Random Duplication gives Random Results

“Random Duplication” is a very often heard term in media agency corridors and even more so these days in the Zoom / Teams meetings online. Those better-read, also sprinkle conversations related to ‘random duplication’ with “stochastic’  or more popular ‘Sainsbury Formula’ and with “Normally Distributed”.

Whats the Context?

The context in which we see these words being used in media agencies is when discussing “multi-media planning”. So, if there is a TV Campaign that reaches 60% of an audience and there is a Digital Campaign which reaches say, 40% of the same audience – then, how does one estimate the net reach of the TV+Digital campaign for the audience.  It is in this context that all the above terms are oft heard. 

Of Course! Multi-media reach is one of the most important element in this increasing multi-media universe that we are getting deeper and deeper into.

While, I don’t claim to be pure-blooded statistician – nor am I going to try to explain these statistical terms to you but, this article will help put the issue in perspective from a media planning point-of-view. Later, I will come back with a more technical answer to the issues with - and alternatives to random duplication along with the help of more statistics-inclined colleagues 😃.  

Why is multi-media reach a challenge? 

Those of you who are in the media domain are aware of the limitations of the measurements systems available. 

• Syndicated research and reports such as IRS, TGI, GWI, i-Cube and some others provide a survey-based estimate of all media but, are often challenged due to lack of vehicle level granularity/ accuracy and dated reporting in a fast-moving digital age. These systems however, only provide “Max-Reach” estimates for various media/ platforms.
• For Campaign Measurement of the non-digital media – none other than BARC (TV Viewership Measurement System) provide any kind of campaign measurement capability. The IRS does offer campaign planning in the IRS Software, but no one in the industry uses it.
• Then there is a huge plethora of Digital Platforms that have their own dashboards/ server reports to map the Max-Reach of the platforms or MAUs which is the more widely used term in digital conversations. These platforms, provide estimates of MAUs as well as of Campaign Reach. There are limitations as some platforms only share impressions and not unique impressions; some don’t provide frequency-based estimates of impressions; the reporting parameters are different across platforms, across formats within a platform, and so on.
• Then there are also the panel based digital measurement syndicated sources such as Comscore, Similarweb, etc. And, there are many other app-download measurement and various other measurement platforms. While, recency of data is not an issue of data on these platforms; the numbers are so different across platforms and there is mush to be done in decoding them yet. 

Of course! there are those who just don’t understand the concept of scientific survey research and discard the syndicated reports due to low sample (but, continue to spend millions on their hunches and beliefs). I find these reports extremely powerful, a good reference and best used layered with assumptions of the changes in the ever-changing real-world. 

And, don’t be so naïve to even try to corroborate the digital universe estimates across any of the digital platforms. Even the most reputed platform claims/ estimates have continued to confound me. Have often seen claims of more females in a certain geo-demographic than estimated to be present in that geography. And, this applies to different demographics across – not only females. Wonder where the world is hiding these people as the Census also could not find them 😄. 

Now again, there are those would say that the estimates that I have are wrong; the Census is too old - as they don’t understand the concept of statistical forecasting. Happy to have a discussion on the universe estimation and forecasting that is in use for the syndicated databases in the industry, 

So , what could be the problems in measurement?

When one executes a multimedia campaign say, across TV, Youtube Trueview, Facebook Video, Disney Hotstar pre-roll & in-stream options – one would have to look at multiple sources of data each with their own idiosyncrasies. 

While ,TV is a broadcast media with certain rules of reach build-up and OTS applicable; on the other hand the patterns of reach and frequency build-up as seen on these digital platforms unique to each:  

• each having their own universe estimate

• own definition of an impression

• own definition of a view

• in fact, even the targeting parameters will be different

• different levels of reporting by period 

• different parameters reported 

• and so on

Don’t expect an easy answer

So, when Clients ask the question – “what is the net reach of the multi-media campaign?” do you think the answer would be simple?

Let me clarify that, I am not advising that reach is the right measure for every campaign or that in every campaign every medium/platform should have reach as a primary metric. There are various reasons for a medium or platform to be included into a campaign and the metric for measurement of that medium/ platform should be based on the campaign/ platform role. 

