Retailing in Marketing Analytics
What is Retail Analytics?
Retail analytics is the use of software to analyze and collect data on physical outlets, websites, catalogs, and online stores to give retailers insights about customer behaviour and shopping patterns. By applying algorithms to data from internal and external sources, such as purchase histories of customers, it can be used for informing and improving decisions on pricing, inventory management, marketing, merchandise, and store operations. Retail analytics also allows retailers to measure loyalty and predict demand. They can optimize the layout of stores, as well as identify buying patterns and track customer behavior.
Takeaways
- In a field that has traditionally relied on intuition, retail analytics makes decisions based on empirical data and science.
- Analyses help retailers to set up inventory, assign staff, price products at levels which will attract customers while allowing the business to make a profit, and gain market share.
- Retail analytics is based on a variety of sources including point-of sale (POS) system, video feeds in store, and systems which track the purchase history and customer service records for each individual.
- AI or machine-learning is sometimes used in retail analytics software to predict trends, make suggestions for next offers and help with pricing and allocation decisions. These tools are easier to understand and use because they have strong visualizations.
Understanding Retail Analysis
The science behind retail analytics involves collecting, analysing, and reporting data related to the operations of a retailer. The art and science of retail are complemented by it.
Retail analytics is a powerful tool that can be used to analyze customer behavior, track inventory levels, measure the effectiveness of campaigns and much more. Retailers can, for example, gain insights about their customers by analysing data such as call center logs and point of sale systems. They can then adjust product pricing, return policy, or even the layouts of their stores and websites. The analytics also help retailers to make more informed decisions on which promotional campaigns to run, which marketing strategies they should focus on and when to increase or decrease staff. Data analytics can help retailers improve sales and customer loyalty, as well as reduce costs.
Why is Retail Analytics so Important?
Retail analytics removes the guesswork from many decisions. Employees with a lot of experience are usually able to offer a wealth of knowledge, but the less-experienced ones will be unable to do so as baby boomers retire. Even the most experienced retail executives have to wade through an abundance of data, both internal and external, on factors such as weather forecasts, labor strikes and merchandise trends. Analyses help retailers to synthesize data, and anticipate events.
The retail industry is highly competitive, complicated by online shopping’s relative newness. Profit margins are thin and there has never been much room for mistakes. Stockouts can be greatly reduced by making even small adjustments to product selection or inventory management. These adjustments can then have a huge impact on your bottom line. fashion retailer, for example, can use data analytics in order to determine which sizes and styles to order and what quantity based on the demographics and buying trends of each location.
Retail Analytics: Benefits
Retail analytics are a collection of tools used by retailers to increase their revenue, lower overhead costs and boost margins. Retail analytics helps retailers achieve these goals by:
- Reduce stockouts and discounts Retail analytics can help users to understand the demand patterns so that they have sufficient product in hand but without resorting to heavy discounts. analytics, for example can be used to determine the speed at which demand drops on fashion products that are driven by social influencers.
- Improving Personalization Analytics allows retailers to better understand the preferences of their customers and capture more sales than competitors. An online book seller can, for example, alert those customers interested in American history to the availability of a new Ron Chernow book.
- Improve pricing decisions Data analytics helps retailers to set optimal prices by analyzing a number of factors including abandon shopping carts and competitive pricing data. By avoiding pricing that is higher or lower than customers are willing to pay, retailers can maximize their profits.
- Improved product allocation: Analyses can be used by retailers to decide where products should go in different geographical regions, distribution centres, or stores. This reduces unnecessary transportation costs. A sportswear retailer, for example, can analyze the data to determine that just a 2-degree temperature difference can affect sales. They can then allocate thermal undershirts to distribution centers located near areas predicted to experience colder winter temperatures.
Data analytics for retail: Types
Retail data analytics can be divided into four types: descriptive, diagnostic and predictive. Descriptive analyses reflect past performances and provide explanations. Diagnostic analytics determines the cause of an issue. Predictive analytics forecasts future results. And prescriptive analyses recommends next steps. Here is more information on the four different approaches.
Descriptional analytics
The foundation of more advanced analytics are descriptive analytics. This type of analysis addresses the fundamental question of “how many”, “when”, and “where” –which is what basic business intelligence software and dashboards provide to report on weekly sales and inventory.
Diagnoses are available.
Retail organizations can use diagnostic analytics to identify issues and analyse them that could be affecting their performance. Retailers can gain a better understanding of their problems by combining information from different sources such as financial data, customer feedback and operational metrics.
