FOUR TYPES OF RETAIL ANALYTICS SHAPING THE RETAIL INDUSTRY
The level of understanding a small mom and pop store has about its customers is very impressive. They know exactly what their customers want to buy, in what quantity, and at what time. But how to achieve the same level of understanding when scaling to a retail giant like Walmart? This is where data analytics comes to the rescue. Data analytics can help retailers understand customer behavior by analyzing massive data sets regarding their information and transaction history. Big data allows retailers to focus on improving customer experience in terms of providing them with what they want at the right time. Retail analytics also enables retailers to introduce dynamic pricing and customize promotions and discounts to each customer. Emerging technologies such as RFID tags, beacons, QR codes, and NFC technology enable retailers to effectively guide the customers in their journey from the
store to making a purchase. Retail analytics offers endless possibilities to retailers in terms of improving their operational efficiency.
store to making a purchase. Retail analytics offers endless possibilities to retailers in terms of improving their operational efficiency.
Types of retail analytics
Descriptive analytics collects data from transactional history, promotional success, and inventory changes to give a detailed summary of such business activities. The retailers have used this type of analytics for a long time. For instance, retailers use measurement metrics such as response rates, cost per lead, and conversion rates to ascertain the success of their direct mail campaigns. However, with the advent of big data, descriptive analytics has started encompassing multiple data points including social media data, time on page, link clicks, conversions, and engagement. Descriptive analytics is an elementary form of retail analytics, which provides a visual summary of past performances.
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The two-minute guide to understanding and selecting the right Descriptive, Predictive, and Prescriptive Analytics
With the flood of data available to businesses regarding their supply chain these days, companies are turning to analytics solutions to extract meaning from the huge volumes of data to help improve decision making
Companies that are attempting to optimize their S&OP efforts need capabilities to analyze historical data, forecast what might happen in the future. The promise of doing it right and becoming a data driven organization is great. Huge ROI’s can be enjoyed as evidenced by companies that have optimized their supply chain, lowered operating costs, increased revenues, or improved their customer service and product mix.
Looking at all the analytic options can be a daunting task. However, luckily these analytic options can be categorized at a high level into three distinct types. No one type of analytic is better than another, and in fact, they co-exist with, and complement each other. In order for a business have a holistic view of the market and how a company competes efficiently within that market requires a robust analytic environment which includes:
Descriptive Analytics: Insight into the past
Descriptive analysis or statistics does exactly what the name implies they “Describe”, or summarize raw data and make it something that is interpretable by humans. They are analytics that describe the past. The past refers to any point of time that an event has occurred, whether it is one minute ago, or one year ago. Descriptive analytics are useful because they allow us to learn from past behaviors, and understand how they might influence future outcomes.
The vast majority of the statistics we use fall into this category. (Think basic arithmetic like sums, averages, percent changes). Usually, the underlying data is a count, or aggregate of a filtered column of data to which basic math is applied. For all practical purposes, there are an infinite number of these statistics. Descriptive statistics are useful to show things like, total stock in inventory, average dollars spent per customer and Year over year change in sales. Common examples of descriptive analytics are reports that provide historical insights regarding the company’s production, financials, operations, sales, finance, inventory and customers.
Use Descriptive Analytics when you need to understand at an aggregate level what is going on in your company, and when you want to summarize and describe different aspects of your business.
Predictive Analytics: Understanding the future
Predictive analytics has its roots in the ability to “Predict” what might happen. These analytics are about understanding the future. Predictive analytics provides companies with actionable insights based on data. Predictive analytics provide estimates about the likelihood of a future outcome. It is important to remember that no statistical algorithm can “predict” the future with 100% certainty. Companies use these statistics to forecast what might happen in the future. This is because the foundation of predictive analytics is based on probabilities.
These statistics try to take the data that you have, and fill in the missing data with best guesses. They combine historical data found in ERP, CRM, HR and POS systems to identify patterns in the data and apply statistical models and algorithms to capture relationships between various data sets. Companies use Predictive statistics and analytics anytime they want to look into the future. Predictive analytics can be used throughout the organization, from forecasting customer behavior and purchasing patterns to identifying trends in sales activities. They also help forecast demand for inputs from the supply chain, operations and inventory.
One common application most people are familiar with is the use of predictive analytics to produce a credit score. These scores are used by financial services to determine the probability of customers making future credit payments on time. Typical business uses include, understanding how sales might close at the end of the year, predicting what items customers will purchase together, or forecasting inventory levels based upon a myriad of variables.
Use Predictive Analytics any time you need to know something about the future, or fill in the information that you do not have.
Prescriptive Analytics: Advise on possible outcomes
The relatively new field of prescriptive analytics allows users to “prescribe” a number of different possible actions to and guide them towards a solution. In a nut-shell, these analytics are all about providing advice. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predicts not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions.
These analytics go beyond descriptive and predictive analytics by recommending one or more possible courses of action. Essentially they predict multiple futures and allow companies to assess a number of possible outcomes based upon their actions. Prescriptive analytics use a combination of techniques and tools such as business rules, algorithms, machine learning and computational modelling procedures. These techniques are applied against input from many different data sets including historical and transactional data, real-time data feeds, and big data.
Prescriptive analytics are relatively complex to administer, and most companies are not yet using them in their daily course of business. When implemented correctly, they can have a large impact on how businesses make decisions, and on the company’s bottom line. Larger companies are successfully using prescriptive analytics to optimize production, scheduling and inventory in the supply chain to make sure that are delivering the right products at the right time and optimizing the customer experience.
Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis.
Data aggregation and data mining methods organize the data and make it possible to identify patterns and relationships in it that would not otherwise be visible. Querying, reporting and data visualization may be applied to yield more insight.
Descriptive analytics is sometimes said to provide information about happened. You might see, for example, an increase in Twitter followers after a particular tweet. Diagnostic analytics is a deeper look at data to attempt to understand the causes of events and behaviors. Predictive analytics, which is used to identify future probabilities and trends, is said provide information about what might happen in the future. Prescriptive analytics is applied to try to identify the best outcome to events, given the parameters, and suggest decision options to best take advantage of a future opportunity or mitigate a future risk.
Here’s a summary of the stages of data analysis:
Descriptive analytics: What happened?
Diagnostic analytics: Why did it happen?
Predictive analytics: What could happen in the future?
Prescriptive analytics: How should we respond to those potential future events?
Descriptive modeling is a mathematical process that describes real-world events and the relationships between factors responsible for them. The process is used by consumer-driven organizations to help them target their marketing and advertising efforts.
In descriptive modeling, customer groups are clustered according to demographics, purchasing behavior, expressed interests and other descriptive factors. Statistics can identify where the customer groups share similarities and where they differ. The most active customers get special attention because they offer the greatest ROI (return on investment).
The main aspects of descriptive modeling include:
- Customer segmentation: Partitions a customer base into groups with various impacts on marketing and service.
- Value-based segmentation: Identifies and quantifies the value of a customer to the organization.
- Behavior-based segmentation: Analyzes customer product usage and purchasing patterns.
- Needs-based segmentation: Identifies ways to capitalize on motives that drive customer behavior.
Descriptive modeling can help an organization to understand its customers, but predictive modeling is necessary to facilitate the desired outcomes. Both descriptive and predictive modeling constitute key elements of data mining and Web mining.
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