Small Business Analytics and Metrics:
How and What Do you Measure Up?
Al Bessin, Principal, Bessin Consulting
Geoff Wolf, ...
Overview
• The Challenge
• Types of Data
• A Customer-Centric View
• Equalizing the Playing Field
• A Basis for Comparison...
The Challenge
3
The Basic Business Proposition
• Don’t lose site of priorities
4
Classic View
• Marketing grouped around transaction channels
• Planned, budgeted and measured independently
5
Spend Justification
• Catalog and internet marketing analyzed
independently
• Different criteria applied
• Customer behavi...
Holistic Marketing Analysis Model
• Consider all channels together
• Find common metrics to permit comparison
7
Types of Data
8
Categories of Metrics―Pre-Sale
• Website
– Bounce Rate
– Visitors
– Page Visits
– Entry and Exit Pages/Abandonment
– Conve...
Categories of Metrics – Buying
• Push Marketing
– Direct Mail
– Email
– Targeted Remarketing
• Pull Marketing
– Paid Searc...
The Channel Challenge
• Consumer channel preferences are changing
– Website is a prominent element in the buying cycle
• E...
Data Types
• Ephemeral Behavioral Metrics
– Useful for understanding interaction of different media
– Not retainable at th...
The Problem
• Behavioral science is inherently “soft science”
• There is no perfect quantitative answer
1. Use the 80:20 r...
The Solution
• Collect Persistent Transactional data
– Assignable to customer records
– Quick view of channel and vehicle
...
Data Elements
• Systems need to be set up to collect source data
• Codes should be passed along with orders to identify
di...
A Customer-Centric View
16
A Customer-Centric View
• Customers see brands and products and what they
represent
• Channels can have synergy or be in c...
Customer Touch Points
Inducement
Decision
Contact
Transaction
Fulfillment
Delivery
Support
Direct Mail Marketing
Internet ...
Customer-Centric Data
• Persistent Transactional data permit customer-
centric analyses
• Imperfect, but:
– Generally pred...
Equalizing the Playing Field
20
The Challenge
• Ban siloing– one team, one company
• Aggregated Demand = 160%
21
Allocation Modeling
• Limitations as to
what data can be
collected that is
customer-
identifiable
22
Developing Models
• Start with collection of
response data
• Customer identifiable
• Estimate behavior
• Model
• Test to v...
An Allocation Model
24
Key Takeaways
• Collect the data―all transactions should carry source
codes
• Develop an allocation model
• Test the model...
Sometimes, “Simple” is Amazing
A Basis for Comparison
27
Calculating Return on Investment
• Demand $s (Matched Marketing Database)
• GM% (plug)
• Variable Transaction Cost (plug)
...
Breakeven Analysis - Defined
• “Incremental” Breakeven
The response rate at which the Order Contribution
equals the Variab...
Breakeven Analysis
30
Tip:
• Transaction costs can be
estimated by taking an
annual P&L and dividing the
variable expense by the
number of order...
Adding Observed Values
32
Marketing Contribution
• Marketing Contribution is the Order Contribution
less Variable Marketing Expense
• When Marketing...
Putting It To Work: 12 Month Contribution
• Measure the value of a customer in the 12 months
after acquisition and compare...
The End Result: Sample Dashboard
35
Tips and Limitations
36
Attributes of Metrics
• Simple indicators of performance
• Timely “heads up” to changes
• Trigger more detailed
investigat...
Frequency of Measurement
• Hourly
• Intra-day variance
• Daily
• Intra-week variance
• Weekly
• Consistent time frame
• Mo...
The Role of Metrics
• Management to numbers
• Budget
• Rolling forecast
• Promote vision
• Warning of sign of other issues...
Challenges
• Standards must be established for
the individual business
• Information must be gathered from
multiple system...
Be certain you understand…
• What drives demand for
yourbusiness
• Your cost to acquire new buyers
• The value of buyers a...
Final Takeaways
• Practical Attribution for Small Business
• Statistics are not enough
• Look High Level First
• Test lega...
Questions?
43
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Small Business Analytics and Metrics: How and What Do you Measure Up?

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Small Business Analytics and Metrics: How and What Do you Measure Up?

