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WHAT IS MARKETING MIX MODELING & WHY SHOULD YOU CARE?

Published On: 2023-09-06

By:Lifesight

Delve into the profound realm of Marketing Mix Modeling (MMM) and its pivotal
role in shaping successful business strategies. This comprehensive blog explores
the intricacies of MMM, offering invaluable insights to elevate your marketing
efforts.

Delve into the profound realm of Marketing Mix Modeling (MMM) and its pivotal
role in shaping successful business strategies. This comprehensive blog explores
the intricacies of MMM, offering invaluable insights to elevate your marketing
efforts.


Most mid-sized businesses use Multi-channel Attribution (MTA) to optimize
cross-channel marketing initiatives. However, as companies scale to more than
three channels and combine offline and online platforms, tracking the
effectiveness of marketing efforts with MTA alone becomes challenging. 

This is where the Marketing Mix Modeling (MMM) concept comes into play. The idea
of MMM has been in existence since the 1960's. However, with time and the
evolution of digital browsers and ad platforms, the MMM has become an aspiration
for large enterprises. 

There is a notion that only Fortune 500 companies can afford to deploy Marketing
Mix Models as they cost over $500K and take as long as six months to show
results. Nevertheless, companies like Lifesight introduced the concept of
automated MMM a few years ago. 

Automated Marketing Mix Models can shorten the processing time from six months
to 10 minutes! While Marketing Attribution still helps, large enterprises in the
scaling phase should use Marketing Mix Models as an ad to optimize their
cross-channel strategy. 

In this article, we will discuss what marketing mix modeling is and why it is
becoming a growing need for bigger brands. 


WHAT IS MARKETING MIX MODELING?

The Marketing Mix, or the Four P's of marketing, is about having the right
balance between your product, place, price, and promotion.

Balancing these factors can be difficult when you don't have reliable data to
make an informed decision. That's why understanding the Marketing Mix Modeling
definition is critical.

Mix Modeling is a statistical analysis method that relies on multi-linear
regression, time series, and budget optimization to analyze marketing data
across different marketing channels and quantify the impact of various marketing
initiatives on your overall sales and marketing performance.

The MM Model helps you answer some fundamental marketing questions:

 * What percentage of ROI is the business generating through different marketing
   channels?
 * Which marketing channels are the most expensive, and which channels are most
   cost-effective?
 * Based on cost and ROI, which channels should I spend on in the next quarter?
 * How are the earnings from advertising contributing to my overall business
   ROI?


TRADITIONAL VS. NEW MARKETING MIX MODELING

While the core concept of MMM remains the same, the channels and sources of data
collection have changed in the last decade.




COMPONENTS AND ELEMENTS OF MARKETING MIX MODELING 

MM Models rely on gross sales, ad spend, product, and customer datasets to
provide a holistic overview of marketing efforts. Let's deep-dive into each of
them.


MARKETING CHANNELS

 * Traditional channels: Television, print media, billboards, magazines, and
   other non-digital channels.

 * Digital channels: Social media platforms like LinkedIn, Facebook, Instagram,
   and YouTube; Communities like Reddit; search engines like Google; email
   campaigns and influencer marketing campaigns.

 * Direct marketing and promotional channels: Customer loyalty programs,
   coupons, and point-of-sale promotions.


TEMPORAL EFFECTS

Short-term temporal effects like time-sensitive discounts push customers to make
purchases faster. On the contrary, prolonged campaigns often delay consumer's
decision-making processes.

For example, when launching a new product, ecommerce stores often offer
discounts like “Buy 1 Get 1 Free within 24 hours of purchase” or “50% off for
repeat customers”. Temporal effects like offers create urgency in the buyers'
minds and can push them to make a purchase instantly.


EXTERNAL FACTORS 

 * Economic indicators - GDP, inflation rate, and recession directly impact your
   potential customer's purchasing power.

 * Seasonality: Seasonality is responsible for influencing customer behavior.
   Running offers and discounts during holiday seasons like Christmas,
   Halloween, Thanksgiving, and Easter often increase sales.

 * Competitive actions: It is also important to note how aggressively your
   competitors promote their products or brands. If one of your competitors has
   a solid promotional strategy, it might lead to a temporary or long-term
   reduction in your sales.

 * Event-driven factors: Unexpected events also play a massive role in MMM. In
   2022, the Russia-Ukraine war caused a significant reduction in the ecommerce
   sales of both companies. Particularly in Ukraine, ecommerce sessions were
   reduced by a record 65%.


BASELINE SALES AND INCREMENTAL SALES

 * Baseline sales define the expected demand for a product in the absence of any
   marketing initiatives. It is directly associated with factors like brand
   equity and long-term trends in pricing seasonality.

 * Incremental sales are the additional sales acquired by promotional activities
   like digital advertising, direct marketing campaigns, and traditional
   marketing initiatives. Total sales revenue is anything over and above
   baseline sales revenue.


INTERACTIONS AND SYNERGIES

Every marketer knows that marketing activities are interrelated. The success or
failure of one initiative can directly affect another's performance.

