Around 60,000 people a month search on Google for information about contact density, and a somewhat larger number search on channel saturation.
Our experience however is that few organisations get much further than establishing some arbitrary rules to limit the overall number of contacts an individual can receive.
They will also tend to acknowledge that the more they spend in a single channel with a similar proposition, that the marginal cost for each additional sale gets greater.
What however is often lacking is any science around how strong these effects are, and what is the optimal point to look for when increasing contact density at an individual level, or spend at a channel level.
There is we believe a further dimension to the problem; not all people respond in the same way as levels of contact density increase. Our findings have been that people with a greater level of engagement with a brand will continue to respond for longer as density is increased, whereas those with a low level of involvement switch off at a much lower level.
The level of contact density and return then needs to be considered against other channels and the media saturation.
- Managing the contact density element
There is no substitute for undertaking controlled tests of different levels of contact for groups of customers to find out how their responsiveness is affected, unless you have been fortunate enough to have done this already.
Some organisations will start mailing catalogues or letters to customers on a regular basis from the initial point of initial contact; this will mean that the longer an individual has remained a customer the more mailings they will have received. In these case the controlled contact density tests have been organised by accident rather than by design.
*Cumulative response rate is calculated Total responders/Total mailed volume
This chart shows the impact of increasing cumulative mailing contacts on response for an organisation that mailed its customers on a regular basis.
Where structured tests have been undertaken, then it is possible to see how important contact density is as a driver of sales. However it is essential to understand this in the context of other factors that determine response.
In a second home shopping case, we discovered that when we looked at how groups that were differentiated by their overall predicted demand in a season responded, the greater their predicted demand the more they were able to provide a good ROI as the volume of catalogue mailings increased.
To explain this chart there are five expected spend quintiles, and for each we have plotted their cost to sales ratio against their overall cost of communications in a season.
For a more thorough explanation of this methodology please click on:
- Understanding channel saturation
To quote Jan Saputra from Seven Plans in Berlin ‘Saturation always occurs in all kinds of advertising and is especially important if our advertising volume is large’.
The problem in understanding it often lies in the fact that the ways in which saturation is measured, will be determined by the nature of the channel in question.
Our approach is to attempt to take historic marketing campaigns in a particular channel, where there is a known spend and trackable response level and value generated, and to plot the cumulative response and value as spend is increased.
This has worked well for such diverse channels as for instance press, door-drops, and inserts.
As this chart shows, the value generated tails off as spend increases, and even reaches a point where increasing the spend has little impact on the value generated.
There is one exception, which is when dealing with Google generic PPC. The issue here is that the relationship between spend and value is dependent on the bidding mechanism. Namely as the budget increases Adwords tend to increase the position and cost per click (CPC) rather than simply gaining more impressions at the same level.
If one can master these Adwords controls, maintaining a consistent positon and CPC, then the spend-value relationship becomes linear as the probability of any impression being clicked (in the first impression instance) is the same.
Where we do however find an interesting relationship between spend and value is when we look at the returns from a higher position. Here we find that the ROI may often decline the higher the position as the CPC increases; however underlying contribution can fall as the click rate reduces.
The importance of developing channel saturation curves in marketing budget planning cannot be overestimated. The best distribution of marketing spend between channels comes when the ROI obtained from a marginal increase in spend is the same across all channels being employed.
At Berry Thompson we have developed a tool which supports marketing budget allocation, and optimisation. This is called BAT (budget allocation tool). Within BAT we can enter saturation curves for different channels and then set an overall budget within which the return is to be optimised.
The BAT tool selects elements from each channel saturation curve submitted until the marginal ROI across all channels is the same.
To find out more about BAT please click on: