Abandon last click now!
– the guide for attribution newbies

With so much to say about the challenges and methods of attribution and the role of machine learning, we sometimes forget that many folks are still using last-click to measure marketing success and considering improved attribution for the very first time.

Perhaps you are new to digital marketing or analytics and you just need to know what on earth it all means!? If this is you, then here is an introductory guide to attribution – what it is, why it’s important and how you can start to get to grips with it.

What is marketing attribution anyway?

Marketing attribution is a digital version of marketing effectiveness measurement, using the deeper richer data available online. The primary question being asked is what precisely is the value of each different type of marketing activity, in terms of the sales it generates?

Answering this helps you understand what are the best opportunities for increased marketing efficiency and sales growth.

What about ‘last click wins’? What is wrong with it exactly?

If you have read anything on attribution you probably heard mention of ‘last click wins’ and maybe something to suggest you should stop relying on it. If this is all a mystery to you let’s briefly look at what it is and why measurement experts have thrown it into question.

What even the most basic digital analytics allows companies to do is anonymously track a user who visits their website multiple times, and observes whether they make a purchase or not. The company generally knows nothing about these users except their very rough location, what time they access the site and what type of browser and device they are using. All ‘last click wins’ attribution does is link any purchases made to the very last visit the user made to the site before purchasing.

So you might shop around, read up some advice, ask friends on social media, click on one or two different sellers, search again for the first one you liked, and then buy something. The final ‘search again’ action would ‘win’ under the last click attribution rule, which means that the web and advertising analytics systems would automatically assign sales to that ad channel, for example to ‘branded search click’, and zero value to the earlier visits.

When the company evaluates their digital marketing, the effect of this last click rule is that their evaluation is locked into to this rather big assumption: that the final marketing interaction was the deciding factor in causing you to make that purchase. When you consider that some of these interactions might just be ‘exposures’ to banners or videos, you can start to understand why there might be a problem here. But the biggest problem with the last click wins assumption is that when buying online, especially for larger complex purchases like financial services or holidays, people generally go through a process, or ‘funnel’, from awareness of what is on offer through to consideration, research and then finally a purchase decision.

Marketing is based on the idea that this funnel process can be influenced which calls into question the logic of only evaluating the final action the customer makes before buying? Last-click is too late in the process: it is often a navigational action taken by a customer after they have already made up their mind to purchase.

This ‘last click wins’ attribution logic is still the primary measurement rule used in digital marketing systems. Last click is easy to measure and suits quick low-value purchases driven by performance only marketing. However, as user behaviour and retail continues to move more heavily online, the length and depth of customer interactions before the purchase have become way too complex to measure in this simplistic way.

Reliance on last-click means many companies are undervaluing digital ‘upper funnel’ marketing activity such as generic search, social, video and email – to name but a few – and overvaluing low funnel channels like brand search, voucher code sites and retargeting ( – some big generalisations here, in fact, all of these channels can work in different ways so you need to evaluate this yourself).

It follows that if the measurement bias due to last-click could be addressed in some way, then companies could make big improvements in marketing efficiency and sales growth, simply by re-allocating marketing budget away from the perceived high performing channels and into the real marketing drivers of sales.

How did it get to this? And why does it matter?

Marketing effectiveness measurement has been around as long as marketing itself. Imagine yourself in a pre-digital age, trying to measure your marketing. At first, it was easy – just count the number of sales you get when do some new marketing activity. This is particularly easy to understand for direct marketing activity, like old fashioned knocking on doors to sell something. For example, you knock on 1000 doors and make 200 sales then your marketing makes 2 sales for every 10 doors knocked on. Add in costs and revenue and this allows you to work out of it is actually worth knocking on any more doors or if it’s better to try a different approach. You can think of this as the real world analogue of last-click wins attribution.

