8 Steps to Optimize the Retail Store with Behavior Analytics

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Behavior Analytics is a framework of metrics that measure, monitor and predict the activities of customers and employees in the bricks-and-mortar store. This high concept presentation provides an overview of the 8 steps for optimization, including Customer Service Models, Demand Analytics, Sales Conversion, Service Intensity, Service Productivity, In-Store Analytics, Queue Management, and Predictive Scheduling.

Transcript of 8 Steps to Optimize the Retail Store with Behavior Analytics

  • 8 Steps to Optimize the Retail Store with Behavior Analytics Ronny Max @SiliconWaves
  • Retail Analytics July 2014 Ronny Max @SiliconWaves 2 Supply Analytics Merchandising Logistics Path to Purchase Marketing Ecommerce Behavior Analytics for the Bricks-and- Mortar Stores Customer Analytics Purchase Patterns from Point-of-Sale Retail Analytics Moves to the Frontline RSR Research January 2014 Guidelines for Analytics What do we want to know? What can we know? What we do once we know?
  • Behavior Analytics is a framework of metrics that measure, monitor and predict the Activities of Customers and Employees in the Bricks-and-Mortar Store July 2014 Ronny Max @SiliconWaves 3
  • Behavior Analytics Customer Service Model Demand Analytics Sales Conversion Service Intensity Service Productivity In-Store Analytics Queue Management Predictive Scheduling 8 Steps to Optimize the Retail Store with Behavior Analytics July 2014 Ronny Max @SiliconWaves 4 Bricks-and-Mortar Store
  • Return on Investment With Behavior Analytics Retailers can Plan better in the Long Term with Demand and Store Layout Analytics, and Optimize the Immediate Term (4-8 weeks) with targeting Demand and adapting the Schedule, and Manage in Real-Time with In-Store Analytics, Queue Management and Predictive Scheduling July 2014 Ronny Max @SiliconWaves 5
  • 1. CUSTOMER SERVICE MODEL Measure, Monitor and Predict Customer and Staff Activities July 2014 Ronny Max @SiliconWaves 6
  • Privacy: Tracking Me! My Digital Identity Email: Whats My Name? Contacts: Who I Know? Calendar: Who I meet? Camera: What I see? Media: What do I think? Web: What do I own? Location: Where I am? Device ID: Connecting ID Me to the physical Me Me, in the Real World My Name My Buying History My Path to Purchase My Current Location July 2014 Ronny Max @SiliconWaves 7 Most optimization models for the physical store focus on group behaviors, and do NOT require the identify of the customer
  • Measure, Monitor, Predict! July 2014 Ronny Max @SiliconWaves 8 Entering (Passing) Visitors Occupancy Browsing (Standing) Zone Area Service Area Exiting (Moving) Queues Frontline
  • Customer Service Models July 2014 Ronny Max @SiliconWaves 9 Convert 45% of Visitors to Buyers (Transactions) Greet 95% of Customers in less than 60 seconds Checkout 90% of Customers in less than 3 Minutes
  • July 2014 Ronny Max @SiliconWaves 10 Service Level Measurement Store B 95% Store C 87% Store D 88% Store E 91% Store A 86% Service Level Measurement is the local stores Success Rate To comply with the Customer Service Model
  • 2. DEMAND ANALYTICS What is the Sales Opportunity? July 2014 Ronny Max @SiliconWaves 11
  • Benchmarks for Demand July 2014 Ronny Max @SiliconWaves 12 Economic Environment Marketing Campaigns Local Traffic Visitors to Store Sales Opportunity is a Person Entering the Store Monitor Demand in Context
  • Demand = Footfall Traffic July 2014 Ronny Max @SiliconWaves 13 How Many People Entered My Store? What is the Ratio of My Visitors to People Passing-By? How Many People Are In The Mall?
  • Marketing Effectiveness July 2014 Ronny Max @SiliconWaves 14 Whats the impact of a campaign on demand? Whats the value of a Visitor per Campaign? Whats the Customer Value for the Store? Campaign Analytics Whats the impact of an Event in the Location, to drive demand? Whats the Ratio of Entering to Passing-By? Whats the impact of Proximity Alerts? Location Analytics Whats the impact of in- store marketing, such as digital displays, stands, and layout, on sales? Whats the impact of a single change, i.e. new merchandise, on sales? In Store Analytics The success of a marketing campaign can be defined by changes in demand behaviors NO ID NEEDED
