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Predictive analytics is a field of data science that uses statistical data modeling and machine learning techniques to identify buying patterns, potential sales, and emerging trends using vast stores of historical data and real-time sales data.
Retail businesses specifically generate vast amounts of data. This data can be turned into a goldmine by using predictive analytics to translate it into data-driven insights on customer preferences and prevailing trends.
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Retailers can utilize predictive techniques and use structured and unstructured data to predict sales growth due to changes in consumer behaviors and market trends. This analysis can help retailers stay ahead of the curve, gain a competitive advantage, increase revenue, and improve ROI overall.
Here are seven examples of how predictive analytics is being used in retail.
Customers participate in hundreds of interactions daily through website advertisements, social media, online purchasing, and offline channels. This data can be used to build a customer’s profile, enabling organizations to understand their target market on a deeper level.
This understanding is based on how they act across each of these digital and physical channels and interaction points, as well as valuable insights gained into which interaction points, inspire certain actions.
Using predictive behavior analytics, retail businesses can identify the optimum channel through which to talk to their optimum audience. They can even personalize in-store services by crafting the right message at the right time and place and increase overall success rates in terms of marketing, acquisition, retention, and upselling.
Behavior analytics helps with:
The value of behavior analytics can be measured in several key metrics, all of which have a beneficial impact on ROI:
These potential benefits offer further proof of the value of data analytics and go a long way to explaining why so many retailers employ predictive analytics to reduce customer churn and help enhance customer experience strategies.
According to McKinsey, companies that have personal interactions with a large segment of customers, “personalization at scale”, observe “a one to two percent lift in total sales for grocery companies and an even higher lift for other retailers, typically by driving up loyalty and share-of-wallet among already-loyal customers”.
Understanding customer behavior and combining it with consumer demographics is the first step in deploying predictive analytics in retail. Retailers can use customer data to offer targeted and highly customized offers for shoppers at brick-and-mortar stores and can do so by tracking behavior across channels to determine when and where customers browse physical stores and e-commerce sites.
Amazon is one very obvious example of how predictive analytics can enable a personalized shopping experience online, but merchants with a physical footprint can also use predictive analytics to make highly personalized offers to customers in real-time.
This can be done by altering the in-store experience by providing offers that incentivize frequent buying, which drives more purchases and achieves higher sales across all channels. In other words, predictive analytics in retail can be used to generate high-value customers.
Retail companies can also harness analytics to upsell and cross-sell. For example, based on a customer’s purchase history, retailers can identify that customer’s preference for buying brand X of candy at the start of every month. That retailer can now offer this customer an irresistible buy-two-get-one-free deal on this candy.
Doing so utilizes the insights of predictive analytics to offer a personalized experience whilst also driving sales. When predictive analytics starts assessing consumers and identifying consistent patterns of their behavior, retailers can use these insights to shape the timing of offers as well as the nature.
Below are some examples of how predictive analytics can be used for improving in-store as well as omnichannel customer experiences.
One way to utilize predictive analytics for in-store shopping experiences is to equip employees with devices, phones, or tablets through which they will be able to access relevant data.
Sales assistants can then identify a shopper through a loyalty card number or an email address to check their purchase history and shopping behavior. Sales assistants will be presented with recommendations based on customer data across channels to help improve their in-store shopping experience.
It is common that grocery customers receive coupons on items that they are not interested in. Predictive analytics allow increased sales as well as customer loyalty through offering highly personalized coupons. For example, if a shopper tends to buy lactose-free milk and cheese, they may receive a 20% discount on lactose-free products or a buy-two-get-one-free deal.
Sephora masters this personalization method by allowing customers to book in-store consultations and makeovers. Through the Sephora app, customers can find a store, make sure that any desired items are in stock, and book a time when a consultant will meet with them.
Makeup artists have access to all the items that the customer has added to their personal profile. Customers can even use the app to find selected products on store shelves. At the same time, they can receive personalized beauty recommendations based on their complexion and various preferences.
Sephora uses predictive analytics to enhance its loyalty program, Beauty Insider. The brand generates customized recommendations, access to recently launched products, and invitations to in-store events based on customer interests and preferences. In other words, analytics leads to the synchronization of experiences across different channels.
Predictive personalization is the art of applying advanced analytics to tailor recommendations to a customer’s personal context.
It can be used to:
Customer segmentation has always been a key part of advertising, but this personalized messaging differs significantly from traditional segmentation tactics in numerous ways.
Crucially, segmentation using predictive analytics is based on insights revealed through automated, data-driven algorithms that can be continually optimized by training machine learning and artificial intelligence programs with further data.
As such, segmentation with advanced analytics offers a far more granular discovery of customer wants and needs than in times gone by. This allows for the discovery of the most relevant, data-driven, and personalized products and approaches to influence the unique customer journey toward the path to purchase.
Product bundling is an efficient way to organize and present recommendations. A recommendation for a customer is presented in a bundle of highly correlated products. In this way, the recommended bundle has more value.
For example, Amazon will recommend an accessory for a high-performing product that might also sell well. In fact, for every item on Amazon, there is a list of items that tend to be bought along with it. Recommended products are often at a reduced price, making the recommendation even more appealing.
French multinational retail group Auchan created a mobile app to improve their omnichannel ecosystem and provide a better shopping experience to their customers. Auchan’s app uses geo-tracking not only to recommend customers go to nearby physical stores but also to send them product recommendations when they are inside a store.
