Posts

Evolutionary machine learning meets marketing analytics

In the past couple of years, we have been working with a genuinely game changing technique, which combines evolutionary computing with machine learning, to produce much more accurate predictive models. This allows us to achieve levels of predictive accuracy using ‘white box’ algorithms, which were previously unobtainable except by ensemble and non-transparent methods. For marketing analytics, this is especially valuable because of the need to be able to interpret how predictions are made, the all important ‘why’ behind data-driven decision making. Marketing Multi-Touch Attribution (MTA) is especially improved by this technique.

In data science terms, the technology is about the application of genetic algorithms to both feature selection and parameter selection. Genetic algorithms are an underutilised early technique in machine learning, which are now experiencing something of a revival. The best way to understand the type of challenge they solve is to think of a genetic algorithm as an intelligent search technique which is able to find the best model from a universe of many trillions of possibilities.

How genetic algorithms help

To explain why this is necessary at all, it is important to understand the scale of the problem. A key challenge facing the data scientist is that before they run any machine learner on training data, they must make choices about what features to use, about how to represent those features as predictors in their model, and what hyperparameters to allow for model configuration. The reality is, most models bake in these decisions through somewhat arbitrary assumptions made early in the modelling process.

A good data scientists seek justification for their feature selection and model configuration, in terms of theory and a heuristic understanding of the problem. Far too many just accept the first model that achieves sufficient accuracy after trying only a few alternatives. While there are methods for feature reduction which have a basis in statistics, very few analysts are even aware of the sheer scale of possible alternative models that could be legitimately trained with the very same data source.  

For example, in a dataset with, say, just 50 alternative features, and for a single model requiring only between 3 and 10 predictors selected from these, there are already around 20 trillion different possible combinations that could be modelled. Add in multiple alternative model types, and allow for each model to be configured in different ways, and you have 100s of trillions of possible models. Many problems have a wider range of possible features that this. Finding the best possible model is what data scientists refer to as an ‘NP-hard’ problem, whereby the computational time required must be scaled in proportion to all the possible options. It’s like looking for a needle in the biggest haystack you can imagine.  

Enter the genetic algorithm. While a genetic algorithm does not guarantee you will find the optimal solution, it gives you a great chance of getting close without having to try every possible option. The way it works is by generating a ‘population’ of random solutions and then evaluating each of these against the objective criteria of desirable model properties, such as predictive accuracy, a normal error and so on. There is a lot that could be said about how to get these objective criteria right, as there are potential pitfalls here – but let’s leave this for another post.

The best models in the population are used to seed a new population in a subsequent ‘generation’ of models, and random ‘mutation’ is also applied to occasionally change these models and keep the population fresh. There are multiple generations in this process, and when we run our genetic algorithms we ensure they spit out each new winner as it appears instead of simply arriving at one final winning solution. The effect is to move towards an optimal solution without merely settling on a ‘local maxima’ i.e. the best solution within a limited range of solutions.

Improved predictive accuracy

The difference between the best guess model and a model derived in this way is simply stunning. Gains of 10-20% in predictive accuracy are not uncommon. For marketing attribution, this means we can produce a model which is validated by the ability to predict on a blind sample, while at the same time being transparent and simple enough to explain how specific marketing interactions work to drive sales. There are numerous other applications, for example at Metageni we use different types of genetic algorithm to help select data samples which match the characteristics of cross-device matched data, to help with that particular attribution challenge.

My guess is that we will hear a lot more about this technique in the next few years, as one of several meta analytics processes in the toolkit which help scale and optimise analytics in many domains. We are very keen to hear about the experiences of others using genetic or evolutionary approaches, so please do get in touch if you are working in this area.

Gabriel Hughes PhD


Can we help unlock the value of your analytics and marketing data? Metageni is a London UK based marketing analytics and optimisation company offering support for developing in-house capabilities.

Please email us at hello@metageni.com

Cross device marketing: the attribution challenge you cannot ignore

Attribution across devices is one of the major measurement challenges for digital advertising particularly affecting how mobile and upper funnel activity is valued. Many choose to ignore the problem and plump for guesswork. Is this wise? And what are the best strategies for dealing with this key source of measurement bias?

