The report, titled “Data, People, and Culture: The Evolving Role of the Modern CMO”, surveyed 300 CMOs across the US, UK, and DACH region (Germany, Austria, and Switzerland) from small to midsize businesses, to better understand how the role of the CMO is changing amid the growing digitalisation of the industry.

However, CMOs are at odds on whether the exponentially increasing sophistication and volume of data helps or hinders creativity, with 41% insisting data does in fact help and 17% remaining undecided.

Importantly, those that say it greatly hinders creativity are twice as likely to struggle with a lack of tools. Furthermore, responses suggest that 30% of marketing teams only review data once a quarter or less, and nearly half (43%) of CMOs feel the greatest barrier to getting value from their marketing data is related to company culture.

At the same time, the need for a dedicated marketing ops role to manage martech stacks has accelerated dramatically. 73% of CMOs are committed to introducing this role to their team: 45% already have someone in place, and a further 28% are currently hiring. Only 12% of CMOs have had one in place for more than a year. 

Harriet Durnford-Smith, CMO, Adverity said: “Modern marketers are faced with huge volumes of data streaming in from multiple sources, so it’s not at all surprising to see CMOs are focused on investing in people dedicated to managing the entire data stack. Without eyes on the big picture, insight-driven decision making becomes nearly impossible.

“A staggering 30% are only reviewing their data sporadically – in today’s data-driven marketplace, brands are in danger of counting themselves out of the race if they don’t have a single unified view of performance, in real time. Now is the time for CMOs to champion change in company culture and embrace the opportunities that data brings.”

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Understanding how to tailor marketing efforts to suit different types of customers is obviously key, but before you can get started on this, it’s important to understand the differences between segmentation and personalisation.

Although a lot of advertisers and publishers swap out the words personalisation and segmentation for one another, they don’t mean the same thing. The strategies are actually completely different, so here is your simple guide to understand each of them and how they work in relation to one another:

Let’s start with Segmentation

Segmentation, or segmentation targeting, is absolutely key within marketing strategies. It involves grouping consumers into segments based on different characteristics which can be used to determine the most effective way to advertise.

Customers can be grouped according to a multitude of factors – here are a few common examples:

• Geographic segmentation: Grouping customers based on their location in the world, a country or a region, depending on how local the available data is

• Behavioural segmentation: Grouping customers based on their habits, for example how much money they usually spend, how many items they usually purchase at once, or browsing history

• Demographic segmentation: Grouping customers based on personal characteristics, for instance marital status, age, gender identity or occupation

• Device or technology segmentation: Grouping customers based on the device they are using to browse, the internet browser or software they are using

• Psychographic segmentation: Marketers can gather and collate findings about users personality traits, online habits, interests and hobbies and ways of receiving information

It’s usually advised that segmentation is implemented at the beginning of a campaign. This means advertisers can start grouping consumers into these segments from the start, meaning as the campaign progresses, marketing efforts can be more effective as marketers can focus on meeting various needs.

As well as obviously driving more successful campaigns, segmentation can also be a really pleasant way of finding out about your customers and getting to know them on a deeper level to ensure your brand is meeting their needs. For example, if you discover that a large portion of your customers are passionate about sustainability, you can make an effort to put more of a focus on your brand’s sustainability, in turn pleasing your customers and forming positive relationships.

Moving on to Personalisation

Think of personalisation as getting even more personal. This method means identifying a particular customer within a segment. For instance, you could have a customer within the skincare segment who is browsing acne treatments, and from this you can personalise your marketing efforts so this particular customer is presented with acne treatment products.

You want to be able to solve a particular customer’s particular desire. This means you need to understand a customer’s reasons for making the journey they have made to your site or product.

It can be really tricky to find out whether a customer always interacts in the same way, or if they are changing their behaviour every time they visit your site. This is why personalisation is key, as opposed to just using segmentation. It’s difficult at the moment, as we are stuck in a moment of limbo, almost, with the loss of third-party data looming but not having yet arrived, to know what type of data is going to be available for marketers. However, there are various touchpoints that shouldn’t be lost that can help with personalisation:

• Where did the customer travel to your site from?

• Which email messages are they opening, therefore are resonating with them?

• What is their current location?

These factors may change their intent, which gives you a clearer view, when you look at the picture as a whole, of the customers wants and needs.

Can the two be used together?

