Werbeplanung.at SUMMIT 15 – Google Shopping und der Long Tail – Performance-Hebel Bid Management – Christian Scharmueller

1. Grundlagen Google Shopping Ads Bidmanagement für Google Shopping Kampagnen Ausgangssituation Kampagnenstruktur Lösungsansatz - Bidding the Longtail Insights – CPC Erhöhungen Agenda
2. Was ist Google Shopping
3. Bis zu 8 Anzeigen - entweder Top oder rechts positioniert Multiple Platzierung möglich Shopping Ads
4. Quelle: Rimm-Kaufmann Group Shopping Ads - Performance
5. Erfolgskriterium Bid Management
6. Ausgangssituation
7. SHORT HEAD LONG TAIL 20% der Produkte generieren 80% des Umsatzes Anzahl der Produkte U M S A T Z I N € 80% der Produkte generieren 20% des Umsatzes Klassischer Longtail
8. Longtail: 80% der Shopping Ad Verkäufe werden von Produkten generiert, die nur 1 Conversion aufweisen. Shopping Ads Longtail
9. 60.000 40.000 20.000 10.000 123456789101112 ANZAHL DER CONVERSIONS PRO PRODUKT U M S A T Z I N € Shopping Ads Longtail - Umsatzanteil
10. Go to: https://gist.github.com/smec/aa6c52a1fd874984f43a Paste in “Bulk Edits” Skript ausführen Analyse unter “Logs” Ihre Longtailverteilung
11. Problematik Hoher Aufwand bei manueller Optimierung Ereignisarmut Wenige statistische Daten Über - oder Untersteuerung herkömmlicher Tools Zeitverzögerung aufgrund fehlender statistischer Daten Herausforderung
12. Ziele Valide Umsatzprognosen für Produkte mit wenig statistischen Ereignissen Frühzeitiges Trennen von Kostentreibern & Umsatzbringern Setzen des optimalen CPC für jedes einzelne Produkt im Sortiment Ziele - Bidmanagementstrategie
13. Kampagnenstruktur Best Practice
14. €0.50 €0.50 €0.50 €0.50 Oft verwendetes Setup: Undifferenzierte Kampagne Überlappender CPC für alle Produkte Problem: Zu wenig Detaillierungsgrad
15. Oft verwendetes Setup: Kampagnen differenziert nach Produktgruppen/Marken Überlappender CPC pro Marke / Produktgruppe €0.50 €0.42 €0.65 €0.74 €0.65 €0.65€0.65 Nike Laufschuhe Adidas Laufschuhe €0.42 €0.42 €0.42 Problem: Zu wenig Detaillierungsgrad Nike Laufschuhe Adidas Laufschuhe Puma Laufschuhe
16. Kein individueller CPC auf Produktebene Gemittelte Statistiken Ausgleichskalkulation - „Bad Products“ werden unterstützt Schnelles Erkennen von Umsatzbringern und Kostentreibern Split nach Produktgruppen: € 100 Kosten, € 1.000 Umsatz (max. KUR: 15%, IST-KUR: 10%) € 75 Kosten € 50 Umsatz € 10 Kosten € 850 Umsatz € 15 Kosten € 100 Umsatz €0.65 Differenzierung nach Produktgruppen
17. Ein individueller CPC pro Produkt (max. 20.000 / campaign) Kontrolle der aktuellen Produktperformance Split nach Produkten: € 25 Kosten, € 950 Umsatz (max. KUR: 15%, IST-KUR: 2,63%) € 75 Kosten € 50 Umsatz € 10 Kosten € 850 Umsatz € 15 Kosten € 100 Umsatz €0 €0,65 €0,65 Item-based Bidding
18. €0.32 €0.30 Individueller CPC pro Product ID €0.33 Empfohlene Accountstruktur Nike Laufschuh Modell 1 Nike Laufschuh Modell 2 Nike Laufschuh Modell 3
19. Bidmanagement Best Practice
20. Clicks Conversions Conv Rate (CR) 3.000 27 0,9% 1.000 10 1% 1.000 1 0,1% 100 5 5% 100 1 1 % ? 50 1 2 % ? 1 1 100 % ? 10 0 0 % ? 300 0 0 % ? Konversionsraten - Was nun?
21. Clicks Conversions Conv Rate (CR) CR-Interval* 3.000 27 0,9% 0,59 - 1,31 % 1.000 10 1% 0,45 - 1,83 % 1.000 1 0,1% 0 - 0,56 % 100 5 5% 1,64 - 11,28 % 100 1 1% 0,03 - 5,45 % 50 1 2% 0,05 - 10,65 % 1 1 100% 2,5 - 100 % 10 0** 0% 0 - 30,85%** 300 0** 0% 0 - 1,22 %** * 95% Konfidenzintervall, Pearson-Clopper-Method -> konservative Methode ** Rule of Three: 95% Konfidenzintervall zwischen 0 und ca. 3/n: zb. 3/10 = 30% und 3/100 = 1% Konversionsraten – CR-Korridore
22. Einrichtung 8% Anzahl der Zimmer 25% Garten 16% Balkon 14% Kücheneinrichtung 23% Parkplatz 14% Unterschiedliche Faktoren beeinflussen die Variable „Mietpreis“ - Ähnliche Beziehungen gibt es auch in Ihrem Shopping Account zwischen Produktperformance und Produkteigenschaften Analogie: Mietspiegel
23. Anwendungsbeispiel I
24. Anwendungsbeispiel II
25. Aggregation – Marke + Kategorie
26. Aggregation – Marke + Kategorie
27. Aggregation - Preis
28. Aggregation - Preis
29. Lineare Regression
30. Product price y = βx + d Regressionsanalyse Zusammenhang zwischen Produkteigenschaften und Produktperformance
31. Product price Search Queries with Numbers Search Queries w/o Numbers Conversionrate 0,8% 0,9% 1,0% 1,1% 1,2% 1,3% 1,4% Regressionsanalyse Zusammenhang zwischen Produkteigenschaften und Produktperformance
32. 6 vor Biddingstrategie 6 nach Biddingstrategie Performance Entwicklung
33. Insights CPC-Erhöhung
34. Steigerung des Bids bei Shopping Campaigns 1. Impressions + 2. Klickraten ++ 3. Klicks: +++ 4. Kosten +++ 5. Konversionsrate ~ 6. Konversionen +++ 1. Impressions + 2. Klickraten + 3. Klicks: ++ 4. Kosten ++ 5. Konversionsrate - - 6. Konversionen ~ + Steigerung des Bids bei Shopping Campaigns Besonderheiten Google Shopping Ads
35. Steigerung des Bids bei Shopping Campaigns führt nach vollständiger Präsenz bei produktspezifischen Termen zu einer semantischen Erweiterung ähnlich des Broad-Matches: Samsung UE55H6270 0,02 Cent Gebot -> kein Erscheinen bei Suche nach “UE55H6270” 0,08 Cent Gebot -> erscheint bei Suche nach “UE55H6270” 0,20 Cent Gebot -> erscheint zusätzlich bei Suche nach “Samsung Fernseher” 0,40 Cent Gebot -> erscheint zusätzlich bei Suche nach “Fernseher kaufen” 0,60 Cent Gebot -> erscheint zusätzlich bei Suche nach “Fernseher” Gepflegte Google Shopping-Kategorie: Fernseher, Marke: Samsung, Product Type: Fernseher Besonderheiten Google Shopping Ads
36. Eine noch spannende Summit!

