So happy to be your instructor for marketing analytics in practice.
Marketing analytics, numbers are always – have always been things that I’m very passionate about and I’m looking forward to translating some of that passion onto you in this course.
So let’s talk a little bit about the objectives that we have for marketing analytics and practice.
This course, through it you’re going to gain an understanding of a step by step approach to data analytics.
You also gain a familiarity with data collection techniques particularly those that are used to acquire some of the most important and valuable data that we find out on the web today.
You’ll earn hands on working knowledge of data management and analysis methods that are used by marketers today.
One is a great book by Eric Peterson which will lay down a great foundational understanding of marketing analytics.
Then we’re going to supplement that with various blog postings and online, contemporary articles to give you real time, really contemporary research that’s done in the field of marketing analytics.
Important things for you to understand but things that we’re not going to address in this course that you can do on your own time outside of the course and the lectures.
Then there won’t be any accreditation in a marketing analytics tool at the end of this course but what there will be with this course is, is a marketer’s approach to marketing analytics with a heavy emphasis on digital data.
Then as I’ve said, hands on demo of how marketers approach all the – from soup to nuts process of marketing analytics.
From the identification of data that they need to the selection of that data, its collection, analysis, and then finally the presentation.
It’s really going to be all you need to know as a marketing analyst to clearly and effectively demonstrate marketing results to stakeholders.
Module 1 is – includes this introduction and then also the first lesson following the map which gives a road map, the marketing analytics process which is a step by step procedure that we’ll be teaching and learning about as we go from – through our analysis journey.
Lesson 3 will be all about collecting unstructured data.
Some really important data sources that are out there today for analysis.
Module 3, lesson 4, begins with a look at structured data and how can – how we collect and utilize that data.
So taking those data that we have and getting them cleaned up into a tidy format so that we’re ensuring that our analysis is air free and the data we have is ready to be manipulated.
Now as you approach this course, as you think about marketing analytics in practice.
You’ll need to be thinking as someone who can bridge that data world and the marketing world.
So you’re going to have to play the role of that person that’s the owner of data and is connecting with that strategy side of your brain.
The last role is what we’ll call data designer.
As you will see, the skills that we’re going to pick up along the way in this course will give you skills and confidence in each of these areas.
Lesson 1: Following the MAP, Part 1
We’ll see how collecting the right data will unlock insights.
The data you will cast out and decide that you don’t need.
Any good insight, any good analysis, is going to involve some pain.
As you go through the process and conduct analysis, you are learning along the way.
You’re finding what data you’re comfortable with and is working for you.
You’re identifying the analysis techniques that lead to those really powerful tight insights for you and then you’re learning as you report things out what your audience likes, what they respond to, what they don’t.
There are a couple things that you’re going to do as you start your analysis with your thorough planning.
First thin is establishing a campaign, brand, company, clear singular objective for your analysis.
You can’t try to do multiple different objectives or serve multiple different objectives with your analysis.
Really fight for this up front to have your executive sponsor, your stakeholders, whoever it is, really get tight on what that singular objective is so that you can then go out and be successful in your analysis there.
Trying to take on too many objectives is going to scatter your analysis and leave you unsuccessful.
Second step is- that you’ll do during this phase is defining the key questions that you’re going to be asking of your data.
A very, very important step, again, as this is going to dictate what data you need to answer those questions and then ultimately the sources.
Next, identifying the type of analyses you’ll be conducting and then the data you’ll need.
Again, you’ve got a question, here’s the analysis I’ll do.
What that leads to is the data that I’m going to need and uncovering that or identifying that in the planning phase is critically important.
Here’s the data I’ll use and then the source that I’ll be using.
First thing you are going to do is locate the sources that require data identified in the planning step.
Now you’re going to go out, find those, and then figure out how you can pull the data out of those sources.
To do that, the second thing you’ll do during this collect phase is utilize data mining techniques and other kind of tactics to pull that data out and get it into a useable form for you.
Third thing is select some data management system that you can use to house that data.
What you’ll need to do as an analyst when you have your data is make that decision on what program gives me the right balance of those two characteristics.
Then the last thing that you want to make sure you’re doing during that collect phase is ensuring that bias in your data is not there.
Right? You’ve stripped out all the biases so that you have good, solid data that will lead to good, solid analysis.
If the data that you’re popping into your data management system is poisoned with bias, the analysis that you produce is going to be poisoned just as well.
The analysis will only be as good as the data you’ve collected.
Lesson 1: Following the MAP, Part 2
Here the first thing you’re going to do is produce tidy analysis-ready data set that is ready for you to start digging into and conducting some analysis on.
We’ll talk a lot later in the course about how you actually do that, but having that clean, well-organized data for your analysis will make that process much more efficient and effective.
Second thing you want to do is proactively address data quality issues.
Look, there are always going to be quality issues about your data.
We are talking- indeed talking about marketing and not academia, so you have carry that attitude in that I might have some data that is just directional, but I have enough marketing knowhow and experience that even with that directional data I can make some kind of decision.
The third thing that you’ll do in this step is perform those analysis techniques that lead to the conclusions and we’ll talk about a number of different techniques.
Each of them have a varying level of depth of analysis and then they therefore produce varying levels of deep insight.
Sometimes a simple analysis with a simple insight is all you need.
Other times you’ll want to go a little deeper and we’re talk about how those different analysis techniques produce that depth of insight.
The fourth- last thing that you’ll do in this analysis phase is then force yourself to really compress the story that you’re starting to form into a really tight digestible little packet.
That’s another term that’s thrown around quite a bit, but if you can’t compress the story that is coming out of your analysis into those 60 seconds, you frankly don’t have a story yet.
Work hard to compress all those learning’s, all those insights that you’re collecting into a nice tight little story.
Be very concise on what it is you are seeing in the data and what then that tell you the organization need to do as a result.
Third is following some simple rules of design to visualize insights with impact.
One is understanding what is your audience is passionate about and then crafting your analysis to fit into that area.
The second half of that are you being passionate about the analysis and the insights that you have.
If you can express those insights with passion, it’s going to stick with people.
The right data answers the right kind of questions, which leads to the right insights that we want.
Finally, the fourth thing we saw was that analysis must point to relevant, powerful insights.
Finally, we saw that reporting should tell stories that people are going to remember and the way that they do that is your ability to connect that story to passions that they have and present them in ways that you are- that expresses your passion.
