Marketing & CXThought Leadership Insights
Bryan Gildenberg on Strategic Data Monetization
September 7, 2023
Bryan Gildenberg, Founder and CEO of Confluencer Commerce, discusses the strategic considerations retailers face regarding data monetization. Retailers must choose between sharing data to foster business growth through active partner participation or monetizing data for short-term financial gains. Monetizing data might limit the pool of partners to those who can afford to buy it, typically larger companies that favor the status quo. This choice involves a risk-return trade-off that retailers need to carefully evaluate, especially in narrow-margin sectors like grocery retail, where the temptation to convert data into immediate net income is high.
Bryan emphasizes the strategic implications of data monetization, cautioning that it could lead to an over-reliance on established businesses that prefer stability over innovation. Retailers must be mindful of these trade-offs to avoid becoming dependent on partners resistant to change. As retailers bring more data to the table, third-party data providers must fundamentally reinvent themselves to remain relevant. Companies like RetailNext, which help retailers optimize various operational aspects, illustrate the potential commercial benefits of integrating deeply with retailers' core propositions.
The evolving data ecosystem is expected to favor larger businesses with the capacity to integrate extensive data streams and apply machine learning for detailed analytics. This trend contrasts with the recent history of commerce, where small startups have often disrupted larger companies. Bryan suggests that the advancements in data and machine learning might shift the advantage back to big companies, enabling them to leverage their resources to outperform smaller competitors.
Over the next five years, Bryan anticipates a significant transformation in the business landscape, with larger companies potentially gaining the upper hand through sophisticated data analytics and machine learning capabilities. This shift could reverse the trend of small startups dominating larger enterprises, marking a new era in the competitive dynamics of commerce.
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Transcript
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I think there's an interesting choice for the retailers in terms of how you think about data monetization. And it's the same choice that there's always been to some degree. The trade-off between if I share data, my business can grow in different ways because my partners can participate more actively. If I monetize data, I get a short-term benefit from that.
But, I probably, to some degree, limit the types and numbers of partners I can have on the journey, because then my partners become the people that can afford to buy the data that I'm selling. And I think that one of the interesting risk return tradeoffs in that scenario is for a retailer to ensure that they are thinking through the trade-offs associated with data monetization, because the short-term potential is so significant and the ease with which you can drop data monetization to the PNL especially in the world I live in, which is grocery retail, which is very narrow margin. The temptation to turn that into net income is super high. But if I'm going to monetize the data it's only the people that can pay for it that are going to help me.
Those companies are going to tend to be larger. They're going to tend to prefer the status quo to what's next. And I can find myself overly dependent on businesses that are overly dependent on the status quo as a result. So I just have to be careful when I monetize data about the strategic trade-offs that are associated with it.
The retailers themselves are bringing more data to the table so that as a result, third-party data providers are going to have to reinvent themselves pretty fundamentally. So that's a whole different kettle of fish. So the third-party providers that are integrated into the retailer's proposition and can tie themselves to multiple aspects of retailer performance. So if I'm a business like RetailNext I’m able to help the retailer optimize labor, optimize traffic flow, optimize operations, what I'm charging people from a media and selling perspective.
There's so many commercial implications to that. There is a really interesting opportunity for the third parties that are really integrated into the retailer's core proposition to really drive that. And I think for the retailers themselves, I think more and more the data ecosystem is getting back to a place where it's going to reward, to some degree, larger businesses rather than smaller ones. Businesses that really have the capability and the capacity to integrate huge data streams, to apply machine learning, which is not cheap, to those data streams, and to be able to turn that into more granular analytics.
I think over the next five years, I think you're going to see the revamping of scaled businesses versus small ones. Like if you look at the history of commerce in the 21st century, it's mostly been small startups taking the bejesus out of big ones. This data machine learning ecosystem I think is the first thing that's happened in a while that may allow big companies to flip the script on that a bit.
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