Once a company gets larger it tends to introduce more brand marketing activity – such as posters, TV or print. This branding activity does not usually lead to immediate sales but instead helps develop a brand reputation and position the brand as having certain features or emotional attributes. The impact of brand marketing is thus generally expected to occur over a longer period of time than more direct activity. What this means for measurement is that future sales, maybe over an extended period of time, could benefit from this brand marketing, and so this should somehow be factored in. Maybe you now sell 20% more, because some of your new customers feel they have ‘heard of you’ before. Getting to this 20% figure is quite hard since the influence of the brand marketing will generally occur at the same time as other more direct marketing that carries on day by day.

Another change that takes place as companies grow is that they introduce more marketing channels which increasingly run concurrently. For example, they might introduce overlapping marketing activity such as maybe posters, plus print, plus in-store promotions. This channel overlap makes the measurement task much harder, as you don’t want to ‘attribute’ all your new sales to all of these channels at once, or else you risk over counting the sales and overvaluing your marketing effectiveness.

Eventually, as marketing budgets grow it starts to make sense to ask some clever stats people to make sense of all the marketing spend and sales data. As nineteenth-century Philadelphia retailer John Wanamaker supposedly said: “Half the money I spend on advertising is wasted; the trouble is I don’t know which half”. Before digital came along, the state of the art in marketing measurement was ‘market mix modelling’ which is a technique still used to find statistical relationships between marketing spend on different channels and sales, with the goal of optimising the marketing mix i.e. the best share of budget for each channel to get the most sales for your budget (incredibly some companies still charge 1000s to do textbook market mix modelling even to evaluate digital marketing! – more on this later…).

The difference with digital is that we now have this new much more granular data about how different visitors interact and make purchases. Enter digital attribution. The challenge it tries to address is how to use this more granular data to do the same thing as market mix modelling but on a much more granular and detailed level, moving beyond ‘last click wins’.

What is an attribution model?

An ‘attribution model’ is simply the algorithm that assigns sales value to the various marketing channels that might be involved in driving that sale. Remember an ‘algorithm’ can be a very simple rule, and so ‘last click wins’ is actually an attribution model (so in a way, everyone is doing attribution modelling whether they are aware of it or not!).

Beyond this then we have to distinguish between ‘rules-based’ and ‘data-driven’ attribution models. A rules-based model (also known as ‘positional attribution’) is a model which takes the data on a series of interactions and then applies a simple rule to share out the value. Typical models are –

  • • Last-click wins – is problematic, see above
  • • First-click wins – good for new customers, but how far back in time do you go?
  • • Linear – credit shared equally between all touch points
  • • U shaped – credit first and last click 40% each, 20% for middle touch points
  • • L shaped – credit the first 60% and 40% to all the rest (%’s may vary)
  • • Time decay – more credit the closer the point is in time to the last click

The key benefit of these rules based models is that they are relatively simple to apply and understand, and so provide a quick and easy way to sense check last click attribution and see how advertising is working throughout their online journey. The downside is that it can seem like entering a hall of mirrors where there is longer a true picture of what is going on.

A ‘data-driven’ model, on the other hand, promises to solve the problem once and for all. Data-driven attribution covers a wider range of algorithms, together with the idea that the model has some basis in evidence defining that particular model as superior or more ‘accurate’ than other attribution models.

(It should be stressed that ‘data-driven’, does not necessarily exclude ‘rules-based’ models, as it might be that data analysis is used to define the particular time windows and positional and channel weights applied in a ‘rules-based’ model.)

While data-driven models seem to offer the perfect solution, they have challenges all of their own. There is a huge range of possible ways to get a data-driven model, and rival data scientists and companies all claim that their approach is the best, so there is, in fact, no one true data-driven model. The quality of the data is always worth considering, as ‘garbage in garbage out’, can skew even the most sophisticated methodology. Major questions exist about how to evaluate such models as accurate or not, and what the right criteria for ‘accuracy’ even are for this problem. Newcomers to the topic are advised to be wary of attribution vendors who sweep all these problems under the carpet and insist that their model is somehow the one true model that will solve the attribution problem once and for all.

What are some of the other key attribution issues?

