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Just how bad is your analytics data?

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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 insight to action

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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.

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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.

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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

Find insight from the user journey

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You interact with your users, customers and potential customers in so many ways, through many channels and at all stages in their journey. So, how do you use data to create insights which improve their experience and and grow your business?

There are three broad approaches to this problem out there. These are:

  1. the digital path approach
  2. the statistical modelling approach
  3. the user experience approach

Let’s explore these and think about how each can lead to new insight: what else we might be able to do with the user journey.

1. Digital path analysis of user journeys

At Google in 2008, a small team in London started looking at custom DoubleClick ad server platform data tracing the ad exposure and click path across digital channels to conversion on an advertiser web site (the team was headed up by yours truly). At around the same time others at Google were looking at measuring the assisting role of generic search clicks to brand search conversions. These analyses became the underpinnings of user path analysis, search funnels and attribution modelling, now built into Google Analytics.

The first task of the attribution pioneers was challenging the built in assumption that ‘last click wins’ when measuring digital conversion journeys. Today most companies still rely on this flawed conversion tracking logic inherited from the earliest days of internet marketing. The issue is that when the customer sees an ad or searches around to research a product, they often decide what to buy first and then only after that do they make the final last click to the sale : last click does not in fact ‘win’.

More recently user path analysis based on behavioural data has become widespread and is now serviced by small army of marketing attribution technology companies. Data on marketing and web analytics has the potential to provide a complete view of the user path both off and on the website. Still many companies are only just waking up to these new ways of measuring marketing impact and a recent eConsultancy survey found that only 39% of marketers believe they understand customer journeys and adapt the channel mix accordingly.

The analytical challenge today is finding the most robust method for data driven attribution, ensuring that data on the non converting paths are used to infer the contribution of each channel, campaign and ad. Several companies claim to do this, and clients of Google Analytics Premium can leverage Google’s own data driven modelling approach to optimise their digital marketing spend.

If you want to know what role a digital click typically plays in user journeys for your industry in your part of the world, you can even access a free data tool by Google to do this at a very high level.

In this view user journey analysis would seem to be a solved problem at least for marketing ROI, but of course it is not that simple as there are huge challenges remaining. Foremost, the data is generally incomplete : users are rarely tracked across part of the journey, even if the tracking is set up right, they use different devices and browsers which miss out the whole picture. People also have a habit of being influenced by channels you cannot so easily track in the same data set as your trackable analytics, like offline ads, call centres and not forgetting, competitor offers. In addition the data you get is usually anonymous, with limited data on who exactly you are influencing, which matters a lot because different types of people are influenced in very different ways.

So much for method one : it’s a great data story when it can tell you how to better allocate your online spend, and optimise your web site, but it is only as good as the incomplete data that goes into it.

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2. Statistical modelling for user journeys

While digital path analysis takes the problem from the bottom up, an older method looks from the top down, tracking sales and channel influences over time, and using regression modelling to identify the role of each channel. The same techniques can be used to compare steps in a user journey on a site, tracking views and and clicks over time and modelling how they influence each other in aggregate.

A great advantage of this approach is that it can look at relationships beyond what can be tracked at user level. Notably the role of offline channels can be included. The statistical techniques are well understood and applied methods like market mix modelling are taught at many business schools and universities. Given the potential to get more of a complete picture, some are advocating this type of approach (for example this post).

The downside is that while you gain in non tracked data, you loose granularity in the tracked data. The data about each step a user takes is aggregated with the steps of all other users, leaving out the important path information. As a result the technique has somewhat fallen out of fashion, despite the advantages in linking all online and offline channels that it potentially offers. Also, if you use statistical modelling beware : a non expert cannot tell the difference between a spurious regression model and a robust one, but the former kind is all too commonly peddled even in large organisations. So make sure you have plenty of data and a statistician who knows what they are doing.

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3. User experience for customer journey mapping

The third approach which many companies follow is to storyboard the customer journey across all touchpoints. The idea is to create a visual map of how different types of customer interact at all stages of the journey through to sale and ongoing engagement, across all touchpoints. Each type of customer is identified by a customer persona. Developed by UX design professionals, the map then becomes a useful way to exploring the blocking steps or friction points in the customer journeys, and for making sure that all channels are working together in synch.

The main upside of a customer journey map is the ability to summarise and communicate the user experience to key folks in product management, marketing and senior management. A good map can create empathy for the customer and focus minds on the central problem of improving their experience.

Because mapping is a communication tool, it makes for a great data story. However of the three approaches outlined here it runs the obvious risk of being strongest on the story, but weakest on the data to back it up. To be data driven customer journey maps can work provided they are grounded in research derived customer personas and each stage in the journey is linked to metrics and KPIs that can be tracked to understand how the journey is building new and repeat business.

The true actionability of a customer map lies it the way it highlights the bottlenecks in the customer journey, so that these can be given due attention. Using market research personas, metrics and KPIs grounds the focus on the steps which matter, and guides data driven priorities. Not a bad approach, however unlike the other two, it is unlikely to yield information about the true incremental impact of any changes you make.

For more information on the customer mapping approach take a look at these this post by Adobe and this one by webdesignviews.

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The best of all worlds

So is it possible to get the the best of all worlds ? The simple but expensive answer is that you could do all of the above and explore areas of agreement and inconsistency. A more manageable answer would be that if you are mainly a digital play, path analysis and attribution make sense. If you have signficant offline to online interactions, and your business is high volume, you should explore statistical modelling. And whatever your business model, a high level customer journey map can help you to understand the overall picture.

Also, if you do make changes to how you interact with your customers, do not forget to apply experimentation and AB testing as a means to validate and optimise the journey.

Finally, if you really want to understand your customer journey, you must try going through the process yourself. After that, try the journey again but this time imagine you know nothing about your product, you are on your ipad and you are in a big hurry. You will soon find room to improve.

 

Gabriel Hughes

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

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RetentionGeni

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Use data analytics to increase customer loyalty and repeat purchases

Customer Lifetime Value is built on customer loyalty and retention. Data analytics can show you precisely what drives customers to return to your brand, and shows you the path to increased retention and loyalty.

Using analysis and predictive models based on your first party data, we help you understand which customer segments repeat and why.

Our approach link customer, transactional, CRM and analytics data to measure and build a rich actionable picture of customer repeat purchases and retention. Machine learning retention models reveal precisely what levers you can use to increase loyalty and CLV on a customer by customer basis.

Key benefits – 

Predicting which customers will make repeat purchases will enable you to target them and understand your retention drivers for increased sales growth.

Profiling your repeat custom is the first step we help with, and then we can evaluate which customer characteristics are the most important. Maybe your repeat customers are mainly retired couples…but is it their age that keeps them coming back, or their relative prosperity?

We will take care of linking your first party data sources together in a robust cloud data warehouse that you can access and use  – this data will then form the basis of our Retention and CLV modelling.

We build a complete analysis of all your retention drivers  – from customer features, product features, marketing and even website and call centre interactions. Then we model what predicts retention to help show what are strongest overall drivers.

All our solutions are custom builds designed to address the specific challenges of each of our clients. We will collaborate with your to make sure the analysis is accessible and useful by all your analyst and commercial stakeholders, to ensure actionable insights and drive real impact.

A predictive model of retention can be used to find which actions work best and offer the highest ROI, each specific customer  – such as as well timed email offer, or search remarketing action. This is your most efficient route to growing repeat customers.

The retention rate is the most important assumption within CLV, and so will work with you to create a complete analysis that is linked backed to a Retention model so you can understand which customers and which drivers are most strongly influencing Customer Lifetime Value.