In my last blog I discussed “how” communication service providers (CSPs) could successfully achieve their goal of transforming themselves into digital service providers (DSPs). Virtualization and cloud play an important role in this process, but innovation in BSS/OSS also needs to happen.
Before you innovate, however, first pay attention to the enterprise product catalogue (EPC). At the moment, catalogues are often all over the place — some of them are still listed in spreadsheets. If you make a change to one product, it’s a laborious process to replicate that change across all the different systems you still have in place.
Hence, your number one priority should be to federate, consolidate and rationalize the EPC. Spend some time on this because it’ll make your life a whole lot simpler by linking all your systems together. Only then can you think about inserting innovation into catalogue-driven BSS with the help of specialists in the BSS/OSS field.
But going back to the EPC: so you have rationalized your catalogue and are now in full control. The result of this process is that you are able to launch products faster than before, and only have to make changes once rather than in 50 places. Congratulations on completing this important step!
But is that enough? This is where the innovation part comes in. Building catalogue-driven OSS/BSS is still very laborious and based on eligibility and availability rules — such as determining whether or not products are compatible and how to price and discount products.
Maintaining the rules of a product is laborious, if not impossible; it is highly error prone because it requires people. So the next step is to incorporate product recommendation engines that can learn autonomously based on a customer’s past and current preferences, and can predict (positive outcomes) with catalogue-driven OSS/BSS. Every time it makes a correct selection, it learns from that experience. It knows who you are and where you are, and it also has knowledge of the type of products and services that you ordered in the past.
If the outcome is not successful, then this is the point when humans intercept the machine. This is what we term the hybrid approach, where humans enrich the data and the learning experience and guide the recommendation programme to a successful outcome. So we don’t have a robot running everything, but instead use a combination of autonomous machines and a hybrid/human enrichment mode.
Is this all enough to become a DSP, however? Stay tuned for the next episode.