The road less traveled in media paid SoftServeInc
Media companies focused on creating digital viewing experiences are standing at a fork in the road with regards to personalization. Recommendation engines are critical components of any user experience—increasing engagement, usage, and time spent with a brand. Front-runners in the space have put a lot of thought and care into the creation of these engines and are benefiting from the results. However, most engines fall short in performance, as does their overall value to the customer and brand.
When searching for the business benefits of a more technologically advanced recommendation engine, one needn’t look far. The most profitable recommendation engine today lives at Amazon, a company that has redefined the customer experience. What is shiny today will be dull tomorrow, so the only way to compete is to keep advancing personalization. This truth compels Bezos and company to raise the bar perpetually. For example, Amazon provides personalized content to each user vs.
The right approach is a matter of being dedicated to customer experience excellence. This means considering and leveraging advanced technology and methodologies available to develop a recommendation engine that acts more as a concierge rather than a coordinator. If audience is king, then providing remarkable differentiation with a recommendation engine that knows the viewer as well, if not better than his or herself, should be the objective.
All intelligent recommendation engines today are based on one of two types of machine learning: supervised, and reinforced.which is driven by historical and outcome labels rooted in: actions, ratings, history, linear progressions, neuro networks, and different data sets. However, this algorithm isn’t optimal—just as one general message will not resonate with every viewer, no singular algorithm can serve each individual uniquely.
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