Before we discuss it in the context of marketing automation, let’s define ALAMO – it is the All-important Leading-edge Analytics-based MIPS mOdel. The acronym MIPS refers to the four-step process in marketing automation: Measurement → Inference → Prediction → Steering.
In a previous article, “The K.I.S.S. Principle in Marketing Analytics“, we listed some of the major benefits of marketing automation to businesses who use it. It has its ups and its downs (which are all good): the ups include increased revenues, leads, conversions, sales, and accuracy of multi-channel attributions; and downs include reduced marketing overhead. Those are outcomes that any CMO would love.
In order to get there, the marketing automation pipeline processes must be put in place. Fortunately, there is an excellent model for such dynamic data-driven application systems (DDDAS – see dddas.org) – that is the MIPS model. MIPS starts with measurement of behavior in the system (e.g., customer metrics), then follows with machine learning and model-building (i.e., inference), which leads to implementation of the model (i.e., predictive analytics and extraction of actionable intelligence), ending with the appropriate action (i.e., “steering” of marketing campaign assets, or prescriptive analytics = taking the right action at the right time for the right customer in the right context).
Data Science occurs in the inference and prediction (“IP”) steps of MIPS – that is where machine learning algorithms are applied to the incoming measurement data, to generate predictive models of customer behavior (e.g., What will they buy? What offer would have highest acceptance rate? What can we cross-sell, or upsell, to this customer?). The “IP” machine learning algorithms may include decision trees, random forests, neural networks, Markov models, Bayesian networks, regression, and association rule learning.
For classical content-based marketing and for inbound marketing (where the customer finds you online and lands on your site), the MIPS model of marketing automation and personalization are essential enablers of marketing success at several levels in the customer journey:
- Brand awareness
- Lead conversion and nurturing
- Customer conversion
- Customer service
- Retention and loyalty
- Passionate subscribers
Being able to target a specific individualized campaign automatically at each of these levels will ultimately improve the ROI from your marketing investments. The good news is that there are synergies and efficiencies of scale across these 7 levels for each of the four steps in the MIPS process, which have many commonalities at all levels – i.e., the need to measure something, the need to infer a model of customer behavior using data science, the need to implement a predictive analytics model, and the need to steer your marketing assets in the right direction for each customer. It can work for your business. Just remember the ALAMO!
The acronym MIPS refers to the four-step process in marketing automation: Measurement → Inference → Prediction → Steering.
For more from Kirk Borne, follow him on Twitter: @kirkdborne
Blog post originally published: January 8, 2015
Advisor to Syntasa | Principal Data Scientist, Executive Advisor, First Data Science Fellow at Booz Allen Hamilton
Dr. Kirk Borne is the Principal Data Scientist, an Executive Advisor, and the first Data Science Fellow at the global technology and consulting firm Booz Allen Hamilton. Before that, Kirk was professor of astrophysics and computational science at George Mason University for 12 years, specifically in the data science program. Prior to that, he spent nearly 20 years supporting data systems activities for NASA space science missions. Kirk has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. He is an elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. Since 2013, he has been listed consistently each year as a top worldwide influencer in Big Data and Data Science on social media.