CoDriver Insights – Forgotten Demand and Hidden Revenues
Gartner’s widely acclaimed definition of Big Data is the “3 V”. The thesis is that the Volume, Velocity and Variety of data grows up to a level which cannot be handled with traditional data management systems. This data would then be enabled with Big Data, powered by Analytics, and used for informed decision making.
Unfortunately, majority of the Big Data industry seems to focus purely on enabling the three V-letters, while the use cases are taken for granted or left in idea level. The typical assumption seems to be that as long as the 3 V’s are captured, sky is the limit for huge business opportunities.
The way I see it, this mantra not only degrades the credibility of Big Data, but also defocuses us from the fact that Predictive Analytics can live and kick even without Big Data.
Most of the Big Data initiatives I have seen by now are mainly focusing on real-time customer management, customer care, just-in-time advertising, and on-the-spot upsell. For these use cases, Big Data is indeed needed. These demands however also set the key challenges of such initiatives:
Big Data might result in big benefits but it surely also means big effort.
While the Big Data trend is essentially about collecting all available data and trying to turn it into an opportunity, we find it more efficient to work the other way around: First set the goals, and then define the needed data. Typically we see that the objectives can be achieved with very manageable data sets. This way we can also tackle the key challenges of Big Data:
Objectives such as Securing customer experience with minimum cost; Maximising data plan profitability or; Optimising network investment ROI can be done very accurately with a more limited set of existing on-demand (= not real time) data. In my view, when it comes to this type of strategic analysis, Big Data is – although beneficial – not really a necessity.
Tu put it simple, this is how mobile business works: Investments improve network quality; quality attracts usage; and usage drives revenues. For us Predictive Network Analytics is essentially about finding and quantifying these causalities.
We analyse and forecast how changes in mobile operator’s service offering, terminal portfolio and network structure will impact on service usage, QoE, revenues and churn. If you are more interested about our approach, I recommend you check on CoDriver™ Predictive Analytics from our services page.
The past 3 years have been extremely exciting to me as I have been responsible for developing our CoDriver™ Analytics service. I’ve been involved in numerous different customer projects and learned a legion of interesting phenomena about mobile data business dynamics. We have seen dependencies that have not been recognised before, and ruled out fads that were believed to exist but factually didn’t.
In the coming weeks we will share some of these insights with you in a series of mini-blogs. We are planning to cover at least following topics:
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The article belongs to a mini-blog series about CoDriver™ Predictive Analytics. In the series we will uncover hidden trends and causalities in mobile data business dynamics.