
  • Variability of the data, adds interesting complexity as it is routinely inconsistent. A lot of social data depends on the trending news in the media. Hence, makes the variability dimension unpredictable.

In the past few years, machine learning has made a significant breakthrough. Many big organizations now offer low-cost options for storing data, which previously couldn’t be supported by bootstrapped start-ups. We now also have faster processors, high connectivity, high throughputs, virtualization, cloud computing, large grid platforms and clustering, which few years ago were the main restrictions to do advance computations. In additions, we now have distributed big data platforms, like Hadoop, that are open source and affordable.

Importance of AI in marketing

The environment of advertising tech is increasingly complex. We now have more data than to know what to do with it. The role media analysts were able to play in ad-tech, a few years ago, is no longer scalable.  Machine Learning provides depth of insights that data analysts simply can’t. The ongoing challenge is to comprehend the analysis on large volume of data streams, as the potential correlations and relationships between disparate data sources are too large for any human analyst to compute.

For effective audience segmentations, it is no longer analyzing the different websites and influencers, rather the consumers are now constantly switching between devices. This additional layer of complexity adds depth to the data inflow. For this machine learning or ‘machine-assist’ is effective than ‘human-only’ analysis. Machine learning is able to test reasonable hypotheses and derive meaningful insights buried in the libraries much faster than humans. For technology platforms using the advanced computation methods, you have to be able to process this information in real-time and draw statistically relevant ad to engage the consumer.

DaVinci IITM #CreateYourMonaLisa

Effects on media buying or RTB

Programmatic media buying is a mechanism through which brands buy audiences. It is a marketplace and RTB is a transaction that occurs on that platform. Think of machine learning as a catalyst to that marketplace. It is now able to match the brands with their current and potential consumers in real-time, across devices, and offer various statically relevant correlations – thereby adding an enhanced degree of certainty for a suggested action.

Machine Learning is NOT a rule-based system that requires an analyst to hard-code domain knowledge into a system. Rather, Machine Learning learns to make decisions based on the inflow of data and experience. As we continue to train the algorithm, we continue to advance the Artificial Intelligence.

So, what does this mean for marketers? Well, brands do not have to worry and figure out the cumbersome process of Machine Learning on their own. They can partner with technology platforms such as DaVinci IITM and utilize #MonaLisa’s Machine Learning capabilities to stay connected with their consumers. 


The method of analyzing the data varies in each company. Some platforms take in all the data, but don’t know what to do with it. Some will determine subset of variables to look at before the analysis of the data and throw away (or not use) the rest of the data. Either of the processes (and perhaps everything in between) are ways in which companies are forming their confidence in their decision making predictions. At DaVinci IITM, we test various variables. It allows us to have a strategy in place for specific vertical with abundant variables to assess correlation.

Aside from the ad-tech, there are many industries using machine learning. So, the journey to that beautiful world of Artificial Intelligence isn’t a lonely one. You may very well already be interfacing with some technologies. 

  • Retail: exploring AI to best market their customers, up-sell products and enhance customer win-back
  • Healthcare: exploring to find ways to improve delivery of patient care
  • Finance: exploring ways to identify ways to minimize fraud
  • Industrial: aiming to reduce waste and increase efficiency

As we continue to refine the algorithms and advance our understanding of recurrent neural networks and deep learning, we are essentially moving towards Artificial Intelligence.  

Follow our blog to learn more on the series.

Want to learn more about DaVinci IITM‘s #MonaLisa, contact your local sales team. We are serving over 20 countries.


Mobile and Machine Learning

In 2017, over 300 million smartphones will have on-board neural network machine-learning capability, according to a study by Deloitte. Machine Learning will power applications for “indoor navigation, image classification, augmented reality, speech recognition and language translation even where there is little or no cellular or Wi-Fi connectivity, such as in remote areas, underground or on an airplane. Where there is connectivity, on-board machine learning may allow tasks to be done better and faster, or with more privacy.”

Contact to get a demo on how DaVinci IITM can power your digital mobile media strategy. We have offices in over 20 countries and are leading mobile efforts across many countries. Let us #MakeYourBrandLimitless.


Digital Adspend Overtakes TV in Taiwan and Hong Kong



Increased smartphone ownership is clearly having a massive impact on the way people across Asia Pacific shop and spend. This trends are reflecting on how brands are buying media on DaVinci IITM platform across APAC.

DaVinci IITM Hong Kong team has seen a steady rise in mobile media buying efforts since the past year as well. This region is leading the trends in terms of tracking the mobile audience and the rising video demand, as consumers trends of viewing content across multiple screens is on sharp rise. We, at DaVinci IITM, are proud to showcase our cross-channel targeting with enhanced machine learning predictive performance analysis. Our cloud platform allows us to make intelligent creative selection to maximize campaign performance.

APAC Mobile eCommerce

As consumers trends shift more on digital and mobile, Taiwan and Hong Kong ad spend have slid away from television advertising budgets. According to an annual Interaction Report by WPP Media Company, GroupM, while television still commands high budgets in the Asian markets, digital advertising platforms are reigning in China.

Furthermore, the study examined the forecasted growth for digital advertising across 46 markets, showcasing that TV accounted for most ad-spend in 2016 standing at 42%. Nevertheless, GroupM projected this percentage would drop to 41% in 2017 due to a 16% contraction of 16 to 24 year-old audience for linear TV.

Other regions – digital trends

Other markets where digital ad-spend has already overtaken TV include Canada, Australia, Denmark, Netherlands, Finland, Sweden, Norway, United Kingdom, New Zealand, and China. These trends reminisce what we have seen across DaVinci IITM since 2016.

Regions where digital ad-spend is expected to overtake TV ad spend in 2017 include Germany, Ireland, and France. GroupM predicts as well that these markets will very soon be digitally dominant like Hong Kong and Taiwan. Across DaVinci IITM, we have noticed vast majority of our brands in Asia (Hong Kong, Taiwan, Singapore) allocating larger ad budgets on digital as consumer’s attention span shift towards their mobile devices.

Want to learn how DaVinci IITM‘s cloud marketing platform can make a positive ROI difference in your campaign? Contact your local DaVinci IITM office today. We are serving over 20 countries – #MakingYourBrandLimitless 


Gearing up for the next Quantum Leap – The Artificial Intelligence

The Next Quantum Leap:


We, at DaVinci IITM‘s research & development lab, have been pushing forward the programmatic digital advertising and overlaying Machine Learning for predictive models. Machine Learning has been playing a critical role to help build accurate correlation models. It is being applied to various components of advertising platform for application for Artificial Intelligence. We look forward to sharing our research on measurement and attribution, cross device correlation as well as predictability on the intent based consumer engagement.

DaVinci IITM is globally focused on pushing the innovation forward and adding talent to our research & development team. On the same note, we are super excited to do our part and mentor the community on relevant subject such as: Machine Learning, Deep Learning and make way towards discovering a full consumer centric Artificial Intelligence UX.  Much work industrywide still needs to be done to harness the full capability of dynamic programming algorithms.

“Recently, I did a mentorship series with the youth in a struggling education system. The fascination of all great things possible, through the cutting edge revolution of AI, bought much energy in the room.

If we look at the recent ‘emerging technologies’, the list would include: self-driving cars, biometrics, chatbots, drones, 3D printing, and VR, just to name but a few.

As with all-things-exciting, the subject sometimes leads to open invitation for the ‘hype’. Perhaps the entrepreneurs all around know the ‘next biggest breakthrough’ facing our human evolution is, “The Artificial Intelligence”. So, let’s work on dispelling the myth, understand what “is” possible today, and ways in which to prepare for this revolution.”