Question:
Discuss about the Big Data Analytics in Australia Post.
The world of business has been experiencing a great never before explosion of massive data in very large scale. And a million dollar worth question is, how is that data used to achieve various objectives of the businesses. Every marketing department in the organizations looks at this large scale data to exploit the market in the best possible extent to increase its segment of sales and profit from the existing market. As sending text messages to the users would not be enough effective to bring the customers to the premises of the organizations, to buy the product and subscribe for the services, marketers now look at the huge database to make the customers enough captive of the products, services, offers and discounts.
Australia Post
Australia post or Australian Postal Corporation in full form, is government owned corporation, which is owned by Commonwealth of Australia. The Australia Postal services were started in 1809 and currently are in operations in over 4429 metropolitan retail outlets, with its 2561 retail outlets present in remote and rural areas. It provides the postal services in entire Australia as well as in overseas territories. AP has the revenue of A$6.37 billion, by 2015. The head office of the AP is located in Bourke Street, Melbourne, which also provides the services of post office.
Big Data Analytics
It is important to understand what is Big Data and what is Big Data Analytics, when considered from the business perspective by the business organizations. Big data is a huge and larger data sets or database that is very complex that the data processing applications traditionally used in current. The term Big Data is often relates and simply elaborates the predictive analysis usage and several advanced methods towards extracting the useful information from the larger sets of data.
Big data has been the target of the organizations, especially, national and multinational large scale organizations, towards converting the potential data base into customer database and it can be made possible, as the accuracy in data sets could lead to making the decisions that are more viable, confident and statistically supportive. These decisions can result in reduction in cost, reduction in risk and increase in the operational frequency.
Having understood the potentiality of the data sets from Big Data, many organizations have spent billions of dollars in the information technology towards specializing the analytics and data management services. Examples of various commercial business organizations that have benefited from the Big Data analytics are Australia Post, Walmart, IBM, SAP, HP, Microsoft, Dell, EMC, Oracle Corporations, etc.
Big Data Analytics In Australia Post
Australia Post is the largest postal service of Australia and implemented the analytics services of big data to improve the spending habits of the customers that has helped the company for optimizing the campaigns of marketing.
The operations of Australia Post are in three core areas,
First Step
Australia Post launched the services through digital, called Digital Mailbox, to communicate through a secure website.
‘Future Ready’ Strategy
Future Ready strategy was announced in February 2010 by Ahmed Fahour, who had been appointed as CEO and MD of the Australia Post Corporation. It has included the structure of new organization and also a renewed foray into a new business trend, digitally, under the strategic business unit, ‘eServcies’.
However, AP announced losses occurring first time in 30 years, in September 2015. The company experienced a loss of $381 million was caused from the huge decline in stamped and addressed mail and also in the mail delivery. However, parcel delivery contributed and accounted for near to half of the overall revenue. The loss was A$222 million that had been down from the profit of $116 from the previous year. Australia Post continues to widen and broaden its range of services and products and continue to invest in the advanced and sophisticated technology, like Big Data and Predictive Analysis.
Customer Address
According to Australia Post, the geographical address of the customer in the database is often considered as a key attribute that can be easily applied to the respective record of the customer, from the vast customer database. It is considered as one of the best-structured and robust data elements that can be easy to work with and also creates a standardising data avenue.
Australia Post has initially has established and set the standards of industry for storing, managing and matching the data of the customer that was centric to the geographical address of the customer. As a part of this standardization, AP had released the PAF (Personal Address File). Now the huge customer base is very well validated and cleansed against the PAF and it has been embedded within the unique household identifier of the Australian Post, which is a DPID (Delivery Point Identifier).
The DIPD of the Australia Post has been applied to each of the address and the process of linking the disparate datasets that have the addresses. The AP corporation has spent enough every year on customer data integration, data hygiene as the data simplified in this process helps the company to present greater benefits for its business.
