This article was authored by Tony Poulos, and was originally posted on telecomasia.net.
Innovation in big data is all about making the data available for any number of people to test out ideas to see what works without impacting the performance of the systems the data resides in.
Promoting and supporting innovation of this type is critical for any business. Big data has become a byword for any business wishing to capitalize on information it has at hand in order to provide customers with the best possible experience.
But what if, with all the best intentions in the world, the customer experience idea doesn’t work out and customers don’t like what you are doing? The “failing fast and failing cheap” theory might work for developing apps, but when it comes to customers, it is a recipe for disaster if it is not well received, and could result in losing them for good.
The drivers for customer profiling and ad-targeting are usually led by those all-powerful marketing departments armed with big campaign budgets, backed by industry experts, consultants and market researchers that are big on theory but not so big on technical know-how. They too have been bluffed into thinking big data is the solution to every analytical need.
Companies like Google and Amazon have been fine-tuning their big data models for many years but they are not averse to making the odd boo-boo. For CSPs, their DNA is still carrying the 99.999% gene where risk-aversion and customer retention are still very high on the agenda.
That could explain why they have taken so long to make use of big data to improve the customer experience. Sure, they’ve been talking about it for years, but how many have reached the nirvana of making offers personally suited to people in real time?
It’s not easy when your customer data is spread across tens, if not hundreds, of systems in different formats. Much of the work has, to date, been focused on the collection and normalizing of that data so it can be used effectively. It is also questionable just how complete and valuable the CSP’s customer data really is.
When you look at the spread of information Google can source, plus a customer’s search habits and subsequent clicks, you understand why it can focus on their habits, interests and spending capacity. CSP data is more focused on which of its services the customer uses. Unless they use invasive tools like deep packet inspection (DPI), they will not have the full picture.
That, in turn raises issues of privacy and associated regulatory controls that CSPs may be subject to, but which digital service providers are not. There is also the question of whether the terms and conditions that CSPs have in place are even remotely close to those that DSPs insist their customers agree to.
All this still does not explain why CSPs are so late on the scene. When you read the latest report that the telecoms industry will spend over $10 billion on big data and business analytics products and services in 2019, you have to ask: “Why wait so long?”
Research house IDC is forecasting that CSP spending will be on a par with counterparts in the government, professional services and retail verticals. However, all four industries trail companies in the discrete manufacturing and banking sectors, which are forecast to spend over $20 billion each.
Will all this be too little, too late in the fight to keep customers using advanced analytics? Or will it be the savior of the industry as DSPs pummel their customers with ads and offers they don’t want? Maybe CSPs could offer them protection from unwanted customer experiences – a bit like how ad-free apps work.
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