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Step up your 4 P’s marketing mix with Marketing Mix Modelling

Today all retail sectors have an online presence and pure play online business is thriving. In the past when offline retail and marketing dominated, the traditional 4 P’s model –  Product, Price, Place, and Promotion – was a widely used way to structure sales strategy and marketing campaigns. Classic marketing analyses using marketing mix models  and econometrics could incorporate data on each of the 4 P’s to build more data driven marketing strategies and for identifying the strengths and opportunities for retail businesses.

In the era of digital marketing and ever expanding e-commerce, it is important to ask if this remains a useful framework for a digital first retail environment and to think about how measurement strategies are evolving. It remains critical to be able to measure your marketing strategy effectively in order to maximise ROAS (Return On Ad Spend) and there is a greater opportunity to get this right given that the huge data volumes created by digital retail offer massive untapped potential for data-driven optimisation.

The retail marketing investment challenge depends on successfully combining the measurement of both online and offline marketing channels to understand what works and what does not, in order to allocate your budget efficiently. Companies spend millions on marketing while having difficulties with accurate measurement and attribution. As a result, spending based on poor measurement and guesswork leads to investing in ineffective channels, while valuable touchpoints are underappreciated. A more solid data-driven approach to marketing is the only option to eliminate the guesswork and make smarter decisions that improve ROAS.  So, what are the options?

Understanding Marketing Mix Modelling

Marketing Mix Modelling (MMM) remains one of the optimal solutions for accuracy in measurement and whole channel optimisation. It is a set of statistical techniques which allow adding the offline data for the whole user journey optimization.  In the digital era, it has been overtaken by granular marketing analytics and techniques such as Multi-Touch-Attribution (MTA) which make use of the raw digital marketing interaction data to pinpoint which customers interacted with which digital channels. However, untracked offline channels remain outside of these methods, and in recent years developments in 3rd party cookies, privacy rules and technology blockers like ios14.5, have increased the importance of more traditional ‘big picture’ methods using econometric methods.

Marketing Mix Modelling brings the advantage of combining both offline and online channels. It can be used to measure channel influence, brand halo effects, and in the post cookie world, social and display impressions effects. Due to the statistical nature of the econometric methodology, there are less restrictive data requirements than are required for machine learning approaches used for the more advanced data driven attribution models. Consequently, MMM allows analysing not only digital marketing channels but other external factors such as economic drivers, competition, pricing and seasonality. So does this mean the traditional 4 P’s approach to strategy still has a place in the era of digital retail?

Econometrics and marketing mix modelling

Econometric methodology and 4 P’s 

Coming back to the traditional 4 P’s model the ‘place’ component is the challenging one, due to cross-device user journeys and multiple touchpoints, which can occur both offline and online. Imagine a user who is completing a generic search online such as ‘buy women shoes UK’. She might end up with a search ad for the UK brands selling shoes and now, she will move to complete a more focused brand search. She does not convert on the day, however, the next day she sees advertising on a tube train or a TV ad from one of the shoe brands she was researching. This moves her from the awareness to the interest stage in the lower funnel. Later that day she sees a display ad of the brand or a particular product category, clicks on the ad and buys the shoes on the brand website. This is a classic example of the attribution challenge and measurement, especially for both offline and online touchpoints. It is very difficult to establish exactly how much credit should be given to the TV ad or to the display advertising. Where should your marketing team invest more resources and which strategy was the most effective one to move the user to the upper funnel and convert?

These kinds of questions can be answered by marketing mix modelling at a high level and with an integrated econometric and attribution model (‘holistic attribution’) it is feasible to aim to answer all such questions with enough accuracy to make smart decisions for both offline and online marketing. For example, should the user decide to complete the purchase in-store, after being exposed to multiple touchpoints both online and offline, this approach allows you to attribute offline sales to your integrated marketing approach and understand which touch-points were most effective. Marketing Mix Modelling picks up the wider environmental and longer term influences which the new digital funnel analytics cannot.

Econometrics and marketing mix modelling

Similarly, econometric models/ MMM can be an effective solution for measuring the ‘promotion’ component of your marketing strategy. Econometrics is the best choice for understanding how your offline marketing channels e.g. print, radio, mailings attribute to digital and work together to drive conversions. With this methodology, you can ensure an accurate and justified measurement of your marketing investments. This is also a great starting point to switch from the last click attribution and measure your clicks and impressions in an independent and accurate way to maximise ROI.

As for the ‘price’ and ‘product’ components of your strategy, it is often influenced by external factors such as regional and seasonal patterns. You can gain valuable business insights and make data-driven decisions about the product categories or pricing strategy based on regional statistical data. Additionally, marketing mix modelling allows to understand any seasonal shifts or out of ordinary events such as Black Friday, based on this data the customer buying trends can be analysed and price/product decisions can be made to drive sales.

Finally, econometric modelling not only provides the analysis of the main components of your marketing channels but also provides an added benefit of bigger picture insights such as brand equity and acknowledging underlying trends that drive sales. Consequently, brand vs marketing performance can be quantified to make efficient financial and media planning.

In conclusion, for the businesses that operate both online and offline marketing mix modelling is the best option for implementing the data-driven approach and measure both online and offline marketing channels simultaneously. The key point is that you do not have to choose between the new world of digital marketing analytics and the more traditional world of Marketing Mix Modelling and the 4 P’s of retail. The data may be very different but the measurement methods should be treated as complementary and indeed can even be combined into a single analysis that incorporates new digital measurement methods without losing the benefits of the well established econometric approach of MMM. Furthermore combining offline and online measurement is essential for a full 360 view of your omni-channel customer. This holistic approach is key for a complete data-driven marketing strategy, which will benefit from incorporating valuable insights into the external non-marketing factors influencing business performance.

