Customer Data to Cash Register: Retail Analytics Close the Gap
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Transcript of Customer Data to Cash Register: Retail Analytics Close the Gap
© 2014 Alteryx, Inc. | Confidential
Customer Data to Cash Register: Retail Analytics Close the Gap
© 2014 Alteryx, Inc. | Confidential 2
Speakers
Ritu JainDirector of Industry Marketing, Alteryx
Mark HeckmanPrincipal, Mark Heckman ConsultingFormer VP of Marketing, Marsh Supermarkets
Jimmy GarrettSales Engineer, Alteryx
© 2014 Alteryx, Inc. | Confidential© 2014 Alteryx, Inc. | Confidential
Agenda…
1. Current State of Advanced Analytics in Retail
2. Top Inhibitors to Wider-Spread Analytics Use
3. Steps Successful Retailers are Taking to Expand Usage
4. Real Life Case Examples
3
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Showrooming/ Webrooming a Norm
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Many Paths-to-Purchase
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Inventory Distortion: A $800B Problem!!
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4000+ and Counting!
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Data is the Linchpin
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Current State of Data-Driven Decision-making
Source: RSR Research Benchmark Report 2014 – Retail Analytics Moves to the Frontline
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Desired State of Data-Driven Decision-making
Source: RSR Research Benchmark Report 2014 – Retail Analytics Moves to the Frontline
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25+ Years of Retail Marketing Experience at Randalls/Tom Thumb &
Marsh Supermarkets
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Marsh Supermarkets….A History of Innovation and Industry Thought Leadership• 90 Stores, Central Indiana-Western
Ohio….• $ 1.1 Billion in Sales• First UPC Scanned in Troy, Ohio,
1974
Mark Heckman• Marketing Executive, VP of
Marketing Until 2011• Author, Industry Study and
Harvard Business Case
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Our Original Aspirations! • Weekly Targeted Programs to Select Shopper
Groups
• Desktop Targeting, Query and Reporting Tool
• Standing Program for “VIP” Shoppers
• Create Personalize Experience on website
• Expand Customer Specific Data into the Merchandising Arena
• Category Growth• Store Layout • Assortment
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Issues That I Faced Behind the Chair
• Limited Use of Customer Data in Decision Making Process
• Lots of Data, Several Data Silos, No Interaction
• No Dashboard Access to Data
• Little Co-operation or Data Sharing Between Departments
• Insufficient Budget and Human Resources to Execute Data-Driven Programs
• No Stated or Accepted Plan In Place to Enhance Data-Driven Positioning in the Enterprise
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The Resulting Customer Data-Driven Roadmap
Customer Identification/Behavior
s
Increase Engagement
Include
Merchandising/Operations
Expand to other Areas of
Enterprise
Phase 1-Discover Phase 2-
Engage Phase 3- Involve Phase 4-Expand
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Phase 1. Customer Identification
• Created a Plan to Accelerate Data Access and Eliminate Reliance on IT for Data Pulls
• Define Communication Options For Near Term and Future
• Evaluate Shopper Engagement Options• Segmentation• Clubs• Geographies• Syndicated Appends
• Determined Shopper Segmentation Approach
© 2014 Alteryx, Inc. | Confidential
Phase 1- Customer IdentificationShopper Segmentation ---Key Questions to Answer With Segments
What is the true value of each
segment to your business?
Is the segment large enough to
move the needle?
How will we communicate to each segment?
How effectively can the
segmentation scheme be
integrated into the core
elements of the business?
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Phase 1- Customer Identification- Shopper Segmentation Variables
• Net Dollars per period shopped
• Trips per period shopped
• Days Since Last Purchase
• Periods Shopped (4wk, 13 total)
• Avg. Net Dollars per Trip
• # Departments Shopped
• # Categories Shopped
• Avg. % Discount
• $ & % Spend on Front Page Items
• $ & % Spend on PL
• % Spend Weekdays
• % Spend Weeknights
• % Spend Daytime
• % Spend Evening
• % Spend Night
• $ & % Spend on Grocery• $ & % Spend on Dairy• $ & % Spend on Frozen• $ & % Spend on Produce• $ & % Spend on Deli• $ & % Spend on Bakery• $ & % Spend on Meat• $ & % Spend on Non-food• $ & % Spend on HBC• $ & % Spend on AO• Category relevancy• Item relevancy
Many customer segmentations stop at only 3 indicators of customer worth – Recency, Frequency and Spend…
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Phase 2- Increase Engagement - Linking Shoppers to Categories
Each Shopper Segment’s Relationship with Categories and SKU’s Was Established.
