Personalizing the retail experience to create demand
Personalizing the retail experience to create demand
Current economic conditions have changed the arena for retail, as business owners struggle to match oversupply with the scarcity of demand. In this case, personalizing the shopping experience may prove to be key to growth and success.
"Know the customer” is a retailing mantra that’s been repeated ad nauseam since the dawn of the 21st century. Nonetheless, today’s successful retailers are only now beginning to achieve customer-centricity by replacing the product focus that dominated the 20th century with a deep understanding of the consumer.
How are these retailers getting there? They start by evaluating detailed consumer data from multiple sources to understand who the best customers are, then combine that information with what and how they like to buy.
Retailers need to anticipate and shape future demand to come as close as possible to satisfying each customer’s unique needs. Achieving this at such a detailed level requires automated processes enabled by solutions with the latest in predictive analytics and optimization capabilities.
The ultimate goal is tailoring the entire shopping experience to create an emotional bond with the customer. In effect, this means turning today’s multi-channel retail enterprise – in a consumer’s eyes – from “the store” to “my store.”
For instance, Korea’s Lotte.com online shopping mall uses analytics to learn more about customers and improve its targeted marketing efforts. Using SAS for Customer Experience Analytics, Lotte.com has laid a foundation to help members receive tailored services that are a better fit for their lifestyle. Through SAS, web traffic data collected is used to analyze the behavior of online customers.
Lotte.com can now analyze unique visitors and additional data from the company’s various websites. Analytics has provided a better understanding of the status of visitors and purchasers, the popularity of each category and product, plus click-through patterns, campaign results and more.
Understand customer segments
How can retailers understand customer segments from data in a timely, cost-efficient manner? Can you personalize customer communications, merchandise each store with the right assortment displayed effectively and optimize pricing in each individual store without hiring an army of analysts? With decision process automation (DPA), the answer is “yes.” DPA includes both the full automation of manual processes and the integration of analytic and optimization routine results into process workflows that increase and speed decision-making capabilities.
"Know the customer” is a retailing mantra that’s been repeated ad nauseam since the dawn of the 21st century. Nonetheless, today’s successful retailers are only now beginning to achieve customer-centricity by replacing the product focus that dominated the 20th century with a deep understanding of the consumer.
How are these retailers getting there? They start by evaluating detailed consumer data from multiple sources to understand who the best customers are, then combine that information with what and how they like to buy.
Retailers need to anticipate and shape future demand to come as close as possible to satisfying each customer’s unique needs. Achieving this at such a detailed level requires automated processes enabled by solutions with the latest in predictive analytics and optimization capabilities.
The ultimate goal is tailoring the entire shopping experience to create an emotional bond with the customer. In effect, this means turning today’s multi-channel retail enterprise – in a consumer’s eyes – from “the store” to “my store.”
For instance, Korea’s Lotte.com online shopping mall uses analytics to learn more about customers and improve its targeted marketing efforts. Using SAS for Customer Experience Analytics, Lotte.com has laid a foundation to help members receive tailored services that are a better fit for their lifestyle. Through SAS, web traffic data collected is used to analyze the behavior of online customers.
Lotte.com can now analyze unique visitors and additional data from the company’s various websites. Analytics has provided a better understanding of the status of visitors and purchasers, the popularity of each category and product, plus click-through patterns, campaign results and more.
Understand customer segments
How can retailers understand customer segments from data in a timely, cost-efficient manner? Can you personalize customer communications, merchandise each store with the right assortment displayed effectively and optimize pricing in each individual store without hiring an army of analysts? With decision process automation (DPA), the answer is “yes.” DPA includes both the full automation of manual processes and the integration of analytic and optimization routine results into process workflows that increase and speed decision-making capabilities.