Get in touch with us for expert advice!

Thanks for reading – we hope you learned something through this high-level tour of marketing effectiveness methods. If you want to learn more about expert data analytics using AI, or are interested in how Metageni can help you use your data to grow online, then do get in touch with us: hello@metageni.com

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

Increase customer lifetime value with predictive analytics

Customer Lifetime Value (CLV) provides your company with an estimate how much profit you will get from a customer over an extended period of your relationship. CLV is quite simple to understand and calculate at a high level; many companies produce estimates of their CLV for financial and marketing analysis. However, the bigger underlying question with CLV is how to create customer loyalty and motivate your customers for repeated purchases to reach a high customer retention rate. This is a much harder question to answer. 

Predictive data analytics methods can be used for understanding how to drive repeat purchases and gain a competitive advantage through building long term customer loyalty. As companies grow larger it becomes increasingly important to focus relatively more resources on building customer retention than on pure customer acquisition, in order to maximise total long term ROI from marketing and CRM investments. It is easier to sell to an existing customer than a new one, and repeat custom should take an increasing share of revenues as the company grows. In other words a high customer retention rate naturally drives higher Customer Lifetime Value. So what lies behind the Customer Lifetime Value concept and how can it be combined with predictive analytics for increased long term profitability?

Customer Lifetime Value

Customer Lifetime Value meaning 

Customer Lifetime Value is a methodology for estimating how much profit you can expect to get from a customer over the lifetime of your relationship. This knowledge will also allow you to map out where, when and how much to invest in your marketing to retain the customers you are most interested in in terms of valuable and profitable relationships. There are several different ways to calculate it at an aggregate level and you can find plenty of examples online – notably several articles by Harvard Business Review. The basic formulation is – 

CLV = Gross margin of purchases over time x Retention Rate – Marketing Costs

Where we estimate CLV at Metageni, we tend to start by defining 4 key types of customer: 

  1. New customers – who are buying for the first time.
  2. Retained customers – who are making regular purchases.
  3. Lost customers  – your churn – who have not bought from you in a while. 
  4. Win-back customers – customers who eventually come back to you after being lost.

Just quantifying each of these, and understanding how they change over time, is hugely insightful for understanding the role of customer loyalty and repeat purchase. It also forces you to answer important questions like, ‘what is a typical time frame between purchases?’ and, ‘how many orders do my loyal customers make in any given period?’ 

Adding CLV allows precise answers to questions about the relative value of retained vs new customers. It makes sense to evaluate how CLV and customer types vary between your customer segments defined by purchase category, time of original acquisition and demographic. For each customer group an idea of long term value through CLV helps define how much emphasis should be placed on trying to increase their engagement.

Using data analytics to increase repeat purchases

Customer data analysis using predictive models allows businesses to understand which particular attributes of the customer are generating repeating purchases and help to diagnose why. Ideally this would be part of a complete, actionable strategy built on a variety of data sources such as transactions, CRM and analytics data. Machine learning based retention models are a fantastic technique for pinpointing how to increase customer retention and customer lifetime value on a customer by customer basis. Customer retention analysis outlined by a machine learning (AI) model, can help you to predict which customers are more likely to complete repeat purchases, which in return allows you to manage your marketing resources accordingly for increased sales and ROI. 

Moreover, cohort analysis can segment your customers and combined with predictive models, help you to tease out the key characteristics of customers with higher customer value. This analysis will not only help you to understand which marketing channels and methods you should use to retain high value customers, but also which particular actions to focus on to encourage loyalty. For example, it could help you to figure out if a follow up communication or a promotional offer really helped move customers to the end of the sales funnel for a repeat purchase, or whether the effort was wasted with more value to be found elsewhere. 

Another major advantage of using predictive models for understanding customer retention as a driver for CLV is the opportunity for in-depth analysis to highlight your main retention drivers, which might include customer and product features, marketing activities, website usability, call center interactions and many more. This approach yields the strongest insights when applied to a wide range of customer cohorts characterised by multiple dimensions, including category and brand interest, customer loyalty and purchase frequency, customer demographics, historic customer buying patterns and behaviour, time since last purchase and other factors which are an interest to marketers reflecting specific business and industry requirements. 

As a following step, the model is built to support retention growth by simulating how various retention drivers drive loyalty and repeat purchase. A data-driven strategy based on a truly predictive approach can greatly increase customer lifetime value and ROI. 

Lastly, the next best action method, which is discussed in greater detail in this article on our blog Predicting Online Customer Intent – Metageni, can help you to understand which action was the best strategic move for encouraging a repeat purchase. Machine learning algorithms are used to precisely identify how to increase customer lifetime value, as well as improving the accuracy of your customer lifetime value calculation. Was it the follow up email? Or perhaps, a personalised offer? Next best action simulates every possible action and combines this with the cost of the action, to pick the one single action that yields the best result for ROI. With CLV analysis, you can focus on the long term ROI as your next best action. Ultimately your customer and purchase data has the potential to do all this if analysed well: predict which customers are more likely to repeat their purchases, what that would be worth to your business and then identify what exact marketing tactics or actions you should implement to make it happen.

CLV and Predictive Analytics

Predictive analytics within your business growth strategy 

When the companies know the exact needs and wants of their customers it is easier to predict the customer intent and plan customer retention strategies. The best way to do this, is to collect first party data about your customers which can then be used for building machine learning models and devising the strategy for high customer retention rate. Once you have collected all the necessary data about your customers, an in-depth analysis conducted by a data science specialist will predict the customer intent and prodive the recommendations for increasing your customers’ lifetime customer value. The ultimate goal is to create data driven personalized experiences for your customers which is unarguably one of the main keys for unlocking retention rate and leading to high 

In conclusion, the main pillars for a successful customer lifetime value growth strategy are communication, personalization and re-engagement. These three pillars of success are difficult to achieve without knowing the needs and wants of your customers. Thus, the data driven approach which is based on your customer data, rather than the generalised information collected from third parties, is a highly effective approach for building strong relationships with more loyal customers.

