On the surface level, if a brand sells its stock as expected, it must be thriving. But is it? Only recently has predictive analytics started to gain traction. Wondering what it is? Keep reading!
Brands strive to have the right product at the right time, given how unpredictable and sporadic customer demands can be.
Inventory management entails acquiring, storing, and processing products to meet customer demands quickly and efficiently. However, maintaining an optimal level of inventory is no easy task. Therefore, predictive analytics is essential to use.
If businesses fail to strike a balance, they risk losing out on sales, customer retention, and customer satisfaction. Poor inventory management practices can lead to stock outs.
This scenario may be a little too relatable:
You set aside a $20-50 bill now and then so you have enough money by the time you go shopping for your wish list items. After a few months of waiting, you visit your favorite retail store only to realize that the product you so dearly wanted is out of stock. Disappointed, you leave the store and hope to find what you are looking for on the online store, except it is not available there either. At this point, as you question whether this out-of-stock problem is your bad luck, you check other similar stores and websites and finally find what you are looking for. This process was time-consuming and inconvenient, but you got what you wanted.
So, what is the problem? Well, this is what happens when stock outs occur. Businesses that fail to forecast demand see a major drop in conversions, risk losing their loyal customers to competitors and struggle to keep afloat.
As a crucial element of supply chain management, mitigating stock outs is essential to business longevity and profitability. In the past, statistics have shown how poor inventory practices have contributed to a staggering loss in sales amounting to nearly $1 trillion.
Every commercial endeavor needs to plan its operations strategically. The viability of this planning depends on how accurately businesses can anticipate their needs before they materialize, which is why demand forecasting is essential.
Poor planning and forecasting can negatively impact business growth, profits, and clientele. Businesses that do not efficiently manage their resources cannot survive and thrive in a highly competitive market.
Inventory is a retailer's most valuable asset. As a business owner, you risk losing productivity, money, and customers if you aren't aware of how frequently, how long, how much, and when your inventory sells.
Changing global supply chain conditions can wreak havoc on businesses and make way for discrepancies to creep in and dismantle business operations and stability. Listed below are some examples of large-scale retailer inventory management blunders.
In 2001, Nike sought help from demand planning software without adequate knowledge and testing. They ended up stocking low-selling products and not enough of their popular Air Jordans. It resulted in a loss in sales of $100 million.
In 2013, Walmart suffered a major out-of-stock issue. Though the stock was available in their warehouses, there was not enough staff on hand to stock the shelves. Had Walmart anticipated demand, they would not have had to lose $3 billion in sales.
Due to a poorly implemented supply chain system, Target failed to expand to Canada. It closed 124 stores in the first two years of operation, resulting in a $2 billion net loss overall. Canadians were appalled as pricing was 15% higher compared to US stores.
Although Target launched an e-commerce site, it failed to cater to Canada’s francophone population. Whenever they tried to shop online, they were redirected to the American store where they couldn’t select a language of their choice, nor could they justify the price increase.
This failed inventory control left some shelves overstocked, some bare, and consumers upset.
Demand forecasting is the process of estimating and predicting future consumer demand using predictive analysis of historical data. It is the foundation of all strategic and operational plans because it aids in risk mitigation.
It also affects the decision-making process regarding cash flow, growth, resource allocation, profits, operation costs, staffing, inventory management, and overall expenditure.
Businesses can benefit from forecasting demand in several ways, from optimizing inventory to analyzing data and making informed decisions, such as:
Relying on intuition and manual stock keeping unit (SKU) data entries can contribute to a significant loss in sales and added wastage of resources. To minimize an excessive stock or inadequate stock problem, Data Pilot assisted a bakery chain by building predictive models for the top 100 SKUs to predict the demand for each SKU.
They also built a web-based application with dashboards and sheets to reflect the demand for each SKU from the model output. This model made it simpler for the managers and production personnel to prevent possible stockouts by minimizing the risk of human error and trusting the data to forecast demand.
The key takeaway is simple; there is no business without demand. Businesses that don’t fully comprehend the role demand forecasting plays in strategic decisions either declare bankruptcy or halt expansion.
Spend less money and keep an optimal level of inventory on hand. Data Pilot builds predictive models that help you fine-tune your supply chain by forecasting growth, reducing uncertainty, and preventing volatility. Now is the time to bid farewell to those late-night sittings where you manually update data in excel sheets and wonder where all your money and consumers went.
Leverage data-driven insights with Data Pilot to kickstart your business success. Real-time customizable inventory analytics awaits you!
Predictive analytics is the process of making predictions about future business outcomes using historical data, machine learning, artificial intelligence, and statistical modeling. Businesses employ data-driven techniques to forecast inventory, manage resources, improve efficiency, increase revenue, optimize performance and productivity, and take appropriate action when needed.
Data-driven processes combined with business intelligence lead to reliable and actionable insights. Brands can benefit from predictive analytics in many ways, such as:
Stocking inventory can be expensive, and stock outs can be detrimental. Brands can leverage predictive analytics to prioritize stock based on demand and profitability by adjusting pricing and offering discounts and promotions to different targeted consumers.
By using IoT in combination with predictive analytics, businesses can plan expenses ahead of time to prevent unnecessary maintenance costs, including reduced waste from damaged items from suppliers. Businesses can then increase downtime and extend the lifetime value of their assets.
Retailers can identify customers that spend the most and target them through proper marketing strategies to increase their profits. By configuring suggestive selling based on best-selling items, excess stock, and buyer trends, brands can target the right audience and improve customer segmentation.
By aggregating data from omnichannel commerce using predictive analytics, brands can forecast consumer behavior, anticipate market trends, optimize business processes, and predict periods in which stock outs are most likely to occur.
It can help them take preventative measures to improve overall supply chain management. We will review the use cases and examples of predictive analytics case studies in retail and e-commerce:
It is an integral part of a thriving business that ensures product availability and timely delivery. Predictive analytics in business helps retailers avoid out-of-stock conditions by anticipating optimal inventory levels. Additionally, it helps create delivery routes that improve operational efficiency, reduce costs, and boost customer satisfaction.
For instance, Carrefour uses data analytics to optimize inventory management. It collects data from warehouses, stores, and websites to reduce stock outages and excess stock and predict demand and supply.
In e-commerce, predictive analytics and big data solutions have allowed brands to make connections between various data points. Brands that offer product recommendations to customers by analyzing behavioral patterns such as previous order history, shopping cart, and purchase frequency, create pleasant online shopping experiences. According to McKinsey, 76% of consumers are most likely to purchase from a brand that offers personalized experiences.
For instance, using predictive analytics, Macy's department store managed to increase sales by 10% in 3 months by sending personalized emails based on user data. This increase in sales for such a large retailer is substantial in less than three months. Also, by analyzing consumer purchases and browsing history, Amazon has generated up to 35% of sales through product recommendations.
With surveillance cameras and IoT sensors, brands can count the footfall at different times during the day, calculate time spent in queues, and see which products and discounts attract consumers the most. This data insight helps configure store layouts, opening hours, and staff schedules, leading to improved customer experiences and sales.
Many businesses today have seen remarkable improvements by employing predictive analytics. In a perfect utopian world, this would mean no problems whatsoever. Predictive analytics cannot solve every minuscule problem unless planned and implemented with proper knowledge and testing. It can, however, offer substantial help. And that is why Data Pilot is here to provide you with data-driven solutions to even the most complex challenges. Never incur another problem of lost money or customers.
Whether you want to employ predictive analytics for e-commerce business or predictive analytics for retail brands, rest assured that Data Pilot can cater to all your business needs.
Discover how we can help you grow your business by visiting our page.