Benefits of Retail Analytics: Optimizing Strategies using Consumer Data

 

What is Retail Analytics?

Retail Analytics is the process of collecting data through various means, like online footprints of visitors on an e-commerce website, feedback forms, point of contact during a purchase at a brick-and-mortar store, etc. and then analysing this data to get helpful insights that are either predictive in nature or is an actionable that can help improve an aspect of a business.

Since retail businesses are commonly medium scale to large scale and consist of a lot of brands, deals, products, customers and now, even channels under their umbrella, Retail Analytics Services can really help retail business owners to zoom into different areas of their business that would otherwise go unacknowledged.

 

Types of Retail Analytics

There are 4 types of Retail Analytics:

1. Descriptive: A general Overview of how the business is currently performing.
2. Diagnostic: Deeper insights and spotting patterns, trends and the relations between the two.
3. Predictive: Predicting trends that are possibly going to occur in the future, using historical data.
4. Prescriptive: Actionable insights fetched using real time data for immediate changes.

 

5 Benefits of Retail Analytics

1. Increased Sales and Revenue: Helps identifying the top selling products and deals that ultimate maximises profits.
2. Improved Customer Service: Get an in-depth understanding of your customers to curate the best shopping experiences.
3. Cost Savings: Spot over-expenditure and make adjustments accordingly.
4. Inventory Management: Insights to make better decisions for ordering and stocking.
5. Marketing Optimization: Where, when and how to allocate marketing budgets and activities

 

Consumer Data: The backbone of Retail Analytics

Data Analysis tools without voluminous consumer data (preferably real-time and historic) cannot do much. A good tool with a large repository can do wonders for a business.

Consumer Data helps gain insights into consumer behavior, preferences and, demographics. This is vital information for consistent growth of a business as it helps make data-driven decisions about pricing, promotions, marketing campaigns, etc.

Consumer data can be collected using various methods, including:

• Surveys
• Purchased Data
• Website Analytics
• Transactional Tracking
• Social Media Monitoring

One of the main purposes of consumer data in retail analytics is to segregate customers based on their demographics and target them for promotions accordingly.

Consumer data can also help identify the top-selling products, most effective campaigns and promotions, most popular times and days for sales, etc.

Go a step further, and you can personalize shopping experiences for your customers using retail promotions analytics.

This can prove to be one of the most rewarding elements of a business strategy as it maximizes customer satisfaction and helps build customer loyalty.

To sum it up, using consumer data for data analytics can help a brand in the following ways:

1. Tailor products, deals and campaigns according to the needs of each consumer.
2. Identify the most valuable customers and target and reach them.
3. Improve your brand’s customer service.
4. Increase sales, revenue and ROI.
5. Better promotions and product development.
6. Increased competitiveness (keep tabs on competitors’ offers, deals, products and campaigns)
7. Fraud Detection and prevention (Find loopholes in a business plan).
8. Identify new marketing opportunities.

ClicFlyer is an analytics firm specialises in Retail Analytics. We provide the best Analytics Dashboard in the market, along with relevant historical data. Our coverage includes supermarkets, departmental stores, brands etc.
For more information, reach out to us at sales@clicflyer.com