What exactly is the problem here?

The challenge is simply that we cannot measure actual user marketing influences before sales when users switch devices. For small purchases, most people research and buy in a single visit on a single device. But for a larger more complex purchase, such as a holiday, TV, or an insurance policy, often a little research is done on the phone, maybe a bit more on a tablet, and then the purchase might be made on a laptop.   

A website visitor is anonymous unless they explicitly sign in at some point. So to be visible as the ‘same’ person more than one device, users have to be an existing customer already. Heavily used online services like Amazon, Google and Facebook have done a good job of streamlining user management but the norm for the rest of the internet is sites cannot tell whether visits from different devices are made by the same person or different people. This is a major challenge for advertising measurement since an ad click followed up by a purchase on a different device shows up as the ad as leading nowhere and having no apparent ROI.

This measurement bias generally gets worse with more complex and expensive purchases which involve ‘upper funnel’ channels and have longer multi-touch paths to purchase. The early touch points are already penalised by last click attribution, and then the cross-device challenge penalises them even further.

How big is the issue?

Comscore produces global stats on this using their usage panels and report than globally around 46% of users use multiple devices within any given month, but the figure is much larger in more developed market at around 50-60% in countries including the US, Germany and UK. The more people use multiple devices in this way, especially during a purchase process, the greater the issue.

Although the growth in the devices is a relatively recent phenomenon, the tracking issue is not new. For as long as internet use has been the subject of research, analysts have worried about ‘home versus work’ use whereby someone might do some shopping research during a lunch break and then pick it up later at home – in fact, this is still a major measurement issue. Also, note that the cross-device issue is strictly a cross-browser issue i.e. a user who switches browser or app on the same device cannot usually be linked across events either.

The bias in marketing measurement is clear: user journeys appear much shorter and less multi-touch than they really are.

Most companies still overvalue the last click and undervalue earlier touch points. With cross device switching even a first click positional model will give too much credit to the last click. This is because a single click is both first and last, and for many advertisers, these are the largest single type of user journey visible in their data. In reality, many of these single touch journeys will be from users who have visited before, maybe even very recently, but just on another device.

Multi-touch journeys will also get broken by a switch to a different device. Maybe you make 2-3 clicks doing some research on your phone, then 2-3 more choosing and then purchasing on another device, with a short break in between. Once again the first few touches via upper funnel channels get undervalued and their true contribution to marketing ROI is partially hidden.

How to ‘solve’ cross-device attribution

Even just thinking about taking this issue into account is a major step which the majority of advertisers don’t take.

The first step is to consider how big a problem it is for your business specifically. As with other attribution issues, the more considered and complex purchases, often the higher value ones like holidays, high tech or financial services, tend to have a long path to purchase and therefore a greater attribution challenge. You can use published research to get cross-device estimates but better is to get some idea of your own customers cross-device behaviour, for example looking at the cross report in Google ads reporting which leverages the benefit of their own cross-device tracking.

Many companies have some customer tracking across devices thanks to user login and sign up, apps, and email/ CRM – and while this data is heavily skewed to the purchasers (more on this below….) it does at least provide a window into your customer cross-device behaviour which you can use to explore just how big a challenge this is for your attribution.

Since most companies just ignore the issue, educated guesswork may actually be a better than average approach. In an attribution course which I periodically run we do an exercise where participants estimate the size of the cross-device bias, simply by considering the proportion of sales which are likely to be affected by the issue and using this estimate to upweight upper funnel attribution models. Maybe you guess around 40% of your sales involve multi-touch cross-device journeys. This suggests that when comparing first and last click models, the shifts that occur for each channel whether up or down, are weaker than they should be – in reality shifting by a factor of a further 40% or so.

This kind of simple analysis may be enough for you to give an apparently low ROI channel the benefit of the doubt, as your estimates could show the channel driving more upper funnel activity than initially appears.

