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Personalised retargeting, dynamic creative optimisation and RTB Greater display advertising reach and relevance October / 2012 1


1. introduction Advertisers don’t have it easy. As the digital media landscape becomes ever more fragmented, it’s increasingly challenging to target specific customers and user groups. This is particularly the case when it comes to display advertising where multiple factors can determine a campaign’s success. Advertising campaigns don’t have to suffer from wastage, however, as developments in technology are helping refine the accuracy with which consumers can be targeted. Getting the right message to the right users at the right time isn’t witchcraft, but rather technological progress that offers a more efficient means of delivering richer and more relevant content that customers respond to. Three approaches in particular are making online marketing campaigns more efficient and effective – personalised retargeting, dynamic creative optimisation and real-time bidding. Each is highly effective, used on its own, but when intelligently combined, the effectiveness increases exponentially. However, even with a high degree of automation, there are certain criteria which will ensure you achieve the best from your campaigns. This white paper explains how this effective trio can be put to best use and what factors need to be taken into account.

Ellie Edwards Managing Director, QUISMA UK

Personalised retargeting + dynamic creative optimisation + RTB = greater display advertising reach and relevance

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2. The database – The foundation for personalised retargeting Being able to continuously optimise campaigns in real-time is a significant advantage of online advertising. A robust database is a must for this. The more that is known about a customer, the more precisely his or her specific interests can be served. In order to build up the data, customers are initially served a cookie on visiting the advertiser’s website and assigned to a particular customer segment (e.g. new customer, existing customer, etc.). Their behaviour when shown advertising is then tracked and stored anonymously. For example if a customer performs a search query in a search engine and then clicks through to the advertiser’s website or if they viewed a display banner ad or interacted with an email newsletter. All of these actions are stored against their cookie within the database. It’s important to note that no personally identifiable information such as name or address is tracked via the cookie. Even the smallest amount of information that can be derived about a particular customer, such as a visit to a single product page, can provide valuable information about his or her potential interests. A personalised product selection can then be created in anonymised form for each customer on this basis. When customers leave the advertiser’s website they are shown advertising which includes these products. This is called retargeting and is most commonly used in display advertising. For example, someone abandoning a transaction on the order page, can subsequently be shown advertising containing the product that was added to the shopping cart but not purchased as well as other recommended items.

A multitude of behavioural attributes are used to generate the dynamic product selection such as the click- and buy-behaviour, onsite search queries and recommendations (see graphic). All of these attributes are stored within the database and updated in real-time as the customer’s behaviour changes. The customer’s response to the advertising shown is also recorded within the database such as when he clicks on a particular product in a dynamically generated banner. In this way, the database is constantly ‘learning’, and the optimisation process is continually refined to ensure the customer is served the most relevant and up to date content suitable to their profile at any given time. 3


The creation of a valid database is the foundation for optimised campaign management.

Search result

• Queries • Typeahead clicks • Search result clicks

Shopping cart

• Click-and-buy behaviour from online shop

• Recommendation on product detail page, in shopping cart and at checkout • Customer ID

Database 001101001 001101001 00101011111 001101001 00101011111 100001010 00101011111 100001010 101000110 100001010 101000110 0101000111 101000110 0101000111 0110110010 0101000111 0110110010 11101101001 0110110010 11101101001 010111000 11101101001 010111000 010111000

Newsletter

• Click-and-buy behaviour from the customer newsletter

• Click-and-buy behaviour from

Produkt ABC

• Dynamic product selection based on user behaviour

retargeting banner

NEW

3. campaign delivery – It’s all about diversity Once a customer’s cookie is recognised, he can be accompanied along his online journey and served relevant advertising at the right moment in order to reawaken his interest in the advertiser’s product. There is a simple formula for this – the greater the access to media inventory, the higher the propensity to ‘find’ the customer across the web. Diversity in both websites and ad placements also play a major role in ensuring the advertiser has the best options for reaching the customer.

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Targeting technology can now determine not just the best creative for each customer, but the best ad placement to show the ad. Both of these can be further optimised as more information is known about the customer’s behaviour. This means that each time a customer cookie is identified multiple decisions are made in real-time to determine which creative should be shown and where on the page the ad should appear. All of these decisions are optimised to ensure the customer has a positive ad experience that ultimately leads to a conversion. Following are examples of ad placements and formats.