However, in case it is so decided that Reach is the campaign measure, then all the complexities stated above in the note need to be managed by the multi-media reach estimation methodology. And, it is not going to be an easy answer. And, I haven’t even added the problem of reach @3+ yet 😝 or yet not added in performance media which is a different ball-game all-together.

Lets also discuss the elephant in the room - NCCS. Most client briefs even today have NCCS as an important audience descriptor but, most digital platforms have no direct design to deliver NCCS-based audiences. I dont even want to discuss what gets delivered using 'surrogates of NCCS'. Again, am not saying that NCCS is crucial and the right way to define audiences. Personally, I believe we should be able to define audiences with far more direct descriptors than identifiers such as NCCS. Am sure things will change but, today most campaigns have NCCS since, TV research too is built around NCCS, 

Things are going to get even more complicated if we now, want to optimize the campaign and define budget allocations across platforms at the pre-planning stage and to also report similarly during and after the campaign.

Maximize from Wavemaker

And, here is a commercial break with a plug-in for my company 😇. Jokes apart, the tool Maximize at Wavemaker is the best that I have come across in the media industry ever. It is conceptually light-years ahead of the competitive tools in other agencies. Statistically so robust that even I don’t try to understand the finer details of the agent-based-modeling techniques that it uses. And, it is never about only having a tool – what matters is the people who pilot the tool and Wavemaker has a very ‘qualified’ team on Maximize. I can connect any one of you to the “Maximize_Desk@Wavemaker” for a deeper interaction. 

In Conclusion

So, if anyone gives an answer to the above question about multi-media campaigns - using just Random Duplication or Sainsbury Formula that are stochastic approaches said to be applicable to Normally Distributed variables – do look at the inferences with a hand-full of salt.

As I say “Random Duplication gives Random Results”. When you invest Millions you deserve better than a random result. 

Investment in Media Strategy is a waste ?

I am a media strategist, and this article is not in jest, but a serious view of the utility of media strategy to drive brands. I will demonstrate this with the use of some media metrics that are often used in the media investment domain.

We often look at the A:S Ratios of Brands within and across categories to arrive at an A:S benchmark which we can use to recommend a starting media budget. Of course! budgeting is a much more complicated subject and far more needs to be done to recommend a media budget; but, this method gives a good reference point. 

Typically, stable FMCG categories with multiple competitive brands have a A:S ratio around 10, ie 10% of the sales revenue for the brand is invested in working media.

Categories with smaller number of competitive brands or categories with dominant brands or categories that are very low involvement are known to have A:S ratios even below 5%. Practically, the brands barely need advertising to survive (Survive is the key-word here).

On the other hand, for brands are in the early stage of life or brands in high involvement categories or in nascent categories - the A:S ratios can be as high as 50% or even higher than 100%. These are not rules, and we need to calculate the A:S value for the specific brand and competitive brands under discussion.

So, we use A:S as a metric that guides budgeting – but, clearly, A:S indicates the importance of  media for the category/ brand. 

Now, lets look at another indicator that we often get out of attribution analytics or more popularly known as Market-Mix-Modelling. As an output of this model, we get the percentage attribution of sales to media. Which means that we estimate - of the total sales what would be the loss in sales if we had not spent on media. Typically, this figure across categories hovers around 10%. Base, Distribution, Pricing, Consumer/ Trade Promotions usually, total up to over 80% or more of the impact on sales. Again, the key takeout is that the % media attribution indicates the importance of media for the category/ brand and is typically around 10% but, needs to be estimated for each brand specifically. 

Though, A:S and % media attribution both vary across brands - to simplify, lets say that media constitutes about 10% of the sales revenue and media contributes to about 10% of the sales. The important part here for the discussion is that the ratio of % media attribution and A:S is 1.0. Lets call this ratio as the Media Utility Ratio (MUR).

                                 MUR = (% sales attribution to media investments)/(A:S %)

So, if we do not deploy media we will lose 10% of sales revenue. But, since media cost is also 10% of the sales, we save 10% of the sales revenue. Hence, we don’t really lose anything if we do not deploy media.

