advanced google shopping by andreas reiffen
TRANSCRIPT
#SMX #11B @AndreasReiffen
About…
• Data-driven online advertising strategist
• Online retail expert
• Entrepreneur
• €3 billion in customer revenues this year
• SaaS product for Google Shopping & Search
• 130 true experts in their field
• Offices in Germany & UK, new office in NYC
… me … crealytics & camato
#SMX #11B @AndreasReiffen
Using Shopping to manage inventory
Topics we‘ll cover today …
How does price influence performance? Will pricing and bidding become one in the future?
Can you use Shopping to push slow sellers? How to avoid common pitfalls?
The role of price in Google Shopping
#SMX #11B @AndreasReiffen
Google Shopping spending already surpasses text ads in in the US and UK
DE spending share
US spending share
UK spending share
72% 67% 57%
H116
43%
90%
H214
23%
77%
H215
28%
H115H114
33% 10%
ShoppingTextAds
36%
H214
59%
H114
64%
41%
56%
H215H115
44% 46%
54%
H116
44%
56% 34%
H116
49%
56%
H215
51% 46%
H214
44%
44%
H115
54%56% 66%
H114
AnalysisbasedoncrealyticsdatafromretailcampaignsinFashion,Luxury,OutdoorandSports;20Mclicksintotal
#SMX #11B @AndreasReiffen
You want to push slow sellers while saving budget for products which are almost sold out
Product Stock level over time Action
Week 1 Mark down!
Week 1 Sold Out!
#SMX #11B @AndreasReiffen
40% of budget is allocated to products that will sell on their own within 3 weeks
Comment
PPC budget should be allocated to inventory that is high in stock or sells slowly At the moment 40% of all budget is spent on quick sellers while only 21% is spent on products that will be in stock after 3 months time
PPC budget allocation by stock projection for top 100 products
* top 100 products, at least 1500 clicks per week / product
40% of budget 21% of budget
10
21 16 14 13 12 11 10
15
24
20
0
20 17 18 23
5
22 19 15 2 1 6 8 4 7 9 5 3
Spend
#SMX #11B @AndreasReiffen
Most people do not buy what they are looking for
Only 34% of conversions match the product that was clicked
#SMX #11B @AndreasReiffen
Same Designer Different Designer
purchase click
34%
16%
14%
15%
21%
64%
36%
Diff
ere
nt
Ca
teg
ory
Sa
me
Ca
teg
ory
We analysed what people actually bought when they clicked on a product ad
#SMX #11B @AndreasReiffen
If people don’t buy what they are looking for, how can you manage inventory via Shopping?
#SMX #11B @AndreasReiffen
With yield we refer to the real value contribution of selling a product via paid advertising
How-to Example
$80
Product
$80
$0
Stock level over time Yield Action
Week 1 Mark down!
Week 1 Sold Out!
$90
#SMX #11B @AndreasReiffen
Track all products which were sold after a click on a product ad
How-to Example
1,000 clicks
11 Items sold
x 4
x 4
x 2
x 1
Conversions Product clicked
#SMX #11B @AndreasReiffen
Normally you take your margin values to inform the bidding about the value of an advertisement
How-to Example
Products purchased
4
4
2
1
$120
$80
$8
$22
Total Margin = $230
Product Clicked Quantity Margin totals
$150
Margin
$30
$20
$4
$22
#SMX #11B @AndreasReiffen
Based on this data the bidding calculates a value per click and suggests a bid
Clicks = 1000
Value per click: $230 / 1000
Existing value per click
= $0.23
How-to Example
#SMX #11B @AndreasReiffen
The blue sneaker is a slow seller which we want to push
How-to Example
Products purchased
4
4
2
1
$120
$320
$8
$22
Total Margin/ Yield = $470
Product Clicked Quantity Margin/Yield Totals Margin/Yield
$30
$80
$4
$22 $150
#SMX #11B @AndreasReiffen
New bid takes yield into account and will be higher
Clicks = 1000
Value per click: $470 / 1000
Updated value per click
= $0.47
Value per click increases $0.47 which will be reflected in a higher bid and more sales of the black sneaker
How-to Example
#SMX #11B @AndreasReiffen
What people buy after a click on a product ad is often very random. Does our approach work anyhow?