For example, successful online ecommerce brands create a perfect marketing
synergy by combining SMS and email marketing. SMSes often have a higher open
rate than emails (SMS - 98% vs. Emails - 20%).

Case in point: Whenever a new sale is live, brands notify the customers with an
SMS and send specific coupon codes through emails to make it more effective.

Beauty brand Sephora often does this:





Source


SATURATION CURVES

Marketers need to clearly understand the relationship between ad spending and
performance and a Saturation Curve helps them understand the optimal level of ad
spending to maximize ad performance.

A Marketing Mix Modeling software considers the saturation curves to identify
this point and helps brands avoid spending additional budgets on the same
campaigns.

A saturation curve looks like:





Source

It typically plots the order values of a product against marketing investments.
After a specific time, it reaches a point where order value or sales remain the
same despite increased ad spend. That's the point of saturation.


COST DATA

Cost data is the overall investment information that businesses make on various
marketing initiatives. To calculate profit, brands must subtract the cost data
from sales revenue.


RESPONSE CURVES

A Response Curve dictates the relationship between various marketing initiatives
and relevant business performance. Marketing teams use them to create media
plans and plan campaign budgets more efficiently.





Source

In a response curve, you should plot sales against marketing efforts. If it's a
linear response curve, it means that returns have been consistent for different
marketing efforts. This shape varies from brand to brand.

Marketing Mix Modeling software aims to analyze response curves to identify the
effectiveness of different marketing campaigns.


MODEL VALIDATION METRICS

Once you develop a Marketing Mix Model, the next step is validating metrics such
as R-squared and mean absolute percentage error to help you determine the
accuracy of Marketing-Mix. Model validation compares the Mix Model with the
real-life environment to measure its effectiveness.


BENEFITS OF USING MARKETING MIX MODELING 

MM Modeling offers a refined multi-channel overview of different marketing
initiatives to marketers. Some of its benefits include:

Accurate forecasting

A Marketing Mix Model analyzes different marketing efforts from the core to
identify which initiatives worked and which didn't, giving a clear picture of
the marketing strategies to focus on in the next quarter.

The outputs acquired from a Mix Model allow you to explore the month-on-month
impact of marketing campaigns on the overall growth of the business. In turn, it
helps predict to what extent the marketing budget needs to be adjusted to reach
the optimal point of maximum return at minimum ad spending.

After receiving the first version of the outputs, you can delve deeper and add
several filters like locations, campaign types, and channels to create an
actionable roadmap of future steps.

Earn trust and confidence from stakeholders

Marketing Mix Modeling attribution becomes a great way to win the trust of your
C-suite executives and build marketing awareness across the organization.

Going a step further, granular MM reports provide a real-time overview of
business segments and their contributions to overall ROI.

Avoid access to confidential data

Marketing Mix Modeling attribution doesn't use users' private data, which is why
it is an ethical approach to privacy threats. It focuses on aggregate datasets
like ad spending and sales to conduct an in-depth statistical analysis of the
effectiveness of your marketing initiatives.

In a privacy-first era for the modern marketer, even though privacy regulations
change frequently, Mix Models will continue to be the flexible approach that
marketers can trust.

That being said, you should integrate Lifesight's AI-enabled, automated MMM
solution that simplifies budgeting, forecasting, and campaign optimization to
eliminate guesswork.

Some reasons why customers implement Lifesight's privacy-centric automated MMM
solution are: 

Allocate budgets automatically to successful campaigns. LifeSight trains your MM
Model with historical data and tests different budget scenarios instantly.Scale
your marketing spend without overspending, predict your KPIs, and allocate the
budget to the right channels. 




The integrated marketing mix modeling platform allows for continuous calibration
of your attribution models, leverages existing data integrations to generate MMM
data feeds, and uses lift values from attribution and experiments to calibrate
models. 





LIMITATIONS AND CHALLENGES OF MARKETING MIX MODELING

Focus on short-term impact

Marketing Mix Modeling is the calculation of sales and ad spend data. Where this
approach falls short is in providing in-depth details on the ability of various
marketing channels to acquire new customers. 

Requires a large amount of data

MM Models require a large volume of data to provide accurate results. This can
be a challenge for small marketing teams with limited data or for newer
organizations that do not have data collection processes and systems in place.

Absence of measurement standards

Marketing Mix Models handle extensive marketing and sales data volumes. If your
marketing team does not have measurement standards, they will not be able to
understand how these models work and the accuracy of outcomes will be impacted.

Not sufficient insights on “why”

While MMM provides insights on the impact of different marketing initiatives, it
doesn't explain the “why”. In other words, you cannot rely completely on this
approach to get granular insights into which channel generated the highest ROI.

However, Lifestight's automated MM Model offers channel-level visibility into
different campaigns and relative spending. Based on the results, it is easier to
analyze why a certain campaign performed well for subsequent budget allocation.






FINAL THOUGHTS

In summary, marketing mix modeling algorithms are powerful for optimizing
resources, making data-driven decisions for budget allocation, and predicting
KPIs and future performance. Going forward, operationalizing MMM within your
brand will be an important modern marketing skill - Book a demo with our experts
now!



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