Device graphs and other technical solutions

The main thrust of technical solutions fall into either ‘deterministic’, or ‘probabilistic’ methods and are generally a mixture of both. This is not as scientific as it sounds. Deterministic means you can actually track a user using some kind of technology, while probabilistic means you make inferences (guesswork) in the most robust and correct way possible given the data you have. Crucially the probabilistic approach depends on some level of deterministic linking since it relies on using information about ‘known’ cross-device behaviour to try to infer the unknown cross-device behaviour.

So the basic idea is to link as many users as you can using a tracking database, and then make a well educated statistical guess as to the rest.

When you consider a cross-device solution you will no doubt encounter platforms which promise that you can leverage their massive ‘device graph’ database to join users up. It sounds like they have all the information you need. A ‘graph’ in this context means a dataset describing a network of relationships, in this case between different devices. The tactic employed by these companies is to draw on data from multiple companies and networks to work map cookies to a wide set of more robust and persistent login based IDs. They therefore ‘know’ that when there are visits from two different browsers, they actually belong to the same user: this is deterministic linking.

Technology providers also use their linked data to train a model to predict what happens when these ID mappings are not available, and which visits are likely to be associated with other visits from other browsers and locations, and following a similar pattern as observed in the deterministic linking. This modelling process is called probabilistic linking.

Before you decide to shell out the large sums required to use these solutions, there are some really major challenges you need to be aware of.

GDPR and user consent, and other challenges

First, the only way a third party can track someone who is not logged is by mapping IDs based on shared login and tracked users from other sources. This type of large-scale mapping is almost certainly a violation of user privacy and identity under the GDPR legislation, and its only matter of time before these platforms have to delete this data. User data which has been properly consented is likely to be very small relative to the total universe of users out there. After all, would you consent to a company you deal with sharing cross-device usable data with a whole network of other companies?

Second, the data gets out of date quite quickly. Users frequently change logins, and suppliers, and also change and upgrade devices. So a large proportion of users who show up have not been already linked to the device graph ID set, which is only ever a small subset of the total number of users who login in. They cannot track everyone, so, for the most part, they have to estimate.

Which brings us to the third issue – and this is actually the biggest one – which is that claims about probabilistic linking are almost certainly overstated. Claims about highly accurate matching rates tend to fudge the difference between precision and recall (look these terms up if you want to understand more). When you dig into the problem, a basic fact hits you square in the eyes: there are many occasions when users do something and then switch devices, and many occasions where users do exactly the same thing, but then do not switch devices. No amount of probabilistic modelling can change this fact.

For example, suppose I see 100 people make 4 clicks on a mobile device, and my data tells me that there is a 3% chance that their next click is on a desktop. This suggests that 3 people should now be modelled as switching to desktop. But which 3 people? There is no way of knowing from the data you have. If you knew, you wouldn’t be using probabilistic linking in the first place! In technical terms, the ‘recall’ is fine, but ‘precision’ is very weak.

DIY linking

Deterministic linking is clearly superior since this is where we simply have all the data we need to match user to user on different devices. What solution providers do then is to effectively offer deterministic linking based on their database, with probabilistic linking used as a fallback option to ‘plug in the gaps’ left by this method.

Again, if you are piggybacking on someone else’s user data, then there are major privacy concerns to consider. However, it is worth noting that most companies already have some data allowing them to join up users across devices – what we might call ‘Do It Yourself’ (DIY) linking. For example, if your website asks users to log in each time, and collects an email address, then if they read their email from you on a phone, you can potentially match them from device to device. It seems there should be a way to leverage that.

Of course, the challenge is that this is always going to be a limited percentage of users, representing a big gap in your knowledge of cross-device usage. However deterministic linking is never 100% complete. So one way to leverage it is to try your own probabilistic matching, using the data you can match to make inferences about the data you cannot match.

If you do go down this route you should be aware of another major challenge with this partially linked data, which is that it’s heavily biased towards fully signed up customers and against non-customers. It would be very easy to use your own sign in data to reach the conclusion that people who buy from you have complex cross-device usage, whereas people who do not buy from you have simpler single device interactions. The problem with this is that the people who sign up and thus become trackable tend to be the buyers, and so you inevitably have more visibility on their complex user journey data than for the non-buyers. This kind of data bias can easily generate misleading conclusions.   

