“Data, Data, Everywhere, Part 1…Data, Data, Everywhere, Part 2…New Media – New Data, New Opportunities, New Dangers, Part 1…New Media – New Data, New Opportunities, New Dangers, Part 2…”
Lesson 7: New Media – New Data, New Opportunities, New Dangers, Part 1
Lesson 7: New Media – New Data, New Opportunities, New Dangers, Part 2
Lesson 6: Data, Data, Everywhere, Part 1
What we’re going to talk about in this lesson is getting to know some very important data sources.
As an analyst, can find some go-to sites for information and data.
We’ll talk about how marketers seek data to better know customers and motivations behind that.
We’ll follow that up with a discussion of why the growth of online data may be an answer to that.
Well, the rise of quantitative data available online could be an answer.
As we’ll see, there are tremendous stores of data out there, and it’s growing every day.
Data that you’ll find really comes in one of two ways.
Now, raw data is typically the original source of data.
It’s much easier to be served up data that has been combed through and metrics have been produced from some of the raw data.
What that then requires of the raw data is some additional degree of processing on your part, the analyst.
Examples of this could include unstructured data, or user-generated contents, things that are out there, that are very valuable certainly.
Processed data, on the other hand, is very smooth, very structured data.
Processing that you may do from processed data would include merging different data sets together, subsetting from some collection of data, or transforming that data in some way.
Doing some things that actually enrich the data that you already have.
Typically, there are well-understood standards for processing and the way that data needs to be processed, to produce analysis ready tables.
Examples of this can include Google Analytics, or really output from almost any sort of reporting program, or reporting tool that is taking raw data in, moving it around, doing some of that merging, subletting, or transforming, even that you may want to do with processed data, and producing metrics ready for insight.
Knowing that targeting is so important that there are companies out there, Google is one of them certainly and that are packaging data and selling them out to brands.
When they do this, there are several different cuts of the data they’ll provide.
If you think of a big segment of consumers a brand may say, we only want to know women between the ages of 25 and 45.
That’s a demographic cut and something that a data vendor is able to do.
Take the big set of all the data they have, and then cut it down to just the demographic that matches the brand’s needs.
Next, comes an even finer view of that data and its taking demo a step further and saying layering in interests.
With each of these steps, each of these cuts of data, you’re getting finer and finer and finer, in your understanding and knowledge of that consumer.
You’re also then, from a data vendor perspective, paying more and more and more for that really valuable data.
Lesson 6: Data, Data, Everywhere, Part 2
Not all the data that is available to brands and certainly not to us as marketing analyst are paid for.
The US Census Bureau site, the Bureau of Economic Analysis site, and the Bureau of Labor Statistics site all provide really valuable information about consumers here and the population here in the US. Data.gov as well as the Center for Disease Control and Prevention also provides additional views of consumers here in the US. Undated, the site there provides information for consumers and people in the world outside of the US and gives much more of a worldly view and worldly set of data for analysis.
In terms of processed data that I like, Gap minder World is a fantastic site full of trends and other information that is very valuable, as well as the World Resource Institute.
Then the American Fact Finder takes that raw data, processes it a bit and serves up something that is- that is very valuable in the form of good, solid metrics.
Visual data is another source of information and there are some great sites out there that provide either graphic visualizations, charts or graphs that you can use.
Good Transparency, Visual Complexity, Marketing Charts, all of these sites provide great visual data that you can use in presentations to pull insights from right away and to use in other forms.
The trick is for you as an analyst to get comfortable with what sites give you the data that you need to analyze your industry, your company or your business venture, whatever it is that you want to- you’re working with.
Very sophisticated techniques for sure but once you master tapping into an API, say Twitter or Google maps, you have a tremendous amount, a very rich- rich data available to you.
So if having sites that are designed to collect information for you to use is not enough mastering these techniques really opens up the whole world of the web to you and turns everything into- into a data source.
There is such great information out there and data available to us but when we are collecting it we have to be clear to be collecting good, solid, clean data.
The value of the analysis that we produce at the end of our process is only going to be as good as the data we put into it.
So if we are collecting voice of customer data and we’re using a survey it’s important for us to realize that that questionnaire can’t be leading towards a consumer.
