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Demystifying Google Shopping:
Uncover buying patterns & optimize for profit
About me
mike.ryan@smarter-ecommerce.com | +43 699 14111 309 | /in/mike-retail-insights
@mikeryanretail
Mike Ryan
Head of Retail Insights, smec
● Deep background in retail operations
● Leadership roles in product & innovation
● Passionate about storytelling with data
● Sold everything and moved to Austria for love
/in/mikeppc
#SMX smarter-ecommerce.com
Agenda
1. The ROAS Pathology
2. The Messy Middle
3. Two Birds, One Stone
4. The Holy Grail
The ROAS Pathology
ROAS = conv. val / cost
A broadly-applicable proxy for profit
● Easily and universally calculable
● Works across all campaign entities
● Reportable from hourly up to yearly
● Google formula is open-ended
and why is it so popular?
What is ROAS
Proxy Metrics
A proxy metric is an indirect measurement of a desired outcome, typically used
when the desired outcome cannot be directly measured or observed. The proxy has
value because of its correlation to the goal – and the higher the correlation, the
higher the value or effectiveness of the proxy will be.
ROAS is a channel-siloed metric
● How accurately is ROAS
modelled to profit?
● What steps can we take to
improve that modelling?
Measurement pains increase as incremental returns flatten
● How accurately is ROAS
modelled to profit?
● What steps can we take to
improve that modelling?
● What steps can/should we take
to replace ROAS?
What’s in a name?
A ROAS by any other name would smell as sweet
Look to ACoS for more rational conversations
● Calculated with the same elements as ROAS: cost and conversion value
● Name clearly describes what it is – a campaign efficiency metric
● Harder to confuse with profitability
Advertising Cost of Sale
cost / conv. val = ACoS
conv. val / cost = ROAS
RANGE CONSUMPTION
Here’s the kicker:
It’s not even necessary to use a proxy metric for profit
Conclusions so far
● ROAS is insufficiently correlated to profit
● The use of ROAS is overextended
● The whole situation is avoidable
The Messy Middle
Your attribution model is wrong
Decoding decisions
https://www.thinkwithgoogle.com/
From quantitative and narrow, to broad and subjective
The ‘messy middle’ [is] a
space of abundant
information and unlimited
choice that shoppers have
learned to manage using a
range of cognitive shortcuts.
“
Browsing and consumption are primal behaviors
● Consumers cycle through exploration and evaluation phases – repeatedly gathering and reducing options
● For merchants, there are opportunities arising from deep-seated biases manifested while shopping
Category
heuristics
Social
proof
Authority
bias
Power of
Now
Scarcity
bias
Power of
Free
Two Birds, One Stone
The Clicked vs. Bought Dilemma
€ 699,-
Entry product Order value
Customer A
Buying behavior
€ 699,-
Entry product Order value
Customer B
Buying behavior
€99.00
€1,090.00
€ 899,-
€ 699,-
Shopping ad
?
The Clicked vs. Bought Dilemma
Click-basket correlations | What will online shoppers actually buy?
€ 699,-
Basket #1
€ 899,-
€ 699,-
Shopping ad
€ 798,-
Basket #2
€ 99,-
Basket #3
Wrong assortment
prioritization
Wrong entry products
Undersized Shopping order
Missing upsell opportunities
Wrong assumptions
Unattractive sales bundles
Expected gross
profit
€ 120
Actual gross
profit
€ 20
Expected gross
profit
€ 100
The Clicked vs. Bought Dilemma
€ 699,-
Entry product Order value
Customer A
Buying behavior
€ 699,-
Entry product Order value
Customer B
Buying behavior
€99.00
€1,090.00
€ 899,-
€ 699,-
Shopping ad ?
The Clicked vs. Bought Dilemma
Click-basket correlations | What will online shoppers actually buy?
€ 699,-
Basket #1
€ 899,-
€ 699,-
Shopping ad
€ 1,090.00,-
Basket #2
Right assortment
prioritization
Right entry products
Upsell opportunity achieved
Attractive sales bundles
Expected gross
profit
€ 300
Expected gross
profit
€ 100
A few questions along the way
● What is the real profitability of my Shopping Ads?
● In which quantity are products sold and at which price?