The app gives customers location-tailored price offers and promotions according to their location in a specific physical store. For example, when a customer scans the barcode of a product, the app instantly provides information about similar products at similar prices that can be found on the shelves. In this way, customers are informed about the best deal for them.
A customer journey is a map that tracks a buyer’s experience. It begins when the customer first makes contact with a brand but does not end with the client placing an order.
The customer journey:
Using predictive analytics helps map out the path a customer takes and gives retailers valuable insights into how prospects and leads move through the marketing and sales funnel to become customers. This demonstrates where and how retailers may need to make changes in this funnel to optimize future customer journeys.
Predictive lead scoring applies machine learning algorithms to evaluate the key behaviors of existing customers and ranks them against a scale. This can distinguish customers more likely to convert, retain, or buy from the company’s products and services.
Through affinity analysis, retailers can also cluster customers based on common attributes and tailor customer journeys in a way that is likely to improve their experiences and drive better sales.
Analyzing customer journeys can help retailers to cross-sell to existing customers. There is so much data on customer journeys that can be utilized with predictive analytics. It can help to promote the right products to the right customers at the right time.
Predictive analytics forecast which customers are highly likely to buy certain products. It is much easier and cheaper to encourage an existing customer to buy a product than to acquire a new customer. Analytics can show who your high-value, high-opportunity customers are.
Retailers can use predictive analytics to identify customers who are at risk of leaving and to find ways to retain them. For example, it is commonly used to detect customers who, based on how they use a service, are more likely to cancel their subscription to the service.
The aim of the predictive model is to identify the churn one month, three months, or six months in advance.
Machine learning uses customer churn history to identify customers at high risk of churn. The customer retention department then takes over to engage with those customers, for example, they might offer a gift or a promotion.
Below are some examples of how predictive analytics in retail can improve the operation and supply chains.
Every retailer wants to have the right product at the right place at the right time. Predictive inventory management allows this. It refers to the integration of predictive analytics to forecast future risks that can disrupt inventory levels. The analytical process considers fluctuating demand, demand forecasting, historical data, and economic trends to manage inventory proactively.
The predictive process tries to eliminate guesswork from the conventional method by filtering and analyzing inventory data store by store to account for many different variables that affect inventory and demand. Organizations can then determine when to pull/push stock, what stock to remove, and predict how many units they are likely to sell. Companies increase ROI as stock wastage is reduced.
Shipping and transport costs often account for a considerable percentage of the final product price. By using predictive analytics, it is possible to determine optimal shipping frequency and quantity to meet demand while minimizing costs.
Predictive route planning can determine the fastest routes considering congestion, distance, weather, and drop-off points. Additionally, intelligent monitoring of fuel consumption, tire pressure, driving style, and vehicle condition can reduce overall costs.
Predictive analytics can anticipate maintenance and repairs for specific machines and by doing so reduce the risk of technical downtime.
For example, a factory would automatically track the condition of its components and the preorder replacement parts before they become faulty. This reduces downtime and increases operational efficiency.
Trade promotion optimization (TPO) is the process of utilizing integrated goals, factoring in promotion and supply constraints, via predictive analytics to create continuously improving trade promotion, strategies, and results. To use TPO effectively, there must be a constant feedback loop in place.
TPO assists organizations to tweak their operational models to become more beneficial and profitable, as stated in this Deloitte report, “A state of the art trade promotion framework links promotion strategy to continuous execution tracking.”
An example of trade promotion in practice is the fruit snack company Welch’s. In 2017, they turned to TPO to help rectify low promotional ROI, siloed planning, and poor budget adherence.
Over a 10-week planning period, they identified areas of ineffective trade spend and moved from manual planning to automated, predictive analytics-based planning. The outcome of this saw the company’s trade investment ROI surge by 16%, and they were able to achieve volume objectives through optimized, profitable promotions.
Analytics can help retail businesses to better understand what drives their sales. Knowing key factors that impact promotional campaigns (whether there is a pull-forward effect, in-category cannibalization, or cross-category cannibalization) should lead to better sales planning.
Businesses can take advantage of predictive models that work to enable sellers to make better decisions in real-time. This iterative model is fed with a continuous stream of historical as well as real-time data.
Insights based on information such as customer profile, visited channel, time of web session, cart abandonment, and price sensitivity can help retailers adjust promotions dynamically.
Predictive analytics allows for immediate adaptations to the market, creating competitive pricing and eventually maximizing profit. Data on competitor prices, paired with predictions of external factors such as weather forecasting and real-time sales data, can inform organizations of optimal attainable prices for customers. It will also suggest what price point would increase sales and determine when a sales campaign would be most effectively run.
Starbucks uses this tactic through its digital menu boards. Using predictive analytics, Starbucks is able to optimize pricing decisions with agility and can easily change and display the prices in-store, timing pricing changes to have the optimum benefit.
There are several benefits that predictive analytics can bring to retail:
Predictive analytics can help retailers increase ROI through targeted marketing campaigns, personalized shopping experiences, smart inventory management, setting competitive pricing, and more.
Utilizing real-time machine learning and artificial intelligence techniques to build predictions of potential trends is one of the many benefits of predictive analytics. This removes key challenges for retailers and creates evidence-based platforms to base business decisions on.
Predictive analytics within the retail industry comes in many forms, and the examples above are just a fraction of its potential uses. As a strategy, it allows organizations to stay ahead of the curve in all aspects, evolving and shifting with their market, reducing waste, and improving ROI.
Without using predictive analytics and harnessing the sheer amount of data on offer, many companies would quickly become lost in the constant fluctuations within the market. Grow with your market and implement predictive analytics techniques today.
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