5 Important Features a Retail Analytics Tool Must Have
Retail analytics software is designed to collect all the raw information from stock levels, catalogue assortments, discounts, pricing information and much more. This information is stored and collected into where it is processed using statistical algorithms to keep a record of trends and patterns. Organisations can use this processed data to make informed decisions on anything- from creating fly price strategies to accurately benchmarking the seasonal discounts. The leading organisations in every industry are reducing their exposure to competitive risks by keeping a close eye on the trends. Here we have listed the 5 must-have features in a retail data analytics tool:
1. Trend Predictions:
It can be challenging to identify the trends while maintaining a balanced stock to create a successful product catalogue. The right intelligence tool can generate reports that assess the rate at which certain trending catalogue items are selling. This allows the supply chain operators and merchandisers to carefully select and identify optimised and profitable catalogues for products. This accumulation of past data paired with analysis can help to create a clear picture of what the demands in the upcoming months will look like.
2. Real-time Competitor Analysis:
In this era of digitalisation, it is becoming easier for customers to effectively compare the prices from different providers online and offline and find the best deals. Therefore, keeping in mind the prices of the competitors in real-time will allow the businesses and their teams to make quick and effective changes to the pricing of your products. A credible retail data analytics platform can do set these benchmarks within few minutes removing the burden of lengthy and manual researching of the competitor’s pricing. Manually doing so often delays the action and ultimately renders the repricing effort ineffective.
3. High data update frequency:
Data pointers like product prices and special offers get worn out pretty quickly. This is precisely why you need to track the real-time data. However, collecting real-time data is not achievable at scale in many cases. An effective customer intelligence tool should present up-to-date data to the possible extent.
4. Onsite product recommendations:
The feature of showing the shopper recommendations of the items that may be of interest to them while they are shopping on the online store is one of the best selling strategies. This is possible via getting access to the user’s search and the trends in the retail analytics platform. These recommendations may include the products that the shoppers are viewing and/or adding to their cart. Slow-selling products or trending items can also be recommended to boost the engagement time of the customer and hence, sales. The business intelligence tools offer retail analytics services that can use various strategies including product-bundling and running offers to maximise product sales and cross-selling effectiveness.
5. Ability to narrow down the data:
Getting a holistic view of the market landscape while still being able to narrow it down to the smallest detail is one of the most effective strategies in profitable markets. From supply chain metrics, merchandising and catalogue assortment to retail pricing and discounting, having the ability to work track data at all market stages allows the stakeholders to make critical decisions at just the right point in time. Having knowledge of where the products are located and which ones are the best sellers allows the retailer to make to act quickly.