An alternative solution

Here is where you have to forgive me for plugging our own Metageni solution which we call a ‘cross-device inference model’. We are interested to know the feedback from the community, so let me explain the principles.

The basic idea is that the known or ‘matched’ cross-device data can be viewed in both its matched and non matched forms, and we can observe how the process of matching itself changes the data distribution. The distributions of interest are relevant data features such as the user path length, device use frequency, and device position in the user path. We use this information to resample from our raw data, to create a sub-sample of non matched data which has a similar distribution of these features.

Thus, unlike probabilistic matching, we give up on the attempt to somehow create new cross-device data out of the unknown and instead settle for trying to make the overall sample more representative of what the complete cross-device data set would look like.

For example, we might find that when known data is linked, there tends to be a reduction in single clicks from tablet devices, as these get matched to multiple clicks on other devices. Let’s say these types of interaction fall by 10%. So before we use the raw data which has no matching ID, we create a subsample which randomly drops around 10% of these single click tablet interactions. We do this but for many different features of the data which we know shift around when the data can be matched. We end up with a data set which is not fully linked across devices, but which includes only non-linked data which is considered representative.

We would love to hear what other experts think about this less aggressive and more GDPR complaint approach.

Cross-device use continues to evolve but is not going away

Whatever you decide to do, hopefully, you can now see that it is best not to ignore the problem. Recent research suggests we may be past the peak of cross-device usage for some users who now just use smartphones for almost all their internet use. Mobile has become dominant over desktop, vindicating those companies that adopted a mobile-first strategy a few years back.  

However cross-device use is in many ways a symptom of a continuing rise in multi-task activity, as we sit listening to music on Spotify, watching a show on YouTube on the TV and playing with holiday ideas on our mobile phone, often all at the same time. Measuring how users move through their digital universe continues to be vital to understanding their behaviour. Marketing analysts ignore this problem at their peril.  

 

Gabriel Hughes PhD


Can we help unlock the value of your analytics and marketing data? Metageni is a London UK based marketing analytics and optimisation company offering custom solutions for in-house capabilities.

Please email us at hello@metageni.com

What is multi-touch attribution good for?

One of my proudest achievements while working at Google was the award in 2011 of a patent for Multi-Touch Attribution (MTA). Since 2007 I led a small team of data scientists and engineers working on the exposure to conversion journey using Doubleclick data. The solution we eventually patented was the culmination of months of research and client analytics support and formed the basis of the Attribution Modelling Tool which is still the most advanced available feature in Google Analytics.

Working in the field of marketing analytics and attribution today I am struck by how many people still fundamentally misunderstand the purpose of rules based attribution modelling as we had originally conceived it back then. So let me put the record straight.

The purpose of rules based attribution is not to provide you with the best alternative to last-click wins attribution. Indeed as many have observed the problem that MTA highlights is that there are many different ways to understand and measure the contribution of different marketing touch points in the user journey to sale. This means it is not immediately obvious what the relative contribution of different marketing channels could be.

It has always been clear that a definitive answer to that question lay in the direction of some kind of data-driven or statistical approach. Indeed this is what my current company attempts to do using machine learning and what Google and others attempt to do with sophisticated algorithmic models. So what is Multi-Touch Attribution good for?

In the first instance, an alternative rules-based model highlights the extent of potential bias in last click attribution. The context for this development was the deeply ingrained adherence to this flawed view of marketing value. Indeed even today almost a decade after rules based attribution was invented a surprisingly large number of major brands still rely on a last click view of the world. Last-click is baked into most advertising reporting systems and as a result, companies continue to under-invest in upper funnel sales growth opportunities and fail to realise marketing efficiencies.

Challenging hidden assumptions

Companies use last click attribution without even knowing about the implicit assumptions that are embedded within this logic. When I give talks to larger groups of agencies and advertisers I often open by asking for a show of hands from all those who are, ‘doing attribution’. Naturally, it’s a trick question. If you are not ‘doing attribution’, then you are almost certainly relying on last-click wins attribution without realising it.

Indeed, if you make any claim at all about the sales impact of your advertising, then you are ‘doing attribution’, and so the question advertisers need to ask themselves is how they can do a better job of challenging and testing their attribution assumptions.