Notes de l'éditeur

Vorstellung Jan
Vorstellung Chris
….

We hope you understand us well, despite the accent, and that you can pick up on all the details.
The accent originates from Austria – from Linz to be more precise.
I started the Smarter Ecommerce GmbH (Company) there in 2007. We are a software firm specialized in SEA automation. Since the founding, we have been developing our ecosystem with direct Google API linkage.
The core is our own script language, respectively, domain-specific language (DSL) which we call “smeco-script.” We enable large retailers to have complex campaign generation and management automations for their performance with Google TextAds. These are tailor-made solutions adjusted to the respective data feed structures that lead to advantages in performance and maintenance expense for over 200 accounts.

The founding idea of the company was the intensive utilization of the long tail through text ads. This highly individual automation not only enables the comprehensive scripting of text ad keyword combinations in the long and mid-tail areas, but also the agile management of your campaigns on the basis of changing feed parameters such as prices, stock levels, etc.
We thus create the reflection of tactical sales measures, such as the up-to-the-minute communication of prices for selected product range segments, the separate handling of stock levels, the reflection of sales prices with price savings or the pushing of product novelties in the text ads.
Coming from this history, it is only logical that Google Shopping Ads are a very crucial cornerstone of our work. Through our own work in the management of Shopping Ads for customers and the reports of our customers with their own SEA departments, a very large need crystallized in the last year. There is no bid management solution on the market that can really deal well with the special requirements of Google Shopping Ads.
Therefore, in addition to the Ad Engine for the generation and up-to-the-minute managing of extensive text ad campaigns, we have been testing and further developing new approaches for a bid management specialized in Google Shopping Ads in our accounts since the summer of 2014. As the algorithm became increasingly more accurate at the end of 2014 and the tests ran extremely positively, we released an MVP with the most important functions in March 2015 and are currently developing a comprehensive app that will be launched in June 2015. Let’s now take a look together at what the special requirements of Shopping Ads are.
Start der Shopping Ads im August
Davor in Österreich seit ca 1 Jahr als Product Listing Ads verfügbar
Experiences gained from more than 200 AdWords accounts show that the creation of a product-specific campaign structure can be considered as the clear best practice.

