How Do You Analyze Retail Data?

How do you analyze retail sales data?

Read about them below, then see if you can put them into action in your operation:Start with the right tools.Use retail analytics to dig into historical data.Mix and match metrics or reports.Use timing to predict what your customers will buy next.Empower your customers to actively share their details.More items…•.

What is retail data analytics?

Retail analytics is the process of providing analytical data on inventory levels, supply chain movement, consumer demand, sales, etc. that are crucial for making marketing, and procurement decisions.

How is analytics used in retail?

Retail analytics focuses on providing insights related to sales, inventory, customers, and other important aspects crucial for merchants’ decision-making process. … Essentially, retail analytics is used to help make better choices, run businesses more efficiently, and deliver improved customer service analytics.

How does excel compare data to sales?

Open a blank workbook in Microsoft Excel. Enter the category you want to compare in cell A1. For instance, to compare different products, enter “Product.” In the cells below, enter the name of each product. You could also use a column chart to compare sales in different regions.

Why should retailers use analytics?

In order to deal with high expectations from consumers, online retailers need to leverage data being collected and actively integrate analytics to improve their decision making process. … They should use analytics to better understand consumer preferences, and further provide them with right product offerings.

What are the benefits to in store analytics?

5 Benefits of Retail Analytics in 2018Benefit #1: Customer behavior insights. The first and foremost advantage of leveraging retail analytics is that they offer tangible and actionable insights into customer behavior. … Benefit #2: Improving Marketing ROI. … Benefit #3: Optimizing In-Store Operations. … Benefit #4: Managing the Basics. … Benefit #5: Enhancing Loyalty.

What is an example of trend analysis?

Examples of Trend Analysis Examining sales patterns to see if sales are declining because of specific customers or products or sales regions; Examining expenses report claims for proof of fraudulent claims. … Forecast revenue and expense line items into the future for budgeting for estimating future results.

Is Excel good for data analysis?

Excel is a great tool for analyzing data. It’s especially handy for making data analysis available to the average person at your organization.

How do you analyze data in a pivot table?

Step 4: Create a Pivot TableSelect the Data You Want to Analyze.Choose “Pivot Table” from the “Insert” Tab.Select the Data You Want to Add to Your Table.Open the New Worksheet Tab.Choose the Fields for Your Pivot Table.Drag the Fields to the Desired Area.Change the Value Field.View Your New Pivot Table.More items…

What is unstructured data analytics?

Unstructured data analysis is the process of using data analytics tools to automatically organize, structure and get value from unstructured data (information that is not organized in a pre-defined manner). … In fact, IDG Research estimates that 85% of all data will be unstructured by 2025.

What are the 3 types of trend analysis?

Trend analysis is based on the idea that what has happened in the past gives traders an idea of what will happen in the future. There are three main types of trends: short-, intermediate- and long-term.

What is POS data?

Your point of sale data is data collected by a business when a transaction happens. On a micro scale this includes any checkout at a retail store, handheld POS hardware and even QR or barcode scanners from apps. … POS software within POS hardware is where the data magic really happens.

What is retail analyst?

Other important responsibilities of a retail business analyst include assisting with new client implementation, providing analytical support to maximize product performance, conducting merchandising and financial analysis, analysing sales reports and evaluating retail performance, and making recommendations.

How do you win in retail?

Own and Adapt. … Know Their Market. … Define Who They Are And What They Do… … Play To Their Strengths. … Professionalize Everything. … Create an Environment that People Want to Visit… … Develop Their People Into Experts in the Category and Customer Experience. … Partner Up: Suppliers, Other Businesses, Community Groups.

Trends: An Overview. … A trend is the general direction of a price over a period of time. A pattern is a set of data that follows a recognizable form, which analysts then attempt to find in the current data.

How do you analyze POS data?

Basic POS Data AnalysisInventory. You can sell a lot of products but still not gain any profit yet? … Top Seller Products. You have a “hot” item in your store. … Sales Trend. You can hardly improve your business if you don’t know your sales trend. … Returns, Exchanges, Refunds. … Customer Insights. … Staff Performance. … What to buy.

Tip #3: Select the right time period to analyse your data trends. … Tip #4: Add comparison to your data trends. … Tip #5: Never report standalone metric in your data trends. … Tip #6: Segment your data before you analyze/report data trends. … Tip #7: Look at a trend line with a lot of data points. … Top #9: Spell out the insight.

What are the key features of a point of sale system?

Point of sale (POS) featuresQuick keys and/or product lookup. … Multiple payment methods/split payments. … Returns, refunds and store credit features. … User accounts and permissions. … Mobile Registers. … Customer-facing display.

What are some caveats with POS data?

Some of the caveats or problems with Point of Sale data is that:It cannot make causal claims/statements.Do not know behaviors and pyschographics and;do not know the exact set of choices faced by the consumer at the time of purchase.

What are retail metrics?

= (total sales / total transactions) AST is a great metric to measure effectiveness of your advertising and promotion campaigns. It can also help identify shopper’s spending behavior. Examining this type of data is essential to benchmarking locations against each other.