What is your measurement strategy? 

Considering the digital transformation in the past 20-30 years combined with the post-pandemic challenges, it has never been more important for businesses to adopt a data-driven approach and embrace the challenges of marketing measurement.

Successful companies and forward-thinking leaders have focused their attention on data analytics, understanding the value of knowing more about their customer’s preferences and what they do during the conversion journey in order to increase marketing ROI, using this insight to make informed strategic decisions.

Can we measure marketing effectiveness for customers who are influenced and buy both online and offline? 

With the right capabilities, knowledge, and data-driven approach, the answer to this question is an emphatic yes. In the following, we are going to briefly review some of the approaches companies can adopt in their measurement process, and try to explore the pros and cons of each, without getting overly scientific.

Get in touch with us for expert advice!

Thanks for reading – we hope you learned something through this high-level tour of marketing effectiveness methods. If you want to learn more about expert data analytics using AI, or are interested in how Metageni can help you use your data to grow online, then do get in touch with us: hello@metageni.com

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

Predict online customer intent with machine learning

When a customer walks into a store, salespeople work to understand what the customer’s purchasing needs are and decide pretty quickly if they are window shoppers, impulse buyers, bargain hunters, researchers, regular customers, or even just wandering around and not shopping at all. How you respond in the physical world is no so different than in the digital world. But how do you respond to customer needs when there is no salesperson in the room? The answer is by using predictive modeling linked to web analytics to predict online customer intent. 

Understanding the customer segmentation by propensity score 

The analogy of a physical shop translates into eCommerce websites and into predictive analytics. Visitor and customer behaviour patterns can be anonymously observed by your first party web analytics data and using machine learning, you can predict online customer intent and who will buy.

A model can score behaviours according to propensity to buy giving a value between 0 and 1 where 1 would be 100% likelihood to buy. Customers with a value closer to 1 know what they want and are heading for till so they will tend to buy without help, so there is no need to waste effort and budget on these customers that would buy anyway.  Customers with lower scores will need much more effort to actually get them closer to any meaningful conversion behaviour, this may not be the best course of action to take given how much resource may be needed to achieve that.

The greatest opportunity lies with focussing on the customers who score around 0.5, the ‘floating middle’, as their behaviour shows they are much more open to persuasion. Those that can be persuaded and influenced given the right nudge can be targeted and brought to sale. This describes how a predictive propensity scoring model ML model can help for a single conversion goal. The opportunity grows wider with more goals added – such as scoring consumer interest across a range of different product categories or offers, and using that to drive a personalised response. 

Furthermore AI machine learning (ML) can also help understand how different responses to the customer can shift the likelihood of them taking a particular action, or buying a particular product. Understanding online customer intent using propensity scores can guide you into offering the most targeted offer or incentive at the right time for the biggest impact. 

Machine learning for predicting customer intent

Customised user journey and ROI efficiency 

One of the main advantages of using predictive modelling is the ability to approach and engage your customer where and when it is the most efficient in order to move the user further in your online sales funnel and to predict online customer intent . This approach can greatly improve and personalise the user experience by allowing your team to identify the customer intent on a case by case basis. For example you can target a customer who is looking for a particular product category with a special offer to increase the conversion potential; or you could provide help to another customer who is identified as having trouble searching for a particular category or product. Similarly, knowing the customers with the high conversion potential will give your marketing team a tool for creating immediate activation actions, like search retargeting.

Having this insight maintains the momentum nudging the customer towards the conversion stage in the decision making process which is efficient and targeted. Just like the in store salesperson. 

Customer journey optimization and attribution

Another valuable outcome of predictive intent modelling is the ability to optimise your marketing channels by analysing the ROI for each feature of the website. These insights are extremely relevant for your UX and developments team and will help you to understand which features and touchpoints to focus on, or which ones should be modified or even removed.

The goal should be to develop a data-driven approach to the buying barriers and create a quick and easy sales funnel, which is extremely important for maximising the conversions. Again using the offline analogy, this would be like asking your in store sales team to use their knowledge and insight to help design your store experience so that it works best for the customers.

Dynamic prediction based on user behaviour change

There is a huge advantage to be gained by assigning a changing score to each user as they move around your site and provide you with more clues as to their interests and intent. This approach will allow you to adapt quickly to any behavioural change as the prediction model responds to the new incoming data about your website users. Moreover, various customer characteristics such as location, interests and buying patterns can be identified and linked to your sales and marketing strategy. This helps build a profile of customer preferences for future use in other digital marketing channels such as social media, search or display advertising. 

Ultimately, these well timed nudges with additional marketing messages or promotions, can change behaviours and ultimately stimulate more sales and drive commercial goals. Knowing your customer has always been a crucial part of marketing strategy efficiency and data driven approach will allow business to make smarter and better informed decisions, while building intelligent and long sustained relationships with customers. 

Get in touch with us for expert advice!

Thanks for reading – we hope you learned something through this high-level tour of marketing effectiveness methods. If you want to learn more about expert data analytics using AI, or are interested in how Metageni can help you use your data to grow online, then do get in touch with us: hello@metageni.com

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

Attribution is changing – how can you prepare?