Around a decade ago Google and others started to address this huge hidden measurement bias through the introduction of metrics such as the assisted click count. So for example in AdWords ‘Search Funnels’ it became possible to see how brand last click sales are influenced by generic search campaign, ‘assist clicks’. This was a major step forward but was not a game changer. Just as the magician’s assistant is a secondary presence on the stage compared to the main performer, then in a similar way the assist click is a secondary player to last-click attributed sales.

This is where alternative models come in: they start with an equivalent status to last click. Using an alternative attribution rule such as, say, first click wins demonstrates the equivalent status of any one attribution rule to another. The point is that without experimental methods or statistical analysis, one attribution rule is no better than any other.

Indeed, last-click is quite a poor choice compared to other positional models. The specific problem with last-click is that it maximizes the risk of attributing a sale too late within a marketing process which influences the user in a series of steps. If marketing has any value at all it is there to help a brand convince and influence a person to purchase from them in preference to spending their money elsewhere. Somewhere within this multi-step process, the consumer makes a choice. By the time customers make the last click, many of them have already made up their mind beforehand. Thus many last click actions are purely the final navigational steps in a consideration research and purchase process.

So, alternative attribution models help us expose the flaws in last-click wins attribution.

Understanding the customer journey

Alternative rules-based models do much more than just move us beyond last click. They also help users understand the complete measured journey to sale. If you compare a first click model to a last click model you are directly able to see the patterns of where users start their journey compared to where they end their journey. If you introduce a linear model you can understand all the touch points that had an influence along the way. A time decay model helps you to understand the closing stages of the process without early focusing on just the final stage. And so on. In other words, rules-based models are an excellent means of summarising the data on multiple complex paths to purchase online.

The need to be able to summarise this data is clear as soon as you try to look at the raw data about each user journey. Many paths are incredibly complex and unique. As an analyst, you need a way to summarise this information so you can understand at a macro level what the most important patterns are. Rules-based attribution models give you precisely this.

One key feature of the attribution modelling tool in Google Analytics is that it allows the user to compare models one against the other. This is because the aim is not to encourage the user to pick a single alternative to last click wins but to compare multiple models for insight into how marketing works throughout the user journey. This feature was central to the original attribution analytics prototype that we created.

Measuring ROI

So can rules based attribution tell us anything about the actual value of marketing? Of course, it can. Think of each rules based attribution model as a hypothesis about how value is created and then you can check this hypothesis against the other hypotheses and against your marketing strategy.

For example, if all models agree that a particular marketing channel drives positive ROI then it looks like a pretty safe bet for ongoing investment. If all models show negative ROI then maybe you should look for a cost saving. If only the upper funnel models show positive ROI for a channel then consider whether your marketing strategy is to use this channel to drive the early stages of purchase such as research, for example, a review based affiliate.

Put simply rules-based models allow you to consider whether your digital marketing is really working in the way that it is supposed to work.

Ultimately decision makers want simple answers. Multi-Touch Attribution was an attempt to simplify what is, in fact, an extremely complex analytical problem. This is proved to be both the strength and the weakness of MTA as marketers continue to struggle with knowing exactly what to do with these models.

Data-driven models still involve hard choices

There was never any doubt that a robust data-driven approach or experimental framework would be preferable to multiple rules based MTA models. The promise of an approach which could yield a single definitive answer is too tempting to ignore. Yet even here the analyst has a duty to ask questions of such models and try to understand the underlying patterns in the data.

One major challenge with data-driven attribution models is that navigational actions like brand search clicks are a good predictor of sales even though they are not a reflection of marketing cause and effect. The issue is essentially that marketing influences sales through a psychological mechanism which is not itself directly observable in behavioural data.

Furthermore, experienced data scientists know that there are multiple data-driven models that can be obtained leading to different results and that a simple objective criterion for selecting between them is not easily available. So, even if you can get to decent data-driven models, it is not nearly so easy as you might imagine to arrive at a single unambiguous solution.

We should always be open to multiple interpretations of the data and to try to avoid oversimplification. So even if you believe you have the perfect data-driven model I would urge any marketer to use the rules-based models as a benchmark and guide to interpretation.

