Industry

Industry Key Challenges in the Retail Industry

1.Technology grows and so does the challenge of managing cybersecurity.

Technological advancement brings along a cyberthreat, which makes the personal and sensitive data of online shoppers the soft target of hackers. The vulnerabilities of threat include DDoS (Distributed Denial of Service) attacks, PoS (Point of Sale) frauds, insider risks, click fraud, cashback frauds, loyalty program frauds that can be mitigated with the help of functional testing and data analytics.

2.Machine Learning algorithms creating a barrier to innovation.

Machine Learning Algorithms usually require huge data to arrive at useful insight, so the retail industry is finding it difficult and expensive to acquire, use, and host big data. The larger the retail ecosystem, the greater its vulnerability. The solutions to combat this challenge can be developed with advanced machine learning solutions that will remove the barriers to innovation.

3.A variety of payment gateways bothering customers.

The retail industry (E-Commerce) has to offer a huge variety of payment options to its customers like a Credit card, e-wallets, debit card, net banking, COD, etc, which is bothering the customers to share their personal information. Many are opting for the COD option, which is increasing the number of return percentages and further poor logistics and supply chain issues.

4.The struggle of retailers in developing technological assets

The silo structure is seriously threatening the existence of retailers. With big companies investing heavily in IT infrastructure, the small retailers are finding it difficult to cope with dynamic technology. Moreover, choosing the right digital model may help to overcome this challenge.

5.Hyper-personalization is not as easy as it seems

With the emergence of hyper-personalization, managing the repository of customer’s data is becoming challenging since more data leads to more choices and more confusion. Using data in the right way and developing IT infrastructure will help to combat this problem; however, integrating the continuously evolving IT system is not easy.

6.Consumer spoiled for choice

The growing competition has spoiled the choices of the consumer, which makes it difficult for the retail industry to attract the right customer, generate traffic, capture leads, and convert visitors to customers (Low conversion rate).

7.Abandoning shopping cart adding to the woes

The retail industry (E-Commerce) is witnessing a high number of shopping cart abandonment issues, which is resulting in losses. The well-designed check-out process may be helpful and convert this challenge into sales.

Real Life Use Cases in the Industry

1.Product Recommendations

Recommendation engines assist customers in getting recommendations about best-suited products on the basis of their requirements. It usescontent-based or collaborative filtering methods to predict items that the user might be interested in. A well-trained algorithm analyses customers’ preferences to make suggestions of products that they are most likely to buy.Although, product recommendations account for just 7% of visits; however, in terms of revenue, it Accounts for over 26% and significantly boosts CTRs, conversions, and other important business metrics. Amid the overwhelming options in products, recommendation algorithms make items more visible to customersbased on how frequently those two items are viewed or purchased together, which increases the likelihood of buying, average order value(AOV), and customer satisfaction.

2.Personalization

The proliferation of shopping channels has profoundly impacted consumer behavior and had created a more diffused shopping experience. The modern-day shopper is hyper-connected with a wide array of platforms, which is why it is essential for retailers to devise communication strategies that are tailor-made and customized to their shopper groups and platforms. The personalized shopping experience that delivers a relevant message to customers can drive engagement, loyalty, and revenue significantly. Customers now expect hyper-personalized orientation that has relevant, targeted, and meaningful messages on every touchpoint. The state-of-art technology can make this possible by creating a repository of the shopper’s purchases on the site which can help to create personalized messages and offerings by segmenting and analyzing customers based on several factors.

3.Supply Chain Optimization

Chain function is complex and collaboration requires much time, effort, and cost. Everything from purchasing, contracts, procurement, warehousing, packaging, transportation or distribution, requires efficiency to quickly answer complex situations. With the advent of AI techniques; these have now become easier and the product can be delivered within a few hours.Machine Learning and its core constructs can find key factors most affecting the supply chain and provide insights to improve its performance.Supply chain disruptions can cause significant negative losses in terms of finances (62%), logistics (54%), and reputation (54%). Technology can enable demand and sales forecasting, optimize inventory with correct stock levels, help logistics planning workbench & warehouse throughput optimization along with providing a 360-degree view of customers, shop-floor yield optimization, and much more.

4.Payments

It is well recognized that an easy, real-time, cashless and frictionless payment system can drive efficiency and consumer confidence in the retail business and can foster trade and consumption. 74% of retail customers prioritize experiences over products, payments can be a critical piece to enable the same. For retailers. It’s common to accept cash or credit card payments, but if these are the only options one accepts, then they are turning away many opportunities and potential revenue. A flexible payment system can allow retailers to accept various payment options, create a better customer experience and nurture customer retention. Moreover, digital payments can help you receive and track customer data and provide you with insights on how the consumers typically spend, how frequently they shop, how much they spend, and other such critical aspects.

5.Dynamic Pricing

Dynamic pricing is the one where prices are adjusted in response to demand and supply using various models. What attracts most retailers is the ability of dynamic pricing to use newly available information to set prices based on the consumer’s willingness to pay. If managed well, it can offer a feasible and attractive path to increase revenue and profit by 8 – 25% and can also be used to reallocate demand to more appropriate times and manage a limited supply base. Similarly, to push slow-moving goods, the prices can be reduced to increase the demand. A slightly different approach of relocating the prices based on consumer demand can help a business to have a lot of benefits. The flexibility and controllability it offers in terms of selling a product at the right price can allow retailers to sell more ata low cost.

6.Inventory Management

Inventory management is an unwieldy beast every retailer attempts to tame and is critical to efficient replenishment decision making and success of any business. To manage the inventory efficiently, it requires pre-planned goals and attention to detail and prioritizing items that require less attention. Mining or extracting customer insight from various data sources is of tremendous importance for inventory management in retail. Data analytics plays a crucial role in keeping up with the change in customers’ tastes, predicting which product will be more profitable, identifying products that are sold in unison, and helping in getting insights that are essential to store products in the inventory. Other technologies like RFID, scanners, etc can track goods and manage inventory efficiently.

7.Demand Forecasting

Be it introducing a new product or opening a new store, the benefits of more accurate consumer demand forecasting in such decisions are apparent. On the other hand, lack of forecasting can lead to poor replenishment decisions, affect profitability, increase storage space, diminish customer loyalty, and loss of stock. Predictable demand is essential to organize orders, determine the store’s product assortment and placement, and manage order shipping, scheduling. Consequently, the efficiency of the entire supply chain would be improved by more accurate demand forecasts. Forecasting techniques like time series, grassroots, market estimates, etc can utilize data to avoid inefficiencies and improve the decision-making processes.According to Mckinsey Digital, AI-powered forecasting can reduce errors by 30 to 50% in supply chain networks. Thus, technology-enabled forecasting serves as a ground for risk reduction and optimized processes.