The marketing mix is a term to describe the multi-faceted approach to a complete and effective marketing plan. Traditionally, this plan included the four Ps of marketing: price, product, promotion, and place. But the exact makeup of a marketing mix has undergone various changes in response to new technologies and ways of thinking. Additions to the four Ps include physical evidence, people, process, and even politics.
- Understanding marketing mix
- Other elements of an effective marketing mix
- Key highlights
- What is marketing mix modeling and why it matters to understand how to balance your marketing mix?
- Understanding marketing mix modeling
- The four phases of marketing mix modeling
- Marketing mix modeling examples
- Key highlights on marketing mix modeling:
- Connected Marketing Concepts
Understanding marketing mix
While many understand marketing as “putting the right product in the right place, at the right price, at the right time”, few know how to implement this in practice.
Identifying the individual elements of a marketing mix and then creating robust plans for each allows a business to market accordingly. It also allows a business to market to its strengths while minimizing or eliminating its weaknesses.
This can include a tangible good or an intangible service. Businesses must understand their product or service in the context of the problem that it aims to solve. If the product does not seem to address any problem, then the potential profitability of the product should be re-analyzed. The target audience, or those who will buy the product, must also be identified.
Price has a direct impact on how well a product will sell and is linked to the perceived value of the product in the mind of a consumer. In other words, price is not related to what the business thinks the product is worth. Thus, it is important to know what the consumer values and price it accordingly. To a lesser extent, price may also be influenced by rival products and value chain costs.
Promotion includes all marketing communication strategies, such as advertising, sales promotions, and public relations. Irrespective of the channel, communication must be a good fit for the product, price, and the target audience.
Place describes the physical location in which a customer can use, access, or purchase the end product. Determining where buyers look for a product or service may seem simplistic, but it has implications for marketing and product development.
For example, place determines which distribution methods are most suitable. It also dictates whether a product needs a sales team or whether it should be taken to a trade fair to be sampled and advertised.
Other elements of an effective marketing mix
Conventional marketing mixes are product-centric, but services and other intangible goods are also commonplace for many businesses. People, process, and physical evidence are three more Ps that these businesses should implement.
People refers to the staff who are directly and indirectly involved in marketing the brand. Employing the best people for the job is crucial since people shape the direction of the brand and therefore the goals and values of the business.
Process covers the interface between business and consumer, otherwise known as customer service.
Process is important because customers often give feedback on their service, which enables a business to improve its systems across the board. Effective processes should make purchasing pleasing and simple while simultaneously increasing brand equity.
Physical evidence describes anything that consumers see when interacting with a brand. Physical evidence can take the form of packaging, branding, and even the physical layout and design of retail spaces and shop fronts.
Physical evidence also extends to how staff dress and interact with customers and the possible impact that this has on sales.
- Marketing mix refers to a suite of actions that a business uses to promote its products or services in the market.
- Marketing mix should as a minimum have strategies devised for product, price, promotion, and place.
- Service-oriented businesses should adopt a broader marketing mix, otherwise known as the seven Ps of marketing.
Marketing mix modeling (MMM) is a statistical method for evaluating marketing campaign effectiveness. The method quantifies the impact of multiple marketing inputs on market share or sales which then determines how much to spend on each.
Understanding marketing mix modeling
Marketing mix modeling uses statistical analysis to analyze the past and future impact of different marketing tactics on sales or profit. The approach is based on the popular 4 Ps marketing mix theory.
In essence, the purpose of MMM is to measure the past performance of a campaign and improve future marketing return on investment (MROI). Conclusions drawn from the statistical analysis then determine how resources can be better allocated across various tactics, products, segments, and markets.
Marketing mix modeling utilizes the multi-linear regression (MLR) statistical technique to assess the relationship between dependent and independent variables. The dependent variable is normally market share or sales, while the independent variable could be price, distribution, or ad spend for different channels.
The four phases of marketing mix modeling
Each MMM project has four distinct phases that we have explained in detail below.
Phase 1: Data collection and integrity
In the first phase, the business collects data on the products to be analyzed, the desired timeframe, and the markets to be modeled. The sales performance metric should also be quantified at this point. Will it be volume, units, sales, or some other metric? Brand margin rates and marketing spend should also be determined so that the MROI can be calculated later on.
MMM also requires the business to use data that will yield the best results. In other words:
- Has the best available data been incorporated?
- Is the data consistent over the entire life cycle?
- Are there multiple years of data to account for factors such as seasonality?
Before moving to the next phase, key project stakeholders should also hold a review session to ensure data integrity. In some cases, data will have to be aggregated or cleansed before moving forward.
Phase 2: Modeling
In the second phase, brand managers must collaborate with their internal analytics staff to discuss statistical details, specifications, and methods. We noted earlier that a multi-linear regression is commonly used, but other methods such as time-series regression are also used.
