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To put it shortly, it’s all about approaching marketing through the scientific method and using it to solve challenges in marketing. First you develop a hypothesis based on your observations and maybe intuition, let’s say it’s “TV is the best marketing channel to advertise Chocolate bars”.
Then you gather data, sales and marketing data in this case, analyze the data, develop a model of the reality, and test your hypothesis. In the end you will learn something. You can then develop a new hypothesis and continue the cycle. The key idea is to experiment, learn new things, and build on your learnings.
Read more about this from our blog: What is Marketing Science?
With the ever-growing piles of data, marketing science relies more and more on different AI and machine learning methods from Bayesian networks to random forests.
Even with all the data and numbers, it’s still important to keep the business aspects in mind: The best model is the one which best helps improve your bottom line results.
There are no absolute requirements, as data quality is often more essential, but sales and marketing data from past 2-3 years usually does the trick.
The more data you have the better, but the data quality needs to also stay consistent for the models to able to compare and analyze different time periods.
As data quality is in key role, we at Sellforte are committed to lend a helping hand along the way. Client's data can be reviewed during discussions by Sellforte data scientist after a NDA is signed, which will provide answers to all questions regarding sales and marketing data quality.
Read more about this from our blog: What data do I need for Marketing Mix Modeling?
Bayesian inference is a statistical technique rapidly growing in popularity among marketers.
The Bayesian approach is popular due to a handful of reasons. First, it allows inputting prior knowledge into your models in the form of informative priors. This makes it easy to leverage any past analyses and business intuition in your model and allows you to reach rapidly reach credibly.
Second, it allows building hierarchical models which in turn allows very granular models without breaking their consistency. Instead of analyzing your weekly total sales, you can see what’s going on exactly at each specific date, product group, brand, and location.
One of the biggest drawbacks in one-off modeling projects is their discontinuity.
By automating most steps in the modeling process, Sellforte AI:
- Provides always up-to-date information to support the daily decision making
- Learns and improves with each update
- Recommends actions that would drive even more business impact
- Tracks the realized business impact of these recommendations
After setting up the data pipelines and automating the data pulls, the process requires minimal work from the client. The onboarding has been designed to ensure as smooth and effortless use of the software as possible after the handover.
In case you have a media agency, the continuous updates don’t require any work from your side.
Our solution differs from other services by a combination of features:
- Continuous updates
- Objectivity
- Granularity of the insights
- Measuring both offline and online marketing activities with common metrics
- Attributing sales and margin uplifts to media and promotion respectively
Certain options may include some of these features, but only Sellforte’s solution ticks all the boxes.
Go ahead and go through related Google Research Papers:
Challenges and Opportunities in Media Mix Modeling
A Hierarchical Bayesian Approach to Improve Media Mix Models Using Category Data
Geo-level Bayesian Hierarchical Media Mix Modeling
Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects
