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Leveraging Customer Data to Create Personalized and Memorable Shopping Experiences
Mar 13, 2023
Retail And Digital Ecommerce

Leveraging Consumer Data and Predictive Analytics to Personalize Shopping Experiences  

Businesses are spending more money on products, tools, and technologies that will aid in their in-depth understanding of their clients. Companies are shifting their focus to current and existing customers since they understand that gaining customers is 6–7 times more expensive than retaining them. 

The best strategy to increase client loyalty and retention is to provide a remarkable customer experience (CX). For many years, businesses relied on surveys to get information about consumer preferences and behavior, which they then used to monitor the effectiveness of their CX. However, as consumer needs evolved, businesses found that survey data alone was inadequate.  

Companies are increasingly investing in a more sophisticated approach that fully utilizes all the data from internal and external sources.  

A study involving B2B marketers suggested that 58% found predictive analytics as the most effective AI-powered technology for hyper-personalized strategies.   

How Consumer Demands Have Shifted Over the Years  

For retailers, the rules of the market have altered. As new technologies and actionable information are incorporated into daily life, consumers are more empowered than ever before. Consumers now have immediate access to a lot of information about e-commerce businesses and their products, thanks to the growing popularity of the Internet and social media. They now have the authority to seek a more personalized and smarter retail experience.  

Retailers must align inventory and brand experience online, in stores, and via mobile devices to stand out from the competition, foster customer centricity, and deliver outstanding service.   

Predictive analytics gives e-commerce businesses a wealth of information they can leverage to distinguish themselves, personalize a shopping experience, and make their operations more efficient, customer-centric, and highly profitable.  

Who succeeds? Those retailers who adjust to new technology and keep up with it, utilizing the insights it offers to offer customers what they want. Retailers may provide a more personalized experience for their customers by learning about them, their unique purchasing patterns, and their product preferences.  

Profitable businesses have used predictive analytics to capitalize on consumer data such as purchasing habits, browsing trends, product comparisons, ordering, and delivery. This makes it possible to market goods in ways that appeal to individual customers. Retailers may then align product sales and purchasing habits to enhance targeted marketing strategies, improve supply chain management, and compete more effectively for improved mind share and wallet share once they have critical insights throughout the organization.  

By leveraging predictive analytics, retailers can address the three key business imperatives given below:  

  • Offering a smarter and hyper-personalized shopping experience to customers  
  • Developing more intelligent retail operations  
  • Building smarter merchandising and supply chains  

That being said, this article explores how leveraging consumer data and predictive analytics can help retailers deliver a smarter shopping experience.  

Importance of Consumer Data in Reshaping Retail Experiences  

In a recent IBM study involving over 1500 CEOs from 25 countries, 93% of the CEOs surveyed said that “better understanding, predicting, and giving customers what they really want” was a top priority for them and their organization.   

A study by Accenture indicates that 83% of customers are willing to share data with retailers to create a hyper-personalized experience.   

Enabling a seamless shopping experience is difficult since it requires matching the right products with the correct placement, ensuring consistent cross-channel promotions, and sending out timely and relevant interactions.   

There is abundant data coming in from multiple systems, channels, and geographical locations. Retailers also produce vast amounts of transactional data rich in information about customer buying habits. If properly analyzed, this data can help retailers enter a new era while aligning with customer requirements and understanding trends that will pay off handsomely.  

Retailers can maximize their marketing efforts by focusing on the right customer base with the right offer at the right time, making each interaction distinct and profitable. This is done by extracting valuable insights from the data. Moreover, they can better match customer preferences and behavior and get the best possible return on their marketing investments by having insight into trends, products, and promotions. Retailers must understand the following to facilitate ROI:  

  • Product sales trends and purchasing preferences to boost profits  
  • What products or combinations of products are most likely to convince customers to make a purchase?  
  • How to boost marketing efficiency and maintain customer loyalty to increase profitability?  
  • How to tailor marketing and customer experiences to cater to individual customers?  

How Predictive Analytics & Consumer Data Takes Retail Experiences to the Next Level  

Businesses can learn, comprehend, and measure the key factors influencing consumer satisfaction with the help of predictive analytics. The technology assigns scores to individual customers using machine learning (ML) and (AI). It enables businesses to anticipate what consumers may desire before those consumers even realize what they want.   

Additionally, it enables organizations to foresee customer experience (CX) problems before they escalate. Hence, companies can gain a competitive edge and identify new prospects thanks to advanced analytics.  

According to McKinsey, businesses that engage in “personalization at scale,” or personal interactions with a large group of their customers, 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.”  

The first step in implementing predictive analytics in retail is to analyze and combine customer behavior with consumer demographics. By analyzing behavior across different channels to determine when and where customers explore physical stores and e-commerce websites, retailers can leverage customer data to provide targeted and highly personalized offers for customers at brick-and-mortar stores.  

Predictive analytics can enable a tailored shopping experience online, as demonstrated by Amazon, but retailers with physical stores can also leverage predictive analytics to present highly customized offers to customers in real time.  

This can be accomplished by changing the in-store experience by offering incentives for frequent purchases, which drives more purchases and increases sales across all channels. In other words, retailers may employ predictive analytics to attract high-value customers.  

Below are the top ways your organization can use predictive analytics to personalize retail experience:  

1. Customer Needs Forecasting  

Predictive analytics and data mining are valuable because they enable organizations to foresee customer needs. Also, they proactively offer their goods and services before customers inquire about them.  

