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Retail Data

Table of Contents

What is Retail Data?

Why is Retail Data so important?

Common Retail Data and Space Planning Terms

How Retail Data is stored in DotActiv

Data transfer methods

How to transfer Retail Data into DotActiv's Data Platform

Data Sources

Point of Sales Data

Loyalty Card Data

Market Data

Retail Analytics

Different types of Retail Analytics

Getting started with Retail Analytics

Free Retail Analytics Ebook

 

 

 

 
WHAT IS rETAIL data? 

 

Retail data refers to any facts or figures that you can collect about your retail business which can then be used to improve your business. That means it can come in many different shapes and forms, including point of sales data, loyalty card data, and market data.

There’s also your customer-centric data, supply chain and operations data and merchandising data. And harnessing all of this data is vital to the future of your retail business.

 
Why is Retail Data so important?

 

Considering the power that your customers have today - they can decide your store’s fate by deciding to either buy your products or not - it’s crucial that you remain a step ahead of them.

That is why retail data is so important. It gives you the information that you need to compete and compete well.

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Retail Data Terminologies

 

It’s easy to get lost in all the terms and acronyms that surround data. It’s a complex topic. At its core, data is information in a raw or unorganised form. It includes numbers that refer to or represent conditions, ideas or objects.

Data is also limitless and present everywhere in the universe. With reference to computers, data are symbols or signals that are input, stored or processed by a computer for output as usable information.

 

COMMON Data and space planning terms

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Caps: This specifies the number of products that can be laid out under or over the stack. When an item is laid on top of another item in a different orientation, it’s called a layover. For example, if cereal boxes are arranged on the shelf in a front end orientation, if sufficient headroom exists you could lay a box on the top of the others in the end front orientation.

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Cubic Space: This calculates the volume or space of a product in cubic feet. It can be used to rationalise the amount of space given to a particular sub-category or segment of product.

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DOS: Days of Supply (DOS) calculates the number of days a product will be on your shelf before replenishment is necessary. DOS is calculated when capacity is divided by Units sold per day.

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Facings: This refers to the number of items with a single SKU on a shelf in linear measure.

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Forward Share: This calculates the forward-sharing space a product occupies across the planogram. It is a relative measure for ensuring brands receive the forward-sharing space they deserve to entice a customer to purchase.

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Fronts: This is the number of items that appear on the shelf above and behind a single facing.

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MDD: Minimum Display Depth (MDD) can be used when wanting to set a minimum limit to the number products that can run deep into a shelf.

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POG: Planogram (POG). A planogram is a visual representation of a department, shelf, or display for allocating products by the number of facings and/or the depth of the display.

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UOM: Unit of Measure (UOM) is assigned to each product as it relates to the measurement of size. The size of an item might be ‘500’ but the UOM defines whether it’s grams, litres and so on.

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Weekly Movement: This is the number of units of a product that are sold each week. It indicates whether an item sells well or not so well. The weekly movement allows you to calculate your days of supply, and is a measure that must be taken into consideration to understand the rate of sales and overall movement of a product within a business.

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Clean data: This is the data that has been reviewed. Before it is reviewed, it’s often referred to as Raw data. During the review, replicated and/or duplicated data should be removed. Each column should contain data. Missing or misspelled words within the columns will affect how the data is read or linked. 

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ETL: ETL is the acronym for Extract Transform Load. Data is extracted using the SQL. It  is then transformed. This transformation includes data analysis, data cleaning, data standardisation and more. It is then integrated into a database, which can happen in  various stages.

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SQL: SQL is the acronym for Structured Query Language and is a computer language that is used to organise and simplify the process of adding, retrieving, editing, and deleting information stored in a database.

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Query: This is a complete select statement that specifies the columns and tables from which data is retrieved, conditions that the data must satisfy, computations that are to be performed on the retrieved column values, and a desired ordering of the result set.

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Relational database: A relational database organises data into tables and links them, based on defined relationships. These relationships enable you to retrieve and combine data from one or more tables with a single query. SQL supports relational databases.

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Key: A constructed column index which allows you rapid and/or sort access to a table's row.

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Primary Key: The unique identifier used to draw links between each row in a table to draw the correct data. It is used to identify a row of data in a specific table. You only have one primary key.

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Relation: There are many cases where data in one table is related to data in another table. This connection between the two tables is known as a relation.

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Foreign Key: Where there is a relation between two tables, these tables are connected by inserting the primary key of one table into the corresponding row of the other table. The field used in such a way to connect the two tables is known as a foreign key.

 

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How Retail Data is Stored in DotActiv

 

DotActiv uses four tables, better known as dimensions, namely Market, Product, Period, and Fact. For the software to function optimally, you need to identify the primary key in each dimension.

