Visit This Link Telecoms Operators have a huge appetite for customer insights. They invest millions to learn everything they can about customers and use the insights to keep customers loyal and happy for as long as possible.
read review Over the past five years, operators have come a long way in predicting whether customers will stay or churn or even what it might take to keep them happier and staying.
http://investingtips360.com/?klaystrofobiya=%D8%A3%D8%AE%D8%A8%D8%A7%D8%B1-%D8%A7%D9%84%D9%81%D9%88%D8%B1%D9%83%D8%B3&b1e=3b This is tapering now. The focus instead is shifting towards loyalty and engagement and how it comes about at the least possible cost and noise.
http://gl5.org/?prikolno=%D9%83%D9%8A%D9%81-%D8%AA%D8%B1%D8%A8%D8%AD-%D8%A7%D9%84%D9%85%D8%A7%D9%84-%D8%AF%D9%88%D9%86-%D9%85%D8%A7%D9%84&d56=8d All along, operators have seen digital marketers develop sophisticated lifecycle analytics that unearth deeper, granular awareness about customers and their decision journeys. They key there is to work with a continuum of behavioural cues generated by customers in real time online engagements. Looking for correlation between a customer action and a desired outcome and responding to it in real time helped internet startups grow faster, cheaper and more efficiently.
خيارات السماسرة الثنائية 2017 Learning from this, Operators have invested heavily in using ‘unstructured’ data from social and digital commerce to somehow amalgamate with massive customer data already residing in their enterprise data warehouses. This big data phenomenon is the next big fad in the analytics industry with a lot of unanswered questions wrapped around it.
بورصة الاسهم الامريكية Here is an example of how this works in theory; Customer Randy produces call and messaging data which operators already hold onto and now with Randy’s social and purchase data borrowed from elsewhere, they have an even clearer picture of what moves her to do the things she does.
explanation This is where it gets off track. With Randy’s data in, operators tend to think that they can now run some models as they have done previously but with fewer attributes to predict where she is going next with her purchases. The problem with this approach is the stitching. So much of this data and there is a lot of it, is out of sync and context that a horrendous amount of compute is wasted in surgery to stitch it up with least marginal benefits. Randy still has no idea why she got the SMS she got or the email to buy a VAS service.
visit To understand what is meant by stitching, consider the inverted pyramid below describing the information required to meaningfully predict customer behaviour or next steps. The top of the funnel (inverted pyramid) is pretty much empty when it comes to operators as they stand today. If they can build this part out, the second tier which is already pretty mature starts to produce massively improved returns. The bottom of the funnel is actually the static non behavioural information that operators hold on their customers. This information sometimes acts as a bias in various models by virtue of being incorrect, invalid, outdated etc and which can be vastly improved if top of the funnel is sorted out and customers can take ownership of this information and improve data on their own.