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Scientific Web Personalisation

How a scientific approach to web personalisation can deliver significant positive returns for marketers. Learn about:

  • The differences between a traditional marketing approach and a scientific approach

  • The common pitfalls of deploying web personalisation programmes

  • How Customer Data Platforms can contribute to success


Web Personalisation and the Scientific Method

By Damian Williams

Digital marketers have a wide range of tools and approaches to choose from when optimising customer experience programmes. Web Personalisation is one marketing approach that is generally poorly understood when compared with higher profile techniques like direct marketing and programmatic media buying. However, this approach (also known as ‘test & target’) can achieve significant returns if used appropriately and deployed well.

Web Personalisation enables the delivery of tailored content to a predefined audience based upon specific criteria. This allows marketers to:

  • Display product-specific banners on websites to visitors that have either visited that product’s webpage or have shown an interest in it offline

  • Trial proposed changes to a website to only a proportion of visitors in order to determine whether or not the change should be made for everyone. This avoids the cost of making changes that provide no tangible benefit

  • Optimise website journeys for visitors based on their previous behaviour. For example, not displaying ‘sign up’ calls-to-action to visitors that have previously logged in

  • Change the content of a website to reflect the country or time zone in which a visitor is located

n3 Hub has seen properly deployed Web Personalisation programmes deliver considerable benefits to marketers through:

  • Higher conversion rates on lead generation websites

  • Greater customer acquisition

  • Increased customer engagement with content

  • Added return visits

  • Longer time on site

  • Lower bounce rates

  • Increased ROI

The pitfalls of poor preparation

Poorly designed Web Personalisation programmes can cause more problems than they solve. They can end up delivering nothing more than additional technical complexity to organisations that attempt to implement them without preparation.

Technical complexity and poor performance can be caused by a number of factors including: incompatibility between the Web Personalisation tool and the website; poor rendering times of personalised content; or the need for extensive development support to create content.  

One of the main causes of a failed Web Personalisation deployment, however, is completely non-technical: it is a failure to grasp the scientific nature of the channel. Indeed, it is usually because a ‘traditional’ marketing approach rather than a scientific marketing approach to delivering the programme has been used.

A traditional marketing approach goes through the following stages:

  • A business objective is generated, either through insight or direct order, then presented to marketing to generate campaigns to meet the desired outcome

  • Marketing creates concepts that it works through with stakeholders until acceptance is gained

  • The concept is turned into creative that is placed into the most appropriate channels with as much targeting as possible to get it in front of the best audience

  • The campaign’s outcomes are measured but usually without the ability to drive performance data directly into the marketing decision-making process

This traditional approach is not effective in the context of Web Personalisation, however, because it is able to be measured very precisely and benefits from a ‘test & learn’ framework. 

What often happens in a traditional marketing-based approach to Web Personalisation is that sweeping changes are made to websites through the Web Personalisation tool - for example, the swapping out of an entire page with another completely different one or making changes to multiple pages or content blocks at the same time. The downfall of this approach is that it isn’t frequent or iterative. It can take a long time for the content to be built in the tool and there are major issues with measuring effectiveness - as it becomes virtually impossible to tell what worked and what didn’t.

Digital marketing, and Web Personalisation in particular, is much better suited to a scientific approach, based upon a ‘test & learn’ hypothesis.

A scientific marketing approach has the following stages:

  • Data analysis, usually using a Web Analytics tool. This can identify areas of a website that are not converting well, or potential reasons for a drop-off in conversions (e.g. poor user experience)

  • Developing a hypothesis based on this analysis that states that if X is changed then it is likely that positive outcome Y (e.g. an uplift in conversion) will result

  • Testing the hypothesis using the Web Personalisation tool to make targeted changes to the website. The goal should be to achieve a statistically significant result based upon a sample of the site’s visitors. A percentage of the audience should see the modified version of the website while the rest should see the existing content. The usual rule of thumb is assigning 50% for the modified version and 50% for the control version

  • Review of the results of the test, again usually using a Web Analytics tool or the Web Personalisation platform itself, to determine whether the test delivered a statistically significant result or not

  • Drawing conclusions from the test results that usually recommend the changes are made live or that the hypothesis is amended and the test repeated

Most Web Personalisation tools have good statistical analysis and A/B testing capabilities and this scientific approach ensures these capabilities are leveraged to their fullest. 

