Be ‘aware’ of Integrated Advertising Mix Planning

Blog by Hidde Smit – PhD student at the University of Groningen

DVJ Insights values cooperation with academia and therefore sponsors several PhD students. One of them is Hidde Smit (University of Groningen), who is researching advertising effectiveness. One of the projects examines the impact of advertising mix planning decisions on different KPIs across the purchase funnel. The project provides practical and valuable insights into what really matters when planning the advertising mix, which can enhance decision-making in the field of advertising. While a number of decisions can potentially impact the outcomes, what are the odds they all contribute equally? In this blog, we will look closer at what matters for awareness.

ADVERTISING MIX PLANNING ENTAILS MULTIPLE DECISIONS

The advertising mix planning (AMP) steps are based on basic marketing literature (Tellis & Ambler 2007; Kotler & Armstrong 2022). An important objective of brands is to create brand awareness (Hanssens et al., 2014; Mintz et al.,2021). Once that objective is set, some decisions have to be made:

• Determining the budget,
• how to divide the budget over offline and online channels,
• how to spend the budget over the available time within a channel (e.g., concentrate spending in a small timeframe or spread over a longer period),
• at what time of the year to plan, and possibly concentrate, on spending?

The goal of the study is to find the relative importance of these decisions toward increasing brand awareness. To determine the relative importance of each decision within the advertising mix planning, a model is first made to predict the impact of advertising decisions on awareness. As input for the model, we use one and a half year of weekly data on aided brand awareness (collected on a weekly level by DVJ Insights), advertising data which we use to model the currently used decisions for several offline and online channels (TV, out-of-home, offline print, online banner, social media Facebook / Instagram), plus control variables.

The data is from the larger category in the Netherlands, which consists of 11 brands. After modelling how current advertising strategies impact awareness, we can use that information to predict what happens if we change the advertising strategy. We simulate a large number of scenarios (approximately 52,000) in which the advertising input for the model is changed, thus changing the current advertising strategy into another strategy.  The changes are as follows:

1. Budget

Advertising is generally treated as an expense instead of an investment, such that successful brands and products tend to receive more budget whereas unsuccessful products tend to lose budget in the following year (Allenby and Hanssens, 2005). Previous research finds this is not wise, as when brands decrease or stop advertising activity they lose market share by losing infrequent buyers (penetration) rather than customer loyalty (Phua et al., 2023).

On the other hand, many brands actively advertise and even seem to systematically overspend (Cheong, De Gregorio and Kim, 2014).

To see how important it is to decide how much to spend on advertising, we take the budget a brand has used and multiply it by 0.5, 0.75, 1, 1.25, and 1.5, to increase and decrease the budget in steps of 25%, while also keeping the original budget.

2. Channel allocation

In general, brands spend the bulk of their budget on TV advertising. However, research finds that using multiple media at the same time is better than using single advertising media (Naik and Raman, 2003; Danaher and Dagger, 2013). Therefore, the budget is moved away from the mostly used TV channel and spread over other offline channels (out-of-home, and offline print).

Online spending remains the same in these scenarios. Lastly, research shows online channels perform on par with television if it comes to brand building (Draganska, Hartmann, and Stanglein, 2014). We also test this, by spreading the budget equally over all channels, such that on average more budget is moved towards online channels (banner advertising and social media spending for Facebook and Instagram).

3. Flighting / Always-on levels for both online and offline channels

There are three types of spending in the scenarios. Flighting (short periods of high spending), always-on or maintenance (long periods with continuous spending), and campaigning where the two approaches are combined (short periods of concentrated spending are combined with continuous spending). For a depiction, see Figure 1. Spending a lot on ads in a short period might make it easier for brands to break through the clutter of advertising, whereas the constant reminder of maintenance strategies might lead to a more long-term connection to the brand.