In order to demonstrate the difference between segmentation and personalisation clearly, here is an example:

• A skincare company is advertising through an advertisement placed on Instagram. Their advertisement shows a group of people using a range of different skincare products displaying different benefits. This, appearing on the feed of a customer who fits into the skincare segment, is segmentation.

• The same skincare company is advertising again on Instagram, however, this time, a user who has been viewing acne treatments online is presented with an image of a model with acne, using acne treating skincare. This is personalisation.

This isn’t to say that segmentation is less effective than personalisation. It can be an easier way for marketers to advertise their products, and also can deliver good results. Personalisation is simply a more specific and targeted approach.

However, the personalisation approach can mean, sometimes, that users become apprehensive about sharing their data. In this article by Yusuf Muhammad, he gave tips on finding the sweet spot of personalisation, instead of ‘creeping’ your customers out.

Segmentation and personalisation can, of course, be used together. But it is of utmost importance that marketers are aware of the difference, especially as segmentation should come first. This is because it determines the reasons you are going to market to particular customers in the first place, and makes personalisation much simpler.

The post Don’t Get This Confused – Segmentation vs Personalisation appeared first on PerformanceIN.

The results revealed that 61% of UK respondents are very or somewhat willing for advertisers to use their data to personalise ads and offers if it keeps content free and data anonymous.

77% said they are very, or somewhat, annoyed when seeing or hearing exactly the same ad many times over.

Intriguingly, over half (56%) prefer, or somewhat prefer, personalised ads that are relevant to their current context over standard, generic ads. 55% are very, or somewhat, more likely to purchase a product or service after having seen or heard a personalised ad.

Given the results, unsurprisingly,  65% of the marketers surveyed revealed that personalisation is a high or very high priority for their advertising strategy, with half saying they use personalised advertising to drive better engagement.

Similarly, when asked what strong or very strong uplift in brand metrics they had seen by using dynamic, personalised creatives, brand awareness (74%), brand consideration (73%) and purchase intent (73%) came out on top. 

Steve Dunlop, CEO, A Million Ads said: “The advertising marketplace has changed forever, requiring advertisers to build a new respect-based relationship with consumers. Bludgeoning your audience with repetitive messaging is a tactic that should be consigned to the past. 

“Instead, brands should leverage the power of personalisation through dynamic creative that adapts to the real-time context of their target audience to boost relevance, engagement and results. Our survey revealed that a clear majority of consumers want personalised ads and are willing to share the information brands need to make it happen – if the value exchange is right. 

“Today, personalisation at scale is more accessible, more affordable and more achievable than ever before. It’s time for brands to reap the rewards.”

It’s very interesting indeed to hear an opposing point of view to what we have been witnessing of late. Brands will have to do some serious research into exactly how their customers feel about their data being used if they are to create effective advertising campaigns whilst still pertaining to their customers wants and needs.

The post Are Users as Worried About Third-Party Data as We Think? appeared first on PerformanceIN.

There are a variety of digital identity solutions being developed to help marketers understand audiences in a post-cookie world, and these fall into three broad categories:

Cohort models

Federated Learning of Cohorts (FLoC) is Google’s answer to digital identity, and it clusters individuals together based on a shared browsing history. More accurate than cookies, this method uses algorithms to build anonymous groups of user profiles based on common interests, which is useful for driving top-of-the-funnel metrics such as brand awareness and recognition. While FLoC delivers scale, targeting is constrained to Chrome, rather than cross browser, and there are some unanswered questions around user privacy and measurement.

Authenticated identity

Authenticated or deterministic identity enables precise targeting through a piece of personally identifiable information – such as an email address – that users provide when they log into a website or app. It enables highly accurate targeting and measurement as it is tied to a real person, and can be privacy compliant as it makes consent simple to track. Authenticated identity enables marketers to reach users at the bottom of the funnel and drive customer retention, loyalty and intent. It does, however, require users both to log in and to opt into data processing, which inevitably limits scale. The authenticated web is only expected to reach around 20% of users for the foreseeable future.

Non-authenticated identity

Non-authenticated or probabilistic identity uses publicly available data signals such as IP address, timestamp or browser user agent, to assign a cluster of devices and browser signals to a single identifier. This identifier can be activated via established pipes, enabling precise targeting across devices and domains, as well as frequency measurement.

