The retail analytics industry is growing at a rapid pace. According to a recent report by Frost & Sullivan, the global market for retail analytics is expected to grow at a CAGR of more than 26 percent between 2016 and 2021. The retail analytics market is valued at more than $2 billion, with an opportunity of more than $6 billion by 2021.

Retail Analytics Definition:

Retailers are constantly working on improving their customer experience and increasing sales with the help of data science and technology. Retail Analytics is the process of collecting data from various sources, analyzing it in order to find patterns, and then using them to make decisions about how you can improve your business.

The main purpose of retail analytics is to help retailers understand their customers better and improve their marketing efforts in order to increase sales.

Types of Retail Analytics:

Retail analytics is a broad category that encompasses everything from customer segmentation to predictive modeling. There are many different types of retail analytics, including:

Customer Analytics: Customer analytics uses data about the customers themselves to make better decisions about how to market and sell to them. This can include information like age, income, location, and other demographic factors.

Marketing Analytics: Marketing analytics uses data about marketing efforts to measure the effectiveness of campaigns and find ways to improve them. This may include things like measuring the cost per click (CPC) of ads or the ROI of specific marketing efforts.

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Predictive Analytics: Predictive analytics uses historical data to predict future trends and make predictions about what will happen in the future based on what has happened in the past. This can be used for both marketing and supply chain purposes – predicting demand for products or tracking patterns throughout the supply chain so that retailers can plan ahead for major changes like holidays or other events that will increase demand for certain products or categories of products.

Retail Analytics Example

Here’s a real-world example of retail analytics in action.

Let’s say you own a small clothing store and you want to increase sales on your latest designer line. You know that the brand has been getting good buzz from social media and word of mouth, so you decide to focus on getting more traffic into the store.

You set up a mobile store in Central Park with some free samples and coupons, but you get only five people stopping by over the course of two hours. Because this is a new brand, you don’t have any historical data about customer behavior. But you do have data about how many people walk by your store each day and how long they stay inside when they do stop by.

Using this information, along with other demographic data like age and gender, you can predict how much additional foot traffic your mobile marketing campaign will generate — or whether it will fall flat on its face.

Retail Analytics Tips

The retail industry is moving at a fast pace. To be competitive in this environment, retailers need to continuously improve their sales and marketing strategies.

Retail analytics is the answer. It can help retailers with sales forecasting, customer segmentation and loyalty programs, supply chain optimization, and more. Here are some tips for getting started with retail analytics:

Understand Your Customers

Customer data is your most valuable asset. If you know who your customers are and what they want, you can create personalized experiences that will keep them coming back for more.

Get to Know Your Data

Retailers collect massive amounts of information about their customers every day — everything from what they search for online to what they buy in-store. You need to understand how these various data points relate to each other so that you can make sense of them all and use them effectively in your marketing campaigns.

Retail Analytics- Definition, Types, Examples, & Tip