Part I
Analytics play a pivotal role in the data flow theme at intervals a retail organization. A typical retailer generates more than thousands of data points through POS machine. It's tough for a retailer to make strategic choices based on this raw data.
A typical retailer has large quantity of sales knowledge stored in their systems. The new technologies have the ability to use these historical knowledge to boost retail productivity. To create sustainable advantage over competition, retailers are making an attempt to reinforce their product offerings, service levels and pricing models. To stop price attrition and to safeguard margins, retailers are making an attempt to cut back their value-to-serve per customer and thereby creating certain that the full price of possession of a customer over time is reduced. Managing promotional plans is another vital space for retailers to concentrate on and target customers more effectively and efficiently.
Small and midsize retailers are facing problem with restricted analytical resources to read the heart beat of their business processes. Retailers are not ready to follow up with routine sales analysis, category analysis and whole share analysis for all the products.
Most retailers collect every transaction from every store, track each movement of goods and record each client service interaction. Hence there is no shortage of knowledge, but how does one translate all that information into actionable data? How this data will be used to make better choices? The main objective of a retail store IT department is to convert the raw data into valuable and useful information.
Business analytics helps to induce insights from the structured data, like sales and productivity reporting, forecasting, inventory management, market basket analysis, product affinity, client clustering, client segmentation, identifying trend, identifying seasonality and understanding hidden patterns for loss prevention and store administration.
Analytical techniques such as statistical analysis, knowledge analysis and analytical tools help in understanding patterns and trends inside giant databases. Once we use them for creating analytical models, they provide the edge to decision making. While descriptive analysis helps to identify problems and examine causes, predictive analytics enhances the accuracy and effectiveness of call making process.
Some analyses applicable to retailers are:
1. Reporting and Sales Analysis
2. Predictive Analysis
3. Inventory Management
4. Promotion-Effectiveness Analysis
5. Demand Forecasting
6. Complete and Category Analysis
Predictive analytics helps a retail organization to reinforce its call making powers by looking at the future with analytical rigidity. Predictive analytics holds the key to taking advantage of these opportunities such that retailers can increase their ability to forecast their customer's behaviour and plan accordingly. Knowledge analytics capabilities cover a number of possible analyses, using statistical software such as SPSS, SAS, Excel and Minitab.
Information analysis helps in call making process with operational efficiency, saves costs by providing high quality solutions, facilitates versatile operating models and state-of-the-art knowledge security. A well trained analytical team can help within the automation of data cleansing, processing and recurring reporting.
Part II
In the constantly changing competitive business environment, informed and intelligent choices are the centre stage for each business organization. Information analytics and statistical techniques facilitate to create business selections and offer valuable insights to an organization.
Data Analytics is the science of playing with sales numbers to arrive at logical decisions by slicing and dicing the information to understand patterns and correlations that could offer the company a competitive edge.
Retailers want to research numerous methods surrounding merchandizing, pricing, promotion, markup and markdown to be in a position to make the correct decision. Statistical and mathematical techniques are used to research current and historical information to form predictions regarding future events. The patterns found in historical and transactional information is employed to identify risks and opportunities.
Information analytics gives a summary on top performers, bottom performers, key value items, sales performance, forecasting, trend and seasonality. Inventory management analysis helps a retailer to keep minimum inventory without running out of stock. Analytical team use the ability of advanced statistical software, super computers and refined mathematics to offer actionable insights to the customer. Advanced mathematical techniques, formulas and statistical methods are used to predict the long run demand of a product. This analysis considers the impact of vacation, seasonality and trend effect.
Retail data analysis helps a retailer to target their customers more effectively by campaigns, to improve response time to market changes, to extend employee productivity and to boost client service at stores. Analytic models examine a client's recency, frequency and monetary worth of client visits along with purchase behaviour and provide a customer's attrition possibilities on which retailers can take corrective action to bolster loyalty. Some of the key analyses are:
o Customer profitability analysis
o Market basket analysis
o Opportunities for up selling or cross-selling
o Client satisfaction analysis
o RFM Analysis
An analytical method will take care of information preparation, modelling activities and generates reports. Analytics offer actionable and powerful decision insights. These decisions are required for developing and maintaining profitable customer relationships. Retail reports give powerful insights and actionable analytics to the desks of retail managers and analysts in real time.
Author Resource:
Howard has been writing articles online for nearly 2 years now. Not only does this author specialize in Retail, you can also check out his latest website about:
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