● Which products drive revenue, which products drive order profit?
● Which items often act as replacement products?
● Which products are often bought together?
Connect the data
Web Analytics
Information about the
transaction and its
components.
Understand the true value of your Shopping campaigns
What is necessary?
Click ID report
Connecting an adclick
to a transaction.
Visualizations
Basket Margin
Product data
Product information to
be connected to the
transaction
Profitability per brand
COGS
gross profit
brand 1 brand 2 brand 3 brand 4 brand 5
70,000.00 EUR
60,000.00 EUR
50,000.00 EUR
40,000.00 EUR
30,000.00 EUR
20,000.00 EUR
10,000.00 EUR
0.00 EUR
Profitability per item
COGS
gross profit
Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 Item 9 Item 10
0.00 EUR
5,000.00 EUR
10,000.00 EUR
15,000.00 EUR
20,000.00 EUR
Clicked vs bought items
Shop overview
Revenue
Category
of bought
item
Category
of clicked
item
● Filter:
transactions where exactly one item
was clicked and exactly one item was
bought
Clicked vs bought items
● 22% of this top category’s
click-attributed revenue actually
converts in a different category
Single category
Category
of clicked
item
Category
of bought
item
Revenue
Basket Margin
calculation
Offline Conversion
Import
Goal
Adjustment
Gross profit
calculation
● Calculate the order profit regularly for every transaction
● Gross profit = Revenue - COGS ( - Transactional costs, optionally)
● Connect the conversion with an adclick (Click-ID)
Offline conversion
Import
Unlock order profit for optimization
● Regular upload of offline conversion feed to Google Ads
● The feed features: Click-ID, conversion time, conversion value, currency
Goal
Adjustment
● Rethink and adjust your goal setting, your current ROAS goal won’t fit
● Ensure that all stakeholders are on board, optimization based on profit ≠
optimization based on revenue
Challenges while planning and performing this task
● Large data volumes
● Data availability, timeliness, and fallbacks
● Intra-day price changes
● Conversion lag
● Returns and refunds
The Holy Grail
smarter-ecommerce.com
God
Metric
Order Data
Demand
Price Stock
A perfect conversion model
Mike Ryan
mike.ryan@smarter-ecommerce.com
/in/mike-retail-insights
Thank You
Reach out any time
https://smarter-ecommerce.com/
digital-momentum/
Listen & Subscribe!

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Demystifying Shopping: Uncover buying patterns & optimize for profit

  • 1. Demystifying Google Shopping: Uncover buying patterns & optimize for profit
  • 2. About me mike.ryan@smarter-ecommerce.com | +43 699 14111 309 | /in/mike-retail-insights @mikeryanretail Mike Ryan Head of Retail Insights, smec ● Deep background in retail operations ● Leadership roles in product & innovation ● Passionate about storytelling with data ● Sold everything and moved to Austria for love /in/mikeppc #SMX smarter-ecommerce.com
  • 3. Agenda 1. The ROAS Pathology 2. The Messy Middle 3. Two Birds, One Stone 4. The Holy Grail
  • 5. ROAS = conv. val / cost A broadly-applicable proxy for profit ● Easily and universally calculable ● Works across all campaign entities ● Reportable from hourly up to yearly ● Google formula is open-ended and why is it so popular? What is ROAS
  • 6. Proxy Metrics A proxy metric is an indirect measurement of a desired outcome, typically used when the desired outcome cannot be directly measured or observed. The proxy has value because of its correlation to the goal – and the higher the correlation, the higher the value or effectiveness of the proxy will be.
  • 7. ROAS is a channel-siloed metric ● How accurately is ROAS modelled to profit? ● What steps can we take to improve that modelling?
  • 8. Measurement pains increase as incremental returns flatten ● How accurately is ROAS modelled to profit? ● What steps can we take to improve that modelling? ● What steps can/should we take to replace ROAS?