As Einstein said, ‘Everything should be made as simple as possible, but not simpler.’

 

Gabriel Hughes PhD


Can we help unlock the value of your analytics and marketing data? Metageni is a London UK based marketing analytics and optimisation company offering support for developing in-house capabilities.

Please email us at hello@metageni.com

Build your own attribution model with machine learning

Sounds too good to be true? Maybe so, but as machine learning and cloud data technology becomes more accessible and scalable, building your own data driven Multi Touch Attribution (MTA) model is becoming increasingly realistic. The advantage of a predictive machine learning model is that it can be objectively assessed on the basis of a blind hold out data set, so you have a clear criteria as to whether one model is ‘better’ than another.

What you need for an in-house approach

First, you need data, and lots of it. Fortunately, digital marketing is one of the most data-rich activities on the planet. Chances are that if you are B2C online business, you have hundreds and thousands or else millions of site visit logs generated every week. These logs contain referrer data which helps you analyse and understand the customer journey to sale, the first essential step in building your own attribution approach.  

Second, you need a solid experienced data science and marketing analytics team. Go for a mixture of skills. Typically some data scientists are strong on theory, but weaker on the application, while others are great communicators and see the strategic angle, but are weaker at data related programming. You also need domain expertise in marketing analytics. You need visualisation experts and data engineerings experts. The fabled ‘unicorn’ data scientist is literally impossible to hire, so instead, you should go for a team with the right mix of skills, with strong leadership to move the project forward.

Third, you need patience. The truth is, getting to an attribution model using machine learning is not easy. It’s not a case of throwing some data at a model and waiting for the answers to pop out by magic. Your team needs to decide what data to use, how to configure it as a feature set, what types of model to use, what an appropriate training set is, how to validate the model and so much more besides. You will need to make multiple attempts to get to a half decent model.

Choosing the final model

The best candidate ML models really depend on your data  – we have had good results with well optimised classifiers and regression models, which we find often outperform even high order Markov models. While a black box or ensemble method may get better predictive accuracy, you need to consider the trade off in terms of reduced transparency and interpretability. The best advice is not to commit to a particular modelling approach or feature set too early in the process, but to compare multiple methods.   

But what then? An advanced machine learning model does not speak for itself. Once you have a model, you then need to be able to interpret it in such a way as to be able to solve the marketing mix allocation problem. What exactly is the contribution of each channel? What happens if spend on that marketing channel is increased or decreased? How does the model handle the combined effects of channels?    

All of this will take months, so it is small wonder than many companies ignore the problem or else go for a standard vendor out of the box approach. It’s worth remembering then that there are some key benefits of a, ‘do it yourself’ approach to consider.

Benefits of an in-house model

 

If you create your own model, you will discover a great deal about your marketing activity and data in the process. This can lead to immediate benefits – for example with one major international services firm we worked with we found significant marketing activity occurring in US states and cities where the company has no local service team. Even with no attribution model defined at that stage, the modelling effort uncovered this issue and saved the company huge sums right away. The point is that your data quality is tested and will become cleaner and more organised through the process of using it, and this in turn supports all of your data driven marketing.     

Another beneficial side effect is that if you create your own attribution model you will also learn about your business and decision making. This process will force your marketing teams to collaborate with data scientists and engineers to work out how to grow sales. Other teams need to be involved, such as finance, and your agencies, and this will often spawn further opportunities to learn and collaborate across all these marketing stakeholders.

Attribution is all about how different marketing channels work together, so your various marketing teams and agencies need to collaborate as well – social, search, display as well as above the line, and brand and performance more broadly. Again, this provides intrinsic and additional value over and above the modelling itself.

Finally, it’s worth pointing out that you will never actually arrive at final model. This is quite a fundamental point to bear in mind. By its nature, a machine learning approach means you need to train the model on fresh data as it comes in. Your marketing and your products are also changing all the time, and so are your customers. So really you need to build a marketing attribution modelling process more than you need to build a single attribution model.

So, go ahead, build your model, be less wrong than before, and then when you have finished, start all over again. As we say at Metageni, it’s all about the journey.   