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          MARKET:

This dimension depicts the information relating to the store. It includes the following: Key Market Detail, Store Detail, Cluster Detail, and Geographical Location.

Key Market Detail: This is the primary key for the market dimension and can't be omitted from any data. It is usually your Store Code or Store Name.

Store Detail: This includes specific store fields such as Store Name, Store Code, Retailer Name and Store Format. The format is based on your product variety, assortment, price, and location.

Cluster Detail: This refers to the grouping of similar stores and includes Cluster Name, Cluster Size, and Cluster Type. You can also include Cluster Description and Cluster Number.

Geographical Location: This refers specifically to the location of your store. It can include Country, Province, and Region. It can also deeper, including Building Number, City, GPS Coordinates, Postal Address, Region Code, Street, and more. It is vital that the right store and format is chosen as the range or assortment may differ accordingly.

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           PRODUCT:

This dimension includes all information relating to the product. It includes the following: Key Product Detail, Item Detail, Dimensions, and Hierarchy.

Key Product Detail: This is the primary key for the product dimension and can’t be omitted from any data. This will be your barcode or the article code as it is unique and can’t be replicated.

Item Detail: This includes all the field related to your product such as Barcode, Brand, Product Description, Product Code, Size and UOM, and Supplier.

Dimensions: This refers to the actual dimensions of your product, namely your height, width, and depth. It is a vital dataset for creating visually appealing and correct planograms.

Hierarchy: This refers specifically to the classification of your products. It includes Supergroup A, Supergroup B, Category, Sub-Category, Segment, and Sub-Segment. It is vital that your products are classified as it will ensure your planogram keeps related items together and in the correct flow.

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          FACT:

This dimension includes data that can’t be altered, including your Sales and Units. While the Market, Product, and Period dimensions are flexible, within Fact, you need the Sales and Units data to be provided. Facts include Retail, Indicators, Stock, and Space Planning.

Retail: This includes field related to the sale of your product, including Sales, Units, % Sales, % Units, Cumulative % Sales, and Cumulative % Units. Cumulative will identify which top 20% of your products drawing in 80% of your Sales or Units.

Indicators: These would be used for your ranging exercises and includ field such as Core Range, Ranging Indicators, and Buyers Indicators.

Stock: All of your fields for stock control are here, including Stock Value At Cost, Minimum Stock, Lead Time, and Case in DC. DC here is Distribution Centre. It is included in an environment that is integrated with the distribution centre to show available stock levels.

Space planning: This will indicate all the vital fields for optimising space. A few examples include DOS, Forward Share, Actual Facings, Weekly Movement amongst others. DotActiv usually formulates these statistics for you.

 

          PERIOD:

This dimension will allow you to report on the time frame chosen and include the following: Period From and Period To.

Both of these are considered as primary keys with Product.


 

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Data transfer methods

The transfer of data is essentially, as PC Mag describes it, the “copying [of] data from one computer to another. When a network is used, data are technically ‘transmitted’ over the network, rather than transferred; however, the terms transfer and transmit are used synonymously”.

When the time comes to transfer data, you have a number of options available to you. Below are two examples:

  • Transact-SQL: This is Microsoft’s and Sybase’s extension of SQL and it is used to send data between relational databases. This method requires the ability to write SQL scripts or queries using Transact-SQL.
  • DotActiv Data Import Utility: DotActiv has a dedicated application that imports data from SQL staging tables into the DotActiv data platform. Once set up, you can schedule the import utility to automatically run its imports at whatever time you choose.
A LOOK AT TRANSFERRING DATA TO DOTACTIV’S DATA PLATFORM

 

At DotActiv, we use Microsoft SQL, which supports any versions of SQL Server from 2014 and onwards. When it comes to transferring data, our category management platform can be approach in two ways:

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By manually preparing and uploading datasets using CSV and Excel files on an ad-hoc basis. This is available for all our solutions, from DotActiv Lite to Dotactiv Enterprise.

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Through automation by integrating DotActiv into your business. This integration is achieved by using staging databases and Transact-SQL statements or queries via the DotActiv data import utility. This is only available in DotActiv Professional and DotActiv Enterprise.

 

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Data Sources 
 
What are data sources?

A data source is primarily a location from which data is being used. Even simpler still, it’s a collection of records that store data that you can use at a later date.

Concerning a retailer business, that could be any data related to the products you sell in your store, any data related to a number of sales you make, or any data your overall stock.

From a simple file to a large database on a Database Management System to a live data feed, data sources take various forms. As for where data is stored, there are only two instances: either it is on the same computer as the program you’re trying to access, or it’s on a different computer on the network.