Predictive Analytics
Retailers can use predictive analytics to predict future events by analyzing a variety of variables. These include weather patterns, economic trends and supply-chain disruptions. The approach is often based on what-if analyses, where a retailer can map what will happen, say, if they offer a 15% discount on one product versus 10% on another, or predict when their stock would be depleted based on different scenarios.
Prescriptive Analytics
Prescriptive Analytics is when AI, Big Data are combined to recommend action based on the predictive analytics results. For example, prescriptive analytics could provide suggestions to customer service agents that they can use to pass on to their customers, such as an upsell or cross-sell, based on past purchase histories.
What is Retail Analytics?
Retail analytics is used by companies to analyze past performance and forecast future demand. They also offer real-time suggestions to store agents and customer service agents to help them cross-sell or upsell products, as well as improve customer satisfaction. All tools aim to boost retailer’s sales, profits and customer satisfaction.
- Tools for in-store analytics analyze data from POS and video cameras in stores to assist retailers in analyzing customer patterns. This allows them to place products in the aisles more efficiently, maintain appropriate stock levels and reduce theft. For example, video footage can reveal whether customers slow down in order to examine a display. POS data, on the other hand, shows how effective merchandising is for customers using loyalty cards.
- Customer Analytics is a tool that analyzes data collected from the systems customers use, such as POS systems and websites. It also uses information collected through phone logs or customer service chats. This data can be used to help retailers identify which items and locations are the most popular. It also helps them determine why items are returned or exchanged or which promotions are best for customers. It can be used to determine the most efficient marketing words to use over chats or on the phone to market a product.
- Inventory Analytics is a tool that assesses the inventory of goods offered by a retailer. Inventory analytics is used to determine more effective warehousing, distribution, and replenishment strategies. For example, it can be used to decide when to use a local warehouse over a larger distribution centre, or when to replace items according to inventory and demand. Inventory analytics, for instance, can reduce labor costs and shipping charges associated with too much safety inventory.
- The merchandise analytics is a tool that helps retailers to determine if they are displaying products effectively. This happens mostly in stores. Its goal is to entice consumers into making a purchase with compelling offers or assortments. Retailers can also use merchandise analytics to adjust their prices in order to maximize profits across all products.
- Web Analytics tracks consumers’ digital footprints as they hover over specific parts of web pages or move from page to page. The tracking follows the consumer from the point where they first arrived on the website to their departure. These analytics help online retailers determine how to present their products on the website, what prices they will charge and which marketing campaigns they should run.
- Reports for Business Intelligence (BI) are often displayed in dashboards and preset to display certain key performance metrics, like inventory turnover or sell-through rates. These reports are primarily used to communicate top-line trends among peers and management.
- Demand Forecasting predicts the demand for specific items that are sold online, based on how customers view them, add them to the shopping cart, remove the items or abandon their cart. These actions don’t represent sales but they are a good indicator of future demand.
- The sales forecasting tool helps retailers to predict the future of sales by using actual sales data and other factors. In conjunction with demand forecasting it is possible to predict the total demand for an item in all channels. This can be used by retailers to ensure that they are able meet this demand.
Shopper Analytics Tools
Data is captured in a number of ways, including at store locations as well as on websites. These are some of tools that can be used.
- Points-of-sale Systems: Retailers use these systems to manage and track customer transactions. Point-of-sale Systems provide information on customers’ purchases, and they can produce reports about sales and trends.
- CRM software. This category of software includes apps that handle sales, marketing and customer service processes. These applications allow retailers to keep track of customer interactions, store data on individual customers and determine potential opportunities for sales, marketing and customer service based upon that information.
- Business Intelligence Tools Retailers use these tools to synthesize data from different sources and large amounts of information. They do this to monitor key performance indicators, such as Customer loyalty, Inventory turns, Sell-through Rate, and Days on Hand. These tools allow retailers to easily create reports and send them on to other executives or decision makers.
- Inventory Management Systems Retailers can use this software for tracking items on hand, monitoring inventory in warehouses, distribution centers and creating forecasts. This software helps retailers determine the best locations to store certain products to reduce transportation costs and to ensure goods are always available for customers.
- Predictive Analytics: Uses data from previous transactions, communications and actions in order to predict future trends. Retail analytics can be classified into four main categories: descriptive, diagnostics, predictive and prescriptive. These types are used to find new growth opportunities and customer segments.
5 Retail Analytics Best Practices
1. Customer data is a powerful tool
Retail analytics practitioners use this data to better understand their customers and identify trends. The best retailers combine customer data collected from ecommerce systems, POS and other sources with the data that they purchase from brokers.