While it is not tied to a known individual, non-authenticated identity delivers the scale that authenticated identity can’t, and is ideal for prospecting and other middle-of-the-funnel use cases. Probabilistic is data minimised with no email, home address or phone number required, scoring points for consumer privacy and ensuring that no brute force attack on an encrypted ID can reveal an email or a phone number.

As each digital identity category has benefits and drawbacks – and is suited to different levels of the funnel – likely the most effective approach is to combine all of the above. Marketers can also include non-identity solutions such as contextual, which enables interest-based advertising by targeting the user based solely on the content of the page. A portfolio of solutions maximises scale and accuracy while respecting user privacy and delivering strong campaign results.

The post WTF is Digital Identity? appeared first on PerformanceIN.

In simple terms, FLoC works by analysing consumers browsing behaviour and uses this data to group consumers into cohorts of like-minded users. These groups are planned to be specific enough for advertisers to use them to show relevant ads, all whilst remaining in keeping with privacy concerns – no browsing history is shared with Google, and consumers can’t be personally identified by marketers.

Initially surfacing in the US, Australia, Brazil, Canada, India, Indonesia, Japan, Mexico, New Zealand and the Philippines, the trial will soon be scaled globally. Due to concerns about GDPR and other privacy regulations, FLoC trials will not take place in Europe.

All Privacy Sandbox trials allow users to opt out of being a part of them, which some may choose to do as there are views that the cohort data is still personal, so should not be available.

What about the implications?

Google’s preliminary tests of FLoC to reach in-market and affinity Google audiences showed that advertisers can expect to see at least 95% of the conversions per dollar spent when compared to cookie-based advertising. This means advertisers may be able to edit their efforts and campaigns to suit this, should it prove a popular and efficient method for both publishers and consumers.

Advertisers may be able to harness the data that will be available to them for certain campaigns, however it seems unlikely that FLoC could serve as a complete replacement for third-party cookies, as there is simply not enough information.

It is worth noting that FLoC is still in its trial phase, meaning that if advertisers do not record results showing success, steps can be taken to ensure it is a more promising alternative.

Marshall Vale, Google’s product manager for Privacy Sandbox, said: “[We’re] excited because we absolutely need a more private web, and we know third-party cookies aren’t the long-term answer. [We’re] worried because today many publishers rely on cookie-based advertising to support their content efforts, and we had seen that cookie blocking was already spawning privacy-invasive workarounds (such as fingerprinting) that were even worse for user privacy.

“Overall, we felt that blocking third-party cookies outright without viable alternatives for the ecosystem was irresponsible, and even harmful, to the free and open web we all enjoy.”

FLoC is still on trial, and it will be interesting to see how advertisers and consumers respond. Sharing varying opinions is crucial to PerformanceIN if we’re going to keep a balanced and well-rounded view of the performance marketing industry. That means we love to hear your opinions, so please get in touch via our Typeform to share your thoughts.

The post Google Has Started Trialling FLoC – Its Alternative to Cookies appeared first on PerformanceIN.

It involves integrating a number of components to provide a model enabling you to conduct risk analysis. It does so by considering a range of possible outcomes, before calculating how likely it is that each particular outcome will happen.

What do you need in order to run an MCS?

To run a Monte Carlo simulation, a mathematical model of your data is needed – for example, a spreadsheet. Within this data, there must be one or more outputs that you’re interested in measuring, for example profit.

There must also be inputs – these are the factors that may have an effect on your output variable. An example of this could be, if trying to measure profit for instance, the number of sales and total marketing spend.

If you knew the exact values of the inputs, you would be able to easily determine what profit you’d be left with. However, if you’re not sure of these variables, an MCS allows you to calculate all the possible options, as well as the probability of each option.

How does it work?

MCS works by replacing all unknown values with functions which generate samples from distributions determined by you. A series of calculations and recalculations are then actioned, producing models of all the possible outcomes and the probability of them being realised.

What could MCS be used for in marketing analysis?

Customer behaviour and whether or not customers will continue to purchase from a brand in the future is something that nobody can be sure of. This makes estimations of customer future value an arduous task.

Imagine a customer owns a few products from a certain brand. This means they are less likely to purchase similar products than other customers who don’t own anything from a brand. Or does it? They could enjoy these products so much that they’re more inclined to repurchase. MCS would be a good way to measure the possible outcomes of this situation. In this example, this would allow a brand to know whether or not to restock a product.

It seems there is an infinite amount of data analysis techniques out there, and they’re worth exploring. Lesser heard of methods could be the secret to unlocking the knowledge you need to steer your brand in the right direction.