 
Examples of data sources

 

Retail Data-WebPage-29.pngMicrosoft Excel: Microsoft Excel is often referred to as the world’s standard for spreadsheets since it’s compatible with just about any Operating System. A few main features include Calculations or Basic Math, Charts, Pivots Tables, and Conditional Formatting.

Obtaining data from Excel into DotActiv is straightforward and doesn’t require any specialist skills.

 

Retail Data-WebPage-30.pngCSV Files: Colloquially known as CSV’s, a comma-separated values file is used for moving tabular data (numbers and text) between programs which operate on incompatible formats.

For example, you may need to transfer information from an application, which stores data in a particular format to a spreadsheet that uses an entirely different format. Similar to Excel, getting data from a CSV file into DotActiv is straightforward and doesn’t require specialist skills.

 

Retail Data-WebPage-31.pngData Warehouse: A data warehouse is similar to any retail warehouse you would usually visit. The only difference is that while your usual warehouse may contain a multitude of merchandise, this one stores current and historical data.

The most common tools used as a data warehouse include Microsoft SQL and Oracle - a data warehouse is a function not the tool itself.

To extract data from any source system into the data warehouse, a process known as ETL - extraction, transformation, and loading - is used. ETL is the process of extracting data from various data sources before transforming it into the ideal format for the purpose of querying and analysis.

 

Retail Data-WebPage-32.png Staging Database: At its core, a staging database is an intermediate storage area for  your data. From this location, your data can then     be extracted.

  It’s primary purpose is to serve as a working area where the data can be organised into a format, which can then be matched to a target    application. They can also be referred to as staging tables, landing zones or staging areas.


 

 
Point of sale data

 

What is POS data?

 The point of sale, better known as POS refers to the place at which a retail transaction occurs. Better put, it’s where the goods are scanned, bagged and paid.

As for point of sale data, it’s collected directly from your retail POS system, which includes a combination of software and hardware.

Regardless of the size of your retail stores, your retail POS system should include a computer, monitor, cash drawer, receipt printer, customer display and barcode scanner. It’s also worth investing in a Credit and/or Debit Card reader.

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The power of POS data

There is a certain power that in collecting POS data. And it’s not just DotActiv who says that. It’s also a point which Jake Freivald, vice president of corporate marketing at software company Information Builders makes.

He’s quoted by Mobiquity, saying, “Point-of-sales data is the lifeblood of a retail company. It’s probably more important than any other single data source, because it helps us understand the past, monitor the present and predict the future.”

With this data captured, you will have a better picture of your overall sales, the daily, weekly, monthly and event yearly movement of your products, and whether or not your sales tactics are working. And that is just the start.

It can also help you to build and drive customer relationships, a point made by Data-as-a-Service platform, StrikeIron in a whitepaper on POS data.

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Loyalty card data
What is loyalty card data?

Loyalty cards, and the customer data that is collected from them, can influence the way in which you run your retail business. That’s because it’ll allow you to know your shopper. And with this type of retail analytics data at your disposal, you can only expect to profit.

Similar to POS data, there is also a power that can be found in loyalty card data. It can tell you the reasons why your shoppers are coming back to your store. It can also tell you how much your shoppers are spending on average per trip.

More than that, though, is what you can do with this data. And that is where its true power lies.

The power of loyalty card data

To make full use of the data that you collect from your loyalty cards, you need to plug it back into your business. One way of doing that is to use to help you build your ranging strategies.

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Tell you which segments to prioritise: Based off the information gathered from your shopper profiles, you’re better placed to understand where you need to prioritise.
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Tell you which strategies to consider for growth: In looking at the basket behaviour of your customers, you can see what they are buying and use align your strategies with that information.

Retail Data-WebPage-38.pngTell you which must-stock SKU lists you need: This data is based off of your SKU performance. In short, it allows you to decide which SKU’s have low levels of transferable demand and which are worth leaving off your list.

 

 Market data
What is market data?

Retailers and suppliers often enter into data exchange agreements with third party market data providers. In this process they share certain information about their own business with the third party data provider in return for information about the market in which they operate.

DotActiv’s software uses both internal data and external market data when calculating assortments and ranges.

Here’s an example as to why this is of critical importance: if a particular product performs poorly in your stores while simultaneously performing extremely well in the external market (your competitors) then the solution is not necessarily to delist the product. Rather, you should try to find out why the product is performing so badly in your stores so that you can correct it as soon as possible. 

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Retail Analytics
What is Retail Analytics?

As a concept, Retail Analytics can become very technical very quickly. That means it’s easy to throw out a technical definition, which would involve also sorts of factors. But it’s not necessary.

Explained in it’s simplest way, Retail Analytics refers to a set of techniques that are used to not only observe but also analyse all things in relation to your retail store. As a result, you have the ability to make smarter decisions to manage your business better.