Online marketers have started to understand that with the imminent demise of cookie tracking, Multi Touch Attribution (MTA) will never be the same again. But what exactly is happening and how can you prepare? During this era of rapid e-commerce growth and increasing use of digital marketing, it is more important than ever to understand the technologies affecting marketing measurement and data privacy. Marketing Attribution, which measures marketing to sale touch point by touch point, has implications for both topics, and the changing nature of browser technology and growing concerns over customer privacy, mean that attribution models will need to change. Marketers will need to adapt and plan for a long-term strategy that allows them to leverage first party data instead of relying on external data trackers. 

So, what are the challenges and solutions? Let us make sure we understand the technology shift first, which comes down to a shift away from a long established tracking technology, the browser ‘cookie’.

What are Cookies again? And are they really going away?

If you don’t already know, cookies are a widely used tracking technology that works through small data text files stored on your machine by your internet browser. Once you have accepted the use of cookies on a website domain, their cookies can  track your behavior on that site. Although strictly limited in data they collect, cookies are a powerful tool to track your user journey from point A to point B. You can think of a cookie as labelling each user with data about their observed behaviour and information they share with that site. The power for marketers comes from using this information for personalisation and even predictions about future visits to the site, including data driven attribution predicting whether a visitor will buy something. 

The most visible application of cookies in marketing has been advertisers using cookies to target consumers with numerous ads and promotions depending on their interests,  including re-targeting you after you visited their website  – this happens especially if the potential customer allowed the use of ‘third-party’ cookies. But what is the difference between first-party and third-party cookies?

What is happening to cookies?

Third-party cookies are dead! – Long live the cookie

The key thing to understand is that only certain cookies are affected by the recent privacy motivated shifts, and that certain cookies, specifically, ‘first party’ cookies, are here to stay. First party cookies are created by the website that you are visiting at the current moment and are quite harmless from a privacy perspective. First party cookies are typically used to personalise and improve your user experience. They cannot ‘spy’ on private data about you or read any files on your computer. It is generally accepted that retailers should be allowed to cultivate automated online relationships through interactions with their customers. Indeed, it is crucial to build up trust with consumers who in return are willing to forgive brands utilising this strictly limited and anonymous data to help market their brand to different audiences.

Third-party cookies on the other hand, are created by other domains usually to harvest your data and understand the underlying information and habits, for advertisers to predict your buying and searching behaviours across different web pages. These cookies have allowed ad-tech companies to create detailed profiles of users for building highly targeted marketing strategies. This is possible because third-party cookies not only allow for tracking the user journey on a particular website domain but across multiple domains, for any domain which shares the third party code, For example, if you visit a healthy eating website and accept the third-party cookie, other websites can be given access to that cookie, and you will likely receive a load of health and food related ads wherever you go online. Although this common use is usually fairly harmless, if sometimes annoying, it is the technical potential of the third party cookie to endlessly accumulate detailed information about a person from site to site. This has caused a concern among data protection advocates and privacy conscious consumers.  

In the end, technology which relies on third-party cookies has lost this battle and they are being phased out. Indeed if you are reading this article in 2023 or later, you will be reading about a technology that used to exist. What started as a tracking blocking feature of less well used browsers  like Safari and Firefox in 2013 has now become the norm, notably since Google announced in 2020 they would phase out third party cookies from Chrome which is used by over 50% of users. Google’s new ‘privacy first web’ will change the digital marketing space forever. 

As cookies are being phased out new tracking technologies are growing in importance, such as user cohorting (e.g. Google’s ‘FLOC’) and anonymous IDs (e.g. Unified ID 2.0). These are interesting but we think will be limited in terms of scale and no doubt will attract privacy concerns as well.  

The End of ‘View-Through’ Attribution

The ‘view-through’ metric was never the best way to measure online ads. The idea was simple enough – when someone clicks on an online ad the performance can be measured by counting the clicks that convert for ‘click-through’ conversions, and so why not also count the ad views that lead to conversions as well, and call them ‘view-through’ conversions!? The only problem was how to count ad views when customers never actually interact with the ad before making a purchase. Enter once again the third party cookie, which could be dropped by the ad-server domain through the ad placement itself and then updated again later in the online conversion for that advertiser, in order to count each view-through sale. 

What seemed like a clever way to understand how ad impressions drive value for advertisers, the ‘view-though’ rapidly became a major source of mis-attribution. This is because the sale could happen many days after the user is shown the ad in which time many other ads and influences both offline and online may have occurred. The concept of measured ‘viewability’ helped this a little, but over attribution remained. One of the huge challenges has been targeting itself: a well targeted ad in a high reach campaign is almost bound to generate a ‘view through’ sale since all the ad needs to do is get loaded into a users browser at some point in the days before purchase. In this way display ads, in particular, have been rewarded for just showing up sometime before a sale with the actual incremental impact of the advertising unknown. With a click, the consumer is usually making a conscious choice to visit the advertiser, whereas most ‘views’ just happen because the consumer is online a lot. 

However, as explained, the view-through metric depends on the third-party cookie which is history. View based measurement has to connect a person buying something online to an ad seen earlier on a different website, which means you need the cross domain tracking capability of third-party cookies. 

Just as the third party cookie will not be missed by most consumers, likewise many serious measurement professionals will be glad to see the end of view-throughs. Before they celebrate though, it is worth noting that major sites like Facebook, Google and Amazon, can still link ad views to sales when they handle both the ad delivery and the sale on their platform, which of course they uniquely have the reach and power to do. So there may yet be an afterlife for the view-through metric.  

Multi-Touch Attribution Moves to Click or Visit Only

Third-party cookies made it possible to track both ad impressions and clicks in attribution models, but now it is not possible to collect data in this way. The ad view (impression) touchpoints are dropping out of the picture, and only direct clicks remain within the measurable customer journey. What people do on the web, including what ads they get exposed to, is now hidden by stronger privacy controls. However, an attribution which is based on clicks to the advertiser site can still be picked up by first party cookies and web analytics. Therefore online sellers can only tell if their ads are being noticed when a potential customer shows up by clicking on one.   

