4 Important Steps for Creating a MAP (Marketing Analysis Process)

We all want to come up with that big idea, actionable insight that will give our company competitive advantage and earn us glory.

But coming up with such an idea or insight is difficult.

To make things worse, an almost impossible way to achieve this is to sit in a room and brainstorm.

That’s why you need these four steps for creating a MAP (Marketing Analysis Process):

  1. Beginning with thorough planning.
  2. Collecting the right data to perform analysis.
  3. Analysis pointing to relevant & powerful insights.
  4. Reports telling stories that people will remember.

These steps are not easy and if you’re an analyst you know how challenging and difficult it can be.

All these steps link to each other, it’s a circular process and you learn along the way.

So let’s dive in!

1. Plan: define success

Establish your campaign’s or company’s clear, singular objective.

Good analysis starts with solid planning.

Defining a singular, clear and quantifiable objective is a good start. Now, ‘singular objective’ is the key here.

You can’t try to tackle different objectives, it will scatter your analysis and leave you without relevant results.

Be up front about this with your executive sponsor, your stakeholders or whoever it is.

Define key questions you will be asking of the data.

This is going to dictate what kind of data you require to answer those questions and then ultimately the sources.

So, getting those key questions right is very important.

Identify the type of analysis you will be conducting.

Identifying the type of analysis you’ll be conducting will also help you identify the type of data you need in your planning phase.

Document it all.

Finally, you need to put all the pieces together.

Document everything, so that you have your objective, your key questions, your analysis technique, types of data and data sources in a single place. This is your MAP!

 

2. Collect: measure what matters

Locate the sources which contain the required data identified in the planning step.

Remember you had this nice laid out document that takes you from your objectives, all the way to your data sources?

Now you’re going to find those sources and figure out how to pull the data.

Utilise data mining tools and techniques necessary to collect the required data.

You need the data mining tools and various techniques/tactics to pull the data out, and transform it into a usable form.

Select a data management system that balances your needs for power and simplicity.

If you think about that, there are so many systems out there, but which ones suit your needs?

The right question to ask here is how to balance the need that you have for the ‘analysis power’ of the program and the simplicity of use?

Usually the analysis power and simplicity of use are inversely proportional.

Something that is super powerful tends to be more complicated to use. Tools that are very simple to use, such as Microsoft Excel, probably don’t have as much horsepower behind them.

When you have your data, you need to decide which program gives you the right balance of power and simplicity.

Ensure the effectiveness of analysis by limiting bias in the data.

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 as well.

The outcome of analysis is as reliable as the data you’ve collected.

 

3. Analyse: monitor and learn

Produce tidy, analysis-ready data-sets to ensure your analysis is error-free.

We’re looking to deliver those powerful, relevant insights that will compel our organisation to change.

That’s why you need well-organised and clean data to make the process much more efficient and effective.

Proactively address data-quality issues and concerns.

There are always going to be data quality issues, nothing is going to be absolutely 100% perfect.

But if I have some data that is just directional, with enough marketing know how and experience I can make some kind of decision.

So, ensure that your data is as clean as possible, but don’t wait around for the “perfect data” to make decisions.

Perform analysis techniques that lead you to draw conclusions from collected data.

Different techniques have varying levels of depth and then they produce varying levels of insights.

Sometimes a simple analysis with a simple insight is all you need. Other times you’ll want to go a little deeper.

Compress your findings into easy-to-understand snippets.

Force yourself to compress the story you’re starting to form into a really tight and digestible little packet.

A great technique that you can employ is to ask yourself, how do I tell this story under 60 seconds?

If you can’t compress the story coming out of your analysis in those 60 seconds, you frankly don’t have a story yet.

 

4. Report: communicate and act

Leverage pre-attentive attributes in visual perception to quickly and effectively communicate your meaning.

This is a mouthful, but a few design tweaks and simple rules allow you to leverage those pre-attentive attributes to really get to your audience’s understanding.

Ensure that recommendations are as clear and concise as possible

Don’t leave room for interpretation or ambiguity.

Be clear and very concise about what you are seeing in the data, and then what is it that the organisation needs to do as a result.

Follow simple rules of design to visualise insights with impact.

This post talks about those design rules and guidelines, if you follow them, then you’re sure to create visuals from which people can pull out meaning right away.

Connect your audience with passion to ensure your story is memorable.

You can start by understanding what your audience is passionate about and then crafting your analysis to fit into that area.

If they are passionate about something, they’re more likely to remember it.

It’s equally important that you express those insights with passion, so that it sticks with people.

 

Over to you

We saw that collecting the right data answers the right kind of questions, which leads to the insights we want.

We saw that the analysis must point out to relevant, powerful insights. It has to be relevant to your audience and it has to be powerful enough to compel them into action.

Then, finally, reporting should tell stories that people are going to remember.

As you build these reports, you’re also building an important base of information for your next planning phase.

Wish you all the best for your analytical journey!

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