Retail analytics software gathers a variety of data, such as pricing, stock levels, catalog assortments, and discounts. Algorithms are used to record and track trends and patterns using this information gathered and stored in large databases.
A large amount of intelligence can be extracted using this data, allowing you to make data-driven decisions on everything from impacting your pricing on the fly to accurately understanding your seasonal discounts. Software like this helps to mitigate human assumptions that can lead to margin loss, overstock situations, and missing market trends.
Retail analytics software enables you to develop a deeper understanding of historical trends and channel them into actionable insight, facilitating the development of an accurate, fact-based case that provides insight into future trends and pricing movements. Utilizing specialized retail analytics tools, many companies reduce their exposure to competitive risk by keeping a close eye on these trends.
When you implement a new system to accomplish this, what should you look for?
Analytics Tools in Retail
Retail software helps you improve your marketing strategies and sell more through enterprise-level sales and customer behavior analytics. By gleaning client information and prevailing market sales trends from a variety of sources, efficient software helps you monitor and improve your business’s performance. Data mining, big data analytics, shop floor management, financial forecasting, and market predictions are all part of retail analytics.
A manual Excel spreadsheet is tedious and time-consuming, while dedicated software combines your revenue, marketing, and performance data into a single interface. With retail tools, you’ll be able to ask the right questions, so you can start getting answers.
Common Features of Retail Analytics Software
Below are some core features that retailers should consider to find the right retail analytics system for their business analysis needs:
Using retail analytics tools is a good way to collect data from multiple sources, including retail point of sale software, retail management systems, etc. They integrate with retail analytics systems and automate the export process instead of having to rely on internal sales teams to collect the data manually.
As part of the data collection process, raw data is cleansed to ensure accuracy and consistency. Erroneous data is dealt with by identifying incorrect or duplicate data, deleting the data, and then rearranging it in the right format.
Using advanced artificial intelligence (AI) and machine learning (ML), this feature of retail analytics software uses clean data and provides insight and information in the form of different visualization dashboards such as tables, graphs, pie charts, etc. By using these insights, business leaders can spot trends, predict results, and make better decisions.
Essential Features in Retail Analytics Tools
Competitor Monitoring That Is Active and Customized
Having control over your data source is essential. Controlling who and what you monitor in the marketplace is a fundamental component of your intelligence. The data set must have a minimum of three sources and ideally five sources, which include your competitors as well as other retailers who sell similar products to what you are selling online or offline.
Effective strategies in profitable departments are the ones that take a holistic view of the market landscape, yet can drill down into granular details as necessary. Data tracking at all stages of the market – from Supply Chain metrics to Merchandising and Catalog assortment to Retail Pricing and Discounting – allows stakeholders to make critical decisions at the right time. This can make the difference between winning a category or losing it. Retailers who know where products are located and which are being sold (or not) can make informed decisions and act quickly.
To create a successful product catalog offering, it is imperative to identify trends and maintain a well-balanced stock level and size range. In the right platform, reports could be generated that explain the rate at which certain “on-trend” catalog items are selling, allowing supply chain operators and merchandisers to identify an optimized and profitable product catalog. Analyzing historical data and trends can give us a better idea of what will be in demand in the months to come.
Alerts and Action Triggers
Dynamically setting pricing and product assortments in real-time automates the process of saving time and effort while maximizing price updates. It is all about being the first one to interpret and leverage a situation as it unfolds based on predictive analytics and retail analytics. Managing multiple product items and outlets can make it nearly impossible to manually adjust the assortment of products across multiple platforms.
Benchmarking Competitors in Real-Time
The price-sensitive consumer landscape post-recession can make pricing agility very difficult. These days, consumers can effectively price check and shop around online and in stores to find the best deals. By benchmarking your pricing against your competitors’ real-time, you and your team will be able to respond rapidly and effectively to changing market conditions. Your repricing effort won’t be hindered by lengthy, manual research of your competitors’ pricing, which often delays actions and defeats the purpose.
Website Recommendations and Cross-Selling Suggestions
A recommendation engine displays items based on a shopper’s browsing history and the trends in analytics as they browse an online store and select items to purchase. Customers may view and/or place in their shopping carts products that are slow to sell as well as trending products. By deploying various strategies such as product-bundling and discounting, the engine can increase product up-selling and cross-selling effectiveness.
When picking a retail analytics tool, it is important to ensure that the above features are available, as well as a highly customizable and user-friendly interface. Analyzing real-time data is essential to developing a retail sales and marketing strategy, merchandising mix, and supply chain optimization. Your bottom line will be affected the most by the interpretations, recommendations, and automation derived from that analysis.
AI and Machine Learning
No longer “the future,” AI is now reality. Look for retail analytics software with AI-powered tools and machine learning capabilities, so that it gets smarter about its recommendations the longer it works with your customer data. AI-powered tools can help continually optimize pricing while taking into account factors such as seasonality, customer behavior, and competitor activity, predict demand and power your personalization activities.
Analytical software provides retailers with a comprehensive view of all aspects of their business. Using key performance indicators (KPIs) such as total sales volume, average transaction size, retail conversion rate, rate of return, etc., they can make more informed and strategic decisions.
Analysts and managers use the software to keep track of the performance of the company across all business units and regions in real-time. Data is identified and analyzed by the retail analytics software to determine what is selling well, when it is selling the most, and what demographics buy it. As a result, sales teams and marketing departments can formulate better sales strategies and design effective marketing loyalty programs, as well as study shopping patterns and uncover consumer trends.