There are lots of attribution topics we have not even covered here. Some major ones that most companies cannot really ignore include –

  • • Attribution for users who switch between devices e.g. research on mobile, then buy on desktop
  • • Online meets offline e.g. where online sales are influenced by TV ads, or where online marketing leads to sales in stores
  • • Managing impression vs. click attribution, including the role of social media
  • • Linking attribution to Customer Lifetime Value, and valuing repeat Vs new customers
  • • Making attribution analysis fast and transparent enough to be tactical and actionable

Each of these topics deserves a long post in itself – for now, be aware that each of these is a complex topic giving rise to many alternatives theories and methods.

How do we get started on attribution?

Understanding the full scope of the challenge means you have already got started. It should be clear from this that there is no simple fix to addressing all the attribution challenges you might face. So a good place to start is to think through and prioritise which attribution issues to tackle first.

To do this, first review the current marketing budgets and how they are allocated. How good is the measured ROI of your largest channels, and could it suffer from attribution bias and error in terms of how value is attributed to each channel?

The quickest way to then challenge your existing assumption is usually not to jump in with a full-blown data-driven modelling project, but rather to look for evidence in the form of simple marketing tests and simple rules based models which explore alternative hypotheses about how value is created.

It is important to understand that developing improved attribution is part of developing a data-driven marketing approach, which in turn is part of developing a broader data analytics capability. This is a process, not a quick fix. You will need to demonstrate the benefits that you will undoubtedly see in terms of more efficient sales growth from your marketing, and then use that evidence to justify investment in a more robust sustained approach.

There is plenty more online about attribution, but hopefully, if you read this far, you should at least now have a better idea of what it is all about.

Good luck getting to the next level and do get in touch if you think we can help.

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

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

Why do companies waste millions on marketing?

We keep hearing companies confess to spending huge sums on marketing without really knowing the value of what they are doing. It seems to be that under market pressure it is often safer to push for more sales through tried and tested ‘spray and pray’ marketing approaches than it is to agonise over petty details like, is our ROAS measurement accurate, incremental and reflecting the contribution of all marketing channels? Here I will explore the broad reasons for this.

Playing it safe

The demands of business growth competitors, seasonality and company cash flow do not allow the luxury of waiting for perfect measurement, so companies tend to try multiple strategies at once in the hope that if they throw enough marketing budget at the wall some of it will eventually stick. This broad market mix approach actually makes sense for spreading overall marketing budget risk, but does so at the expense of optimal ROI.

Yet in all these firms, there is usually at least some attempt at marketing effectiveness measurement. Measurement of ROI / ROAS is very hard to do properly because it requires sound marketing attribution. The gold standard of marketing measurement is to run properly structured experiments with control and treatment groups and statistical confidence measures of uplift. Yet there are so many possible marketing strategies and channels that it is not practical to do this when under market pressure. You cannot test everything at once and often you cannot even afford to ‘go dark’ on even just a part of your market in order to create that all important experimental control group. 

If there was only a way, your ad spend would align with incremental sales impacts and you would grow faster with higher market efficiency.

 

The paradox of cutting ROAS analytics to focus on growth

More scalable approaches to marketing measurement such as marketing effectiveness analytics and attribution are still complex and can be expensive to implement. You need to address issues of data quality and you also need well-managed teams of analysts and data scientists. All this costs money and takes time. So perhaps it’s not surprising that the requirements for sound marketing measurement take a lower priority than the more pressing requirement for sales growth. It’s paradoxical that measuring success is considering secondary to achieving it when there is so much growth to gain, but this is how many organisations regard investment in marketing measurement and analytics.

Given the complexity, advertisers fall back on softer media independent measures of ad effectiveness such as surveys of brand awareness and purchase intent. These softer measures provide reassurance that ad money is not completely wasted, but do not actually attribute any value or ROI to different marketing channels, so leave major questions unanswered.

 

Actor fragmentation and lack of team focus

Another major reason why companies waste money on marketing is because of the fragmentation of marketing teams and agencies. Channels as diverse as TV, search and social require very different strategies and skill sets for sound execution. For larger brands and larger budgets specialisation within marketing channels makes sense, but too often this comes at the cost of poor integration between channels. Once again measurement tends to take a low priority. By their nature, different marketing channels have different systems of measurement and so increase the cost and complexity of getting to an integrated view.

