By Don Cooper-Williams, Executive Director, SAS Asia Pacific | Sep 17, 2010
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.
Automation is the key
Automation is the key to enabling the delivery of just the right offer to each consumer, deploying exactly the right assortment to each store and optimizing the regular price for millions of SKUs.
It is not just about the analytics; the analytics are the critical enabler and the "brains" behind the automated decisions, but they must be complemented by a configurable workflow that allows users to quickly evaluate exceptions and execute any remaining manual activities.
DPA allows the retailer to embrace a consumer-centric approach across all marketing and merchandising activities.
Take for example, Myer, the venerable Australian retailer that wanted to maximize its Myer one loyalty program. By consolidating its customer information into a repository that could mine and analyse data, Myer was able to leverage analytics into its merchandising and marketing processes. This helped segment customers, as well as monitor how customer segments responded to different marketing messages.
With detailed intelligence available, the company can now engage in meaningful dialogue with its customers. Myer's use of customer intelligence extends to finding out its customers' attitudes at the brand level, their likes and dislikes. At the macro level, Myer knows how customers feel about their Myer shopping experience and how it compares with their experiences of Myer's competitors.
Just three years after the launch of MYER one, the company the investment in the program was recovered three-fold. This shows that with analytics in place, merchandisers can stop thinking in terms of what product "we should sell" and instead come closer to providing what each unique customer wants to buy in "her store."