Accompany the customer on his journey:

Premium placements

RTB/long-tail placements

Content placements

Special placements

Format: IAB standard

Format: IAB standard

Format: Picture/text combination

Format: Pop-under

Publisher websites

Publisher websites

Publisher websites

Entry

Publisher websites

Orientation

Content

Exit

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Use of different inventory sources and formats for optimal targeting.


Frequency capping: accompanying, not stalking The success of user targeting also strongly depends on how often the same ad creative is delivered to the customer. Optimised frequency capping ensures that each customer is only served the right number of ads to elicit a positive response and does not feel like he is being stalked by a product or brand. “Ad fatigue” is a common theme used and research has shown that most internet users either become annoyed or stop registering an ad after five exposures. More advanced ad serving technology can identify what the optimal frequency is for each cookie rather than having to install a blanket approach across all customers. After how many repeats of the same online advertisement from an online shop do you normally feel bothered?

50.8%

25.6%

9.3%

After the 3rd repetition

After the 5th repetition

From the 10th repetition

9.0%

5.3%

Not at all

Don’t know

The effect of repetitions on Internet users. Source: Fittkau & Maaß, W3B Report ‘Purchase Decisions on the Internet’ [‘Kaufentscheidungen im Internet’], 2012

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4. Extending reach using real-time bidding Sufficient reach is needed in order to find ‘known customers’ online and to allow for retargeting to work at scale. Real-time bidding can be used to achieve this. What does RTB mean? In the technological interplay of sell-side (SSP) and demand-side platforms (DSP), display ad placements are traded in an automated auction. Inventory is supplied into the auction via SSPs which are integrated with publishers and automate the process. RTB is completely dynamic and based on audience targeting. This means that whenever a customer is identified on a publisher website whose ad inventory is part of the auction, advertisers can bid for the opportunity to show an ad to that customer in the ad placement which is available for bids. Each advertiser in the auction then makes the decision as to whether that customer is relevant for their campaign and if they are then what is the optimal price they should bid for that ad placement. The value of an individual ad impression can vary for each advertiser depending on the customer’s value at that given point in time. The higher the estimated likelihood of that customer completing a transaction, the greater the value for the advertiser and a higher maximum bid can be set as a result. This dynamic pricing increases purchasing efficiency and forms the basis for ROI optimisation. The real-time bidding auction usually follows the ‘second price auction’ principle, so the bidder with the highest bid wins and pays the second-highest bid price with a marginal top-up.

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The bidding principle behind real-time bidding

Website Advertiser

Publisher

1,10 €

Advertiser 1

1,25 €

Advertiser 2 1,40 €

SSP/ AdExchange

Advertiser 3

2 Ad network (purchaser) bids on targeted inventory

100ms

1,00 €

Publisher network

1 Publisher offers inventory for purchase via auction

3 The real-time auction allocates each impression to the

1,26€

Advertiser 3

Source: QUISMA

Highest bid wins

5. Personalised retargeting and dynamic creative optimisation The advertiser with the winning bid can then show their creative in the specific ad placement which they bid on. Relevance which they bid on is crucial to conversion success. Dynamically optimised creatives ensure differentiated and individualised targeting of customers. Two factors play an important role in this – the behaviour of the individual customer - which is known via their cookies – and the general behaviour of all customers. Products determined to have the greatest relevance to the customer at that given moment and which generate the greatest likelihood of leading to a sale being transacted are predicted using both criteria. These are then incorporated into the ad creative. 8


Products previously added by the customer to his shopping cart but not purchased as well as those identified as bestsellers across all customers are usually displayed within the ad creative. The overall behaviour of customers, both on the advertiser’s site and their interaction with the advertiser’s newsletters and previous online advertising is analysed to identify the most relevant products and offers to show in the ad. Existing and new customers can also be viewed differently and the creative tailored to each of their customer segment groups. For example a new customer may be shown highest performing products whilst existing customers could be shown products as well as incentives to initiate return purchases. Different contact frequencies may also be required depending on the customer status. Retargeting via dynamic creative optimisation