We, therefore, transform the advertising strategies by looking at combinations of allocating budget towards either full flighting (100% of the budget in three campaign bursts within the observed period), full maintenance (equal spread of the budget over the observed period), and the combinations in between which is thus a form of campaigning. We do this for both online and offline channels in steps of 5% (e.g., campaign with 15% of the online budget in maintenance and 85% in the peaks, and 65% of the offline budget in maintenance and 35% in the peaks).

Figure 1: Basic depiction of flighting, maintenance, and campaigning spending

4. Timing

If we use either the flighting strategy (spending all budget in set periods) or a campaigning strategy, more money can be spent in set periods. This might be useful as there is demand variability over the year (Gijsenberg, 2017). We can spend that money on (1) peaks: for instance, in the summer more people drink lager and thus this might be the most interesting time to advertise, (2) pre-peak as perhaps managers should start earlier than the peak to mentally prepare consumers before the actual peak, or in the (3) trough, as perhaps it is harder to stand out during these peak moments as other brands also advertise then and advertising in periods where demand is low is more effective for building the brand as you stand out more.

5. Awareness

The pie charts show the proportion of variance that is explained by decisions in the AMP. For instance, channel allocation explains approximately 40% of aided brand awareness for large brands. ‘Interaction’ in the pie charts refers to all possible interactions between the AMP decisions, which has to be included to make sure we observe the true variance of a single decision. This proportion of variance can be ignored for now. We repeated the process for both large brands (Figure 2) and small brands (Figure 3).

Figures 2 & 3: The importance of advertising planning mix decisions for large and small brands

Overall, we notice a strong similarity between large and small brands regarding the effects on awareness. What is different is that channel allocation decisions have relatively more impact on small brands, while online flighting and always-on decisions have relatively more impact on large brands. So what does this mean? It means that the results vary more, either positive or negative, for these decisions. To find out what managers actually should do if they seek to increase awareness, we have to look more closely at the results of the simulations.  For both large and small brands decisions in offline flighting and always-on, timing,  and spending level are not important (meaning their impact on the outcome is small), so we only look at the important parts.

THE BEST SCENARIOS

What is important for managers of both large and small brands are online flighting and always-on, and channel allocation decisions.

1. Large brands

Channel allocation is the most important driver of awareness. The large brands in our dataset can gain more brand awareness if they spread their budget, moving away from the mostly used channel TV, towards the other offline channels and in addition also to the online channels. This makes sense as large brands often already have high brand awareness and in their usual channels there is little to gain. A possible explanation therefore might be that in the other offline channels (out-of-home and offline print) and online channels (banner advertising and Facebook/Instagram), these companies reach unaware consumers.  

Online flighting and always-on. We observe the highest lift in awareness when a pure flighting strategy is used, which means spending a lot of the budget in narrow time windows. This most likely helps in breaking through the clutter and standing out compared to your competition. Also, consumers are more often exposed to ads and thus might better remember the brand. This seems to work best if in online channels an always-on strategy is used of (approximately) > 50%. This means 50% of the budget is divided equally over the weeks and the rest of the budget is spent in narrow time windows just as in the offline channels. Together that means there are periods where consumers are heavily exposed to the brand, which might create strong connections to the brand. Repeated exposure online might help in maintaining that connection longer over time.

The maximum increase in aided brand awareness is around 6% for large brands. While this seems small, it is also logical as larger brands are often already reaching the ceiling of how much awareness they can achieve. If everyone already knows your brand, there is no one left to make you aware. Keep in mind though that maintaining that high level of brand awareness remains important. All the scenarios have no negative effects, which might indicate that spending on advertising at least helps in keeping the current level of brand awareness.

2. Small brands

Channel allocation Compared to large brands, the optimal decision for small brands is different. Spreading over all channels is not wise, and it is better to either stay focussed on TV, or move some budget away from TV towards out-of-home, and offline print, but not necessarily spending more online (compared to current spending). Spreading the budget over all channels and thus focusing more money on online channels comes with a high possibility of lowering brand awareness. This might be because the budgets of small brands are simply smaller. Moving the budget away from TV to other channels could mean these brands are spreading the budget too thin, and they cannot break through the clutter. If people already have weak brand awareness, forgetting the brand might be easy if they are not properly reminded.

