5 Areas Where Data Engineering Gives Retailers a Competitive Edge

New data sources, ranging from log files and transaction information to sensor data and social media indicators, present new potential for retailers to gain exceptional value and competitive advantage in an ever-expanding sector. Retailers will need to empower individuals across their organization to make decisions quickly, precisely, and confidently from a business standpoint. The only way to do so is to use big data and behavioral retail analytics to make the best plans and judgments, gain a better understanding of customers, identify hidden trends that indicate new prospects, and so on.

Let's look at five behavior retail analytics use cases that are now in use in various leading retail organizations to better appreciate the value of big data analytics in the retail industry.

Retail Analytics on Customer Behavior

Improved customer conversion rates, tailoring efforts to increase revenue, predicting and minimizing customer churn, and cutting client acquisition expenses all require deeper, data-driven consumer insights. However, today's consumers interact with businesses through a variety of channels, including smartphones, social media, retail, e-commerce sites, and more. The complexity and range of data types you'll have to combine and analyze will skyrocket as a result.

When all of this data is combined and evaluated, it might reveal previously unknown information, such as who your high-value clients are, what inspires them to buy more, how they act, and how and when to reach them. You can increase customer acquisition and drive customer loyalty with these insights.

Because you can aggregate, integrate, and analyze all of your data at once to provide the insights needed to increase customer acquisition and loyalty, data engineering solutions is the key to unlocking insights from your structured and unstructured customer activity data.

Using Big Data in Retail to Personalize the In-Store Experience

Merchandising used to be thought of as an art form, with no real means to quantify the exact impact of merchandising decisions. As internet sales developed, a new trend emerged in which customers would do their physical research on things in-store before purchasing online afterwards.

The introduction of people-tracking technology has opened up new avenues for analyzing store behavior and determining the effectiveness of merchandising initiatives. A data engineering platform can assist retailers in making sense of their data in order to optimize merchandising tactics, personalize the in-store experience with loyalty apps, and deliver timely offers to encourage customers to complete purchases, all with the goal of increasing sales across all channels.

Retailers can use data engineering to turn in-store customer data sources into a substantial competitive advantage. Cross-selling can be aided by insights, as can promotional effectiveness.

Omnichannel retailers can use data engineering Services to do the following:

• Different marketing and merchandising methods are tested and quantified for their impact on customer behavior and sales.
• Identify requirements and interests based on a customer's purchase and browsing history, and then personalize in-store service for them.
• Monitor customer behavior in-store and provide timely offers to customers to encourage in-store or later online purchases, keeping the purchase within the retailer's fold.

Predictive analytics and tailored promotions can help you increase conversion rates

Retailers must successfully target customer promotions in order to enhance customer acquisition and minimize costs. This necessitates having as accurate a 360-degree image of clients and prospects as feasible.

Customer data has traditionally been restricted to demographic data gathered during sales transactions. Customers today connect with each other more than they transact, and these interactions take place on social media and across many channels. As a result of these changes, it's in retailers' best interests to turn the data that customers generate through interactions into a wealth of deeper customer data and insight (for example, to understand their preferences).

Customer purchase histories and personal information, as well as conduct on social networking sites, can all be linked through data engineering solutions. Let's imagine some of a retailer's high-value customers "loved" watching the Food Channel on television and shopped at Whole Foods on a regular basis. The merchant can then utilize these information to tailor their ads by airing them on cooking-related TV shows, Facebook sites, and in organic grocery stores. What's the end result? The retailer will almost certainly see considerably greater conversion rates and lower client acquisition expenditures.

Analyzing the Customer Journey

Customers today are more powerful and connected than they have ever been. Customers may get almost any kind of information in seconds through channels including mobile, social media, and e-commerce. This helps them decide what to buy, where to buy it, and how much to pay. Customers make purchasing decisions and purchases based on the information accessible to them whenever and whenever it is convenient for them.

Customers, on the other hand, have higher expectations. They want organizations, for example, to provide consistent information and experiences across channels that match their history, choices, and interests. The quality of the customer experience drives sales and customer retention now more than ever. Marketers must constantly change their understanding and connection with customers in light of these trends.

You may combine all of your structured and unstructured data into Hadoop and analyze it as a single data set using big data engineering methods, regardless of data type. The analytical results can show completely new patterns and insights that you never knew existed - and that traditional analytics couldn't even imagine.

Supply Chain Analysis and Operational Analytics

Organizations utilize big data in retail analytics to analyze supply chains and product distribution to minimize costs as product life cycles become shorter and operations become more complicated. Many merchants are all too familiar with the pressures of maximizing asset usage, budgets, performance, and service quality. It's critical for getting a competitive advantage and improving business results.

The key to increasing operational efficiency with data engineering platforms is to use them to uncover insights buried in log, sensor, and machine data. These insights include data on trends, patterns, and outliers that can help you make better decisions, improve your operations, and save millions of dollars.

Assets that generate useful data include servers, factory machinery, customer-owned appliances, cell towers, energy grid infrastructure, and even product logs. It takes a lot of effort to collect, prepare, and analyze this unstructured (and frequently fragmented) data. Data quantities can quadruple every few months, and the data itself is complex, with hundreds of various semi-structured and unstructured forms to choose from.

Data engineering services enables you to easily combine structured and unstructured data, such as CRM, ERP, mainframe, geolocation, and public data. You may then use this data to discover outliers, run time series and root cause studies, and extract, convert, and visualize data using the correct analytical tools.

You may combine all of your structured and unstructured data into Hadoop and analyze it as a single data set using big data engineering methods, regardless of data type. The analytical results can show completely new patterns and insights that you never knew existed - and that traditional analytics couldn't even imagine.

Conclusion

Data engineering that drives action can quickly bring together and study large amounts of structured and unstructured data to identify hidden patterns, new correlations, trends, consumer insights, and other important business data. In order to maintain a competitive edge in an ever-changing environment, retailers must find speedier ways to transform their raw data into analytics-ready data that can be used to make really effective decisions.