Gabriel Hughes PhD


Can we help unlock the value of your analytics and marketing data? Metageni is a London UK based marketing analytics and optimisation company offering support for developing in-house capabilities.

Please email us at hello@metageni.com

Just how bad is your analytics data?

If you don’t know how bad your analytics data is, then the chances are, it is much worse than you think. With data analytics, it’s not the known data quality issues that will cause you the most trouble, not the known unknowns, but the ‘unknown unknowns’ – those issues you uncover and discover as you explore and analyse your data.

Usually, it is only the practitioners who are very close to the data who understand the full extent of the data quality problem. Too often the poor quality of data is kept as something of a dirty secret not to be fully shared with senior management and decision makers.

Common issues in web and marketing analytics

Let’s look at just some of the most common issues affecting web and marketing analytics data. To begin with, do not assume that the data sources provided by the most common analytics solutions are robust by default. Even the best are prone to big data quality issues and gaps. Take Google Analytics referrer traffic, which often reports an unrealistic level of ‘Direct’ traffic, supposedly visits made directly through users typing in URLs, or bookmarking, both in reality low frequency methods of site access. The reason is that ‘Direct’ is, in fact, a default bucket used where no referrer data is available to the analytics server.

Experienced web analysts know that high levels of direct traffic usually mean high levels of broken or missing tags, or other technical issues, that means the true referrer data has been lost.

The major providers are also contributors to that other major source of poor data quality, which is fragmentation and disjoint data sources. Google search marketing tags will track conversions, but only from the Google search and display network. Facebook similarly provides tags which only link Facebook marketing to sales, ignoring all other channels. Affiliate networks do the same thing leading to widespread duplicated over attribution of sales to multiple sources. This challenge is exacerbated by different marketing attribution platform look back windows and rules which are different between platforms.

Having worked with multiple brands of all sizes, I have yet to come across a brand that does not have some level of tagging issue. A typical issue is a big mismatch between emails delivered and emails opened and clicked. Another is social campaigns which are delivered by 3rd party solutions and then appear as referral sources, due to the use of redirect technology.

Tagging and tracking

Tag management systems help manage this, but unfortunately not by linking the data, just by de-duplicating tag activity at source, which is hardly satisfactory if your goal is to understand Multi Touch Attribution (MTA) and marketing channel synergy.

Assuming you solve all your tagging issues and have well structured soundly applied tags, you should not forget that the tag is only as good as the tracking itself. A great challenge here is the gap that exists tracking users across devices. You cannot link visits by the same user on different devices without sophisticated tracking that users have signed up to beforehand. This means your tags cannot tell the difference between the same user visiting twice on two different devices and two different users.

The idea every one of us can be closely tracked and monitored online is an illusion for all the biggest technology companies – and perhaps we should be glad of that. Indeed unique ID tracking and linking is now more closely under scrutiny the age of data security breaches, increased concerns over user privacy and the GDPR. This is yet another source of difficulty for companies looking for a 360-degree view of the customer. Companies have to work with fully consented and well defined samples of data to make progress in understanding their customers.

For the analyst, this is yet another reason why having huge volumes of data is not enough for user insight and data-driven decision making.

So what can you do about all these data quality challenges?

Data quality is perhaps like muscle memory in sport. You use it or you lose it. It’s only by trying to analyse and find patterns in your data that you uncover the issues that need to be addressed. Where there is a need, strategies can be devised to manage these gaps in data quality and take steps for improvement. It is a process.

The best advice is to get stuck in. Pick one data source and run with it, making sure to compare it to others and ask if the data makes sense given what you know about your customers. There are always discrepancies between data sources which in theory should report the same numbers: in my experience, this is a kind of law of all data analytics, so you need to get used to it. Use these differences to help you validate your sources, understand why differences might arise, and just accept that there is an acceptable level of difference – say 2-3%.

In data analytics, as in life, you must not let perfect be the enemy of the good. Be wary of the massive data technology project which promises to link all data together in one big data lake and thereby solve your challenges. Bad data plus more bad data does not equal good data. Face up to your terrible data quality, and tackle the ugliest issues head-on. If you ignore the problem, it can only get worse and you will continue to struggle forwards in the dark.