When a scientific approach is taken, more time will be spent on analysis (to both build hypotheses and measure results) than will be spent on building the tests themselves. Data and its correct and disciplined analysis is vital to the success of any Web Personalisation programme.

A common mistake made by marketers is to measure success by the number of tests that are running at any given time.  This is akin to measuring the success of an email marketing campaign by the number of emails sent. The main problem with a volume-based approach, however, is that it becomes impossible to tell which tests are successful and which aren’t. There is also the practical issue of how to avoid clashing tests (i.e. tests fighting for the same visitor). A better measure of success is the business outcomes that it generates, which should not stop when the test does. 

By focusing on incremental measurable tests underpinned by a scientific marketing approach, Web Personalisation can become a valuable part of any customer experience programme. In reality, nearly every digital marketing channel can benefit from taking a scientific, data-driven approach and in an increasingly data-driven world marketers need to act more scientifically every day.

Where a Customer Data Platform fits in

A Customer Data Platform (CDP) contributes to a Scientific Marketing based Web Personalisation program in the following ways. It:

  • Provides first party data audiences to drive Web Personalisation programmes beyond that which is possible with only web analytics data

  • Creates a single marketing customer view that is augmented with a variety of data to enable better hypothesis generation and results analysis

  • Creates an ongoing attribution model from both online and offline sources to allow the value of implemented tests to be continuously monitored

  • Enables marketers to build omni-channel strategies and target the customer on their preferred channel

TAGS | Web Personalisation, Analytics

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Customer Data Platforms and Programmatic Media

  • How CDPs can work with Demand Side Platforms

  • The limitations of Data Management Platforms

  • Marketing automation platforms and programmatic media


How to optimise programmatic media spend using the power of a Customer Data Platform

by damian williams | June 2018

Customer Data Platforms (CDPs) are most commonly used to drive personalised communications through direct marketing channels including email, direct mail and SMS. They ingest, transform and deliver a combination of first, second and third-party data to marketing automation platforms, enabling more relevant customer experiences.

CDPs are also powerful tools for optimising programmatic media spend (e.g. display, video, social and mobile). Programmatic media buying platforms, commonly known as Demand Side Platforms or DSPs, primarily deal in audiences derived from anonymous behavioural data. However, they also have the ability to upload audiences derived from personalised (first party) data. These audiences can then be used as targets for specific campaigns to reinforce messaging already received through direct channels (or to suppress existing customers from acquisition campaigns).  

In most cases, DSPs will not let you to upload customer Personally Identifiable Information (PII) such as email addresses directly into audiences (and in any case this would be inadvisable due to privacy concerns). Instead they require that the PII data is cryptographically (one way) hashed to ensure that a user is only added to an audience if the supplier is already in possession of the PII as well.

CDPs have the capability to act as central marketing data platforms to power both direct marketing and programmatic media campaigns. As CDPs can store both PII data and anonymous audience data in a single location, they have the ability to generate both hashed PII and identifiable cookie-based audiences and share them with DSPs. This centralisation of audience data can ensure consistency of targeting across direct and programmatic (indirect) channels.

Where do Data Management Platforms (DMPs) fit in?

Data Management Platforms (DMPs) store and transform online visitor data into behavioural segments that can then be shared with other marketing platforms. They differ from CDPs in two key respects:

  • They generally cannot be used to store PII data, as they deal in anonymised audiences

  • They often cannot achieve real-time performance with uploaded data (i.e. there is usually a significant lag between the upload of data and when it can be made use of within the DMP and therefore shared).

This means that they are generally used to generate segments for sharing with DSPs and web personalisation tools, rather than as centralised marketing data platforms.

Although DMPs are good at segmenting online audiences and sharing them with DSPs for targeting of media to specific visitors, they can struggle with allowing first party data to be used in a similar way. For example, they can enable the targeting of customers vs non-customers (e.g. - if a cookie is associated to logged-in customers) but cannot enable targeting based upon customer attributes that aren’t derived from online behaviour (e.g. – age, gender etc).

