data science and machine learning algorithms are transforming the big data community. The growth in big data is well known across all industries and business functions, in particular telecoms. However, one of the biggest complaints from operators is that they are drowning in customer data and struggling to effectively use the insights to improve day-to-day operations and their supply chains.
Unlike general FMCG supply chains, operators’ supply chains have rich datasets about customer preferences and behaviours. This means there is a huge opportunity for operators to use data science to gather, aggregate, store and analyse these trillions of bytes of customer likes and dislikes.
According to IDC, improved customer experience and customer service are ranked as top business priorities in Australia. Telecoms operators are under mounting pressure to improve their data analytics expertise and processes and actually use these insights to deliver a wireless Supply chain that enhances loyalty and provides the truly tailored brand experience customers are demanding.
Operators are a step ahead of other retailers or FMCG companies because they have such close relationships with their customers. They have access to some very powerful insights that can be used to offer a personalised customer service approach. Improved technology makes it possible to collect, retain and analyse data that otherwise would have been discarded. In addition, new advancements in data science allow professionals to use more sophisticated techniques to integrate big data to a level unseen before.
These data mining techniques are used by a new breed of data scientist to interpret rich data, investigate problems and provide exact solutions. Data science is used by many retailers, for example, to pinpoint what customers want, how they buy and what they might be interested in buying in the future. These skills are in such high demand that data analysts with the business know-how are earning almost three times Australia’s average salary. To capitalise on this opportunity, most Australian universities are now offering graduate degrees in data science and analytics.
Another hot topic is automation and using algorithms to classify, predict and optimise customer interactions. For example, if an algorithm is being used at a retailer to predict which stores are going to run out of a popular item, it could automatically send a signal to the Supply chain to push more stock to those locations. Doing this manually for all stores and products would be very time-consuming. This is fuelling much debate about the best way of combining automation and human judgement to create optimum results. For operators automating small-scale decisions based on structured data can be a way of improving costs, quality and timeliness – and delivering a great customer experience.
Predicting demand
With such close relationships with customers, operators can use rich transactional data to accurately predict supply and demand and ensure optimum supply-chain efficiency. Supply chains need to move away from being product forecast driven to become customer demand driven. For example, when a new smartphone is launched, businesses can use data on who is using a similar product, what stage they are at in their contract, how much data they use, what accessories they’ve bought, and other preferences to predict peaks of demand and ensure adequate supply. So, ordering the right product, sending it to the right place, at the right time and with the right price point will help improve speed, accuracy and scalability of order fulfilment.
Big data is transforming all industries and business functions, and data science is the next wave of innovation set to hit the telecoms industry. Unlike the general FMCG supply chain, operators’ supply chains have rich datasets about customer preferences and behaviours. Tapping into this is a big opportunity, but operators need to ensure they are using the right technologies and platforms to manage and drive it into the supply chain to improve the customer experience.