Ultimately, the method chosen will depend on the organization’s goals, data quality, and in some cases the entity providing the statistical analysis on behalf of the client.
Phase 3: Model-based business measures
Once the statistical analysis has been performed, it will produce output data that measures how each tactic impacts sales. The data must also answer or address the overarching purpose of the project, with many organizations choosing to frame project purpose as questions such as:
- What is the best marketing plan to maximize future net profits with respect to the current and future budget?
- For a particular demographic, what are the most efficient or effective marketing tactics?
- What is the impact of advertising on consumer price sensitivity?
- Which competitor advertising campaign is having the most negative impact on sales?
Most MMM projects will also feature a pie chart showing the decomposition of sales where sales volume is broken down according to each tactic. These charts separate two types of tactics:
- Core tactics – or those not controlled by the marketing team such as seasonality, distribution, weather, and competitive trade. Core tactics can also encompass the sales that would occur in the total absence of any promotional effort.
- Incremental tactics – or those that are controlled by the marketing team.
Once a decomposition of sales has been performed, the organization can calculate three important metrics:
- Effectiveness – which is determined by dividing the number of incremental sales by each marketing effort.
- Efficiency – where incremental sales are divided by the expenditure of each tactic. This is normally the total media spend, and
- Marketing return on investment – the MROI can be calculated by dividing the gross profit of each tactic by its total spend.
Phase 4: Optimization and simulation
In the final phase, MMM outputs are transformed into inputs for future marketing campaigns.
Simulations help the organization model the impact of a new tactic before it is used in a real-world scenario. They also enable teams to determine the best combination of tactics that will enable them to achieve campaign goals.
Marketing mix modeling examples
In the past few decades, marketing mix modeling has been adopted by several Fortune 500 companies such as Kraft, The Coca-Cola Company, Pepsi, AT&T, and Proctor & Gamble.
While there has been particular interest from consumer packaged goods (CPG) companies, others now use MMM because of the increased prevalence of companies providing these specialist services. Indeed, marketing mix modeling is popular in the retail and pharmaceutical industries because firms like Nielsen can provide syndicated data on stores, product categories, geographic markets, and distribution channels.
What’s more, the increased availability of time-series data has also seen MMM incorporated into industries such as telecommunications, financial services, hospitality, and automotive. However, in these industries, it is acknowledged that marketing mix modeling is still in its infancy and will require further standardization to be effective.
MMM case study for Facebook advertisers
Facebook (now Meta) is one of several modern platforms that offer a family of services and apps that have dynamic and nuanced advertising needs. Since consumer preferences are in a constant state of flux, this makes it difficult for brands to assess the impact of Facebook advertising compared to traditional channels such as television and print.
A standard marketing mix modeling project assesses data from two or three years. But for online social platforms, data over this time span may become outdated. To counteract this tendency, Facebook recommends advertisers analyze data from a 6 to 12 month period. They should then adjust their methods to account for the statistical power that is sacrificed when analyzing a shorter time frame.
Professional services company Accenture ran multiple MMM analyses in 2021 for disruptor brands requiring a reliable and cost-effective system to optimize their promotional efforts and produce results that were both actionable and granular.
How was this achieved?
Tailored data was first sourced from Facebook, Instagram, and Audience Network which considered standard engagement metrics such as clicks but also paid impressions. Data were then integrated with machine learning techniques such as the Bayesian belief network to analyze potential synergies between multiple channels.
In simple terms, this involved analyzing the relationship between six independent variables (video, display, Facebook app, organic search, Instagram, and paid search) and their dependent online and offline channels. The results of the analysis showed how various marketing channels could drive impacts across other channels. A few of the more significant results are listed below:
- Drivers of paid search – paid search (78%), offline drivers (10.9%), and organic search (5.5%).
- Drivers of Facebook app – Facebook app (87.6%), offline drivers (7.4%), and display (4.0%).
- Drivers of Instagram – Instagram direct (87.9%), video (6.0%), and Facebook app (3.7%).
In summary, Accenture found that disruptor brands that focus their resources on social, organic search, and offline channels could better impact paid search and ultimately, increase their web traffic.
Key highlights on marketing mix modeling:
- Marketing mix modeling uses statistical analysis to analyze the past and future impact of different marketing tactics on sales or profit. The approach is based on the popular 4 Ps marketing mix theory.
- Each marketing mix modeling project should have four distinct phases: data collection and integrity, modeling, model-based business measures, and optimization and simulation.
- MMM is popular among consumer packaged goods companies such as Kraft, The Coca-Cola Company, Pepsi. It is also useful for brands advertising on social media platforms such as Facebook where markets and consumer behavior are more dynamic.
Connected Marketing Concepts