Most DMPs also have a time lag between when data is uploaded and when it is available to be shared with DSPs. In some cases, this delay can be as much as 24 - 48 hours, driving a lack of immediacy which can significantly impact the conversion rate of the programmatic campaign. As CDPs typically have more flexible data models, they can operate closer to real time, delivering audiences to DSPs much more quickly - thereby increasing the probability of conversion.

What about Cloud Marketing Automation Platforms?

Most of the major marketing cloud platforms include some form of programmatic media optimisation capability (known as ‘lead management’ or something similar) as part of their offering – albeit usually at an extra cost. This capability enables the creation of audiences from customer data and the sharing of these audiences with DSPs for programmatic media campaigns. The best platforms optimise spend to improve conversions and can automatically associate conversions of leads with customers. 

However, using a cloud marketing automation platform to feed your DSP has a number of disadvantages:

  • It requires first party (PII) data to be stored inside the marketing cloud environment, which may result in legal jurisdiction or regulatory issues.  

  • Licensing and storage costs may increase as your business becomes increasingly ‘locked-in’ to a particular vendor’s marketing cloud environment. 

  • You may be required to adopt the online ID/tagging/analytics system of the marketing cloud vendor - and this may require relatively significant changes to the setup of your online presence.

  • It can cause significant integration issues if you are using technology from multiple different vendors to power your online customer experience programme.

  • Extraction and management of large indirect (programmatic) audiences can cause performance degradation in the marketing platform, even in those with self-hosted components. 

  • Marketing cloud platforms can struggle to leverage conversion data from offline channels (e.g. – phone and face-to-face) due to issues integrating that data from other systems. This can result in potential customers being excluded from online campaigns during their offline service journey.

How Customer Data Platforms can help

CDPs enable the creation and maintenance of a single marketing customer view which can include online and offline lead information. They also provide the flexibility to deliver audiences to marketing cloud platforms and to DSPs, mitigating the limitations of both. 

Deploying a CDP into your programmatic media buying ecosystem achieves the following:

  • Legal and regulatory compliance through the retention of your first party data within an on-premise or otherwise customer-owned and controlled environment.

  • If your existing marketing cloud platform does not support programmatic (indirect) channels, using a CDP can prevent the need for data integrations to platforms that do provide support. 

  • Reduction in the performance impact on direct channel message decisioning from the extra load required to support programmatic channels.

  • Removal of the need to retag or switch analytics providers to support programmatic channels.

  • Centralisation of campaign targeting to ensure consistency of campaign communication in both direct and programmatic channels.  

  • Increase in programmatic media performance through the ability to supply PII-derived data directly to DSPs.

  • Integration of offline data about conversions and other activities into programmatic media campaigns.

TAGS | PRogrammatic media, display advertising, demand side platform, data management platform, marketing automation

about n3 hub

n3 Hub is a technology business that enables enterprises to rapidly automate personal experiences for each customer, at scale. We provide a next generation Customer Data Platform built to solve real enterprise problems around data integration, campaign targeting, personalisation and performance attribution.

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Breaching the Data Dam

Part One of our two-part look at the data technology required to support modern customer experience programmes.

Why marketing teams need their own data platform

by damian williams | june 2018

Marketing technology is a hot discussion topic at a senior level in most enterprises, particularly between CMOs and CIOs. A common question that comes up from the IT side in these discussions is “Why do I need yet another data platform?”

In reality, that’s the wrong question because:

IT does not need another data platform, Marketing needs a dedicated one.

Many enterprises have spent years and millions of dollars upon realising Enterprise Data Strategies that revolve around consolidation and centralisation. Key drivers of consolidation include cost reduction and simplification of the technology stack. Centralisation of data provides benefits focussed around improved systemic control and compliance.

Although these strategic approaches undoubtedly deliver multiple benefits across the organisation, there is usually one exception – the marketing department. 

The problem with most Enterprise Data Strategies from a marketing perspective is that they are essentially driven by compromise. In many enterprises, there is an overriding need to create and maintain an immutable System of Record (SoR) to satisfy external reporting and regulatory requirements. This need results in compromises because every department in the organisation has different requirements, and it is impossible to meet them all. 