  • Comparing Stores July 2014 Ronny Max @SiliconWaves 15 Define Objectives Define Criteria Categorize by Traffic Categorize by Demographics Categorize by Store Type Define Period of Time Adapt for Seasonality Exclude Outlier Events Judge a Store Against itself
  • 3. SALES CONVERSION How many browsers converted to buyers? July 2014 Ronny Max @SiliconWaves 16
  • Sales Conversion July 2014 Ronny Max @SiliconWaves 17 How many Visitors converted to Buyers? Sales Conversion is an Actionable Metric when monitored in Context with Demand and Scheduling, per Period of Time Sales Conversion In-Store Activities Actual Demand
  • Sales Swing July 2014 Ronny Max @SiliconWaves 18 Sales Conversion Arrivals vs. Exiting Define Transactions Define Period of Time Behavior Anomalies Basket vs. Transaction
  • Retail Reset* July 2014 Ronny Max @SiliconWaves 19 Omniverse Sales Conversion Website Visitors Website Buyers Store Visitors Store Buyers Sales Conversion = Buyers / Visitors The Omni-Channel Transformation Structured vs. Unstructured Data * RSR Research
  • 4. SERVICE INTENSITY How many salespeople we need to schedule? July 2014 Ronny Max @SiliconWaves 20
  • Service Intensity July 2014 Ronny Max @SiliconWaves 21 Service Intensity Is the Ratio of Staff to Customers Service Intensity is a Holistic Metric defined by Customers, Associates, and Period of Time
  • Average Service Intensity July 2014 Ronny Max @SiliconWaves 22 As workforce systems proliferate and forecasting moves away from spreadsheets into sophisticated systems, this opens the door for innovation! Calculating Service Intensity in time segments that reflect the stay time in the store, paints a more authentic picture of how the store operates
  • Optimized Service Intensity July 2014 Ronny Max @SiliconWaves 23 Standard Deviation Targeted Metrics Optimization is the most challenging aspect of working with Service Intensity Service TimeSales Conversion
  • Marginal Value of Salesperson July 2014 Ronny Max @SiliconWaves 24 Sales Staff Rachel + Mike + Abby + Jane Adding 1 salesperson increases revenue but each sales segment is less than the previous slice
  • 5. SERVICE PRODUCTIVITY What is the value of a salesperson? July 2014 Ronny Max @SiliconWaves 25 The Probability that Lindas Sales per Hour is $350 is 87%
  • If We Know . July 2014 Ronny Max @SiliconWaves 26 Past Performance of a Salesperson Probability of Current Performance Within Service Intensity Parameters Probability of Current Performance Outside Service Intensity Parameters
  • F July 2014 Ronny Max @SiliconWaves 27 Probability of Future Performance Per Salesperson We can calculate the How do you view your frontline staff, as Liabilities and Costs , or Revenue Generators?
  • Scheduling Combinations July 2014 Ronny Max @SiliconWaves 28 Traffic Employee Training Employee Preference Skills Service Productivity Provides a Measurable Method to Optimize Who Should Be Working, When, to Maximize Sales
  • Schedule to Demand July 2014 Ronny Max @SiliconWaves 29 Service Intensity Identify the number of employees, per location, per period of time Service Productivity Identify the productive rate of a salespeople, in context to the stores environment Optimization Schedule to sales, skills and training and the employees preferences, while complying with corporate objectives and priorities We forecast individual performance with Service Productivity We measure the stores ability to adapt to actual traffic with Service Intensity
  • 6. IN-STORE ANALYTICS Where, when, and for how long customers stayed? July 2014 Ronny Max @SiliconWaves 30 Even by 2025, bricks- and-mortar stores should still account for approximately 85 % of U.S. retail sales. McKinsey & Company
  • What is In-Store Analytics? July 2014 Ronny Max @SiliconWaves 31 Real-Time Marketing Store Operations Long Term Planning Wi-Fi BLE NFC Video RFID In-Store Technologies for tracking activities In Store Analytics is measuring, monitoring and predicting the activities of customers, specifically where they linger (location), when and for how long (time)
  • Tracking Technologies July 2014 Ronny Max @SiliconWaves 32 Facial Recognition Heat Maps RFID / NFC Bluetooth BeaconsDevice