In a recent article, Bloomberg mentioned that many retailers are shunning Amazon AWS cloud platform because they believe that Amazon is using profit from AWS, which is highly profitable, to discount goods at Amazon.com website. This could be true as most of the conglobate function same way – profit from one business unit routed to grow other businesses. One more concern retailers raised that they are letting their data to being stored at their enemy platform. This is a legitimate concern as data can be leveraged to create huge competitive advantage.
Retailers can leverage data many ways. However, most compelling use cases are
- Personalize engagement with customer,
- Optimize Inventory
Personalized Engagement with Customer
Personal Engagement with Customer has become very important in current era. In a matter of second customers can decide whether they like your marketing message or not. If message is not relevant to them at that moment of time, you might lose the customer. Customers are always connected and bombarded with messages and most of them are irrelevant for them. Data can enable retailers to determine what, when, and through which channel they should send “Next Best Offer”, personalize recommendation , and can enable to track customer path to purchase - visit to retailers website, purchase in store, or clicking digital coupons. According to McKinsey, personalization can reduce cost of customer acquisition by 50%, lift revenue by 5 to 15%, and increase the efficiency of marketing spend by 10-30%.
Retailers new mantra is to keep “Just-in-time” inventory, sometimes demand outstrip supply and can lead to longer wait time until merchandise is delivered. In case of extreme events such as extreme weather, disasters, or viral nature of social media can significantly impact demand. For small retailers, coping with variable demand is much more difficult as compared to big retailer because of scale.
Retailers can leverage data to improve demand forecasting to ensure that the right items are stocked in the right quantities at the right time at the right location to avoid overstocks and out-of-stock. Accurately predicting demand makes it possible for retailers to optimize inventory and use the supply chain effectively. Most of the retailers have adopted omni-channel strategy. To enhance customer experience across omni-channel, retailers can leverage brick and mortar store as a mini distribution center and ensure that stores are stocked with enough inventory.
Conversely, when customers are in the store and decide to have an item shipped directly to them, it’s important to ensure that it’s possible to deliver the items in a timely way. Omni-channel strategies require real-time demand forecasting and analysis to realize enhanced revenue.
With real-time data available for analysis, retailers can predict uptick in demand. Combining real-time data with historical data, such as which stores sell more products on hot days or what geographic location has more affluent trend-following customers, makes it possible to proactively move inventory around and ensure the right quantities are available in the right places at the right time to maximize the number of units sold. On the other hand, same data sets can be leveraged to determine reduction in demand that can enable retailers create promotions and cross sell. It can also enable retailer to determine which items are on-self for longer time and develop a trigger mechanism to alter associates.
So what retailers should do. They have two options
- Store all data on-premise
- Use some other cloud provider
The first option is not viable as it will be very expensive. For second option, the only true competitor of AWS is Microsoft Azure. Retailers need to develop analytics capability to compete with Amazon. Microsoft Cortana Intelligence Suite (CIS) can enable retailers to develop actionable insights that will create competitive advantage for them.
Cortana Intelligence Suite (CIS) is a set of tools and processes offered at Microsoft Azure Cloud Platform that enables retailers to store, process, predict, visualize and drive actionable insights from data in near real-time. The key components of CIS are
- Azure Data Lake
- Azure Machine Learning
- Azure HDInsights
- Azure Cognitive Services
Azure Data Lake
Azure Data Lake services is on demand cloud based data storage and processing platform that can enable retailers to securely store all data with “no limits”,and run on-demand analytics that instantly scales to their needs. It is secure, massively scalable and built to the open HDFS standard. With no limits to the size of data and the ability to run massively parallel analytics, retailers can now unlock value from all unstructured, semi-structured and structured data. For more information, please refer the white paper.
Azure Machine Learning
Azure Machine Learning is a cloud based machine learning service to develop and deploy predictive models. It enables retailers to quickly build and deploy predictive analytics solutions. Data scientists can leverage ready ready-to-use library of algorithms and can build sophisticated intelligent applications. It also provides a fully managed service that can be used to deploy predictive models as ready-to-consume web services. Azure ML has seamless interface with Python and R that enables data scientist, who are currently using R or Python, to leverage power of graphics interface.
HDInsight is cloud distribution of Hortonworks Data Platform (HDP) on Azure. It is offered as Platform as Service (Pass) i.e. it is deployed, provisioned, and managed by Microsoft. It could be very cost effective solution for retailers for processing large data set. Retailers can bring up HDInsights clusters when they need to process large data sets. Once processing is done, result can be stored in either Azure Data lake store or in Azure Blob Storage.
It includes implementations of Apache Spark, HBase, Storm, Pig, Hive, Sqoop, Oozie, Ambari, and so on. HDInsight also integrates with business intelligence (BI) tools such as Power BI, Excel, SQL Server Analysis Services, and SQL Server Reporting Services.
Azure Cognitive API Services
Microsoft Cognitive Services let you build apps with powerful algorithms using just a few lines of code. Cognitive API services enables developers to easily add intelligent features – such as emotion and video detection; facial, speech and vision recognition; and speech and language understanding – into their applications. Retailers can leverage these APIs to track customer movement in store, identify customers’ emotions when they are interacting with an associate, and how much time a customer spends exploring an item.
Microsoft CIS is a strong alternative to AWS platform. It enables retailers to leverage power of Big Data and create sustainable competitive advantage without giving their valuable data and money to their competitor.