iPhone 6 vs Samsung Galaxy

With Apple’s slogan for the new iPhone being “Bigger than Bigger” I thought I’d do some fact finding around that claim. Full disclosure: I’ve had a Samsung Galaxy SIII for about 2 years. Aside from the marketing magic being done by Apple, I just don’t understand how a phone with 2 year old technology is being touted as the next big thing. I’m open to logical explanations which is the main reason I put this list together. What is it about the iPhone that makes people totally ignore superior technology and choose the iPhone instead?

So here’s the list of features comparing the iPhone 6 and 6 plus to the Samsung Note and Galaxy series’. What exactly is the new iPhone bigger than???

Samsung vs iPhone

Based on the features list, the iPhone 6 and iPhone 6 Plus not only have smaller screens than the latest Samsung products, the screen on the iPhone 6Plus has a smaller screen than the Galaxy Note 3, and the iPhone 6 has a smaller screen than the Galaxy S3. The S3! from 2012! Granted, the resolution is very slightly better than that of the S3, but it’s not even close to that of the S4, let alone the S5.

Oh, and I almost forgot about Apple Pay. Near-Field Communication (NFC) has been on the Samsung Galaxy devices since the S3 (again, in 2012). Adoption is low simply because it’s a gimmicky feature that is harder to use than simply swiping a credit card. If Apple can garner better adoption, that would be fantastic. However, this is not a “game changer” for businesses or credit card companies.

Pivot Tables in Qlik Sense

Qlik released their latest data discovery and visualization tool, Qlik Sense, last week, and I’ve been putting it through the paces. Overall, I’m impressed. Qlik Sense competes directly with Tableau and I believe is the better of the two tools.

QlikSense Charts

The most glaring omission I see so far is the inability to create a pivot table (aka: crosstab). I don’t know if it’s on principle that Qlik doesn’t believe you should be creating pivot tables on a BI dashboard, but it’s a feature that I think must be added. There are probably some visualizations that are more appropriate than pivot tables, but the inability to toss a quick summary below one of my charts is just maddening. Of course you can still create a regular table, but each permutation of the data is then repeated as a new row.

That said, I’m writing this post both as a plea for Qlik to add pivot tables to Sense and as a call for hacks. Have you found any tricks for creating a pivot table within Qlik Sense? If so, please let us know! I’ll also post updates on this post as I receive them.

Taking an Average of Averages – Why it’s Wrong

I frequently create reports that show the average (mean) value of a metric based on any number of dimensions. For example, I might report the average cost of a product by state. Usually, I include the overall average cost at the bottom of the report, and one of the first things people do to “check” my work is to highlight the entire row of average costs, to have Excel show them the average of the entire group. These numbers rarely match because taking an average of averages is wrong.


The reason an average of averages is wrong is that it doesn’t take into account how many units went into each average. For example: Let’s say we only have orders in 2 states, New York and Pennsylvania. The average cost of the product in NY is $100, and the average cost in PA is $50. Now…let’s say order volume in NY is 10 orders, and volume in PA is 2 orders. Taking the average of the two averages would give us $75 as the overall average cost. (($100+$50 = $150)/2) = $75 However, if we take volume into account, as we should, the average is $91.67. 10 NY orders at $100 = $1,000 + 2 PA orders at $50 = $100, for a total cost of $1,100, divided by all 12 orders = $91.67. That’s a difference of over $16! Now what if we had 100,000 vs 20,000 orders and we were trying to budget or plan for next year? Instead of reporting the actual total costs of $11,400,000, we would inaccurately report that our total costs are expected to be around $9 Million. Finding out late next year that you miscalculated by $2.4 Million might be considered a big deal…

If you must calculate an average of these averages, and you have volume information, there’s a handy Excel function called sumproduct. SumProduct is a function that takes two arrays (vectors) and multiplies them together, line by line, before adding up the total. In the example above, assuming our state is in column A, Volume is in Column B, and Avg Cost in Column C, we’d use the formula =sumproduct(B2:B3,C2:C3). This takes B2*C2 + B3*C3, giving us the total of $1,100, as we had calculated previously. If we take that formula a step further, to divide by the total number of orders, we can get the actual average cost per order. =sumproduct(B2:B3,C2:C3)/sum(B2:B3)

So, the next time you need to get the average of values that have already been averaged, make sure you take the volume into account.

How to Create a Simple Graph With R

In the process of doing an ROI analysis, I wanted a simple area chart showing the negative burn down of our costs while the project was in development, through to the positive savings of the project after completion. Excel didn’t do a great job for me, so I thought I’d give R a try.

As you can see in the image below, Excel put the x axis right through the center of the chart and since I had to use a column chart, it’s more blocky/chunky than I liked. With R and ggplot2, I was able to smooth out those lines and put the x axis in the right place. I also had a little more control over the labels on the x axis, so I could show the intervals at 12/24/… months instead of 1/13/….. Of course it’s super-easy to create charts in Excel, but with a little extra effort in R, you can have a much better final product.