1) Brands and Categories have specific stock level profiles 2) Stock level profiles per brands and categories stay consistent over time
Our approach will work if …
64% same designer
65% same category
Clicked to bought
#SMX #11B @AndreasReiffen
Designers (and categories) have specific stock level profiles which are stable over time
QUAY AUSTRALIA Inventory average NEW LOOK
41% 45% 38%
30% 31% 35%
29% 24% 26%
week 3 week 2 week 1
39%
22%
avg
38%
21% 26% 31%
76% 70% 67%
2%
week 2
5%
week 1
2%
week 3
normal low stock high stock
Low stock = sold out within 2 weeks
High stock = will last 3 months
#SMX #11B @AndreasReiffen
When we simulate the effects on CPC, clicks and conversions, we see a more effective acquisition
Account result change
Product acquisition change
100%
Yield
100%100%
171%147%
190%
Cost Revenue
MarginacquisitionYieldacquisition
Comment
Our simulation shows that by incorporating yield values more high stock level products and less low stock level products will be sold Yield grows faster than revenue as yield is the KPI we are optimizing towards
lowstock
highstock
normal
+87%
-32%
+20%
#SMX #11B @AndreasReiffen
Pro-actively managing inventory is
possible
People usually don’t buy what they click on
Incorporate yield values to
manage inventory
$.47
Your key takeaways
#SMX #11B @AndreasReiffen
Impressions and clicks in Google Shopping are often very sensitive to price changes
Clicks drop off after product price increase Chart Info
5% price increase coincides with a 60% decrease in clicks
0
20
40
60
80
100
120
0
10
20
30
40
50
60
Pricein£
+5%
clicksownprice Google product category: Apparel & Accessories > Shoes > Sneakers Brand:
Days
Clicks
Price
#SMX #11B @AndreasReiffen
Google will take traffic away from you if your product prices exceed the average market price
Product price change
to avg market price Sum of Imps Sum of clicks Result
+43%
After
106%
Before
74%
-70%
30,002
Before After
100,239
683
-79%
After
3,222
Before
Google takes away 70% of traffic Result: to maintain traffic, you will need to bid much higher *based on 700 products
Product pricing is key to success in Google shopping
#SMX #11B @AndreasReiffen
We compared cheap vs. expensive products and used a test setup to guarantee meaningful results
We compared
cheap products (below avg. price)
with expensive products (above avg. price)
The Idea
• At least six competitors • Available at all times • Similar products (all
sneakers)
The Criteria The Test
• Products were excluded from normal shopping activity
• Results were not influenced by regular bidding activities
#SMX #11B @AndreasReiffen
For all products we often see S-shaped curves, but cheap products generate traffic much earlier
There is a direct relationship between product price, maxCPC bid and impressions Key insights
Impression volume for expensive products significantly lower Cheap products hit Impression limit after 5 days
1.2
1,000 0.6
1.0
0.4
0.2
0 0.0
500
0.81,500
2,000
5-13-165-11-16 5-12-16 5-15-16 5-17-165-16-165-14-16
MaxCPCImpressionsperproduct
MaxCPC
expensiveproductscheapproducts
#SMX #11B @AndreasReiffen
Average CPC is higher for products with prices above average market price
Max CPC bid, Avg CPC while bidding up Key insights
Avg. CPCs of expensive products is about 15% higher despite same bids
5-16-16 5-17-16
0.25
5-11-16
0.43
0.750.65
0.710.65
5-15-16
0.580.54
5-14-16
0.480.35
5-13-16
0.33
5-12-16
0.180.14
cheapproducts
MaxCPCexpensiveproducts
#SMX #11B @AndreasReiffen
Cheap products generate the lion‘s share of total traffic
Traffic of similar products within one shop Key insights
Cheap products generated much more traffic despite similar number of products in both groups
Imps
134%
4.282.611
1.828.412
103.803
135%
Clicks
44.122
2.047
9%
#ofproducts
1.876
expensiveproducts cheapproducts
#SMX #11B @AndreasReiffen
Despite getting more traffic, the performance of the cheaper products is much more efficient
Performance of similar products within one shop Key insights
Number of orders through cheap products almost 3 times higher Higher CR resulting in CPO being ~30% lower Cheap products generate 280% more conversion
40
28
-29%
CPO Conversions
1,042
+280%
274
+61%
0,6%
CR
1,0%
expensiveproducts cheapproducts
#SMX #11B @AndreasReiffen
180
55
0
5
45
15
50
40
10
There is not really a long tail: A small number of products drive the majority of the sales
Conversions per product multi brander UK Key insights
More significantly than in search, shopping conversions are driven by just a few products Concentrate on those products for account optimisation.
Top 10% products = 58% conversions
*Chart displays all products with conversions (448)
ExpensiveCheap
#SMX #11B @AndreasReiffen
Cheap products are converting significantly better than expensive ones
Key insights
Only a few products are responsible for more than half of all conversions
Selling only a few products at a cheap price can make a big difference
Total conversions of similar products within one shop
Conversion Distribution
3% 38%
58%
Top 10% Middle 80% Bottom 10%
20%
Share of top 10%
converting products
80%
Cheap Expensive
#SMX #11B @AndreasReiffen
Your key takeaways
Don’t overbid on expensive
products, rather consider price
changes
Price and bid management
will be merged one day
Discounting only a few select
products could be a killer strategy
$.47 $.47 -50%