How Machine Learning Can Build Brand Value

The traditional purpose of a SWOT analysis is to help evaluate how a business is positioned against its competitors – in short, how its value in the marketplace is being influenced.

As software eats the world, as Marc Andreesen once proclaimed, it’s also dining on marketplace perceptions of business value, turning strategy its head in the process. The events hold lessons for marketers on what degree programming influences value.  

Take Google. Earlier this year, Google was named the world’s most valuable brand according a Brand Finance study, overtaking Apple in the top spot. Google remains dominant in digital advertising: But its investment in machine learning has led to cloud services like Tensorflow, and enhancements for some of its best known products such as AdWords, YouTube, Google Home, and Google Analytics.  Machine learning capabilities have enhanced customer experience, be the customer an advertiser, developer, or an everyday consumer.

Google is not the only valuable tech company with a strong machine learning initiative.  Amazon was named number three in that list.

Meanwhile Google’s competitor Microsoft – ranked fifth – has made a large shift from providing enterprise software to establishing machine learning services that enhances the capabilities of its software. It has incorporated machine learning features into its business software to complement its Azure Machine Learning cloud platform. And it has desktop clients aimed at developers and data scientists, such as the Azure Machine Learning Workbench, that runs various programming languages, and PowerBI, a data visualization tool.  

One of Microsoft’s notable forays is into R programming, offering its own application of the language, and seamless integrating with Power BI.  These efforts are influencing marketshare: CNBC reported that Microsoft Azure is growing at rates higher than Amazon AWS, buoyed by enterprise adoption.

The example of these tech titans shows how machine learning can transform a business. This transformation differs from those created by acquisitions, mergers, and sales – actual resizing of a business. Don’t get me wrong. Business acquisitions still occur.  But the volume of activity is changing. CNBC reported a decline in M&A activity during the first quarter of 2017.  Much of this activity is usually linked to economic conditions and Wall Street sentiment on a given industry or marketplace.  In the CNBC article, Cedric Besnard, a Citi consumer equity analyst, was quoted as saying that “….M&A is actually the quickest route [to growth] – sometimes easier than innovation and less risky than extreme cost cutting.”  However, “erosion in barriers to entry and the slowdown in emerging markets is forcing struggling multinationals into finding new avenues of growth.”

Innovation might increasingly be seen as one of those new avenues, and for more companies than just the next Google, Apple, or Amazon. Competition is fierce, and companies need innovation to survive. A robust analytics strategy has become an essential tool for innovate, helping companies become more efficient, and revealing where to best deploy capital and resources.  Analytics, supported by machine learning, is leading to better operations.

Business analysts are also witnessing the penalty for not leveraging the benefits of machine learning strategies, such as the predictive analytics, which support programmatic marketing.  That penalty has been felt in the retail sector, with H.H. Gregg, Family Christian Stores, Gymboree, The Limited, and Wet Seal among the closed retailers in 2017. All of these retailers failed to leverage the consumer shift to online shopping, where programmatic marketing can be deployed. In fact H.H. Gregg, which shuttered all its stores, just announced it will resurrect as an online-only retailer.

But failure at online retail just scratches the surface to what doomed these retailers.  Consumer data is generated from online activity, and much of it can be modeled to inform back office functions like finance and operationsall of which support, perhaps indirectly, the customer experience.  The value of machine learning lies in the ability to create opportunities from data. The more a business understands the parameters of customer behavior, the better opportunities there are for personalizing and optimizing customer experience.

Indeed, interest in machine learning is building up steam. In a Fortune article on Shoptalk Europe, for example, multiple speakers cited a Gartner statistic that by 2020 artificial intelligence (AI) in retail will manage 85% of customer interaction, with 30% of all companies employing AI to augment at least one of their primary sales processes.  Machine learning, of course, is central to to AI.

So what’s the takeway for marketers? The key is to invest in the portals and activities that promote data mining and advanced modeling. Certainly, that investment has to be incremental over time. But the investment augments brand value, because each steps reflects an honest effort to innovate in ways that can impact customer experiences. It’s the difference between being perceived as a valuable business with an eye on the future, or an outdated business with an eye on the bankruptcy court.

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