On a technical level, it provides analytical data on a number of factors around your retail business, including:

  • Inventory levels: Having the necessary products in the right quantity at the right time will help your store to maintain an optimal inventory level. You can also understand what stock you should have and which stock shouldn’t be replenished.
  • Supply Chain Movement: You’ll have access to information about the movement of each and every product that you have within your store.
  • Customers' Demands: In using the data, you can find out more about your customers’ behaviour and thus, understand where product demand lies. That will inform your assortment planning, which will ensure you have the most valued SKU's or items listed and placed in easy to find locations within your store.
  • Sales Figures: Without effective analytical data, you won’t be able to prove if what you’re doing is helping your bottom line or not.

Why do Retail Analytics matter so much?

In the past, selling to the public was less complicated. Back then, retailers controlled the buying process, telling consumers what they needed, when they needed it, and where they could get it.

Customers are empowered and have a list of demands that you need to meet. They want you to make it easier for them to interact with you. They want you to be able to be available when and if they need you. At any time. They want you to cater for their every whim.

With good data on your side, the above shouldn’t be as intimidating as it sounds.

 

A few benefits of Retail Analytics

It helps you to understand the sales contribution of all categories, sub-categories, segments and SKU’s as well as which are performing in terms of value.

It helps your to understand the units contribution of all categories, sub-categories, segments and SKU’s as well as which are performing in terms of value.It helps youto strategically place yourself in the marketplace.

It helps you to identify who your target market is, which allows you to align your assortments with that market.

 

The different types of Retail Analytics

With the right type of data, your business has the power to gain a competitive edge. To ensure that happens, you have to have more than just the right data. You also need to understand the different types so you can leverage the data.

There are four different types of data, namely Descriptive, Diagnostic, Predictive, and Prescriptive. And each of them answers a question, which helps your business to move forward.

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Descriptive:

This is the simplest form of analytics. It analyses incoming data for insights on how to move forward. In short, it looks at the past so that you can be better prepared for the future.

It answers the question, ‘What happened?’ and looks at the reason/s behind your success or failure.

 

Diagnostic:

This type of analytics digs deeper into the data to better understand what caused the behaviour. With it, you’re attempting to determine where there are problems in your pipeline and why.

It answers the question, ‘What did it happen?’ and helps you to isolate the root cause of the problem.

 

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Predictive:

While understanding what has happened and why is important to move forward, it’s also as important to look at what will happen. And this is what you get with predictive analytics.

It answers the question, ‘What will happen?’ And focuses on forecasting. Just to note, this type of analytics won’t tell you what is going to happen - no analytics can do that. But it can provide you with answer to questions that can’t be answered by business intelligence.

Prescriptive:

This type of analytics goes far beyond forecasting by simulating the future under various sets of assumptions.

It answers the question, ‘How can we make it happen?’ And in so doing, anticipates not only what will happen and when it will happen but also why it will happen.

It’s complex by nature, which means that not many companies use it in their day-to-day operations. That’s because it can become difficult to manage.

 

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Getting started with Retail Analytics

If you are looking to get started with Retail Analytics, the best place to start would be by setting a goal to cover the basics while setting up your infrastructure in such a way that when you decide to make the move to more advanced retail analytics, you haven’t limited yourself.

To achieve any goal that choose, you must follow these six steps:

1. Define the questions that you seek to answer

Before you begin identifying your data sources or anything else, you must first think carefully about the questions you want answered and then write them down.

For example, within your top performing categories, which sub-categories and segments are showing a consistent growth trend?

In defining your question, you’re stopping yourself from getting lost in too much information.

2. Identifying your data sources

After defining your questions, it’s time to identify where your data will come from. Are the required fields in your data warehouse, ERP or POS applications?

3. Identify what technology is required

You need a data platform, hardware to host the data, a data transfer method, and a tool from which you will create customer Retail Analytics.

Excluding the hardware component, both DotActiv Professional and Enterprise cover these requirements comprehensively.

4. Transfer data

It’s now time to transfer your data from your sources to your platform.

If you’re interested in DotActiv as a solution, it’s worth noting that our team of experts will set up your data transfers to occur automatically on your behalf.

5. Clean and classify your data

Once your data has been transferred to your data platform, it’s important to ensure that your data is clean.

If we go back to the example we used above, ensure that each product line item is classified into the most fitting category, sub-category, segment, and sub-segment.

6. Create your dashboards

This is your final step, and it’s where all the magic comes to life. DotActiv Professional and Enterprise both come with a set of best practice retail analytics reports that seek to answer the most popular questions asked.

If your question has not been answered yet, you can create your own customised reports and dashboard.

 
Want to learn more about RETAIL analytics? 

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