The code should be fairly well commented, but here’s the general idea:

  1. Install ggplot2 and scales to build the chart and format it with dollar signs.
  2. Load data from a csv file. Ours simply contains months as numbers, and the total savings.
  3. Interpolate our 60 months into 1000 separate values to reduce the choppines.
  4. Add a valence column to indicate which values are positive or negative.
  5. Plot the chart.

Area Chart in R vs Bar Chart in Excel

Download the spreadsheets here:
ROI_Calculator (For coming up with the necessary numbers.)
SampleROI.csv (Easy format for importing into R.)

### Begin R Code ###
# If you haven't already, install the packages below.
library(ggplot2) # ggplot2 for creating the charts
library(scales) # scales for the dollar formatting of the axis.

#Load the csv file containing the values:
SampleROI <- read.csv("C:/Users/convalytics/Documents/R/SampleROI.csv") # Make sure to use your own path *** # Interpolate the data into 1000 separate values. (to smooth out the choppiness of having only a few values) interp <- approx(SampleROI$Month, SampleROI$TotalCostVsSavings, n=1000) #Rebuild the data frame with the interpolated values. roi <- data.frame(Month=interp$x, Savings=interp$y) #Add a "valence" column to indicate positive vs negative values. #Essentially selects the not-yet-existing "valence" column on a # subset of the positive or negative values in the "Savings" column, # and inserts a value of "pos" or "neg" accordingly. roi$valence[roi$Savings >= 0] <- "pos" roi$valence[roi$Savings < 0] <- "neg" # Plot the chart ggplot(roi, aes(x=Month, y=Savings, width=1)) + geom_area(aes(fill=valence, alpha=.8,stat="identity")) + scale_x_continuous(breaks=seq(0,60,12),expand=c(0,0)) + scale_y_continuous(breaks=seq(-4000000,6000000,1000000),labels=dollar) + scale_fill_manual(values=c("darkred","darkgreen")) + labs(title="Sample ROI",x="Months from Project Start",y="Running Cost vs. Savings") + theme(plot.title=element_text(face="bold")) # R Code by Jason Green : Convalytics.com # ### End R Code ###

I hope this makes for a good example that you can use to tweak for your own uses. If you have any questions or suggestions, please leave us a comment!

Outlandish Measurements

So I’ve been reading “How to Measure Anything” by Douglas W. Hubbard and “Predictive Analytics” by Eric Siegel, and decided to create a post highlighting things that are “impossible,” or at least difficult, to measure. Then, I’ll take on the task of finding ways to measure them. Not everything has to be business-related either. Maybe we can improve the world by measuring something that hasn’t been measured before?

I’ve listed a few ideas below, and was hoping we could use this as a list to challenge the concept that you can measure anything. I believe it’s true, but let’s put it to the test.

Here are the rules:

  • Try to be as outlandish as possible.
  • The thing being measured must provide some benefit by being measured.

That’s it!

My ideas:

  1. Employee Satisfaction: Are employees content, or are they looking to leave?
  2. Management effectiveness: What is the manager’s contribution to the succeess of a department?
  3. What locations on Earth would benefit most from fresh water?
  4. What country will be next to have a banking crisis?

Those are just a few of my initial ideas. Please add your comments and suggestions below. I’ll then create a new post focusing on one of these suggestions and we’ll try to figure out how to measure it.

Is Google Analytics a Waste of Time?

Yes, if you only look at the data and don’t act.

  • Who cares if your bounce rate is 70% or 30%?
  • Who cares if you have 5,000 visitors per month?
  • Who cares if 30% of your visits come from California?

None of that matters if you just look at it and say, “Yay! I have Google Analytics on my site!” Having Google Analytics alone will not help your site in any way. Knowing that you have 100, 1000, or 1-Million visitors per month, doesn’t help you.

For each metric in Google Analytics, you should look at your site over time and note any changes.

  • Is the metric trending up? Down? -> What does that mean?
  • Are there peaks or valleys in the graph? -> Why?

Guide to Google Analytics Actionability

Here is a short checklist to help you determine what to do with your Google Analytics data to improve the performance of your website:

  • Look at the Reports. – Does anything stand out? (Peaks/Valleys/Trends)
  • Ask Questions. – Why? What changed? Outside influence?
  • What do you WANT the data to show? – Do you want an increase or decrease?
  • What can you DO to alter your desired metric? – Design Change? Ad Copy Change?
  • Track it! – Track the results of your efforts. Did they have the desired effect?
  • Repeat with each report or metric you’d like to see change.

The key takeaway of this post is that you (we) all need to stop just looking at our analytics reports. Passively watching your stats go up and down is merely entertainment. We need to ask tough questions and make the necessary changes to improve the performance of our sites.

Good luck!

> If you would rather just look at your reports, we’d be happy to help you with the actionability part. Contact us at BusinessHut.com for our Google Analytics Consulting services.

Google Analytics Referrer Spam

If you’ve launched a new website recently and were excited to see referrals from golbnet or forexmarket, you have been spammed. This is a tactic used by spammers to get webmasters, curious to research their referrers, to visit the desired website. Also referred to as log spam or referrer bombing. It’s not necessarily malicious, but it’s definitely annoying.
Referring SitesIt’s funny that people are doing this now, because I had a chat about how someone could do this with my co-worker, Taylor Pratt, while working at LunaMetrics in 2007.

