Digital marketing is not "marketing through digital media. It should be defined as "marketing based on quantified data" that takes advantage of the interactive nature of digital media (the characteristic of collecting and recording responses). In digital marketing, where targeting accuracy is high and granular communication is possible, continuous trajectory correction and improvement based on data is effective. However, many advertisers have yet to understand this data-driven aspect of digital marketing.
Interactivity and highly accurate targeting enable the measurement of ad responses to specific targets. Based on the response data obtained, communication design and media management can be improved to continuously increase marketing effectiveness and efficiency. Nowadays, this concept can be applied not only to direct response but also to brand marketing. However, in order to obtain data useful for improving performance, marketing initiatives must first be designed to match the data to be obtained.
Audience Classification
By categorizing audiences according to the attributes for which data is available (e.g., age, interests, etc.), marketers can make sense of the responses for each target audience. If the target audience is not segmented, the resulting response data will be a meaningless mass, with no way to see the differences between the different demographics. All you know is how many people achieved the objective, but you don't know who they are. It is fine to set a broad target, but the response data must be attribute-by-attribute. However, the response data should be collected by attribute and made available for analysis.
Communication Design
One of the advantages of digital marketing is that it allows for granular communication. Using the "Perception Flow® Model" developed by Daisuke Otobe (formerly of P&G), communications can be designed based on the attitude change that occurs in the stages leading up to a consumer's decision to purchase, and the necessary stimuli. By adopting the DAGMAR (Definition of Advertising Goals for Measuring Advertising Effectiveness) approach to advertising effectiveness management, it is possible to measure the effects of each stage of advertising contact and its attitudinal change effects.
Different target needs will result in divergent flows. However, branching of flows is bloated with implementation and management effort, so it should be kept to a minimum.
Advertising response data can be collected in a variety of ways. However, before collecting data, marketers must properly understand its meaning. Behavioral data such as video views, page views, scroll depth, and clicks measure contact with the ad and reflexive behavior upon contact. To measure psychological responses and confirm whether or not there is an attitude change or purchase behavior, a survey, called a brand-lift survey, is required. Behavioral and survey data are not substitutes for each other and must complement each other to understand the relationship between ad contact and psychological effects.
Feedback to marketing activities
Analysis of real-time behavioral data and periodic survey data can be fed back into marketing efforts. Marketing "efficiency" can be improved by focusing advertising budgets on those audiences that are most likely to make a purchase. Improving marketing "effectiveness" through better communication is essential for brand growth. Without increasing the percentage of target customers who convert to purchase, the ROI of ad spending will quickly plateau.
To improve communication based on data, first identify areas for improvement, and then create "branches" of communication as well as targets. Because communication branches require creative variations, the cost-effectiveness of production expenses must be considered in advance. The areas where marketing effectiveness can be improved by new creative are those where the ads are viewed but not enough attitude change has occurred (high quantitative response in behavioral data and low qualitative response in survey data). In these areas where psychological yield can be confirmed, target demographics that are barely responding are identified, new communication hypotheses are derived, and their effectiveness is tested. Effective communication may be different for each target. The effectiveness of the branched communication should be verified for each target as well.
Digital marketing allows for the collection of vast amounts of data. However, if marketing initiatives are not designed to collect the necessary data, the data collected will be of no use. However, only a handful of them are truly using the data to improve their marketing efforts. We must continue to challenge quantifiable marketing to prevent the "Garbage in / Garbage out" (analyzing garbage data yields only garbage) results.
Comments