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If not, here’s a quick run down. They really are exactly what the name states – they are a tiny image which is embedded in an email. Usually invisible to the receiver, the pixel can allow a company access to details about their customers.

The 1px by 1px square image is usually a .GIF or .PNG file which is coded into an email, usually coloured suitably to be disguised within, and is activated as soon as the email is opened.

You’re probably familiar as they are seen as a useful marketing strategy, as they are a practical way for companies to be able to find out information about their campaign success, leading to more insight and development in future communications.

Tracking pixels are not recognised by most spam filters, but there is software available which can block emails containing them.

What information can they provide?

Spy pixels can allow access to details such as:

• When an email was opened
• How many times an email was opened
• Where the user was (physical location) when opening the email
• What device the email was opened on

Companies can also use spy pixels to determine the click rate and open rate of their emails, which is why they are so often used.

Marketing teams require information such as this in order for them to tailor their efforts and ensure effective communication. Tracking pixels don’t collect sensitive information such as the recipient’s exact location, bank details, or private browsing history.

In the UK and Europe, it is required that companies state the use of tracking pixels, and they do often require consent. This consent is, however, often included in email terms which users agree to, therefore they’re most likely unaware of what they’re signing up for.

The use of spy pixels is not currently a breach of data protection law. However, some see it as a huge invasion of privacy. Although a company may be using the trackers simply to determine a campaign’s success, many believe that they should state the use in their privacy policy.

British Airways have admitted to using spy pixels, claiming the data is only used internally in order to determine more information about customers, and never shared elsewhere. This received a mixed response, with some customers not understanding why their data needs to be collected in this way.

What does the future have in store for spy pixel tracking?

The conversation about spy pixels is certainly an interesting one, and is one that needs to happen.

Defenders of the trackers say they are a commonplace marketing tactic, and this is true. It is difficult to track an email campaign’s success without the use of them, after all.

However, if companies are able to access information about who has opened their emails and are then following up on them with more emails and even, in some cases, phone calls, that’s when the concerns begin.

The future of reporting may be uncertain if there continues to be misuse of these trackers. Customers will become more suspicious, and with the development of more sophisticated blockers, tracking email open and click rate could be unavailable to us sooner than we think.

What do you think – should spy pixels be allowed? How else could we collect this data? Don’t forget to submit your opinion pieces and all other article ideas via our Typeform.

The post WTF Are Spy Pixels, and WTF Do You Think About Them? appeared first on PerformanceIN.

It is one thing to satisfy your customers, but to ensure your customers are receiving the highest quality experience when interacting with your brand is paramount when it comes to loyalty and retention.

Why is sentiment analysis important?

To ensure customers are happy, sentiment analysis is essential. It enables you to understand what consumers are saying when they talk about your brand and services. It allows you to understand what makes them happy, and in doing this, improve your strategy.

The digital world is booming, and it is now deemed unusual when a brand doesn’t have a heavy online presence. We can only assume that the future will bring with it an increasingly digitised market. This means that the conversations customers have are going to take place in the same way.

Twitter and LinkedIn are great places for customers to share their experiences, and with social listening becoming a technique which is growing in popularity for brands to understand their customers, the two naturally go hand-in-hand. Twitter is, in fact, a common platform for sentiment analysis to take place. Customers flock to tweet about their experiences, and brands can educate themselves and upgrade their services to suit this.

What exactly does sentiment analysis measure?

Sentiment analysis is simply the measure of whether a piece of text – a comment or tweet, for example – is positive or negative. Data analysts can use AI, as well as their knowledge, to identify the tone of a customer’s feedback. This can then be used to influence the techniques which are then implemented in business and marketing strategies.

There are three main categories of sentiment analysis:

1. Knowledge/rule-based techniques: Assessing text according to unambiguous adjectives such as sad, happy, bored, pleased et cetera.

2. Statistical methods: Using machine learning (ML) techniques for deep learning. This enables much more accurate information.

3. Hybrid approaches: Using elements from ML and knowledge representation to detect semantics that have been expressed in a more subtle way.

The different algorithms used in sentiment analysis work to show data analysts exactly what and how their customers are discussing a brand. These techniques are only going to become increasingly important as more and more brands move online.

Using this technique will allow you to truly understand your customers, in turn resulting in a much more positive brand-customer relationship. It should enable you to identify the causes of the problems you may not have been able to solve, meaning you can work on improvement, ultimately boosting your brand reputation.

