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Early this year, the smartphone maker - Xiaomi unveiled “Mi Air Charge Technology,” which could deliver 5W power to multiple devices “within a radius of several metres” and invited customers to a “true wireless charging era”. 5W does not sound too much power nowadays, but looking from the perspective of introducing truly over-the-air wireless charging experience for the end consumer, it would certainly trigger other vendors to join the alley and eventually improve the coverage and charging power. With the increased usage of 5G network, it will no longer be a dream of delivering true wireless experience to the end user with no data delay, engaged media content and limitless power.
It is the best of times, because we can easily access information in no-time. It is also the worst of times, because our ability of digesting and adapting information as human beings has become visibly limited. The technology evolution is no longer on the pace of hardwares’ development, getting the right information at the right time for the right person has become the key. Especially, when it comes to how well you know your customer directly impacts on how engaged your digital value proposition is.
Knowing your customer is not only through tracking and tracing their behavior digitally, you actually have to first define what kind of data insight you want to get from the customer profile perspective. Most importantly, you could also leverage the chance to feed new data insights or tailored features back to the customer in a fast way, so they get engaged in moments of need. Moving your traditional data strategy away from the conventional channel / product-driven targeting marketing, by shifting to an always-on model.
In order to deliver the always-on model, you have to first align on your technology strategy, defining what you want your marketing and analytics stack to look like. The approach many companies are turning towards is assembling a best-in-breed stack, built around a central data layer. This allows you to collect customer data once and then connect it to all of the leading solutions through server-to-server integrations. The data layer itself can be either served by a data warehouse or data lake, or even both. The data layer must consist of a searchable / queryable interface (typically API) in order to provide consistent, compliant and schema enforced data sets.
Once your data layer is in place, it gets relatively easy for serving multiple purposes. You could add multiple tasks in parallel, such as building a machine learning model to automatically provide personalized information while continuously feeding the incoming new data to the model, as well as building live data analytics dashboards in order to discover new business insights or user behaviors.
At the end of the day, Data Strategy is not a hard job, it is all about learning about your business and getting a better view on how you can improve your business value and customer experience based on data insights.
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