Big Data for Meaningful Intelligent Information to Enable Decision Making

3 Mins read

Retail business globally is generating voluminous data due to evolution of Digital Retailing and increased customer touch points. Till just a few years back, data was primarily about stock and inventory or sales and accounting. This Data was being generated at – either Buying House, Warehouse or Store levels. The nature of Data was simple and the results expected from the Data were also single point. However, now that Retail Sales are happening at multi channels, the Data is also being generated at multi points. The traction points with customers for Retailers are also now multiple spreading across stores to social media; mobile phone texts; ecommerce; ecommerce stores and mobile devices. The Data is being collected not only to keep a tab on stock levels but also to study market trends from local to regional levels and to understand customers – their product preferences or shopping behavior.

It is becoming more and more a necessity for Retailers to understand and engage with their loyal customers. In order to stay connected with the customers and cater to their changing shopping behavior, Retailers now need to be present across these multi channels. Data is being generated across these multi traction points and Retailers need to capture this Data. This Data carries not only stock/sales information but also valued Customer and Market performance information. Loss of information for a Retailer as of date stands equivalent to loss of customer loyalty and loss of sales.

The nature of Data is no more simple but is becoming extremely complex. It is collected from various sources like social media or mobile devices; channels like brick and mortar or ecommerce; and different geographies. All this Data once collected has to be utilized to produce intelligent information that can support decision-making. To produce this intelligent information the Data is run through Analytics. This voluminous Data has to be first filtered for relevant Data for the Analytics to be applied. The Analytics need to be designed by Retail experts who possess in depth understanding of Retail business, both from the operational/process as well as the functional perspective. These experts develop the Analytics on complex algorithms that identify relevant Data to produce relevant information that will support critical decision making for Retailers.

For instance, right Pricing plays a vital role during promotions, markdowns and deals for Retailers. The Retailer needs relevant and timely information on stock levels and competitive pricings in order to take effective decision on when to apply Promotion, Markdown or Deals and to arrive at right pricing. Real time Data on category and product availability in warehouses and stores at various locations can be the key. This real time information can enable Retailers to take a decision on buying or disposal of stocks through promotions and deals. The Retailer based on meaningful information can plan sourcing, buying and deployment. This planned supply chain can be managed efficiently to by monitoring real time information especially in the complex Omni-channel retail scenario.

Similarly Data Management and effective Analytics apply towards efficient Customer Loyalty Management. There are as of now multiple transaction points with any one customer unlike when the only contact point was when the customer stepped into a store. The Retailer is now engaging with the customer not only in the store but also on social media, online, through emails or even text messages. At all these points Data is being generated which has to be collected, filtered, stored, analyzed and utilized. This huge volume of Data needs to be managed efficiently for Retailers if they wish to gain relevant information. To sustain customer loyalty Retailers can use information derived from the Data very intelligently in providing a customized and personalized Service experience to every customer.

Of course, Retailers can always go the next step and use Data for research and identifying benchmarks. These benchmarks can be industry benchmarks or meant for their internal analysis for purpose of performance improvements. Use of data from this purpose can help the Retailer not only improve Inventory management, increase profit margins but also stay ahead in the competitive retail space. It helps Retailers to be well prepared in the dynamic market to identify and predict trends along with change in customer preferences and behavior. The Data can help Retailers to improvise and innovate, creating new best practices and industry standards.

Many businesses in todays date are facing challenges in decision making due to data latency, complex data integration points, dynamic cluster & macro segmentation, complex pricing and promotion requirements, inefficient supply chain and assortment thus leading to revenue loss, customer dissatisfaction and impact on bottom line. The business sustainability challenges can be overcome to a large extent by effective use of Data.

Even though current existing Data of an organization may be structured or in an unstructured state, through a systematic and diligent approach to aggregate, drill down, summarize, analyze what-if’s and monitor Data, one would be able to use it for Competitive advantage for Retail and Fashion Business. Managed Big Data with a blend of SAP HANA’s in memory calculation & predictive analysis capabilities is fast becoming a necessary shift in the digital era of Retail. The sooner the Retailers plan and implement the shift, the easier it would be for them to adapt to digital retail and grow their Retail business.

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About author
Amitabh has decades of experience in Retail Solutions, especially in the Retail and Fashion Segment, He manages Enterprise level Global projects as Project Lead and Program Manager. As a founder & CTO at Groupsoft, Amitabh helps businesses in agile and waterfall project management through SAP implementations and advising on industry best practices.
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