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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

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

Why do companies waste millions on marketing?

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

Playing it safe

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

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

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

The paradox of cutting ROAS analytics to focus on growth

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

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

Actor fragmentation and lack of team focus

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

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

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

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

Marketing analytics

Do not let perfect be the enemy of good

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

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

Be less wrong!

Gabriel Hughes PhD


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

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

Metageni is a London UK-based marketing analytics and optimisation company offering support for developing in-house capabilities.

Please email us at hello@metageni.com

Getting from data to insight – best practices in data analytics

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

The gap between data analysis and insight

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

Data-to-insight-2

The challenge of leveraging big data

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

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

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

Telling stories with data

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

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

Data-to-insight-3

Insight often requires a leap in the dark

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

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

Summing up: Seven steps to get from data to insight

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

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

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

Gabriel Hughes


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

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MediaGeni

Econometric market mix modelling for offline and strategic marketing

Market mix modelling is a long established set of statistical techniques which predate ecommerce and big data, and is perfect for adding offline marketing and influences to your models for whole journey optimisation.

Econometric methodology using time series data has much less restrictive data requirements than machine learning. This means external drivers can be analysed such as competitor ads and pricing

If the MediaGeni solution is combined with ClickGeni digital click attribution, then we apply an additional reconciliation algorithm to ensure all ROI figures make sense at both the digital and market mix level, for both strategic and tactical success.

For channels like TV, Radio and Print, econometrics is the best choice for understanding how all your media works together with digital to drive sales. Using market mix modelling you can ensure there is robust and independent justification for your major marketing investments.

Analyse impressions as well as clicks for a full multi touch model replacing faulty last click and view through attribution.

We can attribute offline store level sales to marketing as part of an integrated custom approach, as well as other sales channels and sales categories. So if you want to model (say) how your TV marketing helps Call Centre sales, then we can help.

Reports are actionable, highlighting ROI optimisation opportunities with models which predict sales, so you make smarter marketing choices.

Constrained scenario modelling can be used to work out a realistic channel budget allocation which will achieve more sales for the same total budget, making your media and financial planning really simple.

Reports showing how different levels of spend will yield changes in sales, for each marketing channel, using your predictive model.

We help you understand how seasonal shifts and events like Black Friday interact with marketing to drive your sales, so you can use your campaigns to ride your customer buying trends.

Modelling national sales over time does not provide enough data for statistical robustness, so we model by region as well to leverage more statistical power. This also means you can see how marketing and sales effects vary by region, which provides valuable insights for business with a regional footprint.

With econometrics you have the added benefit of being able to see how underlying trends drive sales, which is often interpreted as long term brand equity. This helps provide a quantified view of your brand vs performance marketing to aid financial and media planning.

Is this for you?

  • MediaGeni is a custom econometrics model pipeline plus reporting built on your marketing and business data.
  • It can be combined very effectively with ClickGeni for best in class digital and offline marketing measurement.
  • We work in close consultation with your marketing and analytics experts.
  • We offer free workshops around customer journey challenges, for brands who may be interested.
  • Interested to know more? Contact us:  hello@metageni.com

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