Advanced Analytics Transformation in Retail
Shopping online is a norm for long among global retail consumers; it is just that COVID-19 has ironically mandated it. Still, retail marketeers have not and mostly never be able to deem retail shopping in-person, as a thing of the past. A recent research, zero moment of truth, from google has clearly indicated, over 70% of global consumers preferring to first homework shop online on their available choices, before eventually buying ‘their things’ in person.
Analytics - The key driver behind online-offline hybrid retail business era!!!
Marketeers no more focus on pure customer service and marketing. The new marketing norm involves genuine, real-time market research data collection, processing and analysing them. The results of such ‘realty’ data analysis provides those ‘like never before’ insights for managers to plan and strategize on their customer service to obtain accurate results. Here are a few of the significant marketing edge that analytics usage provides the retail industry with:
1. Personalized, specifically aimed customer communication
Data Analytics is leveraged to provide those of your product/service information, to those exact customers, who have a necessity for them. Such personalized communication to targeted customers is possible, based on real time, collected customer-data driven advanced analytics. Such advanced analytics tool result based promotions, is far more positively consequential than conventional advertisement-practices. Experts go one step more to deem personalized, customer-communication as the key differentiator of a business from its competitors.
To quote a typical example, haven’t you often wondered how promptly and frequently your usual cloth store sales department keeps calling you on the availability of a particular line of garment you may have enquired casually just a month ago. The interest you’d shown is now a valuable data for analysis and now you will now understand the real purpose behind the prompt, frequent calls.
2. Analytics driven demand forecasting and inventory administration
Extending a bit further, the very same analytics results from multiple customers and additional POS data are vital pieces of information that analytics processes. A collection of such analysed data provides crucial, insightful reports on what exactly customers are buying these days. Enterprises can promptly direct the inventory system to stock up.
3. Market trend detection through predictive analytics
One of the most popular retail marketing promotional principles recommend festive season sales that have the potential to exponentially boost revenues. Retail giants such as amazon have tapped predictive analytics tools that can detect timely, seasonal demands for product lines based on customer sentiment analysis. Predictive analytics tools provide timely insights to marketeers to plan and strategize for product promotions based on seasonal, buying such as festive season sales.
4. Predictive analytics for seamless cost optimization
One of the most striking aspects of using predictive analytics tools is in optimizing on retail prices with fluctuating demands. Divergent to traditional practices of reducing prices after the seasonal demand for retail products ends, modern market research recommends innovative demand-based cost optimization.
Popularised by the US retailer giant, stage, through their research, advanced analytics and market research have found newer, crucial demand concepts to work on. Stage’s researches revealed, when they’d reduced prices of their products just at the moment demand for them started subsiding, the demand actually increased remarkably. Such revelations eventually provided newer avenues of market demand and pricing optimization analysis. Retail giants such as Walmart have capitalized on this concept to find incredible demand and cost management results through advanced analytics implementation on cloud.
5. Enhanced retail customer experience (CX)
Marketeers and strategic decision makers in today’s millennial world rely on pure real-time analytics customer data to augment customer experiences. Relying on analytics has become a newer mandate to increase customer loyalty as well.
A typical example of a wonderful CX in retail would be experiences of buying products online. Customers find delightful time-savers when they find their profile details such as address, email or phone numbers getting auto populated without having to undergo the dragging experience of having to type them all over again repeatedly every time they buy a product online.
Online retail websites also leverage on customer data analytics to recommend exciting product recommendations to customers. For instance, whenever you buy a mobile phone online, the site would also recommend an entire gamut of mobile accessories with an entire list of available discounted options. Today’s advanced analytics utilities have the power to bring in near-real-time retail customer experiences, almost like they are physically present on the shop floor.
6. Analytics to detect high ROI avenues
The most popular advanced analytics utilities among marketeers and managers seamlessly detect the demand for specific products based on customer feedback data. Predictive analytics application use data captured from customer responses, evaluate them, and provide critical insights to decision makers about the number of potential customers willing to buy specific products.
7. Analytics to strengthen customer loyalty
The most widely held application of advanced analytics is in detecting customers who have long not connected with a brand which otherwise in reality they have a need for. The reasons for such customer behaviour can be anything starting from dearth of time to being unable to physically go and shop. Advanced analytics applications of today have the power to seamlessly detect such potential customers who can be made to engage/re-engage as well as gain their consistent loyalty.
8. Advanced Analytics to detect new store locations
Powerful insights provided by advanced analytics utilities have extended their arms to newer retail store owners who would greatly require vital information such as exact location for opening their stores. Advanced analytics applications have the potentials to analyse varied parameters such as demographics, customer-base availability, demands etc and compare them with the brands and product lines to be sold in the new retail outlet. Results of such analysis brings out exact, precise results for store owners to spot locations most apt for opening their new outlets for maximum returns and customer base capture.
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