Agency marketing measurement is of mixed quality. It can sometimes be excellent but at heart agency measurement has a conflict of interest, whereby the agency is essentially marking its own homework. Such bias is rarely malicious or deliberate but more often is a case where there is a systematic tendency to prefer more positive accounts of media effectiveness.

Once again if there are different agencies handling your different marketing channels then you cannot expect much insight into how these channels work together. The result is marketing inefficiency through wasted and duplicated spend and also missed opportunities for finding synergy between channels.

The challenge of cross channel measurement is solvable but it requires focus and resources at the level of senior management. The CMO typically needs support from engineering and finance to push through a properly integrated approach. In many organisations, there is a lack of understanding of the nature of the challenge. It’s often not that there isn’t enough data but that there is a lack of expertise around how to leverage that data for improved decision-making in marketing. This means it is doomed to fail since it is not easy to well. Sound multi-channel measurement means different things for different businesses and so often requires a unique approach for each organisation. Just as soon as you think you have nailed it, business and market conditions change.

 

Analyse that data!

 

Do not let perfect be the enemy of good

One piece of advice we give is just to stick with it and make a gradual transition to a more data-driven approach. It has to be better to make decisions within a rational data-driven framework than to simply burn marketing budget. Some analysis and experimentation is better than none, and more is better still. Strive to improve and do not let perfect be the enemy of good.

The basic idea could not be simpler: money wasted on an activity that is not working can be better spent leveraging activity which IS working. This increases marketing efficiency and sales growth.

Be less wrong!

 

Gabriel Hughes PhD


Does this article resonate with you? Check out other posts to the right of this page.

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

Your attribution model is unique

‘What attribution model should we use?’ is now a question being asked not just by marketing analysts, but by the CMO and even the CDO, CFO and CEO.

It would be fantastic if there was a simple formula to answer that question but of course, life is never so easy. Here are some simple guidelines you can follow to help you answer this question.

Differences in attribution reflect differences in your business

If your business offers a higher value considered purchase, like a holiday or a financial product, then you should expect a longer more complex research to purchase journey, and therefore a higher value given to the earlier touch points. This means attribution models with longer ‘look back’ windows and rules favouring earlier clicks.

If your product is in an especially competitive market, with lots of similar alternatives available to your customers, then you need to focus on the research and comparison phase, again valuing earlier clicks, and also thinking about how to address the cross-device attribution challenge. Users who shop around research on their phones and tablets, and flip through several options, before settling on their final choice. Your product needs to be part of that journey so if you are to have a chance of being selected then leaving it all to the last click is leaving it too late.

If your business relies heavily on repeat custom, then you should attach higher attributed value to new customer acquisition and retention, exploring a cautious application of lifetime value. You cannot assume customers will stay loyal without work, but a repeat purchase is almost always easier and less costly to achieve than the first one.  

Do you have a diverse portfolio of different products, targeting different customer needs and with a wide range of price points? In this case, you may need different models for different product categories. It sounds complex, but if you think about it makes no sense to treat the buyer of (say) an expensive hi-fi system the same as the buyer of a replacement phone charger. Different customers also behave in different ways. Customer segmentation should therefore map on to differences in marketing attribution.

Is your market heavily driven by brand perception and do you invest in TV, outdoor or print? In this case, you will need to explore how to link above the line analysis to your digital attribution.

And so it goes on…. The truth is, each business is unique, with unique complexity around the product offer, their customers, how they engage, and the value of each sale. The only way to address the uniqueness of your business is to develop appropriate unique attribution models.

Incorporate these unique features into your model

Once you accept that your attribution challenge and therefore your attribution model is unique, everything becomes easier. For example, instead of plugging into a standard tag or data collection framework, you can leverage the special and unique features of your data as inputs into your model.

