Nine Examples of How Predictive Analytics are Being Used in Retail

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Customers appreciate suggestions for products and a personalized shopping experience. Examples are, Amazon offers book recommendations that are based on purchases you’ve made in the past, or Netflix customizes your browsing experiences by suggesting movies like other shows that you’ve seen. This is predictive analytics working on the internet. This allows you to gain insight into your customers’ shopping habits and guide your approach to managing your inventory.

There’s a lot you could learn from your customer database that you already have. Predictive analytics can help you understand it.

Predictive analytics also help you determine the best time to promote offers, how to increase efficiency by making more efficient staffing decisions, and when to announce sales or a special offer. Data can assist in making rapid decisions and speed up or even automate repetitive or time-consuming tasks.

Accenture says that investing in AI and Human-Machine collaboration may increase the sales of retail stores by 38% in 2022!

Furthermore, 72% of retail stores have reported that intelligent technology is crucial to their companies to differentiate their products and services. In addition, it is expected that AI will boost 16 trillion dollars in the world’s GDP in 2030.

Nine Examples of Predictive Analysis Being Used in Retail Sector

Retail companies generate huge quantities of data. These data can be transformed into a treasure trove through predictive analytics that transforms it into data-driven insights about customer preferences and trends.

Retailers can employ predictive methods and use unstructured and structured data to forecast growth in sales due to changes in customer habits and market developments. This information will help retailers stay ahead of the pack to gain a competitive advantage in revenue growth and increase the overall ROI.

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If retailers are eager to increase sales and enhance customer service, establishing an online shop is only the beginning. To take full control of your business, from ensuring you have enough inventory to forecasting sales, predictive analytics can assist in taking your business up a notch.

Predictive analytics is a statistical model that analyzes data from the present and provides predictions for future business. It helps you plan everything from future sales to the possibility of shortages in products. It could even help you personalize your customer’s experience.

Cross-Selling and Upselling

The Analysis of customer journeys can help retailers to cross-sell their customers. It could help promote products to appropriate consumers at exactly the right moment. Plenty of information about customer journeys could be used using predictive analytics.

Analytics can help you identify high-value, high-opportunity customers. Predictive analytics predict which consumers are likely to purchase certain items. It’s much simpler and cost-effective to get an existing customer to purchase the product rather than acquiring the new one.

Personalization for Customers

Understanding customers’ behavior and combining that with consumer demographics is the first step to implementing predictive analytics. Retailers can use it to provide targeted and highly personalized deals for particular shoppers

Before the advent of analytics in data, the possibility of personalized promotions was not available or was restricted to large areas of customers who shared the same characteristics. With the rise of online shopping and, eventually, data analytics, it’s now possible to monitor patterns across the various platforms, i.e., observe a person doing research online before purchasing an item in the physical shop.

These insights, combined with retail predictive analytics, allow retailers to offer highly customized offers to their customers on a very specific level. For example, retailers can personalize their shopping experience by offering offers that encourage frequent purchases to increase purchases, which will result in higher revenues across the entire channel.

Better Target Your Marketing Campaigns

In increasing numbers, users are attracted by targeted ads. If Facebook and Instagram provide relevant ads based on little details they share, broad strokes campaigns begin to fall short. Retailers are in a unique position to collect an array of personal data, including preferences, preferences, search and inquiry histories, buying patterns, spending habits, and the best effective engagement strategies.

With this wealth of data is easy to begin analyzing consumers at a more detailed scale. Instead of launching a huge, costly campaign with a limited impact or reach, predictive analytics can tailor the marketing process. Offering more specific messages allows you to influence the content, when it’s displayed, and how and when it is displayed. This improves the ROI and efficiency while creating a more positive customer experience and establishing loyalty.

In-store Recommendations Based on Customer Data Across Channels

One method of using predictive analytics to enhance shopping is to provide employees with phones, devices, or tablets with which they’ll be capable of accessing relevant data.

