The Future of Media Planning
The international media market has been suffering for years from the self-serving and interest-driven business models of agencies. The time is ripe for a true disruption. Innovative technology companies have entered the media market with technology platforms based on algorithms. They enable transparent and efficient media planning based on AI.
Driven by the margin pressure in the agency scene, over the past decades media agencies have become highly creative in the advancement of existing business models. Often the budget of customers is very low. Tis led to agencies having to construct alternative income models. In particular, trading with media service, which implies buying and reselling of media/reach, has proven to be an extremely lucrative variant to earn additional good margins. However, this approach leads to two problems. First, the agency, which should be a neutral advisor and optimizer, leaves its advisor role and becomes a sales-oriented (re-)seller of reaches for its customers.Please Visit moviestars
Software Eats the World
Disruption describes a process, which enables a young company with less resources than the established market partner to challenge established companies successfully. In general, established companies focus on the improvement in their products, which are already profitable, and they neglect the true needs of the market. Innovative companies use this opportunity to produce something novel and efficient, which successfully displace existing products, market or technologies and completely replace them in the end.More Info About George Floyd
New Possibilities for Strategic Media Planning
The innovation drivers of these new players are multi-disciplinary teams of market researchers, statisticians, behavioral psychologists, mathematicians, physicists and media experts. These data science teams work continuously on algorithms, which become more and more precise, for analysis and optimization of media investments. New, transparent business models, swifter decision-making processes and the future vision of just-in-time media make these new players attractive and effective. Blackwood Seven is one of these new players, which has already recognized those market opportunities and which has successfully monetized them.
Media Mix Modelling Approach
The media mix modelling applied by Blackwood Seven is based on a combination of various methods. The basis is the method according to Bayes. The Bayesian statistic is characterized by consistently using probabilities or marginal distributions, which allow particularly valid results. In the course of the enormous processing capacity available today, the comprehensive data basis and use of the Monte Carlo sampling method, complex Bayesian simulations can be applied today more effectively than in the past. Bayesian modelling shows the final utility of individual media channels under various conditions, always dependent on factors such as budget, campaign period, weather, seasonal conditions and spending mood.More Info About mycoolmoviez
Giant Leap in Modelling
Modelling considers various KPIs upon customer’s request. However, for modelling to be successful, it needs to be noted that the KPI is directly influenced by the media, i.e. it describes directly the behavior of consumers.
The created model considers all media channels the customer placed. Tis includes unpaid media such as the customer’s own homepage or his own YouTube channel. In addition, media investments of competitors and information on market changes are also considered. The customer’s individual model also includes macro-economic changes, product variations, weather and other data, which describe external circumstances of the market.
The media mix modelling approach of Blackwood Seven is in many ways different than conventional regression models. Thus far, advertisers had no certainty in media planning and they were only able to explain the past. Today, persons responsible for budgets can simulate with planning certainty the campaign effect and understand it thanks to the machine-learning approach, which will become more precise over time. It reviews and corrects assumptions (so-called priors). The evaluation of the past and the daily model updates with the latest data allows for an exact simulation of the perfect media mix and the campaign results (always optimized to the defend KPI).