Marketing is an essential component of any business, and the allocation of marketing budgets plays a crucial role in ensuring that marketing efforts are effective. Media budget allocation involves finding the ideal budget mix for a company’s media marketing expenditure across campaigns, activities, media channels, product groups, and countries. The potential of media marketing budget allocation to drive top-line growth and profitability is significant and has been widely proven. If companies optimise the allocation of their marketing budget among different media, they can achieve 0.5 % to 3 % additional revenue without expanding their media marketing budgets. Alternatively, companies can make significant efficiency gains (20–25 %) in their marketing spending without losing revenue.
In this case study, we look at three key challenges in using marketing data and how Salesforce MCI (Datorama) can overcome them.
Media mix modelling (MMM) has become established as the data-driven method of choice to enable true quantitative optimisation of marketing budgets and perform what-if analysis of different media mix strategies. These models use statistical techniques to analyse the relationship between marketing activities and business outcomes, providing insights into the effectiveness of different marketing channels and strategies.
The availability of structured data is crucial for MMM, both for simplifying implementation and maximising efficiency of the method. To achieve useful results, a sound data strategy and appropriate tools are therefore essential. The main challenge that needs to be addressed lies in getting data from different sources into the right structure for use in MMM: this ensures its effectiveness and reusability.
In this case study, we will demonstrate how PwC optimised its media budget allocation to maximise incoming leads using MMM. Our approach involved leveraging Marketing Cloud Intelligence (MCI) by Salesforce, a well-known software package for connecting, harmonising and visualising data, and Analyx MMM, an efficient and customisable MMM solution by Analyx.
Challenge Unpacking the complexities of online marketing data: three key challenges
- Lack of transparency in marketing planning
Justifying the strategic marketing decisions is challenging. Indeed, objectively explaining the reason behind them - such as the media budget allocation - is challenging due to the fact that the causal links between a decision and its impact are not easily measurable. The lack of transparency in marketing planning can cause disagreements and create uncertainty within a company. Evaluating the success factors of each campaign is important, not only to see what works best, but also to help justify advertising decisions. - Gathering, organising and unifying data across multiple marketing channels
Gathering and organising data from various sources can be complex, especially for businesses with multiple marketing channels and activities. Failing to unify all data sources can make it difficult to obtain a holistic view of campaign performance and understand the success factors. To overcome this, it is crucial for companies to have an optimised data strategy covering data collection, preparation and analysis using appropriate technologies and a consistent naming convention for campaigns. - Analysing data: in-house or outsourced?
Analysing data requires skilled professionals with in-depth expertise in data science and statistics. Deciding whether to keep analysis in-house or outsource it to agencies can therefore be challenging. Maintaining an inhouse team dedicated to marketing analysis can be costly, while outsourcing to external agencies runs the risk of exposing sensitive strategic information.
These challenges can be overcome by using Salesforce’s Marketing Cloud Intelligence tool as a single source of truth for marketing data and connecting it to Analyx MMM.
The challenges of planning your marketing activities, unifying data and analysing data as explained above are considerable. To overcome these challenges, we at PwC developed a solution by combining Salesforce’s Marketing Cloud Intelligence (MCI) tool for collecting data across marketing channels with Analyx’s MMM for advanced statistical analysis. This approach helps to generate a holistic view of campaign performance and enables informed decisions on budget allocation.
Approach Start with Marketing Cloud Intelligence...
MCI’s strength lies in its ability to drastically reduce the complexity and time involved in collecting and organising data generated through the various channels where campaigns are running. The data collection process can either be done via API connectors, which allow data to be pulled from most marketing channels automatically and in real time, or by uploading external files. Powered by AI, the ingested data can then be mapped into ready-to-use data streams and structured according to prebuilt data models. MCI enables a harmonised view of data by combining data from different sources and structuring it according to predefined data models. With a consistent campaign naming convention, this process allows data to be unified and made comparable across channels, enabling more in-depth analysis and easier visual presentation.
...and then add media mix modelling
Having access to harmonised data is not enough. Properly analysing the data is key to comprehending it. The goal is to learn from past action to make future marketing decisions more predictable. This is where Analyx MMM comes into play, as a leading agile budgeting solution for a higher return on marketing investment. It enables what-if analysis of various media mix strategies, and can provide answers to questions around budget optimisation, such as determining the investment needed to grow sales and market share by a given percentage. Analyx MMM can also optimise budget allocation between different channels or campaigns to achieve a specific sales target, or analyse the impact of a significant budget shift in the short and medium term.
Analyx MMM is based on an econometric model which is specifically designed to analyse marketing-related data, identifying the factors that influence specific outcomes and measuring their impact. The model allows multivariate analysis, which is essential when dealing with data from an uncontrolled environment.
The potential of Analyx MMM to enhance top-line growth and profitability is significant and has been proven in various real-world use cases. Typically, Analyx MMM enables companies to achieve between 0.5% and 3% revenue growth without expanding their marketing budgets, or a 20% to 25% efficiency gain in their marketing spending without losing revenue. With Analyx MMM, marketing managers can make informed decisions based on real data to refine their marketing strategies and budget allocation. Analyx MMM also makes marketing decisions and operations transparent, facilitating clear communication of the reasoning behind marketing decisions and aligning marketing objectives with those of upper management.
