Ask Me Anything: 3 Answers to Data Analytics Challenges in the Retail Industry
The retail industry is one of the oldest industries ever known to humanity. The functioning of the retail industry has changed very little over time. However, the pace of development and adoption of innovation is increasing rapidly at present times due to the advent of data analytics and automation. Big data has changed the retail […]
The retail industry is one of the oldest industries ever known to humanity. The functioning of the retail industry has changed very little over time. However, the pace of development and adoption of innovation is increasing rapidly at present times due to the advent of data analytics and automation. Big data has changed the retail landscape by making operations efficient and improving the forecasting accuracy; thus, boosting the overall profitability.
One of the most challenging tasks in the retail industry is pricing. Price it too high, and you may lose a customer, price it too low, and it hurts your margins. Then, there is a host of other pricing problems including, pricing decisions across different channels, geographies, pricing benchmarks, and markups. Quantzig can help you identify the right pricing model for your business by factoring in various factors such as demand, inventory levels, and activities of the competitors in the retail industry. We can also help you implement predictive data analytics solutions that will help you implement a flexible pricing solution that automatically adjusts all factors affecting pricing and suggests the optimal price points for all SKUs.
How to Forecast Market Trends to Know What Products to Keep in Stock?
One of the most troubling tasks in fashion retail is to predict the trends for the upcoming season. However, predicting what products will be trending in the future is a problem faced by all sorts of retailers looking to make a name for themselves in the retail industry. We at Quantzig, help retailers choose appropriate tools to gain a better understanding of the current platforms trending in the retail sector. As consumers are increasingly revealing their purchasing habits online, our data analytics solutions can help retailers gain access to purchase history, social media accounts, consumer demand, and market trends affecting the retail industry.
What Demand Forecasting Tools to Use and How to Maintain Optimal Stock Levels?
Most of the products in the retail industry are seasonal, and demand forecasting is usually done on a historical basis. But the method is outdated and won’t be sufficient in an age where demand is highly fluctuating, trends are rapidly changing, and consumer preferences are dynamic. With the help of Quantzig, you don’t have to worry about complexities of demand forecasting regarding inventory positioning, long tail items, stock replenishment, and seasonal trends.
Quantzig’s data analytics solutions can help players in the retail industry understand the data regarding demand, trends and seasonality, lost sales, suppressions, and promotions.
Today, managers have access to a large stream of data, and decision-making on the basis of gut-feeling, the rule of thumb, and guesswork are largely eliminated due to the advent of data analytics.
“Without data analytics, companies are blind and deaf, wandering out onto the web like a deer on a freeway,” said a leading data analytics expert from Quantzig.
For more than 14 years, we have assisted our clients across the globe with end-to-end data management and analytics services to leverage their data for prudent decision making. Our firm has worked with 120+ clients, including 55+ Fortune 500 companies. At Quantzig, we firmly believe that the capabilities to harness maximum insights from the influx of continuous information around us is what will drive any organization’s competitive readiness and success. Our objective is to bring together the best combination of analysts and consultants to complement our clients with a shared need to discover and build those capabilities, and drive continuous business excellence.