I’m a big believer in looking at data even when it’s imperfect to see if you can gain insights as some data is better than nothing, but it’s important to be realistic and think of it as an indicator to test rather than a TRUTH to build on. I thought this article did a good job of pointing out several potential flaws that I’ve seen occur in my career. http://www.newyorker.com/tech/elements/how-to-call-bullshit-on-big-data-a-practical-guide

The three legged stool of data science

“Data Science is a three-legged stool that combines business acumen, data wrangling and analytics to create extreme value. Focusing on the hard science skills such as statistical methods is a common mistake when actually, developing the knowledge about a particular business and wrangling the relevant data are often the most important skills to bring to the table.”

In my experience, failure of data science and advanced analytics projects most often is the result of a lack of business understanding or lack of clean data.  These skills are often under valued when searching for data scientists and can result in a diminished or absent ROI on big data implementations and projects.


Christmas Carol as created by an AI

I’m not sure that AIs are really ready to create art that I can appreciate, but there must be a start to it.  Here’s a first attempt of an AI (recurrent neural network) creating a Christmas Carol from an inspirational picture and over 100 hours of music.  Give it a listen and see what you think of it.



Data usage without consumer knowledge

As someone who has used online customer data extensively in multiple roles and at multiple companies, I really believe most companies are respectful in their use of customer data and are attempting to improve the customer’s life. I tend towards sharing my own data with companies that request it that I respect and have a relationship with. Perhaps for me especially that is why it is so difficult to hear that this extremely personal data was being taken without knowledge, consent, or request – and used for purposes undefined.

Ten reasons Data Projects Fail

I found this to be a very blunt and honest warning of why Data Projects can fail. It’s a good (if pessimistic) read for anyone who is running or planning to run a data science project within their company.


Online marketing analytics beyond CTR

I’ve mentioned before that click-through rate (or CTR) isn’t the only method for measuring a marketing campaign. While it’s a useful metric, focusing on it without considering other ways a campaign is performing might not give you a complete, accurate picture of what’s really going on.

In part one of this video, I explained that the goal of the campaign is very important to an understanding of the results because they can impact the measurement drastically. But just as a refresher: Would you use the same measurement for a campaign whose goal was to increase sales and checkouts versus a campaign whose goal was to introduce a product? For the first example, you’d look at the people who were served a goal and determine if their sales and checkouts increased to determine if your ad was successful — whereas when introducing a product, you would see if they explored the product area of your website, searched for the product, discussed the product with a sales representative, or downloaded material about the product. The goal of the campaign is critical to determining overall success.

But there are still more options for evaluating how well a campaign is doing it’s job, and the two I touch on in this video are:

  • A/B testing
  • Marketing Mix Modeling results – Sales impacts in comparison to other campaigns (isolating for other changes to the business)

While multi-billion dollar companies often use marketing mix models — advanced modeling that attempts to isolate changes from a campaign from other impacts to the business, and derive a revenue impact — most companies are content with A/B testing to determine if their marketing is incrementally improving. In A/B testing, you measure two ads (or creatives or other marketing elements) at the same time (sending customers to one or the other randomly) and compare their performance. It is a very useful approach to attempt to determine if a proposed ad is better than the current ad and allows a business to analytically improve their approaches.

In the end, CTR is only one measurement to help measure the success of a campaign and as our industry grows in its intelligence and customization of marketing campaigns, we will need more than just CTR to determine if a campaign is successful or not.

This content was created per request of Multiview and included in their blog posting.  Please see the resulting interview and original post here.

B2B Beat Ep. 3, Part 2: Marketing Analytics Beyond CTR




Evolution of Analytics – BMI


Many people look at work done in the past, find flaws, and are quick to throw away the old in favor of the new. I’m all for evolving our thinking, but I find it difficult to stomach when an analyst or analytics consumer says flat-out, “This measurement is pure BS and doesn’t work at all.” In most cases, there’s a reason why a given measurement received enough acceptance for business to depend on it for decision-making. The measurement may be outdated, and there may be ways to evolve the approach — but it’s important to understand the history before you toss the old measurement away.

Here’s the life of an analyst: You have TONS of data. An avalanche of new measurements. Your first approach is to sort it, and segment the data into categories so you can start to see performance differences in the segments. As your understanding of the data evolves, your measurements also evolve. This is the approach that Adolphe Quetelet took when he created the BMI (Body Mass Index) metric in the 19th century. He had a fair amount of data on height and weight and created a metric that correlated with diseases like type II diabetes. The BMI was proven to be such a useful and predictive indicator that it was factored into life insurance policies post WWII. In the mid-1990s, it became part of the World Health Organization’s approved metrics for obesity, and even popular with members of the general public as their doctors discussed their BMIs with them.

A large part of why BMI was so successful was the ease of measurement. Height and weight became data points easily captured at a doctors visit and were strictly objective measures.

BMI is still widely utilized for trend-setting, population demographic measurements, and in predictive models to determine disease propensity or progression. It performs very well in all of these and will continue to be a worthy measurement for many purposes.

This same principle could hold true with online marketing analytics and click-through rate (CTR). CTR has often been the golden metric for online advertising – but while CTR is a good metric for seeing trends and getting an elementary level of understanding of the data, our application of the metrics must change as our understanding and use of the data does.

The pushback on BMI came when it was considered at an individual level. For statistical samples, BMI can be a great predictor of how many people in the sample will get a disease… but on an individual level, there are so many things that can impact the BMI metric that it might very well be useless for a particular individual.

For example, one person could be an outlier to the model. The general assumption is that BMI is a measurement for obesity, but by purely considering height and weight, it fails in unexpected ways, and for some of the healthiest people, like bodybuilders. Dwayne “The Rock” Johnson has a BMI of 34.3 – ‘obese’ by the BMI model!


Be the hardest (and smartest) worker in the room. #AndHaveBigBalls #TrioForSuccess #SillyWorkoutFacesAreOptional

Should The Rock’s doctor tell him he needs to lose weight? In the same way that you wouldn’t want to hold ALL patients accountable for their BMI in the exact same way, you must not blindly treat all CTRs equally, or you might find out too late that your advertising campaign had the CTR equivalent of bodybuilders – great health, just with different goals.

If, for example, you are running an online campaign with the purpose of branding (where CTR is not as desired as impressions), you may have a stellar campaign with a weak CTR. Should you cut it? “Yes” is the easy answer… but what if I told you sales numbers have increased significantly since the campaign started?

Does this mean CTR is a bad measurement? No, not at all. It is simply one measurement of many, and a very strong one that the industry gravitated to early because of the ease of capture and strong correlation to great campaigns. It is not, however, the only measurement and simply having a low CTR does not necessarily mean that campaigns need to be revamped.

This content was created per request of Multiview and included in their blog posting.  Please see the resulting interview and original post here.

B2B Beat EP. 3: Evolution of Analytics

B2B Beat Ep. 3: Evolution of Analytics