Businesses may deliver the right items at the right time by forecasting customer needs to anticipate (rather than assume) what their customers need.  

Using historical data and machine learning, retail companies can develop models that predict future customer behavior across various aspects, such as product purchases, lifetime value, churn, and more.  

Making future predictions allows retail companies to become more proactive. Companies can use prior purchase history to create a precise picture of what their consumers will likely purchase next. This way, retailers can display the right products before the right customers, even before they know that they really need them.  

An excellent example is that of the telecommunications company AT&T. They have set up a machine learning system for customer service to understand their consumers’ minds.  

Hundreds of data types are consumed by the system throughout a customer project. It determines if a customer will remain a promoter or start to migrate into neutral or detractor territory by considering factors like customer effort, cycle time, retry rates, etc.  

The best part is that the system constantly learns and refines its algorithms as it gains experience.  

2. Increased Customer Retention  

Businesses frequently neglect the loss of existing consumers on the idea of gaining new ones.  

Businesses fail to see that by anticipating a client’s unique needs based on the information gathered from their journey, they may provide an enhanced and flawless customer experience.  

With predictive analytics, retailers may analyze and interpret their previous purchases, viewed products, and cart abandonments. By merging and combining their data with broader profiles, you can segment customers and generate individual customer profiles. By personalizing their experience, you can improve customer retention and return on investment (ROI).  

You can use predictive analytics in a customer service scenario by anticipating a potential issue and resolving it proactively before consumers complain about it. Put simply, you can increase customer lifetime value by leveraging predictive analytics.  

3. Reduced Consumer Churn Rate  

Customer churn is crucial to any business, particularly for subscription-based companies. However, it is essential to note that organizations eventually notice the impact of churn.    

For example, if you have a 5% monthly churn and 10% revenue generation, your client base will grow by 5% monthly. This doesn’t look as bad until, at the end of the year, you notice that half your customers have churned away.  

By using predictive analytics on your data, you can determine which consumers are loyal and which are likely to go. With predictive analytics, you can interpret data in real time and modify your marketing initiatives, customer services, and offers to better serve your target audience.  

The study of customer transactional and non-transactional data can be automated to produce risk scores using a churn model. You can focus on redesigning your methods to have the biggest impact on customer satisfaction and experience by being able to anticipate potential attrition.  

A study by management consulting company BCG indicates that by using a predictive analytics-based churn model, corporate banks can minimize customer attrition by 20–30%.  

4. Hyperpersonalized Marketing  

The hyperpersonalization technique uses predictive analytics to send highly customized marketing messages based on a customer’s digital footprint.  

Retail brands use data, including IP address, location information, and purchase and browsing history, to analyze consumer activity and predict future actions.  

Retailers can identify high-value customers by examining behavioral patterns and then develop timely, relevant marketing campaigns that feel personalized to each customer.  

Getting the timing right is only one aspect of this form of marketing. Personalized texts sent at the right time and speaking to the individual at the right time are also equally important. Making the customer feel as though you personally know them is the goal.  

A study by global management & consulting firm McKinsey suggested that companies can generate 5% to 15% more revenues by leveraging predictive analytics for personalized messages and product recommendations.  

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The Bottom Line  

There is no denying that recent years have been challenging for the retail sector. Businesses had to figure out how to use consumer data more effectively as the emphasis shifted towards data-driven and digital customer experiences.  

Establishing and maintaining customer relationships is crucial, primarily online. Organizations may better develop those relationships with the help of smart analytics tools and solutions.  

For enterprise businesses, it can be challenging for retailers to sift through the abundance of customer data gathered and find relevant information. However, most legacy systems cannot handle the vital need to better personalize the user experience. Retailers may, however, better understand what drives, inspires, and retains customers by using cloud-based smart analytics tools.  

For instance, Designer Brands Inc. had to deal with a vast customer data pool that required a more sophisticated system to better understand their customers and personalize their products.  

The company was able to develop a personalization engine that created prescriptive analytics used for self-service research, integration into digital channels, and leveraged in campaign systems with the help of Pandera and Google Cloud Platform tools.  

This allowed Designer Brands Inc. to increase click rates for channel interactions by 57% above their baseline right away.  

This shows that retailers use predictive analytics to personalize their products and deliver an enhanced shopping experience.   

Given the state of the market and the increased emphasis on client interactions, retailers can make use of such solutions that help them stay competitive.

About the Author

Kevin Gordon

Born in Technology, Raised in Marketing – This is the one-liner Kevin uses to describe his 20+ year career. Kevin is our Director of Marketing and joined the team in 2021, coming from technology start-up, SkipTheDishes.

Starting out in technology, Kevin has a unique blend of technical and marketing experience, with experience as a computer hardware technician, web designer, programmer, Windows & Linux systems administrator, and product development manager, which has allowed Kevin to lead a team of high-performing developers and systems administrators to build integrated omnichannel marketing & sales technology platforms used in retail stores across Canada and the USA.

In addition to his technology background, Kevin has 12+ years of progressive data-driven marketing experience in B2B and B2C industries, including legal, retail, agency, financial technology and more, with over eight years of direct leadership experience in marketing roles.

Kevin has education in Diversity & Inclusion from Cornell University, Business Administration and Project Management from Red River College and additional formal training in Change Management and Business Analysis.

Kevin Gordon