Is it worth investing time and money in marketing mix modeling?

The measurement of the impacts of different marketing strategies on sales and other company KPIs is the goal of marketing mix modeling (MMM), a statistical analytic approach.

Product, price, promotion, and place (distribution) changes are all part of the marketing mix, and MMM demonstrates how such changes affect organizations’ bottom lines.

Marketing analytics seeks to quantify and optimize marketing performance through gathering, analyzing, and interpreting data. Marketing mix modeling (MMM) is an important component of marketing analytics since it allows for a methodical assessment of the success or failure of different marketing efforts, as well as the allocation of resources as a result.

Organization size, advertising strategy complexity, and data accessibility all play a part in assessing if marketing mix modeling (MMM) is worth the time and resources invested.

MMM could be useful for organizations that employ multiple marketing channels and want to know which ones are the most profitable. Organizations that can measure the success of their numerous promotional techniques can better allocate marketing resources and optimize their marketing mix.

MMM, on the other hand, needs large investments in gathering, analyzing, and modeling, which can take a significant amount of time and energy. MMM becomes significantly stronger when used with other marketing analytics tools, such as segmenting consumers and attribution modeling.

It is a validated solution for combining offline and online data, making it appropriate for cross-channel sales measurement. MMM uses historical data to assess the impact of a wide range of factors on sales. These elements may or may not be related to marketing. The model that results provides insight into the variables that have previously generated sales as well as an overview of the prospective outcomes of future marketing campaigns.

Because data collection comes after all variables have been set, the OSEMN model (see below) is useful.

Is this model useful for data analysis?

Because the OSEMN methodology has the potential to be utilized for many other types of data analysis, the answer is yes!

OSEMN (which stands for “Obtain, Scrub, Explore, Model, and Interpret”), with its planned approach to data analysis, may help ensure that all needed phases are completed and findings are accurately comprehended. In general, the OSEMN model could be a valuable framework for data analysis since it provides a systematic process that can help ensure that all necessary stages are completed and that findings are accurately comprehended.

A marketing mix model (MMM) will almost certainly require many more months of data to completely comprehend the brand’s ecology. In most circumstances, at least two years of data are required.

MMM employs a type of multivariate linear regression analysis to assess the impact of each variable on the end result, or KPI. An independent variable is any circumstance that is beyond your control and has the potential to influence your key performance indicator (KPI).

CASE STUDY: A cryptocurrency exchange (CEX)

Consider the following scenario: a cryptocurrency exchange has decided to increase its user base and market share. The exchange has launched the following marketing campaigns:

• The exchange's advertising campaign includes placements in Google AdWords, Facebook's advertising platform, and linked website advertisement networks.
• The exchange has a sizable social media presence, including channels on Twitter, Reddit, and Telegram, among other platforms.
• Articles and interviews about the purchase have emerged in the mainstream media.
• The platform is involved in the cryptocurrency community, sponsoring and attending cryptocurrency-related events.

The following methods could be used to examine a marketing mix model (MMM):

• It is necessary to define key performance indicators (KPIs). The success or failure of the exchange's marketing initiatives cannot be monitored until the exchange decides which key performance indicators will be used. Examples include trading volume, new user sign-ups, website traffic, and social media activity.
• The exchange is required to gather information regarding advertising campaigns and crucial performance measures. Advertising budgets, social media involvement, public relations coverage, event sponsorship and participation, and website stats are all indicators.
• Data Analysis using statistical analytic tools, the exchange may be able to determine the impact of each marketing activity on the KPIs. By doing so, the exchange can evaluate which techniques are most effective in attracting new traders and increasing overall activity levels.
• The exchange can adapt the marketing mix depending on the results of the analysis by distributing resources to the most profitable channels and activities. This will increase the exchange's market share and ROI from their advertising efforts.

Finally, cryptocurrency exchanges that want to measure the performance of their marketing efforts and improve their marketing mix might profit immensely from marketing mix modeling. By analyzing data and selecting the most effective marketing channels and activities, exchanges may increase their return on investment and generate stronger business outcomes.

More case studies will be added the following week! Stay tuned!         

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