1.
What to expect
in this presentation
• An in-depth, technical review of the core
features of the Enhance Ecommerce platform
• Tactical implementation examples and how to
use the resulting data
• My current take on the platform.
@analyticsninja

2.
Product Lists

3.
Product List Performance
• Category Pages
• Search Results Pages
• Cross Sells
• Up Sells
• Promoted products (like homepage
splashes)

4.
Merchandising

5.
Product Scope
Impression list #1 / product impression #9 / ID, Name, Position

6.
Product Scope
• With the product scope, you can send
data for multiple products on a single hit.
• For example, the pageview will now
contain data that allows you to answer
the question  show all products that
were displayed on a category page that
are almost out of stock and what
position were they in.

7.
Import dimensions to enhance data

8.
Custom dimensions to enhance data
• Product freshness
– season / added to inventory date
• Product level promotional descriptions
– On sale, featured item, etc.
• Current product stock amount
• Product reviews
• Product level profit margins
• Product attributes such as color, size, weight,
manufacturer, distributor, warranty

9.
Cross sells

10.
Cross sells

11.
Up sells

12.
Product List Name

13.
Impressions and Actions
• Impressions are meant to model the
choices the user can make.
• Actions are meant to model the decisions
the user made.

14.
Product clicked

15.
Product list position

16.
What do we do with it?
• Cross Sell / Up Sell performance the most
exciting aspect of product list “impression” data
– Recommendation engine optimization (@fastbloke)
• Insights regarding merchandising placement
within category pages can be gleamed, though
sales data is a stronger indicator of when to
highlight products (as we shall see later).
• Revenue from a product list view is reported via
a “last action” attribution.

17.
Product Detail Views

18.
Product Detail Views

19.
Product Performance Reports

20.
Product Performance Standard Metrics
• Good use of standard dimensions that include 5
levels of product taxonomy and brand.
• Big benefit of providing Cart to Detail and Buy to
Detail Rates – many businesses wouldn’t be
accessing those metrics. Propensity to purchase!
• For now, those metrics are based on VIEWS
instead of unique views, which I believe doesn’t
model shopping behavior in the optimal way.

21.
Calculated Cart to Detail

22.
Calculated Buy to Detail Rate

23.
Mix in Product Scoped Custom Metrics

24.
Dimensions vs. Metrics
• Show me all product views that for products that
have a profit margin of 40% or higher
– CUSTOM DIMENSIONS
• Show me the top 10 profit generating products
– CUSTOM METRICS

25.
Enhanced Product Performance Reports

26.
What do we do with it?
• Advertise. Duh!
– Product Listing Ads
– Keyword bidding
• Product placement on category pages and
homepage splashes.
• Help guide merchandising decisions

27.
Overall Shopping / Checkout Behavior

28.
Overall Shopping / Checkout Behavior

29.
Not really different than horizontal funnels
but DOES make these setups available to more users

30.
Quick segmentation capability
from within the interface

31.
Additional Standard Dimension of
Checkout Options

32.
Overall Shopping / Checkout Behavior

33.
Same basic funnel in KissMetrics, but the
denominator is “people”

34.
Current issues with checkout funnel
• Rigid
– Not all sites follow the same checkout funnel, this just
models the most common use case.
– Different payment options have different checkout process
that happen offsite (PayPal).
– Users may log in an automatically bypass many of the
predefined steps.
• Sessionized
– Purchases process may not be within a 30 minute window
• :-/

35.
Refunds – I don’t have data,
but it looks like it’s good!

36.
Internal Promotions

37.
Internal Promotions

38.
Discounts

39.
Discounts

40.
Discounts

41.
Discounts
• Track total value of discounts on order and
product level using custom metrics.
• Discounts may increase demand, but may not be
increasing profitability.
• Make sure to apply the order level discount
across the Product Price for all products being
purchased. A common error is to leave the
product price as a static value at point of sale,
and not apply the discount to the Product
Revenue.
• Follow @minethatdata (Kevin Hillstrom)

42.
Now what?  Segmentation

43.
Now What?  Segmentation

44.
Now What?  Segmentation

45.
Now What?  Segmentation

46.
Now What?  Segmentation
• Prioritize owned, earned, and paid marketing
efforts by page type and product type.
• Create meaningful GDN retargeting audiences in
GA based on segments (winners and losers)
• RLSAs need to be served via pixel to include the
“conversion label”. Armed with a strong GTM
implementation and GA data, create awesome
RLSA lists with a few clicks.

47.
Final thoughts
• Being able to send a large amount of impression
data (including promotions) on a single hit is a huge
improvement in GA architecture.
• Product lists, especially for cross sells and upsells, is
a great feature. Internal promotion tracking also
very good.
• Much of the merchandising related data model in
Enhanced Ecommerce was “doable” before product
launch, but is a very welcome addition to the
platform now that it is built in. User adoption.

48.
Final thoughts
• Session based data is still “meh”, same with Buy to
Detail rates being based on views.
• Vocabulary of product related “verbs” (like add to
cart, purchase) is solid, but a bit rigid (no native
way to track ‘add to wishlist’, ‘social share’, ‘video
play’.
• Product scoped custom metrics for profit metrics
are relatively easy to implement and major in
terms of data value.

49.
Final thoughts
• Generally speaking, I think that there are lots of
“actionable items” that come out of the new
platform.
– Product level advertising insights
• especially good for PLAs
– Smart Remarketing
– On site merchandising decisions
– On site promotional decisions
– Improve performance of recommendation engines

50.
Current @AnalyticsNinja Rating