Predictive Analytics in Retail
The retail industry is on a continuous lookout to attract a vast and diverse customer base. Customer experience is a defining success factor driven by the emotional quotient, customer segmentation, and purchase patterns. Before digitization, a customer’s personal experience with the brand is the primary metric for brand performance. However, with changing times, several other factors like conversion rates, revenue maximization, customer delight, and inventory levels are also concerns for performance measuring. But the importance of Customer experience stood unchanged; it has transformed as a critical factor influencing your market position, competitive advantage, and growth.
Superior customer experience attracts returning visitors, increasing the probability of purchase, and bringing in referrals.
As reports say, acquiring a new customer is five times more costly than retaining an existing customer.
Understanding customer preferences and historical data to facilitate personalization is essential to stay competitive. Research by McKinsey shows that 72% of shoppers expect businesses to provide tailored experiences, and the retailers implementing this will have a greater chance of success. Retailers can accomplish this by adequately analyzing data generated from customer clicks, carts addiction, and online purchases.
Predictive analytics holds the key to simplifying the act and enabling data-driven personalization for better customer experience and revenue margins. Unlock near-accurate forecasting and predictions for bright data-backed decision-making.
More on the why and how of predictive analytics in retail below:
Why predictive Analytics?
Retailers are thriving in a world with tech-savvy customers who have access to diverse digital channels. To stay ahead and offer exceptional user experiences, retailers continuously analyze data points across their business operations, ensuring the best return on investment and a competitive edge.
Data analysis includes three aspects to consider for effective decision making; historical data analysis, real-time analysis and effective forecasting. Analyzing historical data helps key decision makers predict and plan based on future outcomes like sales volume, product reach and anticipate industry trends. Predictive analytics can also help businesses manage their day-to-day operations by giving insights on product demand, daily sales, and proper inventory maintenance on the ground.
We already observe some brands like Amazon and Walmart using the power of Predictive Analytics to provide more personalized recommendations and services by using their data patterns from the past.
To understand more on how it can be implemented, lets walk through some of the use cases
Top 5 Use cases of predictive analytics in retail:
Understanding customer behavior:
Retailers often gain a lot of data through customer touch points like social media, e-commerce, websites, mobile apps, physical stores, etc. Predictive models and analytical techniques enable the observation of interrelationships between different data sets, providing valuable insights into data connections and patterns.
Retailers can gain the information and, through proper data-driven judgments, can find which product demand is upgrading, observe channels that are performing well, what the customer is likely to buy, his purchasing trends and intentions, and recommend the best-desired product in his preference.
 Simplifying inventory management:
Retailers have a large set of Historical data regarding a product, like sales, demand, promotions, and many more. Businesses can utilize statistical data models and mathematical algorithms to identify product demand trends and ensure their availability in the market, facilitating effective supply management.
Predictive analytics helps retailers better arrange inventory and track inventory flow in the supply chain, availability in stores, shipping operations, etc. By undergoing the analytical process and trends involved, it can find the area of delay and automate the process quickly.
Enhance customer segmentation:
Businesses can use predictive segmentation models by understanding their customer behavior patterns in the past. It helps in finding the customer’s lifetime value, customers who risk losing, and plan their marketing strategy accordingly. Retailers can enhance data-driven judgments by segmenting their customer base according to their previous behavioral patterns, trends, and purchases.
For example, Mailchimp says they got more impulsive responses by narrowing down their target audience according to customer segmentation. The stats say they have seen 14.31% more openings and 100.95% clicks higher than non-segmented campaigns.
Leveraging predictive analytics to deliver personalized services to specific target audiences can significantly enhance audience engagement and drive substantial uplift in business growth.
 Establishing insights on product pricing:
Pricing directly has a prolonged effect on your business. By using predictive analytics, businesses can study the attainable price for each client, enabling them to optimize sales strategies, make data-driven decisions on promotional pricing, and ultimately maximize revenue potential. Studying the Price effectiveness of the product, its price-based frequency, and competitive advantage can stimulate the pricing decisions of the product.
 Give Personalized recommendations:
Predictive Analytics can upsell and bring more customer loyalty by analyzing the data coming from various sources. It helps find the performance metrics of online and offline product reach. According to Mckinsey, the positive customer experience will likely yield 20 % higher customer satisfaction rates and boost sales-conversation rates by 10 to 15 %. The engines in predictive analytics can easily understand the customer behavior and offer them the best upselling advice, it can also give the next best offers the store is going to announce and bring in more sales.
Benefits of Predictive Analytics in Retail:
Predictive Analytics empowers businesses with valuable insights, enabling them to optimize productivity, make data-driven decisions, and stay ahead of the competition in a dynamic marketplace.
Here are some other benefits:
Increased Production: By understanding the future demand, sales and & supply chain metrics, businesses can accordingly increase their production.
Decrease Cost: Predictive analyses can find the areas of improvement and allocate the resources which help decrease unnecessary expenses. It also helps executives find a demanding product line and plan promotional advertisements.
Detect Fraud: Using predictive analytics to find future areas of risks or their probability helps businesses take precautions from the start.
Improved customer experience: Providing more personalized recommendations based on customers’ historical data & marketing trends enables businesses to provide the best customer experience.
Sales forecasting: Predictive analytics can provide precise estimations of future sales performance by analyzing product demand, customer behavior, seasonal metrics, and various other factors.
Predictive analytics by leveraging historic data insights finds applications in diverse industries, aiding in targeted marketing, fraud detections, supply chain optimization, and more.
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At Saxon.AI, we help enterprises on their journey to optimize their organizational structure with tailored solutions and services. Get in touch with us to start your journey toward Predictive Analytics today.
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