User visits website

User surfs online

C

Cookie is set

C

Website left x Cookie is detected

Website left x

001101001 001101001 00101011111 001101001 00101011111 100001010 00101011111 100001010 101000110 100001010 101000110 0101000111 101000110 0101000111 0110110010 0101000111 0110110010 11101101001 0110110010 11101101001 010111000 11101101001 Retargeting ad 1 010111000 010111000 Retargeting ad 2

Retargeting ad 1

Retargeting ad 2

Source: QUISMA

Suitable products are calculated using the data

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6. Technical requirements Good technology is required for the successful delivery of dynamically optimised retargeting campaigns. An ad server with retargeting functionality forms the basis for this. At the same time, a special tool is used to generate the ad creative. Templates can be dynamically populated with product data from the advertiser’s product feed according to the individual customer. This sounds simple, but the different solutions available on the market vary considerably – in their scope of service but mainly in their performance. The most important distinguishing features are the control and optimisation algorithms which are crucial to the technology’s effectiveness. As each campaign has its own individual performance goals and very specific framework conditions for campaign control, it must be possible to dynamically identify and modulate the crucial performance drivers. Integrated systems have the big advantage of boasting perfectly harmonised and integrated logic and mechanisms for the individual functionalities (e.g. ad servers, bidders for RTB, ad creative optimisation, customer journey tracking, etc.). In this way, data generated using customer journey tracking can be directly used to dynamically optimise the overall bidding strategy. The ad format also plays an important role in the control and should be adjusted to the campaign goal. More broadly compatible technologies, such as HTML5 and JavaScript, allow for dynamic retargeting on mobile devices as well. (Since the Flash Player cannot be installed on the iPhone or iPad, customers on these devices cannot be reached using purely Flashbased creatives.) This approach ensures cross-device access to the target group and opens up additional inventory sources.

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In addition to these factors, the quantity of impressions offered plays a key role. In the case of RTB, we refer to the number of impressions offered per second (QPS, queries per second). The higher the QPS the greater the volume of inventory available for bids and therefore the higher the chance of the customer being identified for a retargeting campaign. However, as the QPS increases so too does the traffic load on the bidder, with a resulting rise in technological performance requirements.

7. Conclusion We can define five success factors for a successful display campaign: 1. Critical mass

Retargeting should not be used as a standalone marketing measure, but as part of an overall concept. A critical mass of shop/website visitors which can be identified and therefore re-identified subsequently is a must for a successful retargeting campaign. Access to the widest possible inventory and thus a large unique user base must be ensured for this. This can be achieved through a central link to different ad exchanges and sell-side platforms, for example.

2. Storytelling

Depending on the individual customer journey both on the advertiser’s website and across the web, the ad creative should be dynamically optimised and personalised on the basis of product selection. A customer that has only looked at a few products should ideally be shown related bestseller products in addition to the products he/she viewed. Products which were once in a customer’s shopping cart are shown with higher priority while items that have already been purchased are not shown again.

3. Acceptance through relevance

The focus is on the customer and not exclusively the campaign’s performance values. Each retargeting strategy should use frequency capping to limit the number of times the ad is shown. The customer should never feel bothered or stalked by advertising banners or products as the advertising effect is reduced. Equally, demarcation at a product level, i.e. excluding irrelevant products from the product feed, should be part of every retargeting campaign.

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4. Centralisation

Ever more advertisers are tending to centralise their retargeting activities via one service provider. Effective service providers should ideally have a strong reach of over 90% of the online audience which they access via their own inventory sources as well as via different sell-side platforms and ad exchanges. Because of this, it is no longer necessary to spread campaign implementation across multiple service providers. The centralised control also ensures comprehensive frequency capping. This is also crucial to ensure an advertiser is not bidding against themselves for the same customer via two different service providers which can be costly and reduce the campaign effectiveness.

5. Enhancing one’s own data

The possibilities of conventional retargeting are only limited by the advertiser’s own target group data meaning the potential reach is finite. External data can therefore be are added to extend the potential reach. By comparing an advertiser’s own data with external data, new customers with similar profile features can be targeted (statistical twins). This is also known as ‘lookalikes’. It also makes sense to enhance data within RTB to evaluate offered ad impressions. Information on search behaviour and and therefore the purchase decision process phase is valuable in this.

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QUISMA Whitepaper Personalised Retargeting