  • 9. What’s in a name? A ROAS by any other name would smell as sweet
  • 10. Look to ACoS for more rational conversations ● Calculated with the same elements as ROAS: cost and conversion value ● Name clearly describes what it is – a campaign efficiency metric ● Harder to confuse with profitability Advertising Cost of Sale cost / conv. val = ACoS conv. val / cost = ROAS RANGE CONSUMPTION
  • 11. Here’s the kicker: It’s not even necessary to use a proxy metric for profit
  • 12. Conclusions so far ● ROAS is insufficiently correlated to profit ● The use of ROAS is overextended ● The whole situation is avoidable
  • 16. From quantitative and narrow, to broad and subjective The ‘messy middle’ [is] a space of abundant information and unlimited choice that shoppers have learned to manage using a range of cognitive shortcuts. “
  • 17. Browsing and consumption are primal behaviors ● Consumers cycle through exploration and evaluation phases – repeatedly gathering and reducing options ● For merchants, there are opportunities arising from deep-seated biases manifested while shopping Category heuristics Social proof Authority bias Power of Now Scarcity bias Power of Free
  • 18. Two Birds, One Stone
  • 19. The Clicked vs. Bought Dilemma € 699,- Entry product Order value Customer A Buying behavior € 699,- Entry product Order value Customer B Buying behavior €99.00 €1,090.00 € 899,- € 699,- Shopping ad ?
  • 20. The Clicked vs. Bought Dilemma Click-basket correlations | What will online shoppers actually buy? € 699,- Basket #1 € 899,- € 699,- Shopping ad € 798,- Basket #2 € 99,- Basket #3 Wrong assortment prioritization Wrong entry products Undersized Shopping order Missing upsell opportunities Wrong assumptions Unattractive sales bundles Expected gross profit € 120 Actual gross profit € 20 Expected gross profit € 100
  • 21. The Clicked vs. Bought Dilemma € 699,- Entry product Order value Customer A Buying behavior € 699,- Entry product Order value Customer B Buying behavior €99.00 €1,090.00 € 899,- € 699,- Shopping ad ?
  • 22. The Clicked vs. Bought Dilemma Click-basket correlations | What will online shoppers actually buy? € 699,- Basket #1 € 899,- € 699,- Shopping ad € 1,090.00,- Basket #2 Right assortment prioritization Right entry products Upsell opportunity achieved Attractive sales bundles Expected gross profit € 300 Expected gross profit € 100
  • 23. A few questions along the way ● What is the real profitability of my Shopping Ads? ● In which quantity are products sold and at which price? ● Which products drive revenue, which products drive order profit? ● Which items often act as replacement products? ● Which products are often bought together?
  • 24. Connect the data Web Analytics Information about the transaction and its components. Understand the true value of your Shopping campaigns What is necessary? Click ID report Connecting an adclick to a transaction. Visualizations Basket Margin Product data Product information to be connected to the transaction
  • 25. Profitability per brand COGS gross profit brand 1 brand 2 brand 3 brand 4 brand 5 70,000.00 EUR 60,000.00 EUR 50,000.00 EUR 40,000.00 EUR 30,000.00 EUR 20,000.00 EUR 10,000.00 EUR 0.00 EUR
  • 26. Profitability per item COGS gross profit Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 Item 9 Item 10 0.00 EUR 5,000.00 EUR 10,000.00 EUR 15,000.00 EUR 20,000.00 EUR
  • 27. Clicked vs bought items Shop overview Revenue Category of bought item Category of clicked item ● Filter: transactions where exactly one item was clicked and exactly one item was bought
  • 28. Clicked vs bought items ● 22% of this top category’s click-attributed revenue actually converts in a different category Single category Category of clicked item Category of bought item Revenue
  • 29. Basket Margin calculation Offline Conversion Import Goal Adjustment Gross profit calculation ● Calculate the order profit regularly for every transaction ● Gross profit = Revenue - COGS ( - Transactional costs, optionally) ● Connect the conversion with an adclick (Click-ID) Offline conversion Import Unlock order profit for optimization ● Regular upload of offline conversion feed to Google Ads ● The feed features: Click-ID, conversion time, conversion value, currency Goal Adjustment ● Rethink and adjust your goal setting, your current ROAS goal won’t fit ● Ensure that all stakeholders are on board, optimization based on profit ≠ optimization based on revenue
  • 30. Challenges while planning and performing this task ● Large data volumes ● Data availability, timeliness, and fallbacks ● Intra-day price changes ● Conversion lag ● Returns and refunds
  • 32. God Metric Order Data Demand Price Stock A perfect conversion model