Only in this way can every single product be evaluated and managed according to the actual performance.

On the campaign level, a differentiation can naturally be made according to brands or categories; what is important is the setting of an individual product target per product ID among the campaigns to make each product “assailable.”

Only if each product ID has its own product target, every product can be managed according to the actual performance

Which influence this can have on performance was shown by the previous slide. Working on product groups with averaged metrics is very dangerous at times and simply inefficient on a ROAS basis.

This campaign structure thus constitutes a basis for an efficient management of the long tail in the Google Shopping realm.

For the creation of a product-specific campaign structure, we recommend the direct linkage via the Google API – over Bulk Upload in the Google Interface —> it is very slow, and takes 2-3 hours of effort per week, especially with rapidly changing product ranges.
Let’s get down to the bottom of the matter.

So if one wants to make reliable assertions, respectively, estimates about the conversion rates, you have to work with very high confidence interval, in this concrete example with a confidence interval of 95% – the Pearson-Clopper method – in other words, the assertions allow a 95% certainty.

But if you want to make assertions with a high probability there are really large scope the conversion rates show. Especially products with few events do not allow de facto any concrete assertions.

But as we pointed out at the start, big part of your shopping Ad revenue is created by your long tail – that is, products with more than 2 conversions

For instance, products with 50 clicks and 1 conversion? If a relatively certain assertion is to be made here, it can merely be claimed that the conversion rate lies between 0.05% and 10.65%. An online marketing manager cannot do anything with this estimate.

At the same time you have to handle products with 0 sales as well – to predict whether the product will ever perform is a crucial performance lever.

At 10 clicks and 0 conversions, the conversion rate lies with very high probability between 0 and 30.85%. Again, This assumption is not very useful

As you can see, the prediction of the interesting variables like CR and Shopping Cart Values on the basis of classic metrics such as the conversion and the number of clicks is very difficult and, because of the large corridors, not very helpful in part.

And they leave a lot of space for errors.

How could a problem-solving approach look here?
Ebene Product Item
Ebene: Product Type
In practice, we meanwhile use a catalog of more than 100 different attributes, based upon which we calculate correlations with the product performance.

The starting position is always the historical account data with which we determine the existing correlations. This works through a selection of variables, that is, individual correlations are determined for each account on the basis of the actual data. And only those variables that also show the actual correlations to product performance are included in the model.

the algorithm calculates which correlations exist and here are highly exciting correlations for the attributes, which are not very conclusive at first glance.

We see, for example, in the electronics sector that products whose search queries very often contain numbers show a higher CR than product titles without them.

So in the, several regression analyses with different variables are laid on top of each other - dependent on wicht variables correlate to the product performance

In the concrete example, we see a television model for which we exemplarily illustrate different attribute correlations to the product performance. Here we draw on attributes such as the influence of the brand, the influence of the price, the availability, the average number of orders, the size and the age of the product.

So, several regression analyses with different variables are laid on top of each other, so to say, – always with the goal of calculating a correlation with the shopping basket value and the conversion rate. If this is carried out with the proper variables, a prediction of the product performance is also possible without many events – that is, clicks and conversions.

This is analogous to the example of the correlation of the variables of product price and shopping basket value.

As already mentioned, the variables that impact product performance are different in every account. It is also interesting that the variables can also be expanded by the customer at will. A large online shop for sporting goods has, for instance, conveyed the number of sizes per shoe model for the shoe segment. The background is that a shoe with several sizes in stock shows distinctly higher conversion rates.

In sum, the creation of a product-specific campaign structure and the extension of the evaluation basis beyond classic metrics like conversions, clicks, etc., are considered as problem-solving approaches for a valid and quick adjustment of the long tail.

Werbeplanung.at SUMMIT 15 – Google Shopping und der Long Tail – Performance-Hebel Bid Management – Christian Scharmueller

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