Retailers have never had an easy situation with BI solutions. To achieve the greatest possible availability of data, they have needed to integrate across numerous disparate systems, including internal data to capture supply intelligence and (usually) external data to capture demand intelligence. But a data challenge then emerged…

Many retailers have folded in point-of-sale and website traffic patterns in order to understand customer behavior. This attempts to track patterns both in store (at the register), and on the website (through clickstream analytics). But for all the work involved, these exposed an element of customer behavior previously hard to discover: customers’ pre- and post-purchase behavior patterns and decision-making processes.

For the first time, with data tracking, retailers could see what customers looked at before and after they bought (or chose not to buy), what products they compared, or even whether they cross-shopped at competitors’ websites before coming to a final decision. These insights in turn drive more effective marketing and sales strategies based upon quantifiable and targeted approaches geared more precisely to each individual customer.

Retailers are already considering a new kind of data previously unavailable: customer behavior inside the store as a quantifiable metric of sales effectiveness.

Many large bricks-and-mortar retailers are employing the ability to track customers (voluntarily!) within their own stores via their smart phones and the store’s Wi-Fi network. Using this, stores can gauge within 10 or so metres the customer’s approximate location, sufficiently accurately to identify which department they are shopping in and for how long, and in what order they visit parts of the store. They can then deliver targeted notifications of sale items and discounts or coupons to the customer either in the store itself or during the checkout process based upon their in-store shopping behavior. One might think of it as a kind of “physical clickstream” for in-store customers.

This of course requires sophisticated data integration and analytics on the back-end to deliver. In addition to establishing a data delivery pipeline from the Wi-Fi network, through the customer loyalty program in the CRM system, all the way to a geographic internal map of each store down to the department level (or even closer if a higher degree of tracking granularity is feasible!), it also requires correlation of department- and store-level sales objectives against customer behavior in real time, so that opportunities are delivered while relevant to the customer (i.e. while he or she is still shopping in the department). Finally, it requires integration of whatever competitive sales data is available to determine an appropriate discount that would incentivise a customer to buy immediately versus waiting or searching online for a more attractive offer at a nearby competitor.

This is where effective and forward-thinking BI architecture can gain significant traction in creating sales lift, because effective integration of this data capability has the opportunity to leverage one of the most valuable segments of a retailer’s customer population: those who are already shopping there!

DataHub Writer: Douglas R. Briggs
Mr. Briggs has been active in the fields of Data Warehousing and Business Intelligence for the entirety of his 17-year career. He was responsible for the early adoption and promulgation of BI at one of the world’s largest consumer product companies and developed their initial BI competency center. He has consulted with numerous other companies and is regard to effective BI practices. He holds a Master of Science degree in Computer Science from the University of Illinois at Urbana-Champaign and a Bachelor of Arts degree from Williams College (Mass).
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Retailers have never had an easy situation with BI solutions. To achieve the greatest possible availability of data, they have needed to integrate across numerous disparate systems, including internal data to capture supply intelligence and (usually) external data to capture demand intelligence. But a data challenge then emerged…

Many retailers have folded in point-of-sale and website traffic patterns in order to understand customer behavior. This attempts to track patterns both in store (at the register), and on the website (through clickstream analytics). But for all the work involved, these exposed an element of customer behavior previously hard to discover: customers’ pre- and post-purchase behavior patterns and decision-making processes.

For the first time, with data tracking, retailers could see what customers looked at before and after they bought (or chose not to buy), what products they compared, or even whether they cross-shopped at competitors’ websites before coming to a final decision. These insights in turn drive more effective marketing and sales strategies based upon quantifiable and targeted approaches geared more precisely to each individual customer.

Retailers are already considering a new kind of data previously unavailable: customer behavior inside the store as a quantifiable metric of sales effectiveness.

Many large bricks-and-mortar retailers are employing the ability to track customers (voluntarily!) within their own stores via their smart phones and the store’s Wi-Fi network. Using this, stores can gauge within 10 or so metres the customer’s approximate location, sufficiently accurately to identify which department they are shopping in and for how long, and in what order they visit parts of the store. They can then deliver targeted notifications of sale items and discounts or coupons to the customer either in the store itself or during the checkout process based upon their in-store shopping behavior. One might think of it as a kind of “physical clickstream” for in-store customers.

This of course requires sophisticated data integration and analytics on the back-end to deliver. In addition to establishing a data delivery pipeline from the Wi-Fi network, through the customer loyalty program in the CRM system, all the way to a geographic internal map of each store down to the department level (or even closer if a higher degree of tracking granularity is feasible!), it also requires correlation of department- and store-level sales objectives against customer behavior in real time, so that opportunities are delivered while relevant to the customer (i.e. while he or she is still shopping in the department). Finally, it requires integration of whatever competitive sales data is available to determine an appropriate discount that would incentivise a customer to buy immediately versus waiting or searching online for a more attractive offer at a nearby competitor.

This is where effective and forward-thinking BI architecture can gain significant traction in creating sales lift, because effective integration of this data capability has the opportunity to leverage one of the most valuable segments of a retailer’s customer population: those who are already shopping there!

DataHub Writer: Douglas R. Briggs
Mr. Briggs has been active in the fields of Data Warehousing and Business Intelligence for the entirety of his 17-year career. He was responsible for the early adoption and promulgation of BI at one of the world’s largest consumer product companies and developed their initial BI competency center. He has consulted with numerous other companies and is regard to effective BI practices. He holds a Master of Science degree in Computer Science from the University of Illinois at Urbana-Champaign and a Bachelor of Arts degree from Williams College (Mass).
View Linkedin Profile->
Other Articles by Douglas->