Attribution in the future

How Will Marketing Effectiveness Change?

The gap left for online ad view measurement means upper funnel brand activity including display, video and social media all become harder to measure. Performance journeys, such as affiliate and search clicks and including some mid funnel consideration activity like generic search and email, can all continue to be measured using data driven attribution methods. So this type of click attribution must now be combined with other methods to evaluate ad views.

The topic of alternative methods for measuring display and social ad impact is huge in itself, so we will just take a brief tour of the three main options: (1) New measurement solutions offered by the media tech giants (2) AB Tests/ Randomised Control Trials (3) Econometric methods. 

First off, the tech giants are rolling out new measurement systems which promise to track how ad exposure has changed people’s behaviour without the possibility of identifying any particular individuals.  Google has posted an explanation of their approach centred on their Privacy Sandbox, which includes solutions for targeting in a post-cookie world. Google’s FLOC and FLEDGE technologies both get around user ID tracking by aggregating users together in anonymous groups at browser level, with tightly defined criteria for the group definitions which prevent drill down or cross referencing to pick out individuals. FLOC (Federated Cohorts of Learning) groups users by affinity and interest for in-market audience targeting which can then be targeted by advertisers whilst retaining user privacy, while FLEDGE (First Locally-Executed Decision over Groups Experiment) focuses on enabling automated display retargeting at non-user level where interactions with advertisers are stored within the browser and then sold by Google as retargetable segments. Google says it will use these aggregation type methods for anonymous measurement through to conversion. The benefit to users of increased privacy protection also leads to increased advertiser reliance on Google to accurately measure performance with no way for independent measurement or verification.    

A second approach to consider for any kind of ad measurement is to run an experimental AB test, or to be precise, a Randomised Control Trial. This is where a group of people who are not exposed to the ad are compared to a well matched group who are exposed to the ad (control vs treatment groups). Done properly, nothing beats an experiment for accurate measurement of incremental impact. Targeting can help with experiments, helping in ensuring the two groups are well matched and therefore suitable for comparison to make ad impact estimations. The problem now is that targeting is increasingly dependent on the big tech media owners i.e. with Facebook and also with Google FLOC, you can target only within their networks and on their terms. The measurement challenge increases when you want to combine or compare media impacts across more than one media network e.g. a campaign on both Google and Facebook combined. Without 3rd party cookies cross media AB testing is technically very difficult since you cannot be sure if someone in the non-exposed control group on one site might nonetheless be exposed to an ad on another one. Solving this requires some kind of common targeting framework such as matched location geo-targeting (a good option, but not without challenges) or otherwise using an opt-in consumer research panel that can work across multiple networks (requiring research company and media owner support). 

The third type of solution is old measurement tech – Econometrics, or Market Mix Modelling. These are statistical methods which represent the earliest forms of classical machine learning and grew in popularity back in the 1990s as computational power made it possible to estimate a wider variety of more complex models with relatively small amounts of data.  The idea is to use several years of observations of ads and sales, as well as data on economic, seasonal and business drivers, to build a ‘best fit’ model. This model measures how the drivers work together to generate sales, in particular estimating a coefficient (a multiplier) for each media channel to estimate how many sales it generates. The great thing about econometrics is that the data is readily available and can be increased in scope by sampling across multiple locations. Given the fragmentation and complexity of online data, it is not surprising that Econometrics is making something of a comeback. The downside is that it remains a tool of very imprecise high level directional estimation, and requires significant expertise to get right. If attribution is a microscope examining the user marketing journey closeup, econometrics sees it through a telescope in the gloom of a cloudy day. 

The marketing analyst is left with these imperfect tools to try and evaluate the now hidden user journey from ad exposure to sales.  A combination of methods will usually work best – for example using a one off experiment to help validate an econometric estimation.  

What do these changes mean for businesses and how do you adapt?

At the highest levels, the big shift going on here is that big tech companies such as Google and Facebook increasingly keep the data about user interactions with ads within their own walled gardens. This increases the dependency that brands have on these giants especially for targeting and measurement. This suggests the long term strategy for marketers should involve growing their own first party consumer data using sources like web analytics, transactions data, customer data and CRM, rather than relying on third-party data collectors. Likewise, a marketing strategy will have to include mechanisms collecting data from their own data points such as logins, subscriptions, email forms and call centres.

Investing in resources to build brand awareness and capitalising the pull approach of the PPC techniques and keywords targeting will be vital. This allows companies to ‘pull’ potential customers from the web as they are already interested in the product or service as they are browsing.  Advertising partners will continue to be used to reach new audiences and grow the customer base, but ad effectiveness measurement of less tangible brand and impression based impacts will increasingly rely on aggregated data methods which group customers together to protect their privacy. Relying on the media platforms to measure how your customers respond to your campaigns will only increase your dependency on them and provides no independent means of validation on your media spend. 

With the increased focus on first party data it is essential to build a strategy around customer relationship and trust. Since businesses will be focused on their own data collection it will be imperative to build up trust with your consumer, who will, in turn, allow you to use their data. This can be accomplished by following strict data privacy policies and using data for the consumer benefits such as user experience enhancement and content personalisation.

It will also be important to understand the aggregated data, as well as first party individual data, grouping consumers based on their common buying behaviours and search habits. Google’s FLOC and FLEDGE should prove helpful for audience analysis, allowing marketers to choose a target audience, segmented by common buying behaviours and interests. Targeting by the audience and contextual targeting will grow in importance as cookie based targeting falls away. 

