The art in the science of selecting a marketing model

Crafting the perfect marketing model continues to be as much an art as a science. While “traditional” vs “modern” marketing cues images of “Mad Men” style advertisers battling it out against an army of young geeks, most marketers have come round to the realisation that you need both types of marketing strategies to be successful.

Media channels such as TV, print and out-of-home reach wider audiences and boost brand awareness, but they are expensive and determining ROI is difficult. Digital is easier to measure especially in categories where the sale is completed online. Media mix modelling has been around for decades but has had limited success compared to online data-driven attribution. Even with the looming threat of a cookie-less future, marketers across the globe continue to bet on digital to deliver sales numbers, while they are not always able to quantify the impact of spends on TV and print.

In true Bollywood style, however, the India market is a bit ‘hatke’/different when it comes to what works and what does not. E-commerce and tech giants spend a much larger percentage of their budgets on outdoor and print in India than in other markets, and there is data that shows that traditional advertising helps boost the impact of digital spends. Mainline advertising also helps build trust. Digital-only challenger brands have succeeded in some categories, but it is difficult for Indian brands in categories like financial services to establish themselves as trusted providers through purely online marketing strategies.

The solution lies in being able to measure traditional channels better and ensure that they work with digital to deliver a seamless brand experience and maximise ROI. Brand metrics continue to be important, but there is a significant opportunity to adapt techniques developed for the digital world and optimise traditional marketing outcomes. For example:

• Data-driven decision making: Traditional marketing tends to rely on reach/frequency metrics rather than on impact and ROI. Most organisations have a lot of internal data that includes sales numbers, website visits, store/branch walk-ins etc. Data science allows us to combine this with external data sources and market research to identify patterns, predict consumer behaviour and make more insightful marketing decisions.

• Experimentation: Small tweaks at the briefing and campaign creation stage allow one to have multiple versions of TV, print and outdoor ads at minimal incremental cost. These can be tested online or even through traditional channels with slightly different versions of the campaign being run across different locations or channels or with different calls to action. The results of these experiments need to be fed back real time to help optimise subsequent iterations.

• In-flight campaign optimisation: Traditional campaigns are often tested through pre and post campaign market research which is time-consuming and sometimes fails to deliver actionable insights. Optimising a live campaign is possible if planned for during the creative and media briefing process. Experimentation and pulse checks through the campaign period help ensure the most appropriate creative and is used for each channel while spends are diverted to media vehicles that deliver the highest ROI.

• Precision marketing: New solutions are emerging that allow precision targeting in non-digital media including outdoor and on-demand TV and ensure that messaging is more relevant, if not personalised.

Lastly, the impact of digital marketing on the brand cannot be underestimated. Performance marketing relies on techniques that may appear intrusive or annoying if not applied with a brand filter in mind. Balancing traditional and digital media spends with relevant and on-brand messaging seems to be the best way to attract customers and build long-term loyalty.

This article is authored by Shoma Narayanan, Managing Director – Group Strategic Marketing & Communications at DBS Bank India.

Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members

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