The DPID can possibly appended through the software that is accredited by the AMAS (Address Matching Approval System). Minimum match of about 99.6% is to be achieved and to be allowed and accredited towards commercializing their software. When a record is matched, then DPID would be returned with random code of eight digit and stands unique to the PAF’s address record.
There is a certain process to achieve the AMAS accreditation. It needs the best possible ways of breed probabilistic matching to the rules and requireents of the businesses that are precise and comprehensive. After completion of th is process and after DPID is appended, then the DPID is considered to be an important and primary match key to adopt deterministic techniques for matching the disparate data sources.
Eventually, it requires that the databases that are sought to be matched would be based on the address and it has to be processed through the software called, AMAS accredited software. However, in the cases that the unstructured data is to be data, such as non-address related data, and other transactional information, it becomes necessary for the integrator of data to identify various ways of matching it to the geography. Understanding of the ways to ink the data back to the geographical and physical address enables to integrate the entire customer data. If it can be modelled to a CCD (Census Collector District), store or state catchment area, postcode and each of the attributes of the address can very well appended to the DPID and consistent unstructured data linkage can be done towards the structured data.
Prior to this, at Australia Post, processing of files used to be done, with the accuracy less than 60 percent. However, the possible error rates are decreased and the accuracy has been increasing to more than 95%, after applying the Personal Address file.
The software analyse the change of the address by the Australians. The data says that there are about three million Australian shift their houses to new addresses. Changing of the physical address has been a challenge to the company. However, it has gained the ability to track the changed address of Australians, through Mail Redirection Service. The service is enabled to ensure that the mail received by the customer, who shifted his or her house, the mail will be automatically forwarded to the new shifted location of the customer. And such facilitation has helped the company to become hassle free.
Barcoding to Data Quality
After the PAF is released by the Australia Post, many of the databases about the customers were very poor and used to devoid the standards that the release of the initial files processing was done through the AMAS designed, were PAF and the final achievements could match about 60%. It indicates that the 40% of the customer records extracted were inaccurate. However, the customer databases used today have become the regular PAF users through the AMAS accredited software to achieve more than 95% of accuracy in terms of matching. This dramatic transformation has been resulted by enablement of PAF through accurate and comprehensive customer base centric data that support clear business data management applications, clear business rules and functional standards.
Accuracy Beyond 5%
Australia Post has recognized that the accessibility of the PAF has to be increased so that the challenges and needs of the big data world can be assisted and fulfilled. A set of offers that are enriched and designed to suite data package that the teams of the customer relationship management and marketers have to enrich and integrate their assets of data towards generation of the insights and it would allow superior marketing ROI (Return On Investment).
Predictive Analysis
Forecasts of manual cash flow are things of the Australia Post in the past, that includes two software package implementation that crunch the data towards the production of the forecasting reports, daily, monthly and annually. According to Armand Mizan, Astralia Post Business Analytics manager, the previous manual forecasting used to take much more time, even weeks for producing the reports and still the final report was not accurate. Apart from that the staff of Australia Post had little visibility of agency cash flow movement. The company becomes as an agent, for the companies, like telecommunications, ATO (Australian Tax Office) and utility providers. The payements were recived online as well as the retail outlets that are close to 4500.
According to Mizan, there was about $50 billion worth payments were done for each year. To overcome the manual cash flow forecasting problem, Australia Post had decided to go for SPSS Predictive Analysis software, developed by the IBM, along with the Cognos TM1 enterprise planning software. They had a model of monthly cash flow that was based on data of actual figures. It would populate then, the cash flow daily through a translation. By using that, the agency stated the forecasting model that is produced with the help of SPSS and these results were sent back towards TM1.
For checking and determining the accuracy of SPSS in providing the information, Australia Post has taken the reference of historical data that was collected from August 2011 to October 2011 and then applied in the software towards producing the results and reports. According to Mizan, the forecast done with the help of SPSS was compared against the actual data in the year 2011, the accuracy level achieved was 95 to 98 percentage.
