Big Data and Customer Advice: Informative or Invasive?

To recommend or not to recommend?

This is the question firms around the world are asking themselves when it comes to helping their customers make better buying decisions digitally. Each consumer’s action online, on their mobile device, or even in a retail store is a point where data can be captured, recorded and used to make recommendations to customers in a highly customized fashion. Retargeting customers after they visit your site by showcasing your ads on other sites they visit is just the beginning. As consumer tracking tools become more sophisticated, machine learning will drive even more relevant recommendations and customized offerings. Amazon’s own recommendation engine has become the poster child for companies in a multitude of sectors who hope to leverage artificial intelligence and big data technologies to hyper-target customers and drive average purchase value upwards. The Seattle-based eCommerce giant has seem to struck a near perfect balance between hyper-focused recommendations and coming across as a privacy invader, and the rewards they’ve reaped from recommendations have been astounding. According to some digital marketing experts on Quora, 35% of its net revenue – $135.99 Billion in 2016 – is attributable to smart recommendations. With some simple math, that comes to approximately $47 Billion dollars in revenue last year Amazon can partly thank for its recommendation engine.

Finding the balance and the bigger picture

Amazon’s secret sauce, as previously alluded to, is their seemingly magical ability to give consumers smart advice without coming across as pandering or invasive. They’ve managed to refine their formula to make recommendations with “data EQ”, meaning  their systems and algorithms take the human and user experience into account, beyond simple buying patterns. Target’s now infamous product recommendations to a pregnant teen set the internet on fire several years ago; Amazon has seemingly learned well from Target’s mishap in recommendations and their proper delivery.

The truth is… smart product recommendations at a minimum require some consideration of “data EQ”, filtering out invasive recommendations that may embarrass or offend users and clients. As consumers trend towards higher expectations of their eCommerce experience, they will no doubt come to expect four requirements from recommendation engines:

  1. Relevant Recommendations – based on true buying patterns and product relationships
  2. Customization – blanket recommendations for products, even relevant ones, will increasingly be seen for what they are: glorified spam
  3. Privacy and “Data EQ” – recommendations must take into consideration the human element
  4. Timeliness and Context – please don’t recommend a relevant alternative to toilet paper I’ve been buying when searching for a book for my Mom’s birthday!

The Future of Smart Recommendations: Cross-Industry and Cross-Company Partnerships

Recommendations for products and services can be sourced from a wide variety of market participants, and as more investment is made into building the teams, infrastructure and analytics that can drive customer and client recommendations, their advice will become much more sophisticated. We are kissing goodbye the era of “you bought this book… other people also bought this book” gimmicks.  As companies build greater relationships with a loyal customer base, there will likely be greater opportunities for partnerships to form between companies in all industries who wish to leverage the data they collect, allowing for companies to can offer greater value to their own customers with the help of their partners. From my experience in the FinTech industry, it’s quite common for investment software companies working with large enterprises to understand their clients well but only offer up some or a part of the needs they identify. Enterprise client needs are complex, and FinTech companies have an incredible opportunity to form strategic partnerships with analytics consultancies, managed service providers and other financial experts who can solve the problems they see but cannot address with their own limited services.

The truth is, there are similar opportunities in every industry to form partnerships and provide counsel with “data EQ”. This will be the future of smart recommendations. No startup or multinational organization can be everything to everyone, no matter how ambitious. Data and service partnerships with a focus on customer privacy will open up the floodgates and bring in a new era for smart recommendations and data-driven advice. Let us be your guide and expert for finding your strategic partners and leveraging your existing contacts to make your customers happier and bring in new revenue sources. Contact us today.

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