Please note, here we are talking about the short-term impact of the media investments. Also, this above argument will change as per the actual values of A:S and % media attribution for any brand in discussion. 

If the A:S is lower than % media attribution – then, the Media Utility Ratio is greater than 1.0 and the money saved by deploying media causes a far greater loss in sales revenue; hence, MUR > 1 makes a case in favour of the utility of media investments. 

On the other hand, if A:S is greater than % media attribution, then the MUR is less than 1.0 and one needs to question why we should spend in media at all or investigate the nature of media investments. 

As a marketer we need to know the above metrics so that the investment in media is a considered decision and not a ritual. Also, these metrics should be estimated using robust analytics as simple volumetrics, correlations, conversion percentages, etc can give very misleading results.

Now, lets assume that with or without specific knowledge of the above, the marketer does decide to invest in media. Again, for the sake of simplicity lets say, that the current MUR is 1.0. Now, if we invest wisely in media – we may increase the ROI of media and hence the MUR will increase from say, 1 to 1.2 and the inverse will happen if we do not spend wisely.

The below illustrations, show the implications of the values of A:S, % media attribution and MUR.

To improve the Media Utility Ratio, we have multiple avenues:

• Modulate Investment Levels: Check if we are investing at the right levels using the sales response curves. Maybe, if we just increased or decreased the investment levels we may find a point where the MUR is better. We can derive the sales response curve though market-mix modelling. 
• Improve Pricing: Continue to invest in the same manner but reduce the cost of media (the denominator) thus improving the MUR. Here, we improve the pricing or improve on implementation so that we get the best inventory for maximum viewership/ impressions/ etc. This is a very popular approach as it is immediate, direct and tangible. The impact of this is certain but, it will bring in only a small incremental change to the MUR. Do not expect miracles with this approach.
• Strengthen Strategy: Improve the effectiveness of media so that the % media contribution for the same spend improves. This requires one to look at targeting, choice of media/ platforms, manner of usage of each platform, optimization of budget across platforms, monitoring last-mile or intermediate business metrics and optimization campaign to improve effectiveness. If one is able to do this well, the increase in MUR can be exponential. This sounds good but, is not easy nor is very evident and hence, needs bold decisions.

But, coming back to the point we started with, here is the argument against investing in media strategy:

• Media only contribute 10% to the sales or as discussed may be less or more for different brands. You need to think what if the media contribution is higher? 
• One may not be implementing media in the best possible way, but, how wrong could one be. After all, the decision on media investments are joint decision by the best minds in the company even though not taken with formal objectivity. So, if we don’t do media well say the 10% will come down to say, a 7% ? Is that too much to worry about. Especially, when it is not even easily deduced.
• And, whatever one is doing today in media today – if one just improves the pricing, that will anyways impact profitability positively. Will this not be enough to make-up for whatever one may be losing by not doing media in the optimal way.
• So, why bother about media strategy. And, not that one does not do strategy. – lets just not bother about formal media strategy such as investing in as much research, analytics or media strategy people/ tools, etc all of which are expenditure items. 

I am not proposing that one should not invest in advertising. It helps build awareness, brand credentials and imagery. Media is often used to address specific barriers that impact brand choice at different stages of the consumer journey. Media contributes to both the short-term and the long-term effect on sales. The investment in advertising has many considerations beyond just the A:S and % sales attribution to media. 

So, invest in media but, it may not be worth the while to invest too much in media strategy – it’s a gamble anyways 😁. 

Some caveats:

• If the above argument is too simplistic for some media pundits, please pardon the leap taken. The idea is to trigger readers to re-think the importance of media strategy and not enumerate the detailed aspects. 
• If anyone wants to evaluate their current media investments basis the above metrics, I will be glad to assist.
• Despite the above argument, if you still feel that you would want to invest in media strategy – I will be delighted to participate as that’s what puts bread on my table 🙏. 
• If anyone agrees with the title statement entirely - I have nothing to say 😑.
• Special note to my employers – the above is fiction. Media strategy is very important. Pl continue to invest in the strategy resources (including me 👍 ). 
• This article is my personal point-of-view and does not represent the views of my organization.