In conclusion, utilising a data-driven approach for click based attribution modelling will continue to enable accurate insights for commercial decision making and performance ROI, leveraging first party data. Companies will have to rely heavily on their own data collected web analytics and other customer facing systems in order to understand the true value behind each touchpoint in the customer journey. For data around how customers passively interact – for example, ad exposure effects – aggregated data is the only way forwards. At Metageni we only use anonymised first party data for click attribution and then use econometric approaches to understand the branding potential of display, social as well as all offline media channels. While businesses still have time to adjust to the new reality, the digital marketing space has always been subject to change. The question for brands is are they getting closer to their customers or relying on the tech companies to do it for them? 

Get in touch with us for expert advice!

Thanks for reading – we hope you learned something through this high-level tour of marketing effectiveness methods. If you want to learn more about expert data analytics using AI, or are interested in how Metageni can help you use your data to grow online, then do get in touch with us: hello@metageni.com

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

Marketing effectiveness in the new age of eCommerce

The ability to measure success is key to optimising any business strategy and marketing campaigns are no exception for the marketing effectiveness.

Marketing effectiveness = the measure of your marketing plan to optimise efficient sales growth and ROI. This is essential to achieving positive short and long-term results.

Marketing teams typically adopt as many marketing strategies as they can simultaneously hoping to drive leads from one of those to see what works. The reality is that they aren’t then able to say with certainty which strategy is successful and which one is not, since the interactions between these approaches are complex and hard to measure.

This challenge has become greater with the increased rate of business change through digital transformation and disruption, increased competition, and rapid eCommerce growth. This makes it all the more important to understand how customers’ needs are changing and for companies to adapt quickly if they do not want to miss important opportunities.

On top of all this, Covid-19 has now sped up the progression towards increased eCommerce impacting consumer behavior and shifting the balance from offline to online retail. With sudden store closures, many customers were forced to shop online to avoid queuing outside the few shops allowed to open during the lock-down. Globally, brands large and small have shifted efforts into boosting eCommerce with more sales online than in-store for many categories for the first time. Crucially, many believe that new purchasing habits formed during the pandemic will become permanent with a strong long-term shift towards regular online shopping in a wider range of categories.

Marketing effectiveness ecommerce

What is your measurement strategy? 

Considering the digital transformation in the past 20-30 years combined with the post-pandemic challenges, it has never been more important for businesses to adopt a data-driven approach and embrace the challenges of marketing measurement.

Successful companies and forward-thinking leaders have focused their attention on data analytics, understanding the value of knowing more about their customer’s preferences and what they do during the conversion journey in order to increase marketing ROI, using this insight to make informed strategic decisions.

Can we measure marketing effectiveness for customers who are influenced and buy both online and offline? 

With the right capabilities, knowledge, and data-driven approach, the answer to this question is an emphatic yes. In the following, we are going to briefly review some of the approaches companies can adopt in their measurement process, and try to explore the pros and cons of each, without getting overly scientific.

Let’s start with Attribution modelling, a bottom-up digital marketing measurement approach that evaluates the impact of each individual customer touch-point in driving conversations. In other words, a potential customer, before deciding to buy a product, is going to ‘touch’ diverse marketing campaigns on different channels, like a paid search click, a promoted post on social media, an email with a discount, and so on. A multi-touch attribution model assigns credit to different touch-points, in order to understand which strategy is working well and generating sales. This is an amazing source of insight because companies can understand which campaign touchpoints are most highly used and help evaluate which channels work better than others and where in the process they apply. Companies use attribution to adjust future marketing strategies through redistributing their budget to reflect the whole digital marketing funnel. If the attribution model is an accurate reflection of how value is created, then sustained growth and improved marketing return on investment will result.

A key challenge with attribution is that not all attribution models are valid. Simple ‘positional’ models help firms understand the bias in ‘last-click’ sales reporting, but do not give definitive answers. Data-driven models promise more certainty, but even these give results that can vary widely between platforms and vendors. Online retailers should be cautious about black box ‘data-driven’ models which they do not fully understand and do not include indications of objective accuracy.

Nonetheless, multi-touch attribution models (also called MTA) are especially good for evaluating the impact of digital online cross channel campaigns since they provide a more granular and personal level view than traditional aggregate methods like the next approach we will look at: marketing mix modelling.

Market mix modelling (MMM) and econometrics are long-established techniques that use aggregated historical performance data and are useful for high-level analysis of marketing channels and sales drivers. These models estimate the total effect that every marketing channel has on sales while controlling factors like prices and seasonality that impact business performance. For example, if we think about sales happening during the pre-Christmas period, such a model would help you consider the normal seasonal increase in sales as a factor working alongside the marketing plan. 

Market mix modelling provides broader recommendations for how marketers should allocate their budgets to optimize performance and it is especially useful to measure offline channels like TV, Radio, and Out of home. On the other hand, this approach is less precise than attribution and is typically subject to a wider margin of statistical error compared to data-driven attribution, especially when evaluating channels and campaigns at a granular level. 

The key point to understand is that these are ‘top down’ statistical econometric models which estimate relationships between time periods and across geographical areas, rather than the ‘bottom up’ touchpoint-based analysis which can be done with digital marketing data. This is both a strength and a weakness: it is a strength because it can include any sales drivers offline or online, but a weakness because it can easily misestimate if not done correctly, and is only able to reliably pick up larger and more long term relationships.

Marketing effectiveness ecommerce

Finally, marketers need to be aware of the most robust method which is run designed experiments and randomised control trials. These Hold out testing methods are mainly used to check and validate results from other methods since they are most robust for getting a precise measure. The only reason they are not used exclusively for all marketing measurement is that the methods are hard to scale beyond the occasional measurement of 1-2 channels at a time within a single campaign. 

