It’s also impossible to isolate only these sub-optimal spots within the schedule. For instance, imagine a schedule being developed with a host of spots. Since they are being added on the basis of their merit (defined by the planner as CPRP or CPT or any other), the first ones to be added are the best ones. But this ‘merit’ is of each spot individually. Finally, they need to work well TOGETHER with each other within the schedule to collectively deliver the reach and exposure levels.

Lets say we have a plan with 500 spots, which generated 65% reach at a cost of 10 lacs. If the plan objective was 60% and not 65%, then some spots will have to be removed. Typically, the planner will start deleting programs from the schedule that he added last. Now if the removal of the last 50 spots triggers this reach to settle at 60% and reduces costs by 2 lacs then one can say that the task is done. But not quite as we will just see… for there could have been technically some 75 spots in the middle of the schedule, which individually found place within the schedule on their merit but collectively do not add any incremental reach to the plan and their removal could have resulted in a cost reduction of 3 lacs! Hence, the efficiency of the plan depends on the sequence of the spots as well as how they are interacting with each other within that schedule. Again, it’s worthwhile to emphasize that this is impossible with manual interventions and requires an optimiser.

Optimisers can help control number of exposures
While a basic optimiser optimises reach at a budget/ GRP level, a few advanced optimisers have other variations such as minimising costs at particular reach level. Then there are optimisers that let you control the response function curve. You can put in constraints that force the response curve to be as close to the ideal as possible. In this case, the optimiser is simultaneously balancing 3 to 4 constraints – cost efficiency, reach buildup and suppression of reach at exposure levels falling below the effective frequency fixed for the plan.
In fact, over-exposure is a very critical issue that a lot of advertisers have not yet focussed on. In certain cases, where consumers are being bombarded with the same ad for over 15 to 20 times, it can prove detrimental to that brand’s image in the consumer’s mind. There are a lot of experiments to suggest the same from as far back as 1972 when a Social Scientist associated with the University of Michigan published results of an experiment that he conducted with seventy-two undergraduate male students. He used a series of paintings, which he showed to the respondents on a screen each at random frequencies of 0, 1, 2, 5, 10 or 25 exposures. He then asked respondents to rate how much they liked each painting on a 7 point scale. Results were showing a circumlinear effect of saturation as seen in Chart 3. Meaning that too many exposures result in lower favorability or likeability! All subsequent experiments that were inspired in the advertising industry from early work in the social research field found similar results on the relationship of exposures to likeability/ preference.
Some optimisers actually help in this respect by giving out a series of plan options that then can be chosen on that plan’s ability to deliver only within a limited band of exposures (say 5 to 7). Not only does this save costs as you are not draining yourself out by repeatedly bombarding the same set of people at very high exposure levels, but it also ensures that there are no negative associations with your brand due to over-exposure.

Multiple uses for broadcasters and channels
For broadcasters, the Optimiser is a boon to develop price benchmarks to see at what price does the optimiser pick up a daypart in a plan versus at what threshold it stops picking it up. It can also be used as a sales tool to display a network buy. One of the most significant uses at a channel’s end will be to actually optimise the scheduling of program or channel promos. That’s because while a new program will have to target a potential Target Group that’s expected to convert into viewers, it also has to optimally use the inventory available as that’s an opportunity cost as far as the channel is concerned.
With optimisers that have a built-in ‘predictor’ function, a channel could also look at an event scheduled in the future and predict different scenarios. Depending on how the outputs look, a pricing benchmark can be achieved. For instance, there might be a channel that is scheduling a very exciting event that’s expected to attract the bulk of viewership at the time. The channel can then through the optimiser, predict different scenarios and argue with buyers for a premium due to either the high TVRs expected or because there is no other option that exclusively generates that kind of reach at the time of the event. In fact, as the fight amongst broadcasters changes from ‘inclusion within a plan’ to ‘increasing share’ this tool will really come in handy.

The challenge for the future
As we continue to see environment changes around us in the TV industry, what is going to be curious is to know who will be the people who will catch the bus and who will be those who will get left behind. The need for today might be to learn and use the optimiser. The need for tomorrow might be to learn a next generation TV planning tool, who knows. Bottom-line is that each new element will have to be learnt and mastered to guarantee our success! As someone great had said very recently, “If you’re not changing gears very quickly in this warp speed economy, we have a name for you – DEAD!”