Building Customer Loyalty With Predictive Analytics

Predictive analytics is often called “customer loyalty analytics” because of its primary usage in understanding, predicting, and strategizing logistics in a way that enhancing customer retention and satisfaction. A study found that 93% of shippers and 98% of 3PLs feel data-driven decision making is a critical aspect of supply chain activities, and 71% believe data improves the quality and performance of their operations. According to the findings in the study, it is safe to assume that improving customer loyalty with predictive analytics is a very real and actionable strategy.

“Data” is the currency of the future. Investing in data collection and analysis is paying off for companies looking to enhance the efficacy of their marketing, sales, service, and logistical operations. For over half of the businesses in this study, their primary objective of using analytics is specifically to increase customer loyalty. That means leveraging consumer data at every touch-point to create the most targeted and proactive B2C and B2B strategy. 

Want to know how the team at Redwood Logistics is utilizing predictive analytics to build customer loyalty for our clients? With our RedwoodConnect 2.0 platform, we are able to help our clients connect all of their systems and build a data supply chain that allows them to better optimize their customer loyalty strategies amongst many others. Our Innovate team is standing by to help you.

What is “Predictive Analytics?” 

Through online interconnectedness, devices throughout the sales process and supply chain can gather large amounts of data about customers. This enables the collection of hundreds, if not thousands, of variables about customers from marketing goals to purchase history to email open rate and beyond. 

McKinsey research reported that companies with predictive business models, based on customer data, experience a 126% increase in profit margins. At its very core, this means that proactively addressing the needs of clients and consumers is the key to business growth. 

Artificial intelligence and machine learning can then take all of this customer data to extract insights about the purchasing process. The software analyzes all of the data and makes predictions about how the customers will interact with a business in the future. This allows decision-makers to choose how to sell and run operations to best meet the needs of their consumers. 

Read: How Does Predictive Analytics Technology Improve Logistics?

How is Predictive Analytics Being Used in Logistics? 

Projecting sales and purchasing 

One of the primary uses of predictive analytics is to understand consumer buying behaviors in a way that promotes engages and sales. Artificial intelligence can look at historical purchasing data along with other factors to target recommendations to each individual customer. This can help create specific, automated strategies based on consumer engagement and loyalty. It can even determine what sort of personalized rewards to offer for individuals in loyalty programs to best maintain consumer relationships and encourage repeat purchases. 

Although this usage is primarily used for marketing and sales, it’s also highly critical on the logistics side as well. Being able to understand and predict how a customer will make purchases is vital to forecasting manufacturing, inventory, and shipments. Using predictive analytics creates a clear picture of what consumers need, on the front and back ends, in order to have a long-lasting, beneficial relationship with a business. 

Predicting customer value 

Not all clients are created equal. Some will be “one-off” customers who only plan to purchase your products once, will only be manufacturing one line of goods, or who will only be shipping with your company for a few routes. Then there will be customers who want long-term engagement and contracts to purchase and work with you over and over. Of course, we want more long-term clients. 

Analytics can help predict the customer lifetime value (CLV) of a particular individual or organization. From there, you can adjust your marketing and service efforts to those that could be higher value and long term clients. In logistics, this often means prioritizing deliveries for higher CLV clients to ensure fast, efficient transport times. 

Timing promotions 

Promotions are a huge part of pushing sales, releasing inventory, and encouraging new client relationships. Data analysis can help figure out the best time to offer promotions at which point customers will be most receptive. Predictive analytics can predict the season, day of the week, and even time of day to understand when and who will be most engaged with the offer. 

For B2C, this can help determine when to offer that BOGO or run a flash sale. For B2B, this can tell you when to reach out to your clients for a free trial or a follow-up consultation. The data can essentially tell you the best time and ways to engage with your customers and prospects. 

Promotions impact the operational side by determining the volume of inventory and the speed of transport. Proper timing promotions alongside logistics variables, like stock levels, route efficiency, and shipping costs, can make or break customer service during these times. 

Forecasting supply and demand

Predictive analytics can forecast how much inventory is needed based on customer demand and buying behavior. This allows suppliers to prepare months in advance what they need to make, send, and stock in order to meet customer demand. 

This is also the best way to create a distributed warehouse system. AI can determine how much stock is needed in which geographic regions based on customer data and external variables, so you can bring the goods as close as possible to the end destination. This creates a lot less waste along with faster and more on-time deliveries.  

AI Is Really Driving Supply Chain Management

Anticipating maintenance 

Machine learning can proactively predict the failures of certain equipment and machinery, based on historical factors and current machine metrics. By detecting these patterns and anomalies, artificial intelligence can determine the productivity and potential failures of machines in the warehouse. In an industry where robots and automation are quickly taking the reigns, being able to predict breakdowns is critical for improving efficiency and minimizing downtime. 

Improving customer visibility 

The purpose of data collection and analytics is to obtain insights about customers and suppliers. This sort of shared “big data,” like blockchain technology, allows greater visibility along the supply chain. Transparency from start to finish is the most effective way to engender client loyalty. 

Plus, this sort of visibility can help predict future disruptions before they happen. This manages operations proactively, rather than reactively, to enable the most effective customer relations. 

Clients especially love that they have visibility of their shipment status and location. This can help them know when and how their packages are being delivered, which is key to customer service. Predictive analytics can also help manage customer expectations with highly precise and accurate delivery times. 

Prediction vs Tracking: The Race for Complete Visibility and Blockchain-Based Supply Chains

Enhancing supply chain visibility 

Similarly, connecting partners with big data is critical to streamlining the supply chain. Predictive analytics can help avoid late shipments and related expenses by ensuring products are moving through the chain with the highest level of efficiency. In turn, an enhanced level of supply chain visibility along with proactive predictions can open up communications between partners and pave the way for new business opportunities. 

How Big Data Analytics Can Improve Your Supply Chain

Expecting the unexpected 

2020 is teaching us that expecting the unexpected is a necessary part of business. From the news to the weather, from upstream shortages to business interruptions, there are a number of variables that can affect stock, inventory, and shipments. Predictive analytics and machine learning can scan historical data, news stories, and other happenings to best determine what could happen—and how businesses should prepare for it in advance. 

Bringing Predictive Analytics to Logistics 

Businesses are tracking customer activity to develop sales and service strategies that keep customers loyal to brands. Logistics and operations can employ this same goal by utilizing predictive analytics to better understand customer demands and needs. We can extract insights from vast amounts of data in order to bring the right products to the right customers at the right time (at the lowest cost). Predictive analytics is a proactive way to understand what clients need, in order to deliver in the most effective way. 

Predictive analytics offers a competitive advantage by not only targeting customers with marketing, sales, and loyalty programs but also by ensuring inventory, stock, and transportation are constantly one step ahead of the sales process.

Redwood Logistics curates our technology solutions to our clients’ individual needs. We also use our own predictive analytics to best unlock what YOU need to succeed. Let us help you implement the right technologies to expect the unexpected and deliver the greatest service to your customers.