Experts categorize data on customers as a mix of transactional, psychographic, demographic and behavioral points. The process of collecting, consolidating and leveraging these types of data is usually a progressive one, beginning with a broad demographic perspective. Retailers make the distinction between “customers”, or people who already do business with them, and “consumers”, which includes those who could be good prospects. Data from consumers can be used to inform “lookalike models” – for example, when a retailer recognizes Mark as an excellent customer, it will look for others with similar characteristics and target them with offers.
2. Visualization tools
Charts, graphs and dashboards are common BI tools that help users understand data and make informed decisions. These tools are much better at helping you understand information than just staring down rows of data. Business users can also use BI visualization software to perform analytics, instead of waiting for IT to run reports or generate queries.
3. Analyze multiple data sources
Retailers can gain an in-depth understanding of their business by analyzing multiple data sources. This includes sales data as well as historical customer and inventory information. Metrics are also often dependent on each other. Retailers can, for example, correlate their in-store analytics and merchandise attribute analytics so that they are able to optimise the layout of physical stores to convert shoppers into paying clients. The retailer can use inventory analytics to ensure that they have enough merchandise on hand in order to maintain the layout. Retailers must also keep in mind that the data definitions of different applications can be different, leading to inaccurate analyses. It is a good argument for using one platform to perform retail analytics, rather than adopting different applications.
4. Track KPIs
The tracking of key performance indicators allows retailers to measure their performance, and pinpoint areas that need improvement. The most successful retailers use weekly KPI summaries, also known as balanced scorecarding. They compare the current metrics with those of the previous week. This usually begins with an analysis of the facts (for instance, a drop in sales for certain products), and then a more detailed look at why that happened.
5. Prioritize your goals
It is not necessary to measure everything. Retailers have access to new analytic tools, as well as a vast amount of data. However, they must be careful about the recommendations they make or they risk overwhelming decision makers with too many suggestions. Start by identifying opportunities with immediate business impact. According to McKinsey, the best analytics are those that solve a specific business problem with a quantifiable outcome.
Mark Lawrence is a retail analytics specialist who suggests that the five best practices listed above are all related. Lawrence’s recommendation is to start with an objective, and then maybe two or three subordinate objectives. He says that the KPIs which inform progress on this level are called “leading KPIs”. He says that if one of the goals is to get closer to customers, then KPIs might include “increase lifetime customer value by 20 percent”, “achieve 15 per cent year-over-year conversion” and “optimize inventories to support customer-centricity objective.”
Retail Analytics: The Future of Retail Analytics
Retail analytics is expected to become less visible and less talked about in the future. Analytics will be used by users and applications continuously. This is often done unknowingly, just like how smartphones use location tracking constantly to meet user needs.
Retail analytics for business users will be less about creating or reviewing reports every week and more integrated into daily workflows. AI will be a part of everyday business for more people, without them even knowing it. AI data analysis is no longer a hype.
Retail Analytics Software: Increase Revenue with Retail Analytics Software
Consider retail analytics software that is able to ingest data and correlate it from multiple sources. It should also be scalable to keep up with the growth of your company. Oracle Retail’s cloud-based integrated services include analytic software for merchandise as well as inventory management.
Retail Analytics FAQ
What is an example of Analytics?
Analytics is used by retailers for many reasons. They can predict demand, assist managers in buying and allocating sufficient inventory to satisfy that demand, understand the customer’s behavior, optimize prices, or make decisions about staffing.
What type of data are used for retail analytics?
Retail analytics is a method of analyzing data that comes from both internal and external sources. These include customer purchases, call-center logs, navigation on ecommerce sites, POS system, video in stores, and demographics.
What types of decisions can retail analytics assist retailers in making?
Retail analytics takes the guesswork and uncertainty out of retailing. It provides industry executives with information on what to order, where to keep it, what to charge, and which goods are often purchased together.
Shopper Analytics Tools
Data is captured in a number of ways, including at store locations as well as on websites. These are some of tools that can be used.
- Points-of-sale Systems: Retailers use these systems to manage and track customer transactions. Point-of-sale Systems provide information on customers’ purchases, and they can produce reports about sales and trends.
- CRM software. This category of software includes apps that handle sales, marketing and customer service processes. These applications allow retailers to keep track of customer interactions, store data on individual customers and determine potential opportunities for sales, marketing and customer service based upon that information.
- Business Intelligence Tools Retailers use these tools to synthesize data from different sources and large amounts of information. They do this to monitor key performance indicators, such as Customer loyalty, Inventory turns, Sell-through Rate, and Days on Hand. These tools allow retailers to easily create reports and send them on to other executives or decision makers.
