PPC Optimisation Beyond Human Capabilities

Post on 18-Jan-2015

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Take a look at our Technical PPC Manager, Stewart Robertson's,' slides from his presentation on automating large scale PPC campaigns taken from our recent PPC Best Practice in 2014 and Beyond conference.

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© Search Laboratory Ltd 2014. All rights reserved.

Leeds T: +44 113 212 1211

London T: +44 207 147 9980

Maximise Visibility & Minimise Waste: PPC Optimisation Beyond Human CapabilitiesStewart Robertson – Technical PPC Manager

Agenda

Granularity

Difficulties of scale

– Lots of products

– Staying up to date

Bidding at scale

– Low volume terms

– Comparison groups

– Smoothing

Summary

The benefits of a granular approach

Granular ad text

If you searched for ‘purple scarf’ which ad would YOU click?

Granular bidding

Which of these users is more valuable to you?

Granularity

Granularity means:

– More specific, better performing ad texts

– Bidding the correct amount for every possible keyword

– Higher ROI, less waste, better returns

The problems of scale

Example

A company specialising in Red Armani Fedora Hats could end up with ad groups such as these:

– Red Armani Fedora Hats

– Armani Fedora Hats

– Red Fedora Hats

– Red Fedoras

– Red Hats

– Fedoras

– Hats

– Headwear

We also might show ad text such as “Up to 25% off Red Fedora Hats” or “Armani Red Fedoras from £30”, etc.

The problem of scale

But what do we do if the company also sells:

– 10 colours?

– 10 brands?

– 5 sizes?

Plus a similar range of top hats?

… And caps too?

Very quickly the numbers get too big to deal with

How do we solve it?

More people or less granularity?

Equivalently:

– More operating costs or lower return?

How about a third option…

Tailored automation

Unique

Bespoke

Flexible

Creation

Updating

BiddingError checking

Integration

KeywordsAds Ad groupsCampaigns

PricesStockSalesPauses

Keyword-levelStructuredMathematical

Double checksManual & automated

Ad platform API

Process overview

Item Id Brand Product Classification Colour RRP PriceA0001 Armani Headwear > Hats > Fedoras Red £40.00 £30.00A0002 Nike Headwear > Hats > Fedoras Red £38.00 £10.00A0003 Haat Headwear > Hats > Fedoras Red £27.00 £27.00A0004 D&G Headwear > Hats > Fedoras Red £412.00 £299.00A0005 Prada Headwear > Hats > Fedoras Red £38.00 £30.00A0006 Kangol Headwear > Hats > Fedoras Red £29.99 £29.99A0007 Boss Headwear > Hats > Fedoras Red £99.00 £67.00A0008 Nike Headwear > Hats > Fedoras Red £18.00 £14.00

Process overview

Red Armani Fedora Hats

Armani Fedora Hats

Red Fedora Hats

Red Fedoras

Red Hats

Fedoras

Hats

Headwear

Extract fields from feed

•Convert into usable text-strings

•Manually build Synonyms

•Derive categories from taxonomy or classification

Plan targeting item types

•Combinations of fields

•Multipliers

Derive keyword & ad logic

•Synonyms

•Multipliers

•Templates

•Thresholds

Write automation

•Parse feed into targeting items

•Generate campaigns, keywords, ads, etc.

•Sync with Ad Platform

Process overview

Process overview – key points

Product level alone is not normally enough

– Cover higher level categories

It’s not enough to have business-specific templates

– we need business-specific logic

Setup needs a high level of human input

Ask the right questions!

Bidding at scale

The bidding problem

What is the conversion rate of a keyword with 1 conversion from 7 clicks?

Only 95% sure that the Conversion Rate is between 0.36% and 57.87%

The bidding problem

Determining the conversion rate on broad high-volume terms is easy

The problem occurs in low-volume long tailed terms

How can we get the most accurate measure of conversion rate?

– Look to other similar terms

– External data

Similar terms

Group keywords based on user intent:

“Red Armani Fedora”“Blue Armani Fedora”“Green Armani Fedora”

“Cheapest Armani Fedora”“Discount Armani Fedora”

“Armani Fedora”

“Armani Fedora Hat”

Comparison groups - Smoothing

Pool statistics from the comparison group

– But don’t ignore the keyword’s own stats:

Keyword 3%150 clicks

Group 1.5%10,000 clicks

2%

2.5%Keyword 3%300 clicks

Group 1.5%10,000 clicks

Comparison groups - Smoothing

So how do we incorporate external data? We believe our keyword out-performs the group by 50%

2.5%Keyword 3%300 clicks

Group 1.5%10,000 clicks

2.75%Keyword 3%

300 clicksAdjusted

Group Rate 2.25%

BidLabTM – How it works

Custom tree structure based on intention

All keywords will use the most relevant data

Very tightly-grouped in a natural fashion

Hats

Top Hats Fedoras

HeadBrand Based Colour

Based

Head Terms

BlueRedGreen

BidLabTM

Daily process

Download Statistics from API

Calculate Conversion

Rates

Perform Autobidding Calculations

Post New Bids to

Account via API

Summary

Summary

Maximising visibility from PPC campaigns depends on:

Granularity

Up-to-date, focussed, relevant ad copy

Full product & category coverage

Accurate bidding

Minimising waste means knowing how to automatically:

Add or update hundreds of new campaigns

Add or update hundreds of thousands of ad texts & keywords

Accurately manage bids on millions of low-volume keywords

Every single day!

Questions?