In a digital landscape that is increasingly fragmented, with consumers using ever-increasing numbers of platforms and devices, marketers are becoming hamstrung by traditional Enterprise Data Strategies. Common enterprise data environments fail to provide marketing departments with what they need to drive the types of experiences that their customers and stakeholders are demanding. 

Key signs that your Enterprise Data Strategy is not supporting your marketing programme

  • Your data lake is more like a dam. Modern marketing requires quick access to data in order to drive timely and relevant experiences. However, most enterprise data warehouses and data lakes require skilled analysts to access and manipulate the data that they hold - skills that marketing teams usually don’t possess. Consequently, marketing data requests often join a significant backlog for prioritisation and execution. This means that, in an increasingly real-time and reactive world, the opportunity to send a campaign can be lost.  

  • The cost to add data is prohibitive. Systems of Record used for business critical and regulatory reporting typically cannot be changed without going through multiple layers of approval. This usually adds layers of cost and time to the process of adding data to the environment. Marketing departments struggle to justify the cost of sourcing data for campaigns that run for relatively short amounts of time, and the IT change timescales involved are often too long to make it worthwhile.

  • Your data environment is not designed to handle scale. Enterprise data warehouses and data lakes are often built to store and report on large core transactional data sets. However, even in the largest of organisations, the transactional data set may be dwarfed by the amount of digital data that can be generated by web analytics and marketing platforms. To handle this scale and preserve capacity for critical SoR data, digital marketing data can be heavily filtered or summarised, limiting its usefulness for driving campaigns. Speed is also an issue – most centralised data infrastructure is not designed to handle the real-time data feeds and decisioning required by modern customer experience programmes.

Not all marketing data needs to be centralised

Although there will always be a need to feed some marketing data back into the centralised enterprise data store (e.g. opt outs, business process and reporting data), there is a significant amount of operational marketing data that will never need to be centralised.

  • A lot of marketing data is only used by marketers, for example: email opens, complete web analytics feeds and paid media impressions.

  • Marketing data can be transient, in that it may only be required for a one-off campaign or for a short period of time (e.g. – seasonal, promotional or partner data).

  • Some data is platform-specific, for example: specific data tables that are required by a marketing platform for its operation but are derived from a larger data set.

Holding marketing data in a dedicated platform makes a lot of sense for marketers who need to run time-sensitive, reactive campaigns. However, it also makes sense from an IT and data governance point of view because:

  • It preserves the integrity of SoR data sets so that business critical reporting and processes are not affected by relatively unstructured and transient marketing data.

  • Marketing teams can have SoR data access limited to only that required by their marketing platforms. This improves auditability as it becomes easier to track what was shared with the marketing department and when.

  • IT capacity planning becomes easier without the ‘lumpy’ demands for data from marketing teams when large campaigns are being run.

You cannot drive a modern digital customer experience programme from the same data infrastructure that is acting as your System of Record.

The only real solution to the different needs of IT, Compliance and Marketing is to have a dedicated data platform for marketing. This platform needs to be fed from a variety of sources, including Systems of Record, to generate a single view of marketing data. The single customer view can then be distributed in relevant subsets (segments) to downstream marketing decisioning and delivery platforms, and then augmented with marketing campaign response and interaction information.

Only by implementing a dedicated Customer Data Platform (CDP) can enterprise marketers deliver the customer experiences that are increasingly demanded of them. 

But what of the other elements of the modern marketing technology stack? A common question that is asked by CMOs is whether any of their existing marketing technology platforms can potentially be co-opted to act as a Customer Data Platform. In part two of this article, we’ll look at the other types of data platform in the marketing technology stack.

TAGS | systems of record, marketing technology, customer data platform



about n3 hub

n3 Hub is a technology business that enables enterprises to rapidly automate personal experiences for each customer, at scale. We provide a next generation Customer Data Platform built to solve real enterprise problems around data integration, campaign targeting, personalisation and performance attribution.

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Modern marketing decision-making

Why a centralised approach to decisioning is not necessarily the best for marketing. Read on to learn more about:

  • The three key types of decision in digital marketing

  • The pitfalls of centralised decisioning

  • Common tensions between Marketing and IT

  • The advantages of taking a decentralised approach


Making marketing decisions at enterprise scale

by damian williams | May 2018

Decisions, and the process of making them, is an important part of the digital marketing customer lifecycle. Marketing decisioning, put simply, is the process to determine which message a customer should receive through a given channel or medium.