Such experiments are conducted using two matched groups of potential customers: a test group, the audience for whom a test is conducted including a specific marketing variant such as extra spending on a marketing channel; and a control group, an audience group that has the same characteristics of the test group audience but is not exposed to the test marketing activity. Random selection of which people are in which audience group can be used to make sure that the specific marketing variant is the only thing that can explain any change in subsequent purchase behavior, in the most robust type of experiment a Randomised Control Trial (RCT). 

For example, an experiment that wants to test the role of a particular ad allocates specific people to a test group where they are shown the ad being evaluated and to a control group of very similar people where the ads are withheld. Evaluating the difference in the results between the two groups gives a very clear signal as to the net impact of the ad. 

Hold out experiments are the most accurate method of measurement in marketing but they can typically only test one or two things at a time and can be difficult to manage.  Experiments require careful design and do a good job of measuring what specific activity they are designed to measure, but no more. Companies must be prepared to compromise on their marketing plan to get the design to work at a sufficient scale. Despite these challenges, properly designed experiments are the most robust way to measure the effectiveness of specific marketing tactics and so major platforms like Facebook and Google provide tools to allow marketers to test specific marketing tactics in this way and these provide useful benchmarks for other more generalised cross channel measurement approaches. 

Holistic attribution

A complete view of offline and online marketing

It is clear that marketing measurement is critical for determining campaign success, optimizing marketing spend, and driving business growth. Today marketers manage multiple campaigns across diverse media channels and through multiple devices and platforms, so accurate marketing attribution and effectiveness are more important than ever.

The complexity and range of methods represent a huge challenge for marketers and analysts. Companies relying on online and offline channels should consider a combination of both approaches to maximize the advantages of each methodology and to mitigate their limits at the same time. Attribution modelling will assign credit for all sales to digital marketing channels, whereas econometrics will take into account offline channels and non-marketing sales drivers, like price and seasonality. This means they can lead to very different results for the major digital channels, making it very hard for marketers to calculate the true ROI of each channel.

Here at Metageni, we have developed a holistic approach that allows us to combine econometrics and attribution in a single reconciled view of marketing and market drivers. This unified marketing measurement method adopts a data-driven approach that combines the aggregate data obtained from the marketing mix modelling and the person-level data offered by multi-touch attribution into a single comprehensive view.

The aim of this holistic approach is to collect relevant information from all the marketing campaigns taking into account the granular data (MTA) while still considering the broader marketing environment and external factors (MMM). In other words, we get the benefit of granularity and accuracy through digital attribution while also taking full account of the non-digital environment.

Crucial to getting this right is ensuring that attribution is accurate and we achieve this by building predictive attribution models using AI (machine learning) whereby we can say that a model that better predicts sales is more accurate than another. This, by the way, is the same standard that is used to evaluate econometric models. Analytical techniques like the use of ‘hold-out samples and diagnostic metrics help ensure the models are robust.

Using the strengths of both models and combining the different types of data helps to produce consistent ROI results for marketers, allocate marketing budgets with efficiency and drive sustained growth. Great skill and expertise is needed as every business has a unique combination of online and offline factors which drive growth online.

Get in touch with us for expert advice!

Thanks for reading – we hope you learned something through this high-level tour of marketing effectiveness methods. If you want to learn more about expert data analytics using AI, or are interested in how Metageni can help you use your data to grow online, then do get in touch with us: hello@metageni.com

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

Awards winner! Metageni with AO.com win ‘Best Use Of AI’

We were absolutely thrilled that in October 2020 Metageni, as data science partners for AO.com, won the prestigious, ‘Best Use Of AI’ at the 2020 London eCommerce Awards. You can watch the announcement as it happened here.

‘Best Use Of AI’ 

Metageni and AO have been working on data analytics using AI/machine learning since 2018. Now in 2020, the combined Metageni and AO team have been selected as finalists for eCommerce awards in the categories of ‘Best Use Of AI’, specifically for AI-based personalisation and conversion optimisation solutions in the UK and Germany in the midst of upheaval and huge growth in online retail sales linked to the global pandemic.

Responding to customer signals in real-time

Online retail giant AO has a market-leading position supplying an expanding range of electricals throughout the UK and Europe, and in 2019 decided to develop solutions to provide visitors to their eCommerce site personalised shopping experiences. More specifically, they tasked Metageni to create a machine learning solution that could identify types of customers to the website in real-time based on their predicted purchase behaviour.

When a customer walks into typical bricks and mortar store there are salespeople to assist towards making a purchase. A salesperson does this by watching and talking to them, in order to understand the customer. What the salesperson is attempting to do is ‘classify’ the customer to understand what the customers purchasing needs are. Customers may be window shoppers, impulse buyers, bargain hunters, researchers, new customers or regular repeat customers, and more.  

The idea here was to use online customer data signals to predict conversion behaviour with machine learning and respond accordingly. The Metageni team used data signals from the web and sales and explored trillions of possible model and data variants using genetic algorithms to find the best models. Working with AO engineers allowed rapid deployment into live eCommerce production. Rapid iteration of the models was been possible leading to even stronger results.

Results: proven commercial success  

In recent weeks AO conducted independent robust A/B tests to measure conversion uplift due to the AI versus the underlying conversion activity which has increased due to the pandemic related shifts in customer buying –


These A/B tests showed an impressive and significant increase in the AO conversion rate over and above a matched control group in the UK. Furthermore, the result was replicated in Germany with an even big jump in conversion rate compared to the control group. Both tests are statistically significant, and while AO.com do not wish to share specific numbers for reasons of commercial interest, the results translate into long term revenue potential worth millions of pounds.  


A better outcome for AO and for AO customers 

As well as yielding great results, the approach used has allowed AO to understand more about their customers to improve the online purchase experience for everyone. The whole team at AO and Metageni have worked closely together to achieve this success – 


” Metageni have done and continue to do a fantastic job in using machine-based learning to give AO.com substantial ROI.

