What do Netflix, Amazon and Tinder have in common? The endless possibilities of recommendations - and the addictive nature of always asking for more.
Imagine that blissful state, when deciphering analytics and data and browsing dashboards turn into a similar experience like binge-watching on Netflix, mindlessly shopping on Amazon, on endlessly swiping on Tinder. When the experience of identifying insights, selecting customised and contextualised options are aided by algorithms and perfectly put-together, actionable chunks of beautifully visualised, easy to digest data.
5 years ago, the Netflix engineering director, Xavier Amatriain said about Netflix that almost everything they do is a recommendation. He was comparing Netflix to eBay, saying that 90% of what people buy from eBay comes from search, and how it is the exact opposite for Netflix - relying on recommendation is the biggest majority of the time. Netflix has a search feature too, but it's coupled with recommendations to counteract the possible disappointment when the users are not finding what they were looking for. Recommendations have infiltrated its practice so deeply that it's impossible to imagine the brand without it.
Unlike search, recommendation systems attempt to predict the “rating” or “preference” a user would give to an item, action, or opportunity. These recommendations, if used purposefully, can provide far more value to marketers than for the customers.
Recommendation engines, on the one hand, generate useful data for analysing customer behaviours, preferences and desires; and on the other hand, they can serve as a basis to make tactical and strategic recommendations for marketers. Imagine an enterprise-size Netflix, Amazon, or Spotify for marketers; using the very same technology that supports customer choice to now support business decisions.
The “Netflixization of analytics” will enable serious marketers to make decisions based on analytics, preferences, search and predictions.
Digital dashboards will measure and monitor marketing KPIs, and they will also offer data-driven suggestions, options, and advice, as well. The need for “the right answer” will be replaced by the idea of “really good choices.”
We see it already in action, AI, machine learning and chatbot participating in our daily routines. Gmail can already draft professional correspondence based on past e-mail exchanges. LinkedIn proactively prompts value-added introductions. Salesforce software computationally qualifies, and ranks leads. Calendar managers visually and acoustically suggest scheduling options and priorities. Everything that we see that requires digital-related knowledge can become a recommendation, a range of choices - leaving us in control, but more knowledgeable and informed about our own decisions.
It is crucial that business leaders realise that their best people, just like their best customers need and want a perfectly crafted exposure to insightful choices. That's the ultimate goal, especially because the marketing decisions impact significantly the customer experience, thus marketing recommendation systems will have an immeasurable effect on customer recommendation engines.
Many digital marketing agencies develop search engine optimization recommendation engines to support the synergy with user profile themes and characteristics. The ultimate goal is not to sell more, but to learn more about prospects.
As marketing KPIs become more sophisticated, understanding every aspect of decisions becomes more important. Context matters. Recommendation systems are convincing and cognitively appealing: they invite agency; they enable choice. In data-driven, highly educated areas, where business and marketing decisions are supported by technology and artificial intelligence, one of the most challenging decisions for organizations is how to inform and empower their people - not only to serve their customers.
Recommendation systems have both an excellent reputation and great success. To date, marketers have done a better job creating systems of recommendations for their customers than for themselves. Going forward, the most successful marketing organizations will be ones that find innovative ways of aligning recommendations inside the enterprise and out.
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