Online flighting and always-on That small brands have a hard time standing out compared to large brands is also shown in the decision of how to divide the budget over the weeks. Keep in mind that this online budget is relatively small compared to what brands spend in offline channels, which is why it might help to concentrate on their limited spending. When the budget is spread more evenly over offline channels, and thus more people are reached through these channels, it seems there is synergy with online channels and awareness increases the most. Thus, concentrate the online budget in narrow time windows to make an impact there, but at the same time keep a constant presence in offline channels. Allocating more than 50% to always-on spending in offline channels seems to help in keeping consumers reminded of the brand if at the same time online peak campaigns broaden the aided brand awareness.

Small brands have a harder time standing out between all of the messages consumers have to process. This is because smaller brands often have lower awareness compared to larger brands that benefit from higher brand recognition.

Overall, smaller brands have more to gain compared to large brands. Brand awareness can be increased by approximately 16% based on our scenarios. However, in contrast to large brands, the effect can also be negative. Small brands can lose awareness (approximately 15% within our range of scenarios) if their advertising strategy is not right.

KEY TAKEAWAYS

• Both small and large brands benefit from moving money from TV to other channels:
  • Large brands should spend more money on other offline channels and more on online channels.
  • Small brands should spend money in offline channels, and not spend more on online channels compared to the current strategy.

• It pays off to break through the clutter either offline or in online channels by concentrating spending:
  • Large brands should concentrate spending on offline channels.
  • Small brands should not increase online spending compared to the current strategy, but what is spent should be concentrated.
• Large brands have little to lose in terms of awareness. A bad advertising strategy:
  • Means little to no loss in awareness. Spending money overall pretty much always seems to pay off.

• Small brands have more to gain, but also more to lose
  • In the best-case scenario awareness can go up by 16%.
  • It seems like in around half of the scenarios the advertising strategy results in overall negative brand awareness, up to around -15% in the worst-case scenarios.

References

Allenby, G., & Hanssens, D. (2005). Advertising response. Marketing Science Institute, Special Report, (05-200), 8.

Cheong, Y., De Gregorio, F., & Kim, K. (2014). Advertising spending efficiency among top US advertisers from 1985 to 2012: Overspending or smart managing? Journal of Advertising, 43(4), 344-358.

Danaher, P. J., & Dagger, T. S. (2013). Comparing the relative effectiveness of advertising channels: A case study of a multimedia blitz campaign. Journal of Marketing Research, 50(4), 517-534.

Draganska, M., Hartmann, W. R., & Stanglein, G. (2014). Internet versus television advertising: A brand-building comparison. Journal of Marketing Research, 51(5), 578-590.

Gijsenberg, M. J. (2017). Riding the waves: Revealing the impact of intrayear category demand cycles on advertising and pricing effectiveness. Journal of Marketing Research, 54(2), 171-186.

Hanssens, D. M., Pauwels, K. H., Srinivasan, S., Vanhuele, M., & Yildirim, G. (2014). Consumer attitude metrics for guiding marketing mix decisions. Marketing Science, 33(4), 534-550.

Kotler, P., & Armstrong, G. (2010). Principles of marketing. Pearson education.

Mintz, O., Gilbride, T. J., Lenk, P., & Currim, I. S. (2021). The right metrics for marketing-mix decisions. International Journal of Research in Marketing, 38(1), 32-49.

Naik, P. A., & Raman, K. (2003). Understanding the impact of synergy in multimedia communications. Journal of marketing research, 40(4), 375-388.

Phua, P., Hartnett, N., Beal, V., Trinh, G., & Kennedy, R. (2023). When Brands Go Dark: A Replication and Extension: Examining Market Share of Brands That Stop Advertising for a Year or Longer. Journal of Advertising Research, 63(2), 172-184.

Tellis, G., & Ambler, T. (2007). Handbook of advertising. The SAGE Handbook of Advertising, Sage Publication ltd.