4 Benefits of Analytics for Retail Brands

by Your RetailCoach YourRetailCoach

Data analytics is the new-age technology used for determining the health of the organization and taking the necessary steps to enhance business opportunities. The information's are useful in organizing and drafting the future patterns. As the retail market expands globally, data analytics techniques have become a crucial component of every business house.

 From Retail to financial services, every sector recognizes the power of data. Today we look at the retail segment and understand how does data analytics proves to be the 'game changer' in this industry. Also as a 'takeaway', you get the top 4 benefits of analytics for retail brands.

Data Analytics and Retail segment: Identify the correlation

Retailers face an uphill task capturing the correct customer experience. They are eager to figure out the realistic trends. Capturing the data of multiple touch points used by the customer is critical information. As retail data continues to multiply in volumes and velocity each year, retailers are ready to exploit these conditions to maximize the customer traffic. As technology continues to dominate the retail industry, organizations prefer to use professional analytical services for improvising their services and strategies.

Importance of Analytics in Retail

Systematic analysis empowers the retailers to associate and adapt to the dynamic trends of buying and competitive activity among the customers. Modern gadgets and applications allow the retailers to get the 'real-time' analysis which gives a better insight and perspective. In today's condition, even a month old data is of no use as the behavior of retail sector changes at a rapid pace.

4 benefits of analytics for retail brands

§     Excellent knowledge of your customers: Understanding your customers is one of the essential benefits of retail analytics. The raw customer data is calculated on the basis of-

-customer contact

-buying preference

-choice of multiple touch points

-shopping experience  

A defined analysis gives the retailer to customize or personalize their marketing strategies as per customers. For example, Uber matches the customer's personal data along with the drivers as per the geographical locations. As a result of this, customers prefer to receive the offerings from Uber a lot. Personalization is vital in marketing as many retailers lose on consumers due to their generic approach. In today's world, the customer expects a seamless experience across channels. Tracking the customer's shopping pattern and a personalized shopping environment makes it easy for the retailer to manage his activities.

§      Discounts and Offerings- Strategy is the key: The increased online traffic allows the retailers to strategize the flow of their business. Offering discounts, coupons, attractive deals are keys ways to pedal up the sales and gain customer loyalty. However, the question is- how effective this strategy works in the practical environment. Giving discounts without a plan is not the apt way to survive for longer periods. Here data analysis comes into the picture again and retailers need to customize the offerings as per the buying trends of the consumer. They have to determine the long-term objectives. Some of the key advantages of these offerings are below:

-competitive marketing

-loyal consumers

-lure new customers

-removing excess inventory

There is a segment of consumers who only search for coupons and discounts. Data can help the retailers to identify these buying behaviors and design such schemes targeting these customers. Frequent deals help to create a loyal customer base and a good way of meeting the revenues. Often, retailers can add new customers in their database by launching exclusive deals for the 'new customers'.

§       Reduces customer churn rate: Creating a customer base is always a priority for the retailers. The cost of acquiring a new customer is many times high as compared to the existing loyal customers. Data analysis gives the insight as which section of the customer base is about to leave and what can be the possible ways to retain them. The data available helps to segregate the customers and with a decreased churn rate, retail brands can measure the lifetime value and scale faster. Some of the points for increasing retentions:

-personalized approach

-tracking and working on the feedback received from consumers

-attractive deals for loyal customers as per their choice

-monitor customer activity

-analyze the brand's strong and weak points and market accordingly

Data analytics helps to gauge the end-user behavior and preferences which assists the retailers to prepare their action plan to retain the existing customer base. Technology has transformed as a guide for the retail brands in improving client interactions and maximising customer satisfaction levels.

§  Predictive Analysis can do wonders in retail: For newbie's, predictive analytics is a study of current and past data to forecast future results and trends. Combination of analytical queries, techniques, and defined algorithms assist in building predictive models. These models are highly accurate and business houses rely on these inputs while drafting their future expansions and roadmaps. Although predictive analysis stands on facts and figures, the data is not precise and not always be 100% accurate. It's just a predictive calculation to judge the probability of future outcomes and prepare you accordingly. The motive is to minimize expenditures, save precious time and reduce waste of resources and products. Predictive analytics has flourished for 2 prime factors:

-High-end technology makes it accurate

-intensifying competitions

Predictive analysis is applicable in diverse industries such as retail, healthcare, automobiles, manufacturing to name a few.

Conclusion:

With the highly competitive market, optimizing business and a seamless customer service has become the priority for retailers. Universally, data analytics is applicable to every stage of a retail process. Few impact areas where data analytics can play a vital role in retail business:

-future performance

-price optimization

-maintaining the customer base

-forecasting trends

Mitigating the risk factor and improving overall performance are the benefits organizations reap through utilizing data analytic methods and procedures. With such amazing features, data analytics will continue to evolve and keep reshaping the future of retail business. It would be a commonly used tool for enhancing customer relations in the future. In the past few years, retail business has witnessed a major transformation, driven by innovative techniques and digital assistants. This is just a beginning of a long road ahead.

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Created on Sep 26th 2017 02:33. Viewed 916 times.

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