Sales associates can find customers using the number on a loyalty card or an email address to examine their past purchases and shopping habits. The sales assistants are provided with suggestions based on customer information across all channels to improve the shopping experience in the store.

Customer Journey Analytics

Nowadays, it is simple for consumers to access any information they want using mobile phones, social media, and the internet. This makes buying and making purchases easy for consumers.

Marketers have to evolve by understanding and interacting with their clients constantly. This is feasible when retailers are equipped with data-driven insight, which allows you to know the customer’s history and profile across all channels. In the same way, consumers are now demanding greater from companies, such as providing continuous information, a seamless experience across channels, and a reflection of the past, preferences, and desires.

You’ll be able to respond to more difficult retail issues like:

  • Every step of the customer journey
  • Your most valuable customers and the way they behave
  • The most effective method of reaching them

Inventory and Supply Chain Management

One area that is frequently ignored is the back-office processes. Insufficiently maintained inventory is a nightmare for retailers everywhere. Supply chains have to be improved to improve efficiency in operations. Storing up on items that don’t move as fast or not having enough popular items can be a problem. Predictive analytics help solve questions such as which items to keep and when to store, as well as what to throw away and when. This information can improve performance and lower expenses. Therefore, predictive analytics for retail eliminates the uncertainty of any purchase based solely on an inclination.

Mobile App Encouraging Customers to Visit Offline Stores

Sephora is a master of this method of personalization by allowing customers to schedule in-store consultations and makeovers. With the Sephora app, customers can locate a store, check if the desired items are available, and schedule an appointment with a consultant who will meet with the customer.

Makeup artists can access every item the user has included in their profile. In addition, they will receive specific beauty tips according to their skin tone and preferences. Customers can also use the app to locate the products they want to purchase in the store.

Trade Promotions Optimization

A study by Booz Allen reveals that a substantial part of retailers loses about one-third of the amount spent on trade promotions. This is because of the inability of the decision makers to assess the effectiveness of trade promotions and their ROI and make profitably efficient use of their money through the use of information.

Most CPG businesses still depend on spreadsheets, ERP, or TPM systems to optimize trade promotions. Any effective Trade Promotion Optimization software should come with sophisticated analytics. Predictive and Prescriptive analytics can draw large amounts of real-time, structured, and unstructured data collected from various consumer and market interactions and translate it into actionable suggestions to ensure the proper trade-related promotions. An optimization tool for trade promotions equipped with predictive analytics can assist you in creating what-if scenarios to predict sales for different promotions.

Make Better Pricing Decisions

Pricing for many small retailers is more like an art form than actual science. At present, many companies still base their pricing on historical data and accepted notions like trends and seasonal patterns. But, eCommerce solutions has done away with myriad factors that influence the price, such as traditional times and seasonal sales. Most retailers do not reduce prices before the traditional sales times and lose the advantage of advanced sales. This affects revenues because of the huge price fluctuation.

AI and predictive analytics can monitor the inventory level and competitors’ prices and gather demand data to decide what price levels should be. Instead of using predictive analytics, they will help you determine the most effective time to begin reducing and pushing the prices slightly in either direction. Research has shown that gradual price fluctuations are more efficient than sudden price increases. Being proactive when moving prices can make it easier to differentiate your store’s performance and help you have better control over promotions while remaining one step ahead of business.

Retail is now an increasingly important part of anticipating customer needs as it is about offering nice goods. Businesses that adapt to changing technology and use analytics to improve their operations achieve more results due to innovative strategies based on real-time data.

The Key Takeaway

Predictive analytics has allowed the analysis and fusion of huge amounts of structured and unstructured data to find hidden patterns and connections between customer insights, trends, and other valuable business data.

To stay competitive in a rapidly growing market increasing, retailers need to find innovative ways of using new and comprehensive information sources in new ways. Analytics can help retailers in gaining a greater understanding of their customers’ data and provide actionable insights that can turn the market’s weakest player into a market leader.

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