Retail Data Analytics – Top 10 Reasons you need it in 2020

Next time you see a popup ad on your browser, based on your searchrest assured that it is retail data analytics at its efficient best.

Retail industry has evolved into a diverse economic activity from being a single store to super markets, chain stores and e-tailers. With the explosion of buying options,definition of success has changed for retail industry from beingjust their capability to inundate market with a plethora of products and deals to their ability to anticipate customer needs and preferences.

Data analytics and the Retail industry

Data from a retail establishment can originate from conventional sources like promotional tools and electronic sources like Point of Sales (POS) consoles, security cameras,sensors and the omnipresent social media. With the ever changing customer demands retail industry has to move away from traditional intelligencegathering and processing to data driven approach.This will help retail industry to provide better services and gain an edge over competition. In the retail industry data analytics not only provides profound insight into customer preferences, which is vital for marketing and procurement decisions but also influence supply chain management and inventory levels. Predictive data analytics is used to forecast trends and customer buying patterns.

Retail industry need data analytics yesterday, but 2020 is not too late, some of the reasons include:

Improved decision making

Retails industry encompasses a gamut of functions like procurement and merchandising, operations, advertising and marketing, accounting and personnel. These often work as independent departments with data splintered across the company costing the company important insights which are vital for strategic decision making that effect growth. Our retail analytics offers an overview of all the metrics aiding business houses to take timely decisions to effectively engage all the key stake holders.

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Price optimization

Price of a product or service is a major determining factor that drives shopping trends. But it is a tight rope walk for retailers to balance cost of the product, marketing cost and competitor’s price with their selling price.  Data analytics can help retailers to arrive at optimum price for their products or services, applying algorithms on a large number of transactions. These algorithms use various parameters like buying trends, inventory levels, and competitors’ price, location of the stores and foot falls to provide valuable real time insights to arrive at optimum price. Predictive analytics can forecast variations in price based on seasonal changes in demand for products and customer response to price variation.

Demand prediction

Retail businesses can identify products/services that have high demand by gaining a good understanding of the buying trends of customers using data analytics. This is turn will help them to predict demand and improve operational practices.

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Forecasting Trends

One of the important benefits of data analytics in retail industry is forecasting trends. A strong data analytics solution like our Consumer Trend Analytics will incorporate technology to mine and analyse data on the most frequent or high spending shopper, last year and current year’s bestselling product or popular designer or supplier. This information will help retails to arrive at better ordering decisions and promotions and also identify trends that need to be promoted or discontinued.

Marketing analysis

Marketing data analytics presents deeper insights into trends and customer preferences by measuring and analyzing marketing performance. Marketing analytics an important tool in our (Phygital Insights) retail analytics solution helps analysis of marketing plan which is the road map to realising business objectives and marketing performance to optimize return on investment.

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Estimation of effectiveness of Promotional material

Historically retail industry has thrived on promotions and offers to improve sales, but effectiveness of these tools have always eluded industry. Data mining and analysis can unearth reasons for disparity in promotional activities and revenue generation. We (Phygital Insights) offer Promotion Effectiveness Analytics that help retailers plan and execute effective and result oriented promotional campaigns. Analytics can even influence decision on locations for new establishments.

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Vendor management

Efficient vendor management goes a long way in ensuring timely delivery of quality services and goods.  Data analytics can stream line processes like vendor selection, monitoring/setting level of excellence, managing individual contracts, timelines, terms and other parameters. Our (phygital Insights) Vendor Analytics offers retailers’ seamless vendor management, including vendor expenses and delivery timelines there by mitigating risk due to delays and disruption.

Cost control and inventory overloads management

Improved operational performance, efficient business practices and effective vendor and inventory management are the key factors that aid reduced operational costs for retailers.  Data analytics has helped retailers to identify goods/services with seasonal demand and goods that are fast moving or in vogue so that implementation of  just in time inventory practices will reduce inventory overloads and wastages.

Killer Merchandising

Ever changing consumer demands and innumerable new products and newer sales channels are the challengesmerchandiser face while devising displays and offers to grab customer attention.  A robust data collecting and analysing tool is the need of the hour. Our (phygital Insights) Store Planning and Optimization Analytics solution can help garner actionable insights into in-store shelf stocking, store layout optimisation, store level traffic and customer affinity towards specific brands and products.

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Customer is the king – Retail industry thrives on enhancing customers’ shopping experience.

Improved customer satisfaction

With the ever changing customers’ expectation retails industry has to be on its toes to anticipate customer preferences to ensureimproved customer engagement. Customer analytics can help to deliver good shopping experience improving customer satisfaction and gain customer loyalty which will result in recurring sales, referrals and revenues. Using predictive analytics stores can even predict what, how and where their customers want to buy.

Personalised shopping experience

Customer Trend Analytics from the bouquet of our (Phygital Insights) retail analytics solution mines data about customer behaviour from telephone calls and emails, chats, surveys and social media. This data can be used to personalise customer experience by devising customer specific products and services. Improved customer communications and relationship help retailers cater to shoppers’needs resulting in better customer engagement that improves bottom lines.

Development of New Products and Services

Today customer data analytics can motivate companies to develop newer products and services precipitating in customer delight.

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Newer opportunities in data analytics

Omni Channel Retail Experience:Availability of multiple shopping channels asboth physical and digital outlets has redefined the customers purchasing journey.  Omni channelretail experience is the next big thing in retail data analytics to identify customers, understand and meet their needs and improve sales.This unique experience can aid:

  • Implementation of focusedmarketing efforts.
  • Merchandizing efficiency enhancement
  • Optimization of supply chain management 
  • Store operations improvement

 Implementing augmented reality: Augmented reality is a futuristic approach to retailing where a customer can own the product virtually in order to make an informed buying decision resulting in fewer shopping cart second thoughts. This will help retailers operate at reduced inventorylevels and shop returns. Data analytics when applied to implementing augmented reality can vastly improve customer experience.

Hidden benefits of data Analytics

Some of the lesser know benefits of data analytics in retail industry include:

Ability to combine data with human insights: Notwithstanding the wealth of information that data analytics puts at retailers disposal, there is no denying the importance of human perspicacity, judicious combination of these two makes data analytics a strategic tool.

Generation of customised reports: Data analytics de-silos data intelligence from various departments and allows retailers to generate custom reports by marrying parameters from both intra and inter departments and offers retailers an in-depth overview of vital metrics, enabling better strategic business decisions.

Challenges in Retail Analytics

Given the vast amount of data that aids data analytics including customer and organisational information, data analytics in retail industry does come with its own challenges.

  • Data security and privacy issues
  • Handling liability and intellectual property rights issues
  • Managing multiple channels of customer engagement
  • Meeting and exceeding ever-changing customer expectations

Today, successful retail businesses rely on solid facts, metrics, and up-to-date data to make key business decisions. Retail industry has notched growth of only 2.2% in 2020 as against 2.5% in 2019 all the more reason for retail business to adopt data analytics aggressively for a speedier turn around.

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