🔮Future trends in retail analytics📈

 Upcoming trends in retail analytics

What is Retail Analytics?

Retail data analytics is the act of gathering and analysing retail data (such as sales, inventory, pricing, and so on) in order to identify trends, forecast outcomes, and make better business decisions. When done correctly, data analytics enables retailers to get faster and greater insights into the performance of their stores, products, customers, and vendors –thus making better business  increase profitability. 

Almost all retailers use data analytics in some way, even if it’s only examining sales figures in Excel. However, there is a significant difference between an analyst using Excel to pour through spreadsheets and employing purpose-built AI to evaluate billions of data points simultaneously. 

To know more about the difference between them, there are four types of retail data analytics that you must know about. Each plays an important role in providing today’s retailers with key insights into their business operations. 

·       Descriptive Analytics – the most common one: helps retailers organize their data to tell a story and uses it to describe “what” is happening in their business. 

·       Diagnostic Analytics – the simplest form of “advanced” analytics: It enables retailers to use data to answer the “why” of specific business concerns. 

·       Predictive Analytics – the second most advanced type: It forecasts the future using findings from both descriptive and diagnostic analytics. 

·       Prescriptive Analytics – the most advanced type: it gives retailers good recommendations to do. 

 

Why is Retail Analytics Important?

1.   More effective sales and marketing strategies: 

Retail analytics provides insights into customer behaviour, allowing businesses to understand the return on investment (ROI) and make informed decisions. It helps identify what works and what doesn't, such as determining the impact of campaigns on conversion rates or analysing social reactions to products. This information enables businesses to develop effective sales and marketing strategies, such as replicating successful in-store displays or tailoring messaging based on customer behavioural data.

2.   Optimizing operations: 

Implementing retail analytics helps improve operational efficiency. For example, analysing peak hours and traffic patterns allows businesses to optimize worker rosters and ensure adequate staffing levels for better customer service. It also guides decisions on store layouts, product placements, and service quality, enhancing the overall operations management as the business grows.

3.   Stop wasting money: 

Retail analytics enables better resource allocation by focusing on strategies, products, and projects that drive growth. Regular tracking of analytics helps identify and address issues promptly, such as optimizing inventory levels to prevent missed sales or avoiding excess stock that ties up warehousing resources. By analysing customer data, market trends, and consumer behaviour, businesses can forecast future demand and optimize their vendor supply chain accordingly, improving sales and efficiency.

 

Future of Retail Analytics

Below are significant retail analytic trends that businesses are using to achieve a competitive advantage in the market. 

1. Create more hyper-personalized experiences for a one-to-one market

The ability to track consumer interactions at such a fine level allows management to obtain a far deeper insight into key shopper’s wants and expectations. This means that shops may now provide the unifying experiences that customers expect while communicating distinct offers to highly refined segments. As a result, providing hyper-personalized retail experiences to your customers can increase total sales by increasing loyalty and share-of-wallet. 

If you would like to apply the same to your business, your firm should think of having a single data and analytics platform in order to develop and expand this level of personalization.  

2. Spending and demand can be predicted

Retail analytics trends are leveraging advanced analytics, which use computers and machine learning to predict trends detected in customer data. These complex computational models inform businesses how much of a certain product or service customers will wish to purchase during a specified time period. 

Demand forecasting is being used by business owners to bring their most profitable consumers back into the store through timely notifications and good offers on related products. As a result, businesses can ensure that shipments are timed to get the products their customers desire on the shelves while also optimizing their supply chain. 

Retail leaders are applying predictive data analytics to determine each customer’s lifetime value in order to increase retention. 

3. Develop automated, dynamic pricing models

To remain competitive, retailers frequently need to maintain a percentage of their prices very low. These low-cost items, known as doorbusters, and key value items (KVIs), are frequently the top sellers and traffic generators that establish a retailer’s price image. As a result, while KVIs can account for up to 80% of revenue, they only account for half of a retail company’s profit. To compensate for the low margin on KVIs, merchants raise the cost of their higher-margin items and strategically put them alongside doorbusters and KVIs in creative ways to encourage buyers to add higher-margin products to their carts. 

Retailers can stay in business and grow by optimizing product prices to increase profit margins. With this in mind, dynamic pricing algorithms, in particular, have proven to be game changers for merchants. Dynamic pricing models will offer price suggestions automatically, allowing management to make better informed and timely decisions that benefit the company’s bottom line. To be genuinely effective, we strongly recommend collaborating with a data analytics consulting firm to construct a custom solution tailored to the retail company’s business objectives, operational processes, and client base. 

 

4. Selling online is non-negotiable

Online shopping is important for customers and crucial for retailers. Today, 37 percent of monthly retail purchases are made online, and many businesses are addressing those demands. Independent merchants can interact with customers locally while simultaneously expanding their reach by selling online and retaining a local presence. 

Going online can boost the prospects of success as many retailers change their business strategies to pursue new revenue streams. In fact, among shops who sell online, internet sales currently account for 51% of total income. Moving to the first new channel highlights the need to employ digital tools to assist retailers in continuing to innovate. 

Conclusion

Embark on a retail analytics journey with Synodus today!  Retail has always been a data-intensive industry. Hence, in the near future, adopting retail analytics, making the most out of data, and extracting insights from it to make informed business decisions should be the top priorities for any business to compete in a consumer-empowered economy. No matter what you are retailing, you could realize value by adopting data analytics and utilizing critical trends in retail analytics to drive your business growth!   To get ahead, you may build an in-house team on your own or turn to data analytics consultants (like those at Synodus) to help build and deploy the required data infrastructure to embark on a retail analytics journey soon.

 

References:

https://synodus.com/blog/big-data/future-of-retail-analytics-trends/

 

#retail #futuretrend #trendanalysis #analytics # Nanobi #hunnarvi #supplychain

 

Gokul G

 

ISME Student Doing internship with Hunnarvi under guidance of Nanobi data and analytics. Views are personal.

 

 

 

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