Solution Marketing budget optimisation with MCI and Analyx MMM
As a large company, PwC employs multiple marketing channels to conduct its marketing activities. These channels generate huge volumes of data that can be used to obtain valuable insights and continuously improve our marketing strategy – but doing so is a challenge. We realised that combining MCI and Analyx MMM had great potential to tackle this challenge, and we decided to conduct a test of our approach in the Swiss market.
Phase 1: MCI
- Establishing a global classification strategy
The first step was to establish a global classification system at PwC, with a consistent naming convention to structure data throughout the organisation; the various campaigns across marketing platforms were then named following this convention. The naming convention includes essential information such as the fiscal year, overarching campaign names, goals, and target groups. This enables a clear distinction to be made between campaigns, and typically incorporates delimiters between the various pieces of information.
- Gathering historical data
We used MCI to gather data from several marketing channels over the past two years. The marketing channels were connected using MCI’s embedded API library, making the process quick and efficient. Next, we mapped the various dimensions and metrics retrieved from the marketing channels to the fields of the most appropriate MCI data model, ensuring that the data was unified and ready for analysis.
- MCI harmonisation centre
After unifying the data, MCI’s harmonisation centre was used to deconstruct the naming convention and create “harmonised dimensions” for each element in the naming convention system. These harmonised dimensions always contain the same piece of information regardless of channel and campaign (e.g. specific target group, campaign name), thus creating a harmonised view of the data. Besides creating this harmonised view, adhering to these naming convention standards also helps save time and costs by reducing the need for an extract, transform and load (ETL) process.
First results
After implementing MCI, we saw improvements in automation, efficiency and scalability of our marketing activities. Having a single source of truth for marketing data minimises the time required to handle reporting requests. The API connectors for various marketing channels allow data to be refreshed automatically on several dashboards every day, resulting in seven times fewer reporting requests. We were also able to achieve a 20% efficiency increase in our marketing budget. This improved efficiency stems from the ability to make data-driven decisions, optimise marketing strategies based on real-time insights, and allocate resources more effectively. With a comprehensive view of marketing performance, we can identify underperforming campaigns, eliminate wasteful spending, and reallocate funds to areas that generate higher returns. MCI also facilitates quick connection of new data sources, streamlining the process of harmonising additional data from various marketing channels.
Click here to read our case study on implementing MCI.
Phase 2: Analyx MMM
At this point, we were able to download a comprehensive and harmonised dataset from MCI covering our paid marketing channels, including Google SEA, LinkedIn Ads and programmatic ads. This dataset was uploaded as an Excel file to Analyx MMM for further analysis.
Our objective was to maximise the number of new leads generated by optimising the allocation of a constant marketing budget among the paid marketing channels in use. For this case study, we focused only on lead generation, and did not consider brand building, which has a more long-term impact. Analyx required three pieces of information to conduct this assessment: the target variable (in our case, maximising lead generation), the variables of interest (the paid marketing channels used) and the constraint to be respected (the constant budget).
An initial assessment showed that paid media marketing activities was responsible for 7% of the total new leads generated and that LinkedIn Ads was the strongest paid channel, contributing about 3%.
Based on the analysis, Analyx MMM recommended a revised budget allocation strategy for the paid media budget, which included the following:
- 29% budget increase for LinkedIn Ads
- 11% budget increase for SEA
- 50% budget cut for programmatic advertising
The media mix optimisation analysis conducted by Analyx demonstrated that by allocating the paid media budget optimally across various channels, it is possible to increase new leads by approximately 0.9% while keeping the budget constant.
Conclusion Summary and conclusion
Making data-driven decisions to optimise media strategies doesn’t have to be complicated. Given the right methods, data and technology to implement a well-prepared and well-structured MMM, companies can optimise their marketing strategies, rationalise their marketing budgets and increase growth.
Our collaboration with Analyx offers a practical example of how these goals can be achieved. We started by implementing MCI, which reduced the overall data structuring workload and improved the final quality of data. We successfully gathered current and historic data from various media channels to create a well-organised and comprehensive dataset, primed for further analysis. What’s even more valuable is that this infrastructure enables real-time reporting and analysis of new data, ensuring continuous integration of this data for future insights.
Analyx MMM then delivered actionable insights by carrying out in-depth data analysis and reducing the time required to develop the next marketing strategy. The model demonstrated a high level of accuracy with a mean absolute percentage error (MAPE) of 14.3%, which is a strong result in the B2B context. Our media mix optimisation analysis revealed that allocating budgets optimally across the various paid media channels could increase new leads by approximately 0.9% without increasing the budget.
To sum up, combining MCI with Analyx MMM is a beneficial approach for businesses looking for ways to optimise their marketing efforts and achieve growth with a data-driven strategy. The two tools complement each other and allow management of the entire marketing data lifecycle from data collection to analysis, resulting in a shorter time to market for an improved strategy. In addition to this, the infrastructure as implemented is designed to be scalable, facilitating future reassessment and enhancement of marketing strategies as needed. This flexibility ensures the system can adapt and grow alongside evolving business needs and objectives.
Authors
We would like to thank our PwC Germany colleagues for producing this case study: Mathias Elsässer, Julian Röhl, Sascha Stürze and Stefano Belardi.
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