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?

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.

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

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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

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.

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.

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