 

Gabriel Hughes PhD


Can we help unlock the value of your analytics and marketing data? Metageni is a London UK based marketing analytics and optimisation company offering support for developing in-house capabilities.

Please email us at hello@metageni.com

Getting from data to insight

Survey after survey tells us that organisations are still struggling to fully leverage big data to get to insight and data driven decision making, despite investing billions. One major commonly overlooked reason for this is the difficulty of bridging the gap between pure data analysis and actionable insight.

The gap between data analysis and insight

Getting to insights is challenging because it requires both the science of data analytics and the art of data storytelling. Data storytelling for insight goes beyond pure data analysis since it involves communication tailored to an audience, such as a decision maker. It also goes beyond pure analysis because it must incorporate factors which the data alone cannot reveal no matter how much analysis is applied, such as idiosyncratic knowledge, uncertainty and even intuition.

Data-to-insight-2

The challenge of leveraging big data

A recent report by CapGemini The Rise of Insight Driven Business highlighted the importance of using data and insights for action to drive business growth. Yet in an extensive survey they also found that only 27% of senior executives believed their big data initiatives were successful. The survey highlighted data silos, and found that 52% of executives believed the IT development process constrained their ability to quickly create insights.

Of course it is not data or technology alone that constrains insight, but the inability to find the right people who are able to leverage it. As KMPG found in their global 2015 CIO Survey, 36% of technology leaders suffer from a shortage of big data analytics skills, higher than any other function asked about, and also high on the list, 29% say they are short of business analysis skills.

Clearly getting from data to insights is not easy, and needs investment in technology and people. But can the lack of investment alone fully explain why organisations are struggling to create data driven businesses? What happens when you have the data and the systems in place, but the actionable insight still feels thin on ground?

Telling stories with data

As Google have reminded us, data analysis is not about graphics and visualisations, it is about telling a story. This means identifying the audience, and speaking to their aspirations and concerns. Too many data analysts and analytic platforms create more information from data without clear direction as to precisely why the recipient, the decision maker, should be interested, and what they should actually do with it.

There is no shortage of data: organisations are drowning in it. Unfocused and poorly communicated analysis just adds more fuel to fire, more information for the time poor executive to navigate as they try to pinpoint what actions to take. Bridging the gap has to be about tailoring the analysis the needs of business decision makers. It is also about making sure the complexities of analysis are made easy to grasp and compelling, and essential points break through for those who need to know. This is data storytelling.

Data-to-insight-3

Insight often requires a leap in the dark

For data storytelling to work you need a receptive audience and many rightly stress the importance the creating a data driven culture (for example this post by McKinsey and this one by PWC). Typically the move to data driven culture is understood as move away from decision based on gut instinct and opinion. However as anyone who has tried to use predictive modelling for business forecasting will know, often it is impossible to remove all uncertainty and to resolve the decision problem you have without making some very big assumptions, and relying on intuition.

In fact data analysis and intuition are not mutually exclusive means to making smart choices, rather organisations must be comfortable with exploring problem solving from all angles and understand the limitations as well as the opportunities inherent in their data. Blind adherence to the dictates of data analysis which is patchy, has a margin of error or which cannot be generalised, may be even more dangerous than ignoring data altogether. A post by MIT Technology Review makes this point very well, notably in respect of risky decision making for innovation.

Summing up: Seven steps to get from data to insight

No online article is complete today without a list to summarise the thinking, so here goes – the first four are widely advocated as essential, while the fifth, sixth and seventh are about bridging the data to insight gap –

  1. Integrate your data
  2. Accelerate big data technology
  3. Invest in smart people
  4. Communicate insight by creating data stories
  5. Tailor your analysis to your business goals
  6. Drive for a data driven culture
  7. Tolerate some intuition and uncertainty

So there it is, in just seven easy to understand steps. In practice it will take years and is ongoing process. Getting from data to insight is a major overhaul of how your organisation behaves, requiring commitment, imagination and effort at all levels.

Gabriel Hughes


Can we help you become more data driven?
Please email us at hello@metageni.com