+25% INCREASE IN BASKET ATTACHMENT BY KNOWING THE SHOPPER

Create a planning function for assortment and inventory to bring customer information to the forefront of the decision process for a multi-billion-dollar retailer.

First-party transactional data, especially as it related to top tier loyal customers and non-traditional third- party data were introduced into the decision-making process. Understanding how best customers shopped and what they purchased informed a range of actions from assortment planning to in-store merchandising. Competitor’s online assortments were scanned for opportunities both for web and in- store localized opportunities. Understanding competitors’ localized pricing enabled a window into promotional intensity to drive margin improvements.

Dashboards were created to ensure data was consistent and validated by finance. Iterative changes to dashboards and reporting metrics were easier to implement because data inputs and attributes were standardized. These micro customizations cemented ability for each category to analyze their specific business and gain organizational adoption of the project.

Stores’ space planning was also taken into consideration. Things like adjacencies, shelf space, and margin rates were analyzed to maximize performance. The insights and opportunities gleaned from these reviews kicked off a 9-month store remerchandising program informed by data to affect every store in chain.

+25%

BASKET ATTACHEMENT INCREASE BY KNOWING THE SHOPPER

~3.5

REDUCED PRODUCT LIFE CYCLE FROM 7 MONTHS TO 3.5 MONTHS BY STREAMLINING INCORPORATING WEB & STORES FOR THE FIRST TIME

+$2B

BASKET ATTACHEMENT INCREASE BY KNOWING THE SHOPPER

+$1B

INCREASE FROM LOCALIZATION WITH BETTER ASSORTMENT PLANNING AND PRICING

+$200M

IDENTIFIED IN LOCAL MARKET OPPORTUNITIES

2X

ORIGINAL PLAN BY CREATING CLOUD-BASED DATA ACCELORATOR THAT STREAMLINED DATA ACROSS CROSS-FUNCTIONAL TEAMS

Create a process for a multi- billion-dollar specialty retailer to free up cash tied to slower moving inventory.

Qualitative and quantitative customer research had been done in previous efforts to understand what was important to customers. However, this data was never integrated into the planning process. Item selling information paired with the customer research revealed new insights—specifically the impact on different customer cohorts. This analysis married customer data and localization opportunities to help make smarter decisions. They learned which items meant the most to their best in-store shoppers assuring their biggest spenders remained satisfied.

A common data lake was created using Alteryx and SEQL servers to wrangle data. Structured data like loyalty, credit card, product selling information, store productivity and margin per linear foot were added. And then unstructured data like IRI and Neilson were added. The pivot point was in how the data was standardized based on how the customer looked at the product. This new process added attributes that were important to customers

Attribution was created as a dynamic model so future changes to customer behavior could be quickly adopted. Attributes were weighted differently and flexibility was built-in so as the process generated results, the planner could adjust weighting for future line reviews.

+15%

INCREASE IN PRODUCT PRODUCTIVITY PER FOOT BY KNOWING THE SHOPPER

$250M

IN POOR SELLING INVENTORY WAS DIALED BACK

+10%

OF THE TOPLINE GREW

LINE REVIEWS

SHIFTED FROM SEASONALLY TO TIMING BASED ON OVERSEAS VERSUS DOMESTIC TIMELINES ENABLING ALL BUSINESSES TO MOVE AT THEIR OWN SPEED

3 STEP APPROACH

WAS CREATED TO ALLOW FOR NEGOTIONS AND COVERSATIONS WITH A LEADERSHIP REVIEW, PLANOGRAM & FINAL REVIEW AND FINAL SIGNOFF

MORE INSIGHTS

OPPORTUNITY Senior management needed to free up cash tied to..

OPPORTUNITY Create a planning function for assortment and inventory that..