Using Data to Personalize the Customer Experience in Retail

Today’s consumers place a high value on receiving a personalized shopping experience. According to the 2021 Inmar Intelligence Shopping Survey, 90% of consumers say that a brand’s ability to provide them with a personalized experience directly impacted how much money they’re willing to spend. These customers are looking for personalization across all channels: through their physical mailboxes, email inboxes, and in-store, as well as web, social, and digital channels.

Brand leaders are listening. A 2017 Periscope by McKinsey survey found that 95% of retail CEOs identified providing a personalized shopping experience as a key priority. To deliver the type of shopping experience consumers value, innovative retail brands are using data from a variety of sources to transform the brick-and-mortar and online shopping experience. Let’s explore how data can be used to optimize the customer experience in retail.

How Data Can Improve the Customer Experience

Today’s consumers enjoy a plethora of choices. As a result, they have high expectations. Optimizing the customer experience by providing high levels of individualization offers a way for retailers to differentiate themselves from competitors and develop a loyal customer base. Retailers can use data in a variety of ways to improve the customer experience.

Better understand customers’ needs and desires

Retailers can use customer data to gain a better understanding of customers and what they are looking for in a shopping experience. Personal data, engagement data, behavioral data, and sentiment data can all help provide a clear 360-degree view of the customer. 

Personalize customer communications

Using customer data, retailers can create personalized customer journeys to strengthen buyer relationships. Insights from this data allow retailers to send customized emails and direct mail tailored to specific interests based on customer profiles. Data also enables fine-grained personalization, serving up relevant content to a website visitor in real time based on the user’s engagement data. 

Improve product recommendations

Browsing patterns and past purchase history can be leveraged to increase the relevancy of automated product recommendations. This type of data can also help inform suggestions for additional purchases that pair with products already in the shopping cart. 

Optimize customer experiences 

Retailers can transform customer perceptions of their physical locations from mere places of commerce to gathering places for a community of shared interests. Customer data can reveal what target audiences value and what would bring them together. Additionally, beacon technology can personalize the in-store shopping experience by allowing retailers to send relevant notifications to customers’ mobile devices, providing helpful information and alerting them to new offerings, and discounts. Additionally, as digital channels continue to become more integral within customer journeys, retailers need to focus on building seamless experiences, from search to purchase and fulfillment.

Predict trends 

Being able to predict what customers will want to purchase in the future enables more effective inventory planning and more-strategic marketing. Predictive analytics can be used to analyze consumer behavior, social media trends, and a range of other data inputs such as weather, time of year, and more to see what lies ahead for demand.

Common Challenges to Improving the Customer Experience in Retail 

Optimizing the customer experience in retail holds enormous promise. But the move from theory to practice can pose challenges. The following roadblocks will need to be addressed before a company can implement a data-driven approach to personalizing the retail experience. 

Variety of data

Retailers need the ability to combine customer purchase behavior data, email contact and response data, and external data such as lifestyle interest data into a single, scalable, consolidated customer data hub. The ability to ingest data from many different sources, including multiple clouds, as well as the ability to integrate directly with many of the top marketing platforms, is crucial.

Relevant data

A robust personalization strategy requires sufficient relevant data. Top retailers are taking advantage of third-party data sources, including demographic, purchase intent, point-of-sale, weather, and consumer mobility data, to more thoroughly personalize the customer experience. 

Real-time capabilities

Website personalization relies on the ability to execute in real time. As visitors click through a site, retailers need their analytics capabilities to keep pace each step of the way with visitors’ changing searches, clicks, and interests. The closer retailers can get to real-time personalization, the better the experience for website visitors and prospective customers. For this reason, a retailer’s data platform must be able to process streaming data quickly and integrate with top marketing and ecommerce software platforms. 

Scale

To deliver targeted, personalized communications and experiences, retailers must have scalability in their data and technology. For this reason, companies must have a cloud-based infrastructure that has flexible storage resources and compute power that will accommodate the large amount of data needed for personalization.  

Snowflake for a Better Customer Experience in Retail

Today’s retailers are seeking to harness the power of granular data. With Snowflake, organizations can leverage data to build personalized, multi-channel, and in-store marketing strategies that drive a new level of conversion and profitability. In addition, they can securely share and exchange data to strengthen partnerships and obtain customer insights. Snowflake enables retailers to leverage their own data in combination with a vast network of other data sources to drive their business forward.

See Snowflake’s capabilities for personalizing the customer experience in retail. To give it a test drive, sign up for a free trial.