Business Outcomes
And now Australia Post has completely integrated the actuals and has been forecasting the actuals monthly cash flows for following 4 years. Finally, both the daily and monthly cash flow models have been aligned completely. In addition that, there is also improved agency financial data visibility that the company had not before.
Technical Difficulties
However, according to Mizan, TM1 and SPSS project has its own teething problems. For instance, when the dates of transactions and agency settlements, the data it has taken out from the general ledger started reflecting the data of transaction. But, this date appears varied from the when the actual cash settlement was done. This settlement date was quite important during the forecast of the cash flows. So, to overcome this challenge, Australia Post applied lags for the transactions to finally reach at the required settlement dates.
There was also another challenge, according to Mizan. The challenge was the effect of the special events and public holidays of territory and state based. The SPSS software needed to take the holidays and special events into account and, so the forecasting model is entered with the calendar of holidays.
Future Uses
Austrlia Post having tasted the fruits of the predictive analytics, the company has been looking to scale up the projects with additional analytics, in addition to the analytics that have already active.
Australia Post has been exploring currently, to extend the use of SPSS in the forecasting of the daily revenue by retail outlet individually and detection and prevention of the fraud. Since Australia Post is the largest retailer in Australia, the fraud risk would be significant. So, the company looks to detect the fraud of the credit card in its retail outlets that are more than 4500. Apart from the fraud detection, Australia Post wants to implement TM1 by the sales and marketing staff for the purpose of customer analysis, for understanding, the details about the customers of the company, the products and services that they are buying, habits of them and also to identify the churn of the customers.
Australia Post started evaluating several ways to deploy and use the predictive analysis throughout its organization. The future plans include daily customer sales prediction, reduction of the customer churn through gaining improved insights into the interactions and predicting the products profitability. Specifically, it is looking at customer churn in the business of parcels, according to Mizan. As the business fo the mail has been declining and parcels will be the current and future growth in the company. So, protecting the market of parcel business market and terminating the churning of the customers is noted as a higher position in the list of priority.
According to Mizan, automating the armaguard cars sending process down to the retail branches of the company towards the collection of the cash, after the amount reaches on a specific threshold is also listed in the priority list, by using predictive analysis. It is important as there is a lot of money need to be collected at the network of retailers.
Another significant shift the company is looking for is a giant jump with the social media analytics using the same SPSS, which can be very well suitable.
However, setting and implementing the new technologies in all of the new adapting methods are to be looked very carefully, so that the funding requirement is determined, which also needs timeframe for the implementation. it is going to take approval, time and funding to start defining the requirements of business. Australia Post has to decide, where it has to go by scaling up the predictive analytics and based on that it needs to make the investments in the new software and hardware. Meanwhile Australia Post has decided to fine tune the current operations by predictive analysis, by feeding more and more information so that the better accuracy in the predictions of cash-flow can be achieved.
Australia Post advises to focus right at the beginning of the goal, so that it can improve the chances to get the seeker to reach those goals. Though there are plenty technical challenges, clear understanding of the goal of what to be achieved and common sense have taken the Australia Post to the goal (Schutz, Plant, 2012).
Conclusion
The business world is experiencing a never before explosion of large scale data that is made available as raw data that can be processed in many ways, by customizing to the needs and requirements of the businesses. Australian Post is a biggest postal service retailer in Astralia with about 4500 outlets in the country. Big Data is large data sets that can be made available, about the customer and their detailed information. This information can be potential for the company and it helps to make increased business, along with the increased retention of the customer.
Australia Post uses the big data technology towards delivery of the better value to the better support or service and business through the data analytics improvement, which helps to map out in optimizing the practices and strategies of marketing and the customer journey. The technology is applied to its three of core areas, like parcels and logistics services, letters and associated services and agency services and retail merchandise. Predictive analysis has been employed for various operations of the company and the company has been making up the arrangements to scale up these applications and technologies, within the organizations.
References
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