You can structure your attribution model to reflect the different customer journeys that you see for each customer segment and product group. As you learn more and more about your customer journeys, your data-driven modelling can adapt.

At Metageni we believe a customised data-driven approach unique to your business is the only way to get to grips with this complex business challenge and turn it into opportunities to increase marketing efficiencies and grow sales. You won’t look back!

Gabriel Hughes PhD


Can we help you with your custom attribution model? We collaborate with our clients’ organisations, helping marketing & data science teams create integrated on & offline investment analytics solutions for optimising sales growth.

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

Getting from insight to action

While getting from data to insight is challenging, the final hurdle for creating value is about translating insight into actions. Data insight can actually help boost sales, customer engagement and critical business KPIs, but only if it is actually translated into action.

Insight to action is about people

Most organisations are sitting on huge and growing amounts of data, and then rely on analytics technology tools applied by teams of analysts to extract value. This arrangement means the analysts finds themselves as an intermediary between the decision maker and the data. The hand off from analyst to decision maker is therefore at the heart of an organisation’s ability to drive impact through actionable insight.

Many organisations cannot take full advantage of their data because they simply do not have the people or the culture to get the process to work. While big data grows in abundance, people with the skills to get from this data to insight are in short supply. The global 2015 CIO Survey by KMPG found big data analysis and business analysis skills in most short supply affecting 36% and 29% of those interviewed.

Insight-to-action-2

The demand for social skills and strategic understanding

Analysts and data scientists attract a certain type of person, the same kinds of people who are attracted to computer science and mathematics, and who are in short supply across all industries.

At risk of stereotyping, it has to be admitted that all too often this particular analytical skill set is associated with weaker of social skills and an inability to ‘zoom out’ and see the bigger picture. Many of us who have worked with statistical analysts are familiar with the experience of trying to get the analysis out the technical details and into actual implications and inferences. This is the problem of the big ‘so what’ that plagues so much analysis where despite many hours of work the question is still, ‘yes but what does this mean, what we should do differently ?’.

Research confirms that the folks who are in the highest demand of all are those who combine both technical skills and social skills. A 2014 University of California study of the US labour market found an ability to combine cognitive skills like mathematics together with social skills has become a key and growing determinant of career success.

A strategic view is also important, to understand the context of analysis. Strategic consultants often succeed where analysts fail because they approach problems from the top down and not the bottom up, meaning they start with the big questions that the organisation needs to answer and bend the analysis to that. The risk is they go too far in the direction of over claiming on the basis of weak data.

Insight-to-action-3

The data never speaks for itself

Somebody has to make the conceptual jump from analysis to implications, and then the organisation has to be ready to take action.The successful analyst has to be able apply themselves to the questions that the organisation needs to answer, and make strong recommendations when the price of inaction appears greater. This means creating data stories, and communicating results, which requires much more than technical analytic abilities. For it to work, decision makers in the organisation need to do their part to be ready to listen to what analysis is telling them, and put preconceived notions and internal politics aside.

Easier said than done

A review of the current evolution of analytics capabilities by the Gartner consulting group defined prescriptive analytics addressing the question ‘what should we do’ as the highest stage that an organisation can attain (for example in this presentation by Gartner). They rightly identify analysis which drives action as having the highest business impact and requiring the most advanced skill sets, to get beyond descriptive and predictive analysis.

Getting from insight to action is easier said than done.The difference between success and failure is how well people can work together to align around what the data analytics means for action. Organisations need to hire the best talent and develop the right organisational culture.

A culture of data driven decision making means that a junior analyst should feel comfortable telling a senior executive that their product is not right for the market, or their ad spend is wasted, or even that there is no good data to know what the best course of action should be. There should be no fear of ‘shooting the messenger’. Any debate about the right action to take should be grounded in a robust predictive model of the likely consequences of action for KPIs.

Your organisational culture must accept that even imperfect analysis is preferable to flying blind. You will find unexpected insights and opportunities along the way, and will learn the that process of trying to make data driven decisions is valuable in itself as everyone gets closer to the analysis.

 

Gabriel Hughes


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