In an era of uncertainty, with consumer behaviour altered greatly by coronavirus, marketers must utilise all of the tools at their disposal to make budgets stretch further. Matthew Teale, Data Analyst, Metrix Data Science lloks at why sifting through large amounts of data from different sources and applying the correct analytical techniques is a worthy investment.
In an era of uncertainty marketers must utilise all of the tools at their disposal to make budgets stretch further. Failure to grasp the complex dynamics between ad spend and response could leave businesses in a vulnerable position.
Econometrics is the answer to this conundrum. It is the language of attribution, the statistical underpinning of marketing mix modelling. It is also the most scientific and reliable way to discover which levers of the marketing machine can be pulled to achieve a desired outcome.
Done right, econometrics gives marketers the answers to crucial questions:
. Which channels are driving sales?
· Which are virtuously impacting each other?
· Where does diminishing returns set in for each media channel?
· How does brand marketing affect sales?
· What is the optimal mix of channels to use and when should they be deployed?
HOW COVID-19 CHANGED CONSUMER BEHAVIOUR
Covid-19 has significantly accelerated an already burgeoning ecommerce channel. During lockdown, huge numbers of consumers flocked to online shopping, including those who had previously been reluctant to do so, such as the over-65s.
With consumers spending longer indoors and consuming more content, there are undoubtedly opportunities for brands, if they use the appropriate analytics. Procter & Gamble and Unilever, for example, have adopted a counter-intuitive approach by increasing marketing budgets to help them understand the changes Covid-19 has wrought.
With marketing resources becoming scarcer there is a greater incentive to make every pound count. Companies cannot afford to waste money on ineffective channels, nor can they spend beyond what is appropriate on fully functioning channels.
Applying winning data science techniques in the shape of econometrics is business’s best bet for navigating uncertain terrain and ensuring that budgets are optimally allocated to give the best returns.
The new lifestyle imposed on many people by the pandemic will invariably lead to digital having a bigger presence at the marketing attribution table. Measuring the correct impact of digital media, taking into account the activity of other channels as well as external market-related factors, requires the scientific tools of attribution.
GOOD VERSUS BAD ECONOMETRICS
Only by sifting through large amounts of data from different sources and applying the correct analytical techniques will econometrics be a worthy investment.
Good marketing econometrics starts from the assumption that the omnichannel consumer is constantly being subjected to advertising. They will sometimes see and hear hundreds of messages before committing to a purchasing decision.
The answer is to build a model with the complexity to recognise this multi-dimension marketing environment yet simple enough to produce intelligible and actionable results.
The features of this model and its specification are what separates ‘good’ marketing econometrics from ‘bad’. Good marketing attribution models have:
Appropriate lag structure – ensuring the statistician is not restricted to contemporaneous time analysis. There is often a time lag between channel deployment and an impact on an individual’s behaviour.
Brand analysis – marketing attribution is incomplete without considering how brand advertising is behaving in the mix. Analytical approaches evaluate the impact of marketing activity on key brand metrics and further investigate how this feeds into sales.
Diminishing returns – there is a limit to how much channel activity has a positive impact. It is important to use the appropriate methodologies to investigate the spend threshold that attains optimal response results.
Interactions – the interplay between channels needs to be measured because none works in complete isolation in an omnichannel world. For example, we have observed the virtuous relationship that exists between TV and door-drops play out for many brands. Accounting for this in the analysis is crucial, otherwise marketing channels are treated as siloed entities.
THE CASE FOR SCIENTIFIC ATTRIBUTION
Without a scientific approach to marketing attribution, organisations will not have the strategic vision they need to combat the tricky economic times ahead or maximise opportunity during the next growth cycle.
Simply knowing the latest consumer trends isn’t enough. Businesses must understand what changes to shoppers’ behaviour means for them. They require the tools to determine the correct media mix, to know how to engage prospects and when to deploy those channels.
Econometrics, the scientific approach to attribution, is the answer brands need.
Omniatt is our econometrics attribution tool to evaluate which channels and communication touchpoints drive customer sales and other behaviour. Find out more and download our econometrics white paper here.
CASE STUDY: THE SALVATION ARMY
Metrix Data Science has helped many clients to navigate the complicated world of marketing attribution. Applying econometric analysis that recognises the nuanced dynamics between marketing activity and sales allows our clients’ marketing resources to go further.
The key reason long-standing client The Salvation Army (TSA) came to us for attribution analysis was the charity’s shifting marketing focus. Due to the new regulatory framework of GDPR, TSA had decided to drop cold mail as a marketing channel. It wanted an impartial view of the best media allocation available to compensate for the loss of a key channel.
The novelty of our approach started with data collection. Since TSA only runs campaigns around Christmas, data availability is limited to three months in the year.
We therefore collected data going back three years to maximise the number of data points. This would not only enhance the robustness of estimation, but also facilitate a longer-term view of media performance; a vital element of the project, since previous marketing analysis was based on short-term ROI.
To understand a multi-faceted environment we built a suite of econometric models, paying specific attention to model specification for each. Our analysts produced models to predict the response for each marketing channel. This approach ensured the complex relationship between media spend and channel response was factored in.
We incorporated the insights gleaned from the modelling into a tool that enabled TSA to predict the outcome of different media budget allocations in terms of donation income, response volume and ROI. The tool was complemented by a plethora of recommendations to optimise the potency of TSA’s media allocation strategy. Results included:
• Increased donation income by £680k for lower spend
• ROI increased from 1.04 to 1.24
• Increased average donation value by 7%
By Matthew Teale
Metrix Data Science