Chapter X: Top retail technology trends to watch out for in 2023 - Part 2

Various retail giants have come up with varied DTC strategies. For instance, a US retail giant and a leading apparel company have come up with differentiated value offerings such as buy anywhere — return anywhere.  However, for a DTC strategy to be successful, technology support is important. Retailers should have a robust order management system that can seamlessly pass online orders to stores, and vice versa. 

An advanced order management system is also important to build an endless aisle experience at stores. An integrated platform is needed to manage stores and online checkouts. Many apparel brands have this system at present. 

To orchestrate a personalized journey, organizations need marketing technology software that captures consumer data. To get the best results, this should be embedded with product recommendation engines. A scalable and modular architecture is critical to add/modify components as the scale of operations and complexity grows without disrupting daily business. Retailers should also build the right API structures and create a unified data layer.  

Building a profitable DTC model, however, also requires a critical review of the unit economics of the business. A strong consumer lifetime value is important to build the margin pool. While there should be disintermediation of channels as needed, there is also a need to strike a balance to ensure appropriate proximity to consumer location. Retailers should build hooks and content for consumers to keep returning to the brand. 

Customer first-party data 

Traditionally, companies would curate personalized experiences through third-party data. However, that is changing. With privacy concerns and major browsers blocking third-party cookies, retailers will, in the near future, not be able to collect and harness third-party data. 

Broadly, there are three types of customer data in play. 

First-party data: This is the data captured by a brand/retailer directly from the consumer with explicit consent via channels they own or can influence. For example, websites, apps, surveys, CRM, feedback, offline activations. 

Second-party data: First-party data of other platforms, or walled gardens, which the brand/retailer is directly working with. For example, data from platforms like Facebook and Google. 

Third-party data: This is data that is owned by various apps in content, publishing, social network, etc., where the brand/retail is not directly working with but leveraging ad networks and intermediaries, such as Google. 

Of these, first-party data has a big role to play as retail brands can use it to offer personalized offers and services. According to research by a major tech company, worldwide, 36% of marketing professionals expect customer purchase history to be their most valuable source of data once third-party cookies are gone. Also, companies find first-party data gathered through sources such as chats and emails more reliable to better understand customer preferences and analyze behaviors to deliver improved customer experiences. In addition to that, customer data will also help brands create innovative advertising campaigns by stitching together data in a compelling and interesting way.

With more data and predictive analytics coming into the picture, brands also need to change their marketing models. Rather than relying solely on marketing agencies, brands now have deeper engagements with technology partners and are using analytics to learn more about customer preferences to create more intensely customer-centric strategies. Brands are taking a closer look at their customers and redesigning marketing strategies that are a closer fit. 

However, acquiring customer first-party data is by itself not enough. Turning that into meaningful business results is the key to brand success. Typically, value from consumer data can be translated into the following benefits:  

• Efficiency in acquiring consumer data at scale, which will reduce customer acquisition costs
• Improvement in conversion and value of order via personalization of experiences
• Driving efficiency in broader marketing spends
• Access to real-time consumer insights and preferences 

While retailers also must comply with data privacy regulations, and should manage their data policy accordingly, many brands have built entire business models using deeper analytics solutions on qualitative and quantitative consumer data. With digitization of the physical shopping experience and integration of new retail and data capture technologies, retail 4.0 is now a reality. The transformation of retail is benefitting the end consumer, who now has greater control over the retail experience.  

Watch out for the first two trends in the first article