The power of machine learning is leveraged today by P&G to improve advertising targeting and ROI, especially in search and programmatic. In the near future, ad fraud is an area that might also benefit from this new trend.
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Cops and robbers, fraudsters and verification companies. Given the size of the issue and the fast growth of digital media, this problem is definitely becoming more and more relevant.
Very good point – this is the direction the industry is taking i.e. having media capabilities in-house (including data, tech and analytics), as previously there was a huge value leakage as agencies may have had the wrong incentives (i.e. maximize their profit vs. the cost of their clients).
The tension described here is spot on – today, in the industry, buyers and sellers have usually the agreement to leverage 3rd parties that assess if accounts are fake and so execute financial transactions only for real humans.
The good part is that there are many data in this space – trillions of transactions happen for second, and all of them are assessed by different tech systems to understand how to best allocate media budgets. More than data, the actual problem is speed i.e. how to be able to analyze so much data in milliseconds.
The problem of ad fraud is very present in the industry – many fraud detection companies are in the space (e.g. Moat, Integral Ad Science, Double Verify), but it’s always a game of cops and robbers, with fraudsters trying to engineer new ways to extract value from the system, and 3rd parties that try to catch up.
Interesting perspective – this is exactly what happened in the last decade, but unfortunately the outsourcing / collaboration with agencies did not work very well. The problem of transparency came out very strong in the last two years and now advertisers are in-sourcing more and more their media operations (including P&G).
The topic of space economy is fascinating – from one side, there are the endless opportunities, from the other the current limitations. As we learn over and over again in the space of exponential technologies, we should not draw conclusions based on today’s limitations, as technologies keep on evolving and doubling their capacity/speed/power and reducing their costs by half at a very high frequency. In this case, it would be interesting to understand the economics of space economy and of the related applications. Both from a financial and operational side, it could be interesting to run scenarios to understand what could be possible when, as well as to understand what are the levers we should pull to make greater things happen still within our generation!
The perspective taken by the author is quite interesting – believing in something before trying to even sell it outside. In this case, GE could have several opportunities to leverage this trend internally, but this is not happening (or is happening at a limited scale). This issue is pretty common in large corporations, where different groups have different objectives and incentive-systems. Here, strong leadership and strategic vision is missing – leveraging this trends should start from a committed CEO and then cascade down to committed BU leaders and Operations leaders. Once the company can start with the right cultural approach, there could be a real impact on internal operations and then external proposition. Net, I believe strong factors contributing to the success of operations are strategy, leadership and culture.
Open Innovation is a disruptive trend that is having huge impacts on many organizations – in this case, the point on “what can make OI successful” is very interesting. Especially from an organizational standpoint, it is important to define the right tools, capabilities and frameworks which can enable OI. As an example, based on my own experience, while I know that OI can be support many different functions and processes in a company, in different companies and industries, I learnt it is important to define well the OI process itself, making sure that two things happen:
1) that people contributing to OI have the right incentives (as with multiple entities in the value chain, with misaligned incentives the final result of the system would be sub-optimal or anyway worse than what it could have been had all entities be part of the same organization)
2) that OI happens very upstream in the design phase (to make sure there is the right amount of time and resources to diverge first and converge just at the end, where more disruptive ideas actually have the time to be generated).
Nice paper on one of the most critical challenges today i.e. bricks-and-clicks vs. Amazon. One area that I find pretty fascinating is exactly linked to how physical presence can be leveraged vs. online only (even if also Amazon is in the offline business now, with Whole Foods and Amazon Go, the self-checkout grocery store). In fact, while many technologies linked to AI/ML are active online (with a high level of sophistication), there is still a big gap vs. offline (where sophistication is limited). The ability to bridge these two worlds will be critical. For this, AI/ML focused on facial recognition, consumer attention and shopping behaviors can power the omni-channel experience and help connect the physical world to the online one (e.g. with data that may be used for advertising and/or even product development). This is an area where it could be interesting to see if the Walmart-Microsoft collaboration will explore.
This is an interesting paper for an interesting topic – two points called my attention:
1) correlation does not mean causation – the example on Jane Fonda presence is spot on, as sometimes data may propose ambiguous and sub-optimal results. It would be interesting to understand though whether in this specific case big data actually present this risk. In fact, this seems a problem of dynamic creative optimization (quite common in the advertising space), where with millions of consumers’ feedback it is easy to run experiments (A/B tests) by changing different parts of the creative (including the presence of Jane Fonda).
2) the past is not always a good predictor of the future – in the media and entertainment industry, like in many others, trends can abruptly change and machine learning (which focus on the past) might not be a good tool to help predict such changes, hence pushing companies (Netflix in this case) in a “follower” position. On the other side, as machine learning gets more sophisticated, it can also learn about specific patterns which are an early predictor of an upcoming change (similar to earthquake forecast of the dragon king theory in finance). Still, predicting a disruption, may not help in the case of the media and entertainment industry, where results are not binary (earthquake yes/no, financial crisis yes/no). Open question.
The topic of changing consumer taste and Open Innovation is very interesting. From one side, OI democratizes decision making, ensuring that multiple and diverse points of view are considered. From the other, I’m wondering whether OI in this case is not just a bridge / intermediate step to crowd-sourcing. Said differently, as brands need to be where consumers are, ideally the process of defining which products to develop and where to invest could be fully outsourced to consumers. In fact, there are today several tools for “social listening” (e.g. Crimson Exagon) which can help companies understand the “sentiment” of consumers towards certain products or brands or choices (including packaging, colors etc.). Net, it could be interesting to explore this approach as an alternative or complement to OI as well.