The last few months have been an unprecedented time for us and many retailers. In this period, Metageni has delivered again, completely retraining our custom machine learning models in both the UK and German markets, responding to the dramatic shifts in customer behaviour.

I wish the Metageni team every success with this award entry. They’re a great partner for our eCommerce function.”

David Lawson, Managing Director, AO.com


The teams at AO and Metageni continue to look for ways to use AI technology to better improve commercial and customer outcomes. 

Thank you for reading about our success. Can Metageni help unlock the value of your analytics and marketing data? Feel free to email us at hello@metageni.com for advice on how you can use data analytics and AI to grow online. 

giffgaff case study- Becoming data-driven with Metageni

Telefonica brand giffgaff have worked with Metageni to develop data-driven analytics which combines and offline and online marketing, and cross-device attribution.

The complete solution combined both offline media and online user journey data in a system of machine learning algorithms designed to predict sales.


“This is an awesome piece of work. The feedback internally has been fantastic….everyone is clear that this is it with regards to what we can do with the attribution data which is great, and the fact that we can assign CPA level metrics to each channel was something that went down very well internally.

Thank for all your help on this project”

Matt Roberts, Strategic Analytics Manager, giffgaff


These bespoke predictive models are used as the basis of an attribution modeling system. The algorithms distinguish the ROI of separate channels to help with marketing optimization, with spend from low-performing channels switched in to boost higher performing channels.

The outcome is increased sales growth and marketing efficiency.

Other features of the system included a cross-device inference model for improving device-based optimisation, and additional modelling for upper-funnel and offline to brand navigation channels such as brand and organic search, direct and re-targeting.

The complete system implemented by giffgaff included live backward integration with a big data cloud and custom live visualisations and query and tools.

The whole system is designed to create a data story for accessible insights to improve ROI through improved transparent attribution.

Predictions and attribution can be dynamically updated whenever new data is available in the data warehouse, and the machine learning models are re-trained to boost accuracy.


Find out more about giffgaff mobile here

Can we help unlock the value of your analytics and marketing data?

Ask us about your marketing optimisation at: hello@metageni.com

AO.com used machine learning for marketing optimisation

Online retail giant AO.com has a market-leading position supplying an expanding range of household appliances throughout the UK and Europe. Cross channel and cross-device marketing are of critical importance to their sales growth.

To optimise marketing spend the team at AO chose to develop a multi-touch attribution (MTA) model which is based on machine learning, developed for them by Metageni, an emerging provider of customer journey algorithmics.


“ Metageni have done a fantastic job evaluating marketing ROI across online and offline and between devices. The new machine learning model replaces our positional model in our e-commerce and analytics platform, and we can also see how channels like TV and print play their role.

We especially liked the way the Metageni machine learning models are not a black box but were produced as a set of easy-to-understand rules, which means we can use them for targeting and bidding.

I would recommend the Metageni team to any company looking to supercharge their marketing optimisation. ”

David Lawson, Managing Director, AO.com


Metageni models are trained on the unique data of each client to predict sales with objective accuracy and enable each click to be precisely evaluated. The platform includes a cross-device inference engine to improve accuracy where customers switch between mobile and desktop use, and analytics service to combine these custom ML models with econometrics.

The outcome is a live integrated view of marketing ROI, informing both strategic and tactical marketing actions.

Gabriel Hughes, CEO of Metageni, said,

“It has been a huge pleasure and a privilege to work with the team at AO. Their focus on innovative approaches to e-commerce optimisation has been a key driver for this project and we are delighted to be allowed to deploy our unique solutions for such a major brand”.

Metageni continues to provide customer journey algorithmics to AO.com.


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

Loveholidays -huge growth with machine learning attribution

Loveholidays.com is a rapidly growing online travel agency. Travel is a sector that has a major marketing attribution challenge, facing all of the challenges that multi-touch attribution (MTA) tries to address –


High levels of cross-device use with research on mobile and purchase on desktop

Long complex customer journeys with extended research before booking

Competitive space with many companies chasing each click


The Challenge

Online travel agency Loveholidays has data for millions of anonymous user interactions, but their attribution model was not identifying how different marketing channels and devices worked together to drive sales.

They needed data-driven marketing optimization to increase marketing efficiency and ROI.

The Solution

In 2017 Loveholidays partnered with Metageni to develop a custom attribution model. The team explored multiple supervised machine learning classifier models, including decision trees, high order Markov chains, and ensemble methods of predicting holiday booking to determine the best model.

The best performing model tended to credit ‘upper funnel’ marketing activity more strongly than ever before and showed interesting patterns of variation between channels. It also highlighted key opportunities, including their email newsletters to past customers.

The Results

Loveholidays has been able to use its custom attribution model to re-estimate its marketing Cost Per Sale and adjust digital bids and budgets to drive efficient growth. The model can also identify, at any stage, the likelihood that a user with a given set of interactions will go on to make a booking.


Surpassing all expectations, Loveholidays achieved year-on-year growth of more than 190%, and in 2019 came UK number 1 in the Sunday Times Profit Track 100 list.


Loveholidays continues to work with Metageni, providing dynamic bidding for further ROI optimisation.

 


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

Abandon the 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 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. The last click is easy to measure and suits quick low-value purchases driven by performance-only marketing. However, as user behavior 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 generalizations 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 doing 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 analog 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 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 that increasingly run concurrently. For example, they might introduce overlapping marketing activities 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, intending to optimize the marketing mix i.e. the best share of the 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!).

Last click attribution

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 touchpoints
  • • U shaped – credit first and last click 40% each, 20% for middle touchpoints
  • • 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 no 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 prioritize 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