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Pitney Bowes Software
The Five Best (& Un-Common) Practices for Unlocking Value in Customer Data to Drive Growth and Real Estate Strategy
October, 2014
Al Beery - PBSGary Faitler - PBSDave Bolan - Rexall
Who we are…
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Since 1959, the PBS Strategy and Analytics division (formerly Thompson Associates & MapInfo) has
provided innovative research solutions and developed sophisticated sales‐forecasting models for
more than 1,600 clients in the retail, restaurant, and financial‐services industries.
The Five Best (& Un-Common) Practices for Unlocking Value in Customer Data to Drive Growth and Real Estate Strategy
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Let Pitney Bowes Software and Dave Bolan, Director of Analytics, Rexall Canada, share with you the five not-so-common best practices for unlocking the maximum analytic power from your data. It begins with leveraging data for powerful visualizations and reporting, thereby facilitating the creation of actionable information.
Pre-Cursor to this Webinar…
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The Five Best (& Un-Common) Practices for Unlocking Value in Customer Data to Drive Growth and Real Estate Strategy
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Dave Bolan
The Five Best (& Un-Common) Practices for Unlocking Value in Customer Data to Drive Growth and Real Estate Strategy
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1.Harvest Your Data for Maximum Yield
2.Optimize Insight → Display and Report
3.Extract Meaning ~ Prepare for Action
4.Construct Tools for Forecasting
5.Build Bridges for Busting Silos
The Five Best (& Un-Common) Practices for Unlocking Value in Customer Data to Drive Growth and Real Estate Strategy
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1.Harvest Your Data for Maximum Yield
Harvest Your Data…
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The Five Best (& Un-Common) Practices for Unlocking Value in Customer Data to Drive Growth and Real Estate Strategy
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2. Optimize Insight → Display and Report
The Five Best (& Un-Common) Practices for Unlocking Value in Customer Data to Drive Growth and Real Estate Strategy
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3. Extract Meaning ~ Prepare for Action
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Extract Trade Area Insight
What is the extent and
consistency of my store’s draw?
Extract a Clear Customer Profile
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Establish a general understanding of “Who is YOUR Customer?”Index clusters based on contribution to performanceCreate a uniform ‘effective’ household base for model development Identify demographic characteristics which correlate with sales
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Competitive Impact Quantification
The competitive analysis evaluates the competitiveness of each individual geography, so it is important to understand the type of competitive situation impacting each.
Types of Competitive Situations• Adjacent – competitor is in same center or across the street• Impacting – competitor is in a similarly advantageous/convenience position• Intercepting – competitor is more convenient to the census tract customers
CustomersCustomers
Adjacent
Intercepting
Impacting
Quantify Significance of Site Characteristics
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Site characteristic analysis measure model calibration against thepresence of site specific attributes as provided for all database stores.
Model Distance Decay Patterns
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Basic premise is that customers who are more conveniently served by the store will visit more frequently and thus contribute a higher annual volume
The Five Best (& Un-Common) Practices for Unlocking Value in Customer Data to Drive Growth and Real Estate Strategy
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4. Construct Tools for Forecasting
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5. Build Bridges for Busting Silos
The Five Best (& Un-Common) Practices for Unlocking Value in Customer Data to Drive Growth and Real Estate Strategy
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Leveraging Results Across the Organization
Sample Range of Analyses:
• Data Visualization
• Trade Area Analysis
• Maturity Analysis
• Customer Profiling
• Competitive Assessment
• Site Attribute Analysis
• Site Forecasting
• Benchmarking
• Store Clustering
• Omni-Channel Analysis
• Target Marketing
Benchmarking Foundation• Performance varies by environment (e.g., market size and
density, competition, customer profile, synergies, etc.)
• The PBS model is best-fit to the client database, controlling
for measurable qualities within the environment that have
been statistically shown to drive store performance
• Benchmarking utilizes the value of the predictive model in
the context of goal-setting, which is often an arbitrary
process
• Actual store performance and model-predicted potential are
scored in decile, with (-5) being the lowest and (+5) being
the highest.
Benchmarking results should be measured against P&L metrics to determine the full
picture of viability
Store Benchmarking
Exceeding Potential
Not Meeting Potential
Store Benchmarking
Developing a national demand surface with pre-defined criteria, PBS can run a national supportable store analysis and provide the results at the market level
Supportable Store Estimation
• Interaction between brick & mortar stores with online sales
• How does saturation of brick & mortar impact my online
growth?
• Established markets v. greenfield markets
• What is the most appropriate assignment?
Omni-Channel Analysis
Recap – Interdepartmental Value
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The Five Best (& Un-Common) Practices for Unlocking Value in Customer Data to Drive Growth and Real Estate Strategy
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Dave Bolan
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Analyses to accounts for ‘roll-in’ acquisitions & impact of FTE doctors in associated clinics
Upcoming: Model adjustment for rebranding
Store clustering & benchmarking to support model
Renovation lift calculator
Partnership at a Glance
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Flyer Optimization
Existing sales penetration Market-share
Potential – Trade Area only Competitive Intensity
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Q&A