Many organisations take a relatively simplistic approach to decisioning and attempt to use a single platform to make decisions. This platform then pushes data to a delivery tool (or tools) for message generation and presentation.  

This works well for small organisations that have a limited set of relatively simple campaigns to decision for, and a simple set of rules for deciding which messages an individual should actually receive. This rule set is commonly known as a contact framework.  

In these simple cases the decisioning platform can also be the delivery platform. However, in most large enterprises, the decisioning process and contact framework is far from simple. 

The single-platform decisioning approach does not scale to larger organisations that communicate through multiple channels, with a large set of campaigns and a complex contact framework.  

In large enterprises, there are typically three different types of decisioning that influence marketing campaign delivery:

  • Campaign Decisioning (also known as customer targeting and personalisation) determines which contacts should be targeted by a given campaign and generates personalised data to populate that campaign.

  • Message Decisioning: this is where the actual message or offer that a customer should receive is determined. This is particularly important for multi-step campaigns, where a contact may receive several different messages based upon custom logic and data.

  • Contact Decisioning: when a contact is a candidate to receive multiple messages or offers in the same time window, this is the process that determines how many of these messages the contact will actually be presented, based upon a prioritisation and/or suppression framework. 

Although each of these types of decision is distinct and relevant to a different stage of the campaign journey, many large enterprises do not split out decisioning into distinct stages. A key reason for this is that large organisations tend to centralise processing - because it generates a sense of control and makes it easier to trace what processing has occurred.

An outcome of adopting a centralised decisioning approach is the provisioning of large platforms and associated infrastructure to perform marketing data processing. Also, under this approach, delivery and presentation platforms perform no decisioning and are only responsible for message rendering and/or delivery.  

Unfortunately for enterprise marketers, a centralised decisioning approach has a number of key issues that result in poor marketing outcomes:

  • Long campaign turnaround times due to complex campaign setup and the significant operator expertise required. 

  • Under-utilised capability of marketing delivery platforms, resulting in campaign experience being less ‘rich’ and engaging than what is potentially possible. In a centralised decisioning approach, the maximum level of experience that can be delivered is limited by what the decisioning platform can do, not by what the delivery platforms can achieve.

  • High cost of change as all contact rules and decisioning logic are centralised in a single enterprise-wide platform. 

  • Confusion and complexity as decisioning is merged into a single process without distinct stages and separation of concerns. Complex decisioning configuration in a single platform leads to a lack of clarity within the enterprise as to what the contact rules are and the impacts that they have.  

Of course, the main reason for the friction between a typical enterprise IT-led approach and an ideal marketing approach is the appetite for change. Marketers need to be inherently flexible and reactive to match changing consumer needs, while IT managers are driven by needs to centralise operations, control costs, and reduce the need for change.

The ideal enterprise approach to decisioning

To achieve a winning balance between the needs of marketers and IT managers, enterprises need to focus upon developing a decisioning capability that can provide all of the following:

  • Fast campaign turnaround times to meet changing customer needs and mitigate against competitor activity.

  • Reduction in the need to engage costly IT resources to deploy marketing campaigns.

  • An environment where test and learn approach can be adopted, enabling marketers to optimise campaigns based upon performance.

In this approach, decisioning is not centralised in a single platform – rather it is divided between platforms that have capabilities best suited to perform each type of decisioning and for the type of operator that will be driving it.  

  • Campaign decisioning in a Customer Data Platform or dedicated decisioning platform that delivers a set of campaign independent records per contact with targeting flags and personalisation.

  • Message decisioning in a marketing platform that includes journey building capability. This delivers fully personalised messages for delivery to contacts for the given time window.

  • Contact decisioning in a marketing platform with contact framework capability that allows for the delivery of prioritised and personalised massages to contacts.

A key advantage of this approach is that it does not require any specialist IT or development resource to design, decision and deploy campaigns, instead using existing marketing and data analyst resources more effectively.

This approach also allows marketers to utilise the full capabilities of modern multi-channel marketing platforms to build out complex and rich customer experiences. Having the contact framework closer to the point of delivery also allows alternative models to be generated and evaluated as all of the potential messages are available for review.

