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By Mikhail Templeton
Holiday spending is expected to reach record levels in 2023.1 The retailers that win outsized portions of this historical spend will be those who move customer service and support beyond historical norms.
It all starts with data.
Today’s consumers expect omnichannel experiences and rapid delivery within three days. With the choice of so many retailers at their fingertips, they also seek out the ones who share their values of equality and sustainability.2
The organizations that harness the power of retail data strategies to meet these expectations will win mindshare, wallet share and loyalty for the foreseeable future. Here are inventory management, pricing and personalization use cases I expect to see play out in the year ahead, as well as five essentials to enable them.
Retail data strategies to reveal why, when and how purchases are made
Data analytics offers more than just numbers—it provides a window into the psyche of consumers. Retailers can gauge not just what consumers are buying but also why, when and how they’re making those purchases.
One way retailers can capture some of that raw data is by enabling shoppers to seamlessly transition from their phone’s cellular data to a store’s Wi-Fi without having to sign on.
Once on Wi-Fi, the customer’s information becomes first-party data, which allows the retailer—with proper notification and consent from customers—to capture rich data sets such as dwell time and product interests. This data can be used to enrich the retailer’s app experience, empowering the app to become an actual tool and not just an information hub.
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By analyzing data, retailers can identify individual preferences and trends and even predict what a consumer might want in the future to create a unique and memorable customer journey.
Retail data strategies for personalized shopping experiences
Shoppers crave experiences. By analyzing data, retailers can identify individual preferences and trends and even predict what a consumer might want in the future to create a unique and memorable customer journey.
Retailers can use customer purchase history and browsing data to customize their website’s homepage. When each customer logs into the retailer’s website, the customer will see a selection of clothing items in their preferred styles, sizes and colors.
Additionally, the retailer can send personalized email promotions showcasing new arrivals or sales on items similar to what the customer has previously ordered. This targeted approach not only enhances the shopping experience by making it more relevant and efficient but also increases the likelihood of the customer making a purchase.
Retail data strategies for inventory management
An empty shelf may not only let down customers and end in a missed sales opportunity for the retailer, but may also speak to ineffective use of data.
Data analytics allows retailers to anticipate demand based on historical data, seasonality and market trends. Retailers can use this foresight to their advantage. By utilizing data from past sales, customer engagement and social media, retailers can predict which items will be most popular leading up to the holiday season.
This knowledge can help retailers prioritize items before peak timing. For example, a retailer selling holiday decorations will be able to ensure that the right products are available in the right quantities, maximizing operational efficiencies and sales while minimizing costs.
Retail data strategies for dynamic pricing
Data-driven retail introduces the concept of dynamic pricing, where prices can be adjusted in real time based on market demand, competitor prices and other external variables.
When a customer uses their rewards card at the gas pump, the retailer can use customer rewards data to customize the video ads playing while they pump. These ads can display custom pricing offers based on customer preferences. For example, if the data shows that the customer often purchases coffee while filling their car with gas, the video ad can display a BOGO coffee deal.
Five essentials for any retail data strategy
1. Agree on your tech stack
Retailers will need to build technology stacks that are appropriate to support their data strategies. This involves selecting the hardware and software solutions that can efficiently collect, process, analyze and store data. The tech stack should include tools for data analytics, customer relationship management, inventory management and point-of-sale systems.
It’s important that these tools are compatible with existing systems to ensure a smooth flow of data across different business functions. Line-of-business leaders and IT cannot have these conversations in siloes. Both the business and IT leaders need to align on a roadmap that will rationalize aspirations with current and desired tech capabilities.
2. Ensure data security and compliance
One of the easiest ways to lose customer loyalty is to lose customer trust. That’s why it is so important to implement robust data privacy and security measures to protect consumer information from breaches and cyberattacks. These measures include encryption, secure data storage and regular security audits.
Additionally, compliance with data protection regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) is essential to maintain customer trust and avoid legal penalties.
Beyond compliance, retailers should also consider the ethical implications of data usage. This includes being transparent with customers about how their data is being used and giving them control over their personal information.
3. Implement data quality management
For data to be helpful, it needs to be accurate. Retailers need to establish protocols for data entry, validation and regular cleaning to avoid issues like duplicate records, outdated information and inaccuracies that can lead to poor decision-making.
4. Integrate customer data for a 360-degree view
Integrate customer data from multiple touchpoints, for example, online, in store, mobile and social media. Using data from multiple sources is key to gaining a 360-degree view of the customer. This comprehensive perspective allows for more effective personalization, better customer service and improved targeting in marketing campaigns.
5. Prepare for AI everywhere
Generative AI has been one of the most talked about topics of the year, with transformative potential across various industries. Retailers already look to use generative AI for creative content generation, data synthesis or to personalize customer experiences.
We’re seeing retailers use generative AI to customize product descriptions by using customer data such as age range to tailor descriptions for specific customers.
Retailers need to prepare for the integration of AI technologies by ensuring their data infrastructure can support these advanced applications. This preparation includes training staff in data science and analytics, investing in AI-capable tools and establishing processes for data-driven decision-making.
We are still learning all the ways generative AI will transform the industry. But before retailers can even begin to harness the power of AI, corporate lines of business and IT must come together to rationalize envisioned use cases with the technology foundations they’ll need for them to succeed.
Mikhail Templeton is Vice President and Senior Partner at Kyndryl.
1 2023 Holiday to Reach Record Spending Levels, The National Retail Federation, November 2023
2 The five zeros reshaping stores, McKinsey & Company, March 2022
