INTERNET RESPONSE FUNCTIONS

Media modeling demonstrates over and over again that online’s true ROI can exceed the ROI performance indicated by machine metrics alone, and that offline and online activity can work together to improve overall cam-paign ROI.

The chart below displays response functions for eight different media channels on a one sale/media expenditure scale. Channels that rise up on the far left-hand side of the chart (search) have higher (steeper) ROI rates than channels on the far right-hand side (newspaper). These patterns of response were derived from media modeling work that was performed at PHD Canada in 2012 for a client in the telecommunications business.

There are a couple of important findings contained in this chart that have been confirmed through media modeling work performed through-out the PHD global network. Note that online channels tend to be more efficient producers of ROI than offline channels. But also notice that the online channels have sharper diminishing return curve response function patterns than offline channels. In other words, online channels are great first choice channels in a media mix, but they become increasingly ineffi-cient as weekly expenditures rise. In effect, online channels hit a sales ceil-ing sooner than offline channels, a function of the online medium’s lower reach capacity compared to the more established offline media.

Response Functions

(relationship between Unit Sales and Expenditures)

0 $50

The correlation between low-reach/sharply diminishing curves and high-reach/more gradually diminishing return curves is particularly noticeable when it comes to direct mail. In the telecommunications case (see chart) the direct response (DR) channel’s response function is lin-ear, suggesting that there is no diminishment in effectiveness with this medium as weekly expenditures increase. DR is a channel that is effec-tively capable of reaching 100% of a consumer population by virtue of its delivery system (home and newspaper delivery).

The TV response function curve (see chart) produced an ROI that, in this case, was not as beneficial to clients as the radio medium. Not sur-prisingly, both the radio and TV curves began to flatten at similar weekly spend levels. These two media have similar depths of consumer reach each week.

Newspaper was the least responsive channel in this particular media mix. In this case a “perfect storm” of characteristics existed; shallow sales impact coupled with high weekly cost. In this case a free transit daily paper was utilized. This ROI picture is not representative of most heavy daily newspaper executions.

The relationships plotted in the chart reflect each channel in isolation, as if the channel alone was being employed in the service of the marketer.

Media modeling work also provides for examination of combinations of two, three, or more channels working in tandem. Sometimes simul-taneous usage of multiple media creates synergistic impacts on target

variables (sales). In other words, sometimes TV + Internet display create a positive impact on sales that is greater than the sum of TV and Internet display in isolation. The addition of the one channel improves the perfor-mance of the other channel. This dynamic is also called an assist, and it is critical that the software used for media modeling is capable of capturing this dynamic.

The synergistic effect or “lift” effect is often seen with combinations of TV and search. TV drives awareness, which drives exploration, which drives search and e-commerce transactions or store visits. In more general terms, lift is most pronounced when creative/message consistency exists between channels and when website identification is featured in the offline creative.

Of course the efficacy of the ROI insights derived from the response functions is only as reliable as the legitimacy of the media modeling work.

And modeling legitimacy is driven to a large extent by the accuracy of the historical time-sensitive channel costing data that serves as the model’s drivers. Modelers must build channel cost files over the campaign time period that properly reflect when the brand messaging was consumed modified by when the messaging was digested.

The need for correct representation of media channel cost by week (or other applicable time unit) applies to all channels utilized in a campaign, both online or offline channels.

Identifying the timing of brand messaging consumption is a straight-for-ward process. Channel cost must be allocated to the campaign time units (weeks) in proportion to the impressions generated by the channel of the campaign time period. If 10% of the campaign’s online display impressions occurred in week 5, then 10% of the campaigns display cost must be allo-cated to week 5. The assignment of the right channel cost to the right week in the campaign is the first step in building an accurate time sensitive driver.

The second step in building accurate time patterns for channel costs requires adjustment to reflect how consumers digest brand messaging.

Message digestion refers to recall and persuasiveness—properties that diminish over time. This diminishment is called memory decay (recall decays by 20% per week, for example). The reciprocal of memory decay is called adstock (if week one recall is indexed at 100, the week two adstock index is 80). Adstock is an industry term that describes the extent to which message impact deteriorates over time. An adstock value of 80 (a half-life of 80) reflects full impact week 1 (100), 80% of week one impact in week 2 (80), 80% of week 2 impact in week 3 (64), 80% of week 3 impact in week 4 (51), and so on, until impact virtually disappears.

Different media channels have different average adstock values. The pat-tern of brand message recall spreads over a longer period of time for the TV medium (80 on average) than for, say, the daily newspaper medium (0) where message recall tends to be exhausted within the medium’s week of impres-sions. Radio adstock factors are low (20-60). Search adstock is nonexistent.

Magazine adstock is in the 60 range, as long as costs reflect the very long average issue readership impression “tail” that exists among monthly maga-zine readers. Out-of-home is usually purchased in 4 week blocks. Therefore the 4 week unit cost must be assigned evenly over each of 4 weeks and then each week must be adstocked at a value ranging between 40 and 60.

Online display can exhibit a wide range of adstock values because the ad units employed by advertisers are varied; from high impact “rich” ad units, to lower impact static display units. Online video adstock resembles TV.

A channel’s adstock value (or memory decay) is affected by factors other than the size and nature of the ad unit being used. High levels of brand

“equity”, built through consistent messaging and long term advertising support, produces larger adstock factors for the carrier channel. Hot com-petitive activity diminishes a channel’s adstock value; low comcom-petitive heat increases a channel’s adstock value.

Each channel’s average adstock value serves as a starting point for the modeler. Optional adstock patterns should be prepared and tested within the model for each channel. The model fit will improve as each channel’s adstock option approaches consumer reality. When the model fit can no longer be improved, the most relevant channel adstock patterns will have been achieved.