- Inventory Management Systems Retailers can use this software for tracking items on hand, monitoring inventory in warehouses, distribution centers and creating forecasts. This software helps retailers determine the best locations to store certain products to reduce transportation costs and to ensure goods are always available for customers.
- Predictive Analytics: Uses data from previous transactions, communications and actions in order to predict future trends. Retail analytics can be classified into four main categories: descriptive, diagnostics, predictive and prescriptive. These types are used to find new growth opportunities and customer segments.
5 Retail Analytics Best Practices
1. Customer data is a powerful tool
Retail analytics practitioners use this data to better understand their customers and identify trends. The best retailers combine customer data collected from their loyalty programs, POS and ecommerce systems with other data sources. They also purchase data from brokers.
Experts categorize data on customers as a mix of transactional, psychographic, demographic and behavioral points. The process of collecting, consolidating and leveraging these types of data is usually a progressive one, beginning with the demographics. Retailers make the distinction between “customers”, or people who already do business with them, and “consumers”, which includes those who could be good prospects. Data from consumers can be used to inform “lookalike models” – for example, when a retailer recognizes Mark as an excellent customer, it will look for others with similar characteristics and target them with offers.
2. Visualization tools
Charts, graphs and dashboards are common tools in BI. They help you understand data and make informed decisions. These tools are much better at helping you understand information than just staring down rows of data. Business users can also use BI visualization software to perform analytics, instead of waiting for IT to run reports or generate queries.
3. Analyze multiple data sources
Retailers can gain an in-depth understanding of their business by analyzing multiple data sources. This includes sales data as well as historical customer and inventory information. Metrics are also often dependent on each other. Retailers can, for example, correlate their in-store analytics and merchandise attribute analytics so that they are able to optimise the layout of physical stores to convert shoppers into paying clients. The retailer can use inventory analytics to ensure that they have enough merchandise on hand in order to maintain the layout. Retailers must also keep in mind that the data definitions of different applications can be different, leading to inaccurate analyses. It is a good argument for using one platform to perform retail analytics, rather than adopting different applications.
4. Track KPIs
The tracking of key performance indicators allows retailers to measure their performance, and pinpoint areas that need improvement. The most successful retailers use weekly KPI summaries, also known as balanced scorecarding. They compare the latest metrics with those of the previous week. This usually begins with an analysis of the facts (for instance, sales for certain products dropped), and then a more detailed look at why (for instance, stockouts).
5. Prioritize your goals
It is not necessary to measure everything. Retailers have access to new analytic tools, as well as a vast amount of data. However, they must be careful about the recommendations they make or they risk overwhelming decision makers with too many suggestions. Start by identifying opportunities with immediate business impact. According to McKinsey, the best analytics are those that solve a specific business problem with measurable results.
Mark Lawrence is a retail analytics specialist who suggests that the five best practices listed above are all related. Lawrence’s recommendation is to start with an objective, and then maybe two or three subordinate objectives. He says that the KPIs which inform progress on this level are called “leading KPIs”. He says that if one of the goals is to get closer to customers, then KPIs might include “increase lifetime customer value by 20 percent”, “achieve 15 per cent year-over-year conversion” and “optimize inventories to support customer-centricity objective.”
Retail Analytics: The Future of Retail Analytics
Retail analytics is expected to become less visible and less talked about in the future. Analytics will be used by users and applications continuously. This is often done unconsciously, just as smartphones use location tracking constantly to meet user needs.
Retail analytics for business users will be less about producing weekly reports or reviewing them and more integrated into the daily workflows of their employees. AI will be a part of everyday business for more people, without them even knowing it. AI data analysis is no longer a hype.
Retail Analytics Software: Increase Revenue with Retail Analytics Software
Consider retail analytics software that has the ability to ingest data and analyze it, as well as those tools which can scale with you. Oracle Retail’s cloud-based integrated services include analytic software for merchandise as well as inventory management.
Retail Analytics FAQs
What is an example of Analytics?
Analytics is used by retailers for many reasons. They can predict demand, assist managers in buying and allocating sufficient inventory to satisfy that demand, understand the customer’s behavior, optimize prices, or make decisions about staffing.
What type of data are used for retail analytics?
Retail analytics is a method of analyzing data that comes from both internal and external sources. These include customer purchases, call-center logs, navigation on e-commerce sites, POS system, video in store, and demographic information.
What types of decisions can retail analytics assist retailers in making?
Retail analytics takes the guesswork and uncertainty out of retailing. It provides industry executives with information on what to order, where to keep it, how to price it, as well as which goods are often purchased together.
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