Your best option is to simply ignore these referrers. Do not visit the websites. They’re spam, so they don’t deserve your business, but there’s also the chance that you’ll wind up on a site filled with viruses and other malware. If you don’t want the referrals to show up in your Google Analytics account, you also have a few options for removing them.

How to get rid of referral spam:

1. I always recommend keeping at least one GA account with no filters. Make sure you have one profile that will show these referrals, just in case there’s a problem as you create new filters. (You always want to have access to your raw data.) If you don’t already have a separate profile, create a new Google Analytics profile and start anew.
2. On what will now be your “good” profile, you can create a few filters to eliminate the golbnet and forexmarket referrer spam entries.
Create an “include” filter that only includes your domain name. If someone uses your Google Analytics account ID on another domain, this will prevent them from showing up in your analytics.

  • Filter Type: Custom > INCLUDE
  • Filter Field: hostname
  • Filter Pattern: yourdomain\.com
  • *The filter pattern is RegEx, so you should escape the period with a backslash.
  • Case Sensitive: No

Hostname Include Filter

The above method is probably the easiest way to solve the problem, but there are still loopholes. The “perpetrators” could actually be visiting your site through some automated means. If they do it this way, our hostname filter won’t have any effect. Instead, we’ll have to eliminate any referrals from the suspected spammers. *If you find more spam domains, please leave a comment below. I’ll keep this list updated for anyone that wants to cover all bases.

For now, we’ll create custom filters to eliminate any Referral Source (aka: Campaign Source) with text that matches our spammers:

Current Referral Spammer List

  • golbnet
  • forexmarket
  • ForexTradingStrategies
  • acessa.me
  • is.gd/UnlimitedWebHosting
  • is.gd/ForexTrading
  • tinyurl.com/ForexTradingSystems
  • tinyurl.com/MakeMoneyWithYourWebsite

*There are many variations of the golbnet spam, so capturing any referral containing “golbnet” is necessary. However, if you’re actually in the forex market, eliminating any referrer with “forexmarket” in their URL might be overzealous. You’ll need to tweak these values for your individual situation. Luckily, there’s a link to learn more about Regular Expressions right in the filter creation screen.

Ok, onto the filters:

  • Filter Type: Custom > EXCLUDE
  • Filter Field: Campaign Source
  • Filter Pattern: golbnet
  • Case Sensitive: No

*This will eliminate any referrer with the text “golbnet” anywhere in the referring URL.
To exclude other referrers, such as forexmarket, you could create another filter, OR you could simply add a “pipe” which acts as an “OR” operator.
(eg. Filter Pattern: golbnet|forexmarket|anythingelse )
*You can get the pipe by pressing Shift and Backspace.
Referrer Exclude Filter

That should eliminate these spam referrers from your Google Analytics reports. Remember, the most important thing is that you don’t visit these sites. If you have any questions, or additional solutions, please leave us a comment below. Also, if you have any additional spam referrals to report, please leave them below and we’ll add them to the list.

For more information, we’ve also republished this post on our technology website, BusinessHut.

What is My Conversion Rate?

If you’re just getting started with internet marketing, your first question might be, “What is a conversion rate?” Put simply, your conversion rate is the percentage of visitors who complete your desired action.

If your goal is for your visitors to buy your product, a conversion would occur once that has happened. If you had 100 visitors that month, and 1 person bought your product, your conversion rate would be 1%. (The formula for conversion rate is: Goals Completed divided by Total Visitors = Conversion Rate.)

Conversion Rate

How Do I Track Visitors and Goal Completions?

Now that you know what conversion rate is, you might be asking how to get these numbers to plug into the formula. This is the part where you go to www.google.com/analytics and get your Google Analytics account set up.

Google Analytics is a fantastic free service that allows you to track many statistics about the visitors to your site. Just follow the instructions provided by Google, and get the snippet of Javascript onto your web pages. (all of them) Google updates the reports every few hours, so be patient if you don’t see data right away.

Google Analytics also allows you to define and track goals, such as when a visitor goes to a specific page, or clicks a button. Try to set up a few goals with the instructions given by Google, but you can also expect a more detailed explanation soon.

How Do I Improve My Conversion Rate?

That’s the heart of what this site is about and we’ll cover it in detail soon. For now, get Google Analytics on your website and set up some goals. Spend some time understanding those numbers, and when you’re ready to come back, we’ll be here with a new post to help you along.

Welcome to Convalytics.com

Convalytics.com was originally created to focus on the analytics and statistics behind marketing conversions.  I’d like to take that a step further and devote this website to the demonstration and discussion of all types of analytics.

I will be writing about data visualization, the latest programming languages, and the latest business intelligence applications. Specifically, QlikView, Tableau, and Spotfire, and I’m sure there will be talk of Excel.

We welcome all comments and suggestions, so please, fire away…