The post Sentiment Analysis: the Key to Effectively Engaging Customers appeared first on PerformanceIN.

The study highlights what marketers are doing to overcome challenges preventing improving customer experience and exceeding business goals. The study also sheds light on how some of these challenges develop, as well as their solutions and the impact those solutions could have on businesses in the coming year.

Generally speaking, using AI and machine-based learning will become the new standard with 98% using AI/predictive modelling tools reporting marketing improvements, as well as 60% of marketers planning to increase their use of AI in the next 12 months, including 41% of those already using AI.

In terms of challenges, Blueshift found that most marketers (95%) can’t deliver real, cross-channel personalisation at the 1:1 level while 36% reported they spend more time collecting/distilling information than executing against it when it comes to tech and data.

“Our study with Kelton Global reveals how sophisticated technologies are needed now more than ever, as these platforms allow marketers to keep up and bridge the gap between existing customer data, tools, and processes,” said Josh Francia, Blueshift’s chief growth officer.

“The report findings make it clear that in order for marketers to win and exceed their goals they need to: unify their data, integrate AI, and invest in technology that’s built for scale,” he added.

The post Marketers Using AI/Personalisation Exceeding Revenue Goals by 30% appeared first on PerformanceIN.

2018 was a hard year for tech giants, and the beginning of 2019 has not been much kinder to the likes of Google and Facebook. The days of marketers placidly placing their ad spend with the two platforms have certainly come to an end, with more and more chief marketing officers and marketing teams questioning the best way to reach their customers and deliver ads to the masses.

While some in the industry have come out strong and suggested marketers should pull their spend from the platforms altogether, the fact remains that they will, and should, form part of a balanced marketing strategy. Many marketers are clearly following this path; the numbers point to the fact that Facebook’s share of the almost $280 billion worldwide digital ad market will have grown to 19.5% as of the end of 2018, trailing Google at 31.5%.

The analogy “you need to fish where the fish are” rings true to an extent – if your customers are using Google, Facebook, Messenger and Instagram, then it makes sense that this is where you’ll direct your ad spend. But as anyone who has actually gone fishing will understand, it takes more than just knowing where there is a massive school of fish to actually catch one. You need to recognise what kind of fish they are, and what kind of bait will work.

Back on the solid land of advertising and marketing, that means you need to have access to robust, insightful first-party data that allows you to shape a campaign around your audiences’ behaviour, likes, dislikes and preferences. This is currently the biggest hurdle at which the likes of Facebook and Google fall.

Marketers want more from walled gardens

Sizmek’s survey of more than 500 marketers across the UK and US showed that two-thirds believe that walled gardens are a major block to improving first-party data. That’s a significant chunk of advertisers who think they’re not getting bang for their buck. Without access to this data, how are brands meant to effectively analyse and measure campaigns?

It’s a big question and one that needs to be asked. If a staggering 88% of marketers say they want to own the data created from their own campaigns, clearly there is a reason for brands to start looking elsewhere in the digital landscape for platforms that will deliver better data ownership.

After all, consumers don’t browse the internet with blinkers on; there is an enormous opportunity for advertisers that are willing to follow the gaze of consumers and look outside of the walled gardens.

What marketers really want

Clearly, brands are clamouring for more and better insight into their data. They need to compete for the attention of consumers, and data is the biggest and best way to ensure they are creating the content that counts; and that they are delivering it in the right place at the right time. In fact, the same study showed that 83% said having improved insight into data was a critical or high priority in 2019.

So, what kind of data are people are looking for? Well, when asked which insights they found the most valuable, “understanding audience behaviour” came out on top (92%), followed by “informing overall strategy” (88%) and “viewability” (87%). It’s this information that makes all the difference to whether a campaign is seen by more consumers.

It’s time to take stock of your surroundings

It may be increasingly hard for marketers to navigate today’s complex digital ecosystem, particularly when it comes to improving their campaign effectiveness or increasing the efficiency of their media spend. But it’s high time to start asking the questions of the walled gardens and taking advantage of the plethora of adtech solutions that offer simple, transparent control over the first-party data generated as part of ad campaigns.

Marketers must put themselves firmly in the driving seat and ensure their campaigns are being executed effectively and are delivering meaningful returns on their ad spend. Put simply, if you own your data, you’ll own your future.

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