  1. 1. Small Business Analytics and Metrics: How and What Do you Measure Up? Al Bessin, Principal, Bessin Consulting Geoff Wolf, EVP Marketing , J.Schmid Jude Hoffner, Principal, Hoffner Marketing
  2. 2. Overview • The Challenge • Types of Data • A Customer-Centric View • Equalizing the Playing Field • A Basis for Comparison – Break Even and Contribution • Tips and Limitations 2
  3. 3. The Challenge 3
  4. 4. The Basic Business Proposition • Don’t lose site of priorities 4
  5. 5. Classic View • Marketing grouped around transaction channels • Planned, budgeted and measured independently 5
  6. 6. Spend Justification • Catalog and internet marketing analyzed independently • Different criteria applied • Customer behavior ignored …a gross overstatement of program performance 6
  7. 7. Holistic Marketing Analysis Model • Consider all channels together • Find common metrics to permit comparison 7
  8. 8. Types of Data 8
  9. 9. Categories of Metrics―Pre-Sale • Website – Bounce Rate – Visitors – Page Visits – Entry and Exit Pages/Abandonment – Conversion Funnel • Call Center – Calls Handled – Abandonment Rate – Talk Times – Upsells – Conversions …what can be associated with customer records? 9
  10. 10. Categories of Metrics – Buying • Push Marketing – Direct Mail – Email – Targeted Remarketing • Pull Marketing – Paid Search (branded, competitive) – Natural Search (branded, competitive) – Direct URL Entry – Affiliate Programs – Marketplaces – Comparative Shopping Engines …associated with customer records 10
  11. 11. The Channel Challenge • Consumer channel preferences are changing – Website is a prominent element in the buying cycle • Enterprises remain organized around old models – Direct Mail (or catalog) and Website Divisions – Call Center and Website Divisions – Should be Direct Division • Consumers should see the enterprise, not individual channels • Measurement and Analysis mustlook acrossthe business 11
  12. 12. Data Types • Ephemeral Behavioral Metrics – Useful for understanding interaction of different media – Not retainable at the customer level • Persistent Transactional Metrics – Tied to the sale – Assignable at the customer level 12
  13. 13. The Problem • Behavioral science is inherently “soft science” • There is no perfect quantitative answer 1. Use the 80:20 rule 2. Be aware of opportunity cost 13
  14. 14. The Solution • Collect Persistent Transactional data – Assignable to customer records – Quick view of channel and vehicle – Useful for predictive purposes • Model response across channels • Adjust models with Ephemeral Behavioral trends • Validate models with testing This achieves the quickest quantitative read on performance at a reasonable cost 14
  15. 15. Data Elements • Systems need to be set up to collect source data • Codes should be passed along with orders to identify direct sources Call Center Collected Source Code Call-In Number Code Assigned Source Code Unsourced Website Email Source Direct URL Entry Paid Search Source Affiliate Source Marketplace Source CSE Source Unsourced 15
  16. 16. A Customer-Centric View 16
  17. 17. A Customer-Centric View • Customers see brands and products and what they represent • Channels can have synergy or be in conflict To maximize your relationship with your customers – Look at the business from their perspective 17
  18. 18. Customer Touch Points Inducement Decision Contact Transaction Fulfillment Delivery Support Direct Mail Marketing Internet Marketing Advertising Call Center Website Store Lifetime Value 18
  19. 19. Customer-Centric Data • Persistent Transactional data permit customer- centric analyses • Imperfect, but: – Generally predictive – Allow better contact relevance – Normalize the multichannel view Critical to use a clean and accurate database 19
  20. 20. Equalizing the Playing Field 20
  21. 21. The Challenge • Ban siloing– one team, one company • Aggregated Demand = 160% 21
  22. 22. Allocation Modeling • Limitations as to what data can be collected that is customer- identifiable 22
  23. 23. Developing Models • Start with collection of response data • Customer identifiable • Estimate behavior • Model • Test to validate • Refine model 23
  24. 24. An Allocation Model 24
  25. 25. Key Takeaways • Collect the data―all transactions should carry source codes • Develop an allocation model • Test the model, ongoing • Measure ROI the same way for all vehicles • Use source information to plan acquisition and contact strategies 25
  26. 26. Sometimes, “Simple” is Amazing
  27. 27. A Basis for Comparison 27
  28. 28. Calculating Return on Investment • Demand $s (Matched Marketing Database) • GM% (plug) • Variable Transaction Cost (plug) • Marketing Cost (plug) 28
  29. 29. Breakeven Analysis - Defined • “Incremental” Breakeven The response rate at which the Order Contribution equals the Variable Marketing Expense • Variable Marketing Expense includes only those costs which are truly variable (unlike a financial breakeven analysis) For example: Do not allocate overhead such as CEO pay! 29
  30. 30. Breakeven Analysis 30
  31. 31. Tip: • Transaction costs can be estimated by taking an annual P&L and dividing the variable expense by the number of orders taken 31
  32. 32. Adding Observed Values 32
  33. 33. Marketing Contribution • Marketing Contribution is the Order Contribution less Variable Marketing Expense • When Marketing Contribution on a first order is negative, it is the Acquisition Cost • This establishes thresholds for selecting prospecting sources 33
  34. 34. Putting It To Work: 12 Month Contribution • Measure the value of a customer in the 12 months after acquisition and compare to the acquisition cost 12 Month Order Sales Less: Cost of Goods Less: Fulfillment Expense Less: 12 months of Marketing Expense = 12 Month Value 34
  35. 35. The End Result: Sample Dashboard 35
  36. 36. Tips and Limitations 36
  37. 37. Attributes of Metrics • Simple indicators of performance • Timely “heads up” to changes • Trigger more detailed investigation Avoid Analysis Paralysis 37
  38. 38. Frequency of Measurement • Hourly • Intra-day variance • Daily • Intra-week variance • Weekly • Consistent time frame • Monthly • Tie to monthly financials 38
  39. 39. The Role of Metrics • Management to numbers • Budget • Rolling forecast • Promote vision • Warning of sign of other issues • Example - gross margin 39
  40. 40. Challenges • Standards must be established for the individual business • Information must be gathered from multiple systems • Fewer significant metrics are better than more • Information is dynamic • Never let metrics displace intuition 40
  41. 41. Be certain you understand… • What drives demand for yourbusiness • Your cost to acquire new buyers • The value of buyers and how it differs by source • The impact of other factors on demand 41
  42. 42. Final Takeaways • Practical Attribution for Small Business • Statistics are not enough • Look High Level First • Test legacy • Look at Dependencies (example of chart that shows orders timed with major marketing activities) • Internal benchmarking vs external • Why metrics must be tailored to the business – attribution tuning 42
  43. 43. Questions? 43

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