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Injecting data science and big data into the wireless supply chain

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 telecommunications. However, one of the biggest complaints from telcos is they are drowning in customer rich data and are struggling with effectively using the insights to improve day-to-day operations and their supply chains.

 

Unlike general FMCG supply chains, telco 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. Telcos 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.

 

Dr Gregory Hill, Head of Business Analytics at Brightstar Australia, explores the top industry trends in big data in Australia and provides some top tips for operators to embrace the power of data science to improve their supply chains. How can they best gather business insights from the big data behemoth to enhance their operations and wireless supply chains, and most importantly their consumers’ loyalty?

 

  •         Recognise big data

Operators are a major 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 are gold dust and can be used to offer a truly personalised customer service approach. Improved technology makes it possible to collect, retain and analyse data that otherwise would have been discarded. Plus, new advancements in data science allow professionals to use more sophisticated techniques to integrate big data to a level unseen before.

 

  •         Upskill in data science

According to LinkedIn, in 2014, statistical analysis and data mining was the no 1 hottest skill that got people hired. And the Harvard Business Review claimed that data scientist will be the sexiest job of the 21st century. A new approach to business management and data analysis is being seen across Australian businesses, which is based on science, mathematics and statistics. There is a growing emphasis on hypotheses, algorithms, cause and effect, and experiments to extract knowledge from large data volumes, and this is set to keep growing. 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 hot property and data analysts with business know-how are in such high demand and low supply that they 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.

 

  •         Use insightful algorithms

Another hot topic is the growth in 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 were 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 around 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 sure fire way of improving costs, quality and timeliness – and delivering a great customer experience.

 

  •         Visualise the data

Communicating all of this information clearly and efficiently is another discipline we’re seeing a big growth in. This involves building compelling visual representations of complex data. Using graphics, maps, tables, diagrams, graphs and charts are all ways of making big data more accessible and usable to non-mathematicians and non-scientists in an organisation. Well-crafted data visualisation helps uncover trends, develop insights and explore scenarios. Underpinning all of this is story-telling – working across teams to uncover the story behind all of this data and communicating it in an engaging and simple way.

 

From a business perspective, data science is an integral part of analytics that encompasses data mining and business intelligence. But what do these emerging trends and science mean for the wireless supply chain?

 

  •         Streamline omni-channel

All of this data and scientific analysis is geared to achieving the Holy Grail: a seamlessly positive customer experience. Allowing customers to interact across multiple channels is an expectation that must be met by retailers. Better knowledge of competitor pricing, demand trends and customer buying preferences (online and in-store) can initiate sales and promotions that help avoid losing business and retain customers. This also impacts customer service and enables telcos to provide a more tailored interaction, while improving supply chain efficiency and creating a “smart supply chain”. Using analytics tools, such as cloud-based platforms, enables a real-time optimised experience, which is crucial to achieving this.

 

  •         Supply chain demand

With such close relationships with customers, telcos 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. The essence of demand shaping is knowing about your most profitable customers and products, and protecting and promoting them. Consumers will not be disappointed by an “out of stock” notice and retailers won’t have excess product piled up in their store rooms, so it’s win-win for everyone involved.

 

  •         Design a winning portfolio

Data scientists can also help with product design and ensure a portfolio meets customer requirements. For example, a retail phone store in a small country town is likely to stock different products to one in a CBD location, because customer demand will differ – features like enhanced network connectivity and being ruggedised may be more important in a rural or remote setting than colour variety. Knowing the trends and predicting behaviours based on insights means the right phones, accessories, bundles and services are all available across all channels, when the customer demands them. It’s important to use data science algorithms in customer segmentation and clustering. They can tell which customers are the lowest cost to serve and which are likely to buy the highest profit products. Creating a balanced menu of bundled offers based on data to address customer needs across brand, price, function, accessories and value-added services (e.g. insurance and upgrade programs) is key to success.

 

  •         Integrate internally

Most telcos have their own large analytics departments, working across Customer Relationship Management (CRM), Marketing, Business Intelligence and Reporting. Ensure they are feeding into the right people on the supply chain, so the company is working together end-to-end. Use data and modelling on the customer and how they are using products to tailor offerings. By working with the relevant teams, this insight can be used to drive the supply chain and ensure customers are at the heart of everything the business does. The current crop of big data and analytics tools provide a way to integrate data from sales, marketing, action of customers, product reviews, competitor information, warranty data and supplier status in real-time to make demand-driven supply chains a reality.

 

Big data is transforming all industries and business functions, including telco and supply chain, and data science is the next wave of innovation set to hit the industry. For telecommunications, harnessing this new approach will position them at the forefront. Unlike general FMCG supply chain, telco supply chain has rich datasets about customer preferences and behaviours. Harnessing this is a big opportunity and ensuring telcos are using the right technologies and platforms to manage and drive it into the supply chain to improve the telco's customer experience is imperative.

 

Dr Gregory Hill is Head of Business Analytics at Brightstar Australia and a member of the Industry Advisory Board at Melbourne Business School’s Centre for Business Analytics. Brightstar, a SoftBank Group Corp. subsidiary, is the world’s largest specialised wireless distributor and a leading provider of diversified services focussed on enhancing the performance and results of the key participants in the wireless device value chain: manufacturers, operators and retailers. In 2014, Brightstar reported global net revenues of more than US$10 billion and employs about 9,000 people on six continents. 

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