Data Data Everywhere, what did we talk about? We saw that marketers seek data to better understand customers, that targeting is king for brands and the use of data to do that.
Getting an understand from a company’s view of how they can use data can help you as an analyst as you’re conducting your analysis.
Lesson 7: New Media – New Data, New Opportunities, New Dangers, Part 1
Lesson 7, new media, new data, new opportunities, new dangers.
The world of new media has certainly opened us to a whole host of new and interesting and robust data sources.
Vast stores of data and information out there for us as analysts.
We’ll see how brands can answer critical questions using that data.
Then we’ll also see how deceit and some poor data quality problems have plagued digital analytics.
The zero moment of truth, that time between a consumer receiving a trigger or knowing that need to buy a product to the time that they actually make that decision is a vital, vital series of time for brands.
Getting to know what consumers are thinking in that period of time.
The data sources that we can use then to get a sense for what’s going on in ZMOT with consumers include web traffic data, web server performance data, web transactional data, usability studies, and user submitted information.
The Consumer Decision Journey produced by McKinsey is a great framework for just that.
At every step along the way of the consumer from the trigger that – when they realize that there is a product need to the initial consideration set of when they first here are the brands that I may be purchasing from, the active evaluation process when they go out and evaluate those different choices to the moment of purchase, the post purchase experience and then hopefully for brands who have become the loyal partner of consumers, a loyalty loop.
Smaller organizations probably more attune to acquisition and acquiring consumers.
Medium organizations may be looking into behaviors and large organizations taking a much more sophisticated view into outcomes with consumers.
Your objectives into those consumers and then that again will feed some of the metrics and the decisions of data that you’re putting into your tools.
Lesson 7: New Media – New Data, New Opportunities, New Dangers, Part 2
Then interpretation bias, basically meaning that, as we’re walking into our analysis, we’re not going in with some preconceived notion that we’re looking to then substantiate with data, rather we’re walking in with possibly some hypothesis on where we might go, but open to whatever the data is telling us.
These are not the only dangers that we need to be aware of as we are analyzing digital data.
Not true senses of that actual measure, so it makes you then need to be suspicious, and need to question some of the data that you are collecting, never look at it and just collect it on its face, because there are operations such as this going on.
The tweet was taken down after only 2,000 of the fans had retweeted, which calls into question were those four million actually four million people or had that number been boosted in some way? Rita just represents one of those stories;there are several stories like this, of people either being duped, or willfully entering into some kind of data, data manipulation.
Which for us as analysts, again, in this new world, makes it incumbent on us to be suspicious of the data that we’re collecting, and not to just take that quality as a given.
You own that, and you can be pretty comfortable that the quality of that data is good.
Now as you start with paid, as I said, you can be pretty clear with the quality of that data.
As you move out towards earned, you’re going to be less and less in control of that data and therefore, as a result, less and less clear of the quality of that data.
If you are very concerned about data quality, focusing on paid work that you’ve done and the owned properties that you have, will provide certainly the clearest, the most sound set of data, and ultimately the most sound set of recommendations.
Now, earned data, as we’ve spent a lot of time talking about in this lesson, is very, very important, the things that go on in social media are rich and robust and very valuable for you as an analyst.
In no way do you need to shy away from data you collect through earned sources, but when you do it, it’s important to be aware of the different pitfalls that may be accompanying that data.
By jumping in there, collecting data, and making decisions based off that and making them quickly, you are really capturing the value of the web.
Now if you’ve been fooled into utilizing some bad data along the way, it’s one more thing that you’ve learned, and, frankly, in a relative sense, you probably haven’t invested much time.
Silence ] So summing up lesson seven, New Media, New Data, New Opportunities, New Dangers, what did we learn? We saw that the web has created untold measurement opportunities for brands, and that brands can answer critical questions using data, critical to their marketing, critical to which they are as brands.
We saw that data and tools are accessible to anyone anywhere; there are so many free, very powerful tools out there that can be used.
That deceit, poor quality, data quality, continues to plague digital analytics, it’s something that, as an analyst, you just need to be aware of and account for, and can counter by focusing on what is known, again those paid, those owned assets, the marketing activities that you’re taking, those things that you’re very sure of the data quality, and just being a little more suspicious of the earned activity that you’re collecting data on.