Categories were Defined for Each Shopper Segment According to the Categories “Breadth of Appeal” and “Spending Index”
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Phase 3—Involve Merchandising and Operations
Retailers need to combine traditional marketing and advertising activities with merchandising, each have assets needed by the other.
As a transitional tool away from mass to a more targeted approach…..affinities to front page circular items are scored to better understand what the optimal mix of FP items to drive sales among key customer segments
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Phase 3- VIP Mailer ResultsDepartment Level Impact
We decided to focus several key offers in the Produce Department as many of the top shoppers in the segment communication indexed extremely high in Produce.
The results were encouraging. Nearly 10% lift in the entire Produce Dept. and in 9 of the stores with very large Produce Departments, 20% Departmental Lift was experienced.
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Phase 4---Solving Retail Problems with Customer Data
Out of Stock Prevention Represents HUGE Incremental Dollars to Both Retailer and Brand. Customer Data Both Household Level and Not are Key Elements to Staying In Stock
SKU Assortment Now is Frequently Driven by VIP and other Critical Shopper Segment’s Affinities to Categories and Products
Store Layout Can Now be Refined and Adjusted to Leverage Traffic Patterns of Key Shopper Segments within the Store
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Organizational Inhibitors
Source: RSR Research Benchmark Report 2014 – Retail Analytics Moves to the Frontline
Cu
ltu
reTe
ch
nolo
gy
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Large IT Project
Lots of Tools
Many Months…
The Current Approach to Insight
FAIL
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Rep
ort
The Alteryx Approach
All Relevant Data
Access, cleanse, and blend data with unique packaged data options
Rapid design of predictive analytics with unique spatial
understanding
Packaged Market & Customer Data
Consumerize the use of sophisticated analytics
Enrich
An
aly
ze
Ble
nd
Text
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Customer Success
Customers across the world
300+Strong Foundation
Renewal rate
Ranked “Visionary” by
Gartnerin
Both “Advanced Analytics” and “BI and Analytics”
magic quadrants
95%+Corporate Info.
Associates across North America & Europe
165+
Leader in Data Blending and Advanced Analytics
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Big Box and SpecialtyRestaurant
sGrocery
Consumer
Goods
Our Retail Customers….
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Marketing CorporateMerchandisin
gSupply Chain
Results Speak…
Reduced Site
Selection
Time 95%;
Franchise
Cannibalizati
on Analysis
Assortment
Optimization
>100,000
SKUs for
10,000+
Locations
Reduced
Inventory by
31% and
Freed-up
~$20M in
Working
Capital
Doubled the # of
Customers
Shopping Online
& In-Store.
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Recommendations
1. Ensure access of data, analysis and reporting to those that need the data, minimizing dependence on legacy systems and IT
2. Build cross- functional relationships and joint goals to ensure inter-departmental cooperation, tear down fiefdoms
3. Tie metrics and results of data-driven initiatives back to organizational goals -- departmental sales, # of units, shoppers, etc.
4. Take small, iterative steps with data-driven programs, gaining quick wins -- and use those wins to build executive support, promote data-based culture
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Questions?
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Key Terms
• Retail Analytics• Customer Analytics• Customer Loyalty• Merchandising Planning• Demographic Analysis• Customer Data• Customer Scorecards• Multichannel Analysis• Big Data Analytics• Big Data Analysis• Predictive Analytics• Data Blending• Advanced Analytics• Data Artisan• Data Analyst
• Alteryx• Mark Heckman• Marsh Supermarkets• Ritu Jain• Alteryx Analytics Gallery