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Master the Holiday Buying Season with the Next-Gen Analytics and Scalability of the Cloud

Over the last several years, holiday shopping has grown exponentially, and retailers have experienced a significant shift from in-store to online shopping for holiday purchases. Through this growth, retailers are generating massive amounts of data that can be utilized to predict buying trends, increase logistical efficiencies, optimize pricing based on current market trends, and numerous other applications. However, many companies are not able to maximize the potential of their data due to on-prem infrastructure constraints or lack of access to analytical tools. These are two of the leading reasons global retailers are seeking the scalability and next-gen analytical capabilities available in the cloud.

Scaling to Meet Your Data Needs

Prior to the Cloud, the difficulty of estimating how much technology is needed over a multi-year period made scaling hard to do. An article from NGDATA illustrates this issue, “…with the exponential increases in the volume of data being produced and processed, many companies’ databases are being overwhelmed with the deluge of data they are facing.” In order to prevent this, retailers are having to plan for peak workloads and are forced to make heavy capital expenditures of on-prem infrastructure.

However, with cloud technologies, companies can scale instantly as needed. An article from Stratoscale wraps the concept up neatly, describing that “in essence, scalability is a planned level of capacity that can grow and shrink as needed.” If online traffic surges, the cloud provider can scale in real time to handle the additional workload. Then, it can automatically scale back when the surge subsides. This ability to scale dynamically allows companies to provide a positive customer experience during the holiday season, without needing to sustain excess infrastructure throughout the rest of the year.

Leveraging Next-Gen Data Analytic Platforms in the Cloud

Shopping has transformed into an omni-channel experience where a customer may see an ad online and then come into the store to physically hold the product prior to purchase, or they may see an item in passing and then purchase it later online. For retailers the key is to ensure they are providing a positive customer experience, regardless of where the interaction takes place. Here are three top ways retailers can utilize next-gen analytics and machine learning to enhance their business and customer experience:

  • Predict Customer Behavior – Every time a customer views an item online, saves an item in their cart, or completes a transaction in-store, companies are gathering data on their customer’s preferences. Has a customer purchased five blue clothing items in the past 6 months? Yes, then send them a curated email highlighting blue items you sell. Does a certain customer’s total purchase increase when they have a 15% off coupon instead of a 10%? Yes, then you can provide them with a 15% coupon, while predicting their total purchase should increase 40% after the discount.
  • Realtime Price Adjustments – Rather than attempting to manually track how pricing is affecting purchase trends, next-gen analytical tools can analyze internal purchasing data along with competitor pricing to predict how pricing fluctuations will impact purchases. This information can then be utilized to adjust prices across the entire company or split A/B test pricing for further analysis to increase purchases and maximize revenue.
  • Automated Inventory Logistics – Basic analytical and inventory management tools can identify when a company is low on an item and plan a restock. However, next-gen analytical tools can incorporate machine learning to provide significantly enhanced predictions. For example, instead of only analyzing past purchases, a company can integrate weather data to predict an increase in snow jacket sales in a region due to a likely cold front moving in. The company can proactively move snow jackets from an unaffected region to the impacted area. When the storm hits and other retailers quickly sell out of inventory, the company who proactively shifted inventory will increase sales and customer satisfaction.

A recent McKinsey article on personalizing the customer experience through analytics stated, “Highly personalized customer experiences, when offered to millions of individual customers by using proprietary data, are difficult for competitors to imitate. When executed well, such experiences enable businesses not only to differentiate themselves but also to gain a sustainable competitive advantage. Moreover, our research has shown that personalized experiences drive up both customer loyalty and the top line.” The messaging is clear – companies that take advantage of these technologies are going to benefit.

Get to the Cloud This Holiday Season

It’s simple to understand the benefits the cloud can provide, including: scalability, cost efficiency, and next-gen analytics. Where most companies run into a critical roadblock is, “How do we get there?” For years, the traditional cloud migration approach meant spending thousands of hours analyzing reports, refactoring applications, writing new ETL, and then hoping everything worked when the cutover to the new cloud platform occured. This strategy is risky, time consuming, and often completely offsets the projected increased revenue and savings.

Just as companies are wanting to modernize from old-school analytics, they now have the ability to modernize their migration approach as well. Gluent Data Platform provides the ability to migrate data and processing the cloud, without needing to write any code.

  • Advisor – Gone are the days of spending thousands of hours analyzing reports and databases. Gluent Advisor is an automated analysis tool that will review your databases and create a detailed report illustrating database metrics, offload-ability, and a projected ROI for migrating the database to the cloud.
  • Automated Offload – Writing new ETL pipelines to move data to the cloud can be a time consuming project. Gluent Offload Engine simplifies this process by offloading the data and keeping it up-to-date automatically. No ETL required.
  • Transparent Query – The most complex and risky part of a migration is refactoring applications. Gluent Query Engine completely eliminates this step by allowing applications to transparently access the offloaded data, without any code changes.

Getting your data into the Cloud is important to the growth of your business. However, not all paths to the cloud are created equally. Gluent Data Platform helps customers de-risk cloud migrations, reduce the project length to weeks instead of years, and significantly increase speed to ROI. For a cloud migration, you want to partner with a company that understands cloud technology and cloud native data platforms completely, as well as one that can help your company improve its business results. At Gluent, we will evaluate your current situation and help guide you on the right path to implement the perfect cloud solution.

Learn about our checklist that can help you make the migration easier or give us a call to get started now.

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