Adopting a decentralised decisioning approach for marketing does not mean increasing complexity or adding additional cost. In reality, done well, it is a ‘fit for purpose’ approach that utilises existing resources and technology better without impacting the integrity of the enterprise data framework.

n3 Hub enables marketers to automate the decisioning process across the three key types of decisioning, and can be deployed quickly with minimal impacts upon existing IT infrastructure. For further information on the capabilities that we can bring to your marketing technology stack, please contact us.

tags | marketing automation, digital transformation, data decisioning, customer journeys




Unlocking customer data

How to drive customer experience transformation using data from legacy systems


How to transform your customer experience without breaking your systems

By Rory Watt | May 2018

Digital-led customer experience transformation is one of the hottest topics for CMOs in today’s market. According to IDC, over two thirds of global enterprises are looking to make the strategic shift from traditional to digitally-led strategy, with over one third expecting to have fully adopted digital transformation by the end of this financial year.

As with many major strategic shifts, digital transformation is largely consumer-led at its core. Consumers are now demanding to deal with brands through their own channels of choice, and new market entrants are primed to disrupt industries that are slow to react to the pace of digital change. Senior marketers have been well aware of this trend for some time and have now started to lead transformation initiatives at the C-level.

According to recent research by Gartner, CMOs are now outspending CIOs, making significant purchases of marketing-related technology from their own capital and expense budgets as they look to engage customers through a rapidly-expanding number of channels. However, the purchase of modern marketing technology systems is only one part of driving successful customer experience transformation. 

CMOs now have access to a range of powerful solutions to deliver great customer experiences across multiple digital and offline channels simultaneously. However, the performance (and ultimately payback) of these systems can be seriously impacted by an inability to access customer data. 

In any large organisation, customer data is usually held in a range of legacy systems and databases, and in formats that end delivery platforms struggle to use effectively. Liberation of this data – and creation of the near-mythical Single Customer View – is often pushed to IT as a systems architecture challenge. As IT teams serve the entire organisation, marketing then has to compete with a range of other areas for resource, and data integration gets wrapped up into broader change programmes that can take years.

The challenge for CMOs is that their experience transformation programmes can’t wait years for access to customer data. This leads to executives purchasing a range of point solutions to meet the specific requirements of new and emerging channels, and ultimately, fragmented multi-vendor marketing technology stacks that don’t work together seamlessly. In fact, even single-vendor stacks have integration issues as they are often the result of a series of acquisitions by the lead vendor attempting to gap-fill capability and pull together an end-to-end solution.

Mixed vendor environments are, and will remain, a reality for CMOs in the digital age. The challenge isn’t really about technology consolidation, it’s more about working out how a range of technologies can work best together, using and enriching a common set of marketing data. The answer to this challenge is in an emerging sector of marketing technology, the Customer Data Platform.

Customer Data Platforms act to unify marketing data from a wide range of sources, transforming it into usable, targeted information. Essentially, they are the ‘marketing glue’ that acts to stick together a disparate range of legacy databases, third party data sources, CRM systems, and marketing delivery platforms, making the whole stack work together to deliver the end experience.

Of course, data integration solutions have existed in the market for years. However, many of these solutions are complex, requiring specialised IT support to maintain, and expensive data science resource to operate. The advantage of the Customer Data Platform is that it only looks at data relevant for marketing and is ‘owned and operated’ by the marketing department. 

Customer Data Platforms optimise data specifically for marketing outcomes. Marketing-specific data integration isn't just a matter of getting two systems to talk to each other - the data also has to be optimised to make campaigns easy to implement. There is little point sending complex data models to delivery platforms that will then require in-depth work by marketing delivery teams to get campaigns live. Customer Data Platforms make the campaign deployment task easier by optimising data before it reaches the end delivery platform, reducing complexity and increasing the number of campaigns that can be deployed within a given timeframe. 

Modern Customer Data Platforms, like the n3 Hub, are also relatively inexpensive and quick to implement. And as they are designed with easy integration in mind, they typically require minimal IT change – meaning they can be up and running, delivering value within weeks, not years.

tags | Data integration, customer data platform, customer experience, digital transformation


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