How to Optimize Your Fashion Store by Using Retail Analytics Solution?
Technology has transformed every retail industry including the fashion store. Analytics has turned out to be the new fundamental idea in today’s unsteady retail environment which allows fashion retailers to work efficiently. It’s a challenge for retailers to gather and analyze data on customer preferences, their demographic profile data, and buying habits. Identifying potential areas is essential for growth. Identification of the potential of your fashion store helps you to profit by the sales opportunities.
Fashion is always changing. It’s essential to track trends to survive by managing seasonal fluctuations in the fashion retail industry. Collections change with the seasons. So, changes in trends may result in loss of the charm in the coming year. But more than ever the market has stepped up another level during the last years. Retailers take a different approach to using their physical space in recent years. Today, fashion retailers are concentrated on customer experiences rather than only looking at stores as a space full of ever-changing products. Now, retailers need to offer their customers a flawless customer journey.
Customers demand more personalized services. Collecting data is necessary to understand customers and meet their expectations. Retail analytics can create value in the fashion retail industry. Investing in technological solutions helps retailers in increasing revenues, sales, and customer retention rates. Thus, retailers who invest in technology will survive in uncertain markets.
The cloud, mobility, social media and analytics-based solutions provide data for the fashion store to enhance customer experience and gain loyal customers. Retail analytics provide new metrics about inventory levels, consumer demand, customer behavior, etc. for
- Marketing & Pricing
- Staff Management
- Checkout Line Optimization
- Stock Optimization
- SKU Range Management
- Store Space Optimization
Retail analytics provides fashion store a comprehensive and essential data and knowledge about customer preferences and their reactions to changing trends. The collected data is used to increase customer retention and enhance the customer experience to gain loyal customers. The prediction of customer behavior by using retail analytics solutions helps you to set a course for better marketing strategy, optimize store space, manage SKU range, optimize stock, optimize check-out line and manage staff. Furthermore, the data that is collected by using retail analytics give insight to fashion retailers about customer behaviors based on specific areas. It’s easier to give the customer the most ideal customer journey through their visit to the fashion store by setting the design of your store, product layout and promotions depending on the customer preferences. Moreover; counting the number of visitors in the fashion store, discovering the yearly, monthly, weekly, daily and hourly traffic of the store, calculating the conversion rate and abandonment rate of the store is possible by using retail analytics solutions. These data are so crucial to increase the market share of your fashion store.
Marketing & Pricing
The retail analytics help fashion retailers to determine their cross-selling and upselling strategy through personalization. Fashion stores can use collected data to set a course for an effective marketing strategy to increase the market share of the fashion store. Moreover, it’s possible to analyze the effectiveness of the valuable areas in the fashion store such as; campaign area, promotion area, check-point area, cabin area, etc. The prices are changed frequently in the fashion store due to changing collections. Retail analytics solutions can also be used in pricing strategy since it’s possible to observe reactions due to the changing prices by calculating spent time and the number of visitors in the specific area. Additionally, it’s also possible to determine the number of the person in the specific areas of the store such as cabin area and optimize staff management in the fashion store by measuring the number of visitors and service time.
Staff Management
The number of staff and staff interest is so important in the fashion store. You can learn about the most visited and most time spent areas in your fashion store and direct your staff to the proper area at the right time. Having a knowledge of the rush hours and the number of visitors in your fashion store helps you to optimize your staff management and personnel shifting to not to lose the interest of your shopper due to long waiting time. In the fashion store, the customer needs to learn more about the product like the available size, color, etc. That makes efficient staff management so essential. Efficient staff management decreases the abandonment rate. Thus, you don’t lose sales opportunities in your fashion store and maximize customer retention.
Checkout Line Optimization
Through monitoring waiting times and densities in the checkout area, store managers are notified instantly and automatically when the number of people in the queue or waiting time exceeds the level. The retail analytics can also give you insight into the preference and efficiency of the usage of the multiple-server queueing system or single server queueing system.
Stock Optimization
Optimizing stock is so important in the fashion store since the collection is changing consistently. And, old-fashioned clothes are undesired since there is a need for space for the new collection. Retail analytics helps the fashion store in stock optimization. If you learn about the dead-points and busy areas in your store, you can optimize your store layout, prevent the accumulation of stock and out of stock problems as well.
SKU Range Management
There is a wide range of products at the fashion store depending on gender, age, style, collection, campaign, etc. Determining the SKU range of merchandise has vital importance in the fashion store. The retail analytics helps the fashion store in predicting future trends of fashion stores to determine the optimum SKU range. An optimum SKU range also prevents the accumulation of stock. You can determine the optimum product range by collecting the data about customer behavior. The retail analytics calculate the spent time in a specific area. You can analyze if the range is too wide by observing spent time in the area. So, you can prevent problems such as not being able to find what the customer is looking for. It’s also possible to track if the customer passes a specific product area over. In that kind of situation, you need to have a look at the efficiency of the SKU range of the area.
Store Space Optimization
Most of the retail analytics also give heat map solutions to detect the most visited areas in your store and change the store layout to increase the potential of the dead-ends in store. Furthermore, you can compare different areas in your fashion store by using A/B testing. You can also compare a specific product group between other stores. So, you will be able to observe the areas in your fashion store that has a higher performance and take action to reach the potential sales of the areas that have low performance.
Data drives your business. Hidden insights or undiscovered anomalies can hold big business potential. Thanks to progress in technology, fashion retailers analyze huge amounts of customer data to understand patterns and personalize their offers to customers. Acquisition of customers, engagement campaigns and maximization strategies helped in increasing customer satisfaction and loyalty. Fashion retailers are able to make smarter decisions by understanding customer behaviors, their purchasing habits and identifying fashion trends.
Did you check our retail analytics solution? You can start using Udentify that analyzes the costumers’ behaviors at the brick and mortar shops. Udentify provides local conversion rate, head counting, heat maps, and order analysis. Thanks to the image processing technology, it can follow the customers anonymously, with which it gives meaning to the data that is gathered from the tracking by converting them to statistical models. It enables company managers to take data-oriented decisions by its management interface.
