How do remittance companies stack up against TransferWise on Google Trends?

The numbers from Google Trends below show how various companies focusing on international remittances stack up against TransferWise. TransferWise is the flat line, and the other companies' numbers are in relation to TransferWise's. Companies with a large offline foot print such as Western Union and Moneygram are in the lead world wide. Xoom, a remittance company acquired by PayPal in 2015, also do well.

If we zoom in on the UK, the story changes. While Western Union is still the most Googled brand in the remittance industry, the lead shrinks from 13X to 2.2X over TransferWise. Over time, the declining share of search volume of Western Union becomes quite clear. This might explain part of the reason that they are so eager to build their own online currency transfer platform.

World Wide

TransferWise is flat line.

Companies	Search interest relative to TransferWise
western union	13.00
moneygram	4.14
xoom	1.38
transferwise	1.00
post office money	0.47
worldremit	0.39
ria money transfer	0.24
azimo	0.20
transfast	0.17
moneycorp	0.17
caxton fx	0.14
fairfx	0.14
remitly	0.12
currencyfair	0.09
hifx	0.08
transfergo	0.06
worldfirst	0.02
ukforex	0.01
tawipay	0.00

United Kingdom

Companies	Search interest relative to TransferWise
western union	2.19
transferwise	1.00
post office money	0.83
moneygram	0.58
moneycorp	0.41
fairfx	0.33
caxton fx	0.28
azimo	0.22
worldremit	0.22
transfergo	0.13
hifx	0.09
currencyfair	0.06
xoom	0.06
ria money transfer	0.05
ukforex	0.04
worldfirst	0.04
transfast	0.02
remitly	0.00
tawipay	0.00

If we look at the market over a longer period of time, the decline of Western Union's share of search volume becomes clear.

Google Trends bulk download

I put together an API to make it easier for everyone to download data from Google Trends. You can find it here: http://46.101.74.214:8000/getgt.html

Here are a couple of examples of how it can be used:

• http://46.101.74.214:8000/getgt.html?query=amazon,quora,google,transferwise (downloads one file)
• http://46.101.74.214:8000/getgt.html?query1=amazon,quora,google,transferwise?query2=spotify,airbnb,supercell

It generates the download links for Google Trends and to be able to download the files you need to be signed in to Google.

Greeks use Revolut's app to skirt capital controls

Judging from the interest on Google for the prepaid card and payments app from the London based fintech company Revolut, the are experiencing rapid growth at the moment. Interestingly, most of the searches are coming from Athens. 

With capital controls still being in place in Greece, and many Greeks having a hard time getting cash out, it seems like locals have discovered the benefits of having a foreign card. A bit of Googling shows that it is being recommended as a way to skirt restrictions. A novel use of the company's product. Will fintech play an increasing role in the future in times of financial uncertainty?

Download link for Google Trends data

Enter keywords

You need to be signed in to Google for the link to work. You can enter up to five words.

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4:
5:

Once you have downloaded the data, you might want to parse it to get a clean table.

Load file to parse data

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Get Google Trends download link with javascript

The URL_GT function I use to collect Google Trends data was previously implemented with R. To make it easier to download Google Trends data, I created a javascript implementation. Use the code below, or get it from Github.

If you want all data, it's enough to enter a keyword. You can add an array of up to five keywords to compare. To download the data, simply click the link generated.


'use strict';

function URL_GT(keyword, country, region, year, month, length){
  
  var start = "http://www.google.com/trends/trendsReport?hl=en-US&q=";
  var end = "&cmpt=q&content=1&export=1";
  var geo = "";
  var date = "";
  var URL = "";
  var month=1;
  var length=3;

  
  //Geographic restrictions
  if(typeof country!=="undefined") {
    geo="&geo=";
    geo=geo + country;
    if(region!==undefined) geo=geo + "-" + region;
  }
  
  if(typeof keyword==="string"){
  var queries=keyword;
  }
  
  if(typeof keyword==="object"){
  var queries=keyword[0];
    for(var i=1; i < keyword.length; i++){
      queries=queries + "%2C" + keyword[i];
    }
  }
  
  //Dates
  if(typeof year!=="undefined"){
    date="&date="
    date=date + month + "%2F" + year + "%20" + length + "m"
  }
  
  URL = start + queries + geo + date + end;
  URL = URL.replace(" ", "%20");
  return(URL);
}

Revolut jumps ahead of Azimo in search volume and turns off their invite program.

Revolut jumps ahead of Azimo in search volume in January 2016. Quite an impressive increase in interest for Revolut's card and mobile wallet. Was the interest too much for the company? A couple of days ago, they turned off their invite program.

Google Trends för influensasymptom och spridning i Sverige

Hur influensan sprider sig i Sverige. Data från Google Trends.

Sedan 2004

Områden med hög risk för influensaspridning

Trend det senaste året

Revolut jumps in search volume and is now on par with Azimo on Google Trends

Google Trends shows Meerkat was a fad

Google Trends data suggests that the Meerkat video app was a fad.
Also, it never became popular outside of California, New York and London.

Twitter Q3 2015 MAU growth forecast

Twitter just released their Q2 earnings, and with their stagnating user growth in mind, I wanted to take a look at their expected user growth in Q3 given the current trend in Internet search volumes for Twitter.

Past log changes in monthly active users (MAU) has had a strong correlation with log change in average quarterly search volumes. That's what we'll use for the prediction.

Then, let's assume that the current search volume trend continues into Q3 (forecasted figures in red below).

Based on the historical correlation, we can then forecast the change in MAUs from Q2 to Q3.

That should mean an average of 335 million monthly active users in Q3. If we extend the forecast to Q4, that gives us 346 million MAUs.

Twitter has stopped growing in the US, and all the growth in Q2 is from abroad. Are there any markets where search volumes trends are positive? There are stable or slightly decreasing SVI volumes in Germany, Austria, Switzerland, Japan, Australia, and New Zealand.

The trajectory is negative in France, Spain, Italy, Ukraine, Russia, Indonesia, Brazil, South Africa, Canada, and Kazakhstan.

There only places I've found with a positive trend is Portugal, Argentina and South Korea. 

It will be interesting to see how actual search volume interest develops during Q3.

Has Supercell peaked?

Last year's revenue hit new record highs for the Finnish game maker, but the latest data from Google Trends show a worrying picture for their hit game "Clash of Clans". Data from Google Trends show that interest for Clash of Clans peaked in February and has since been on a downward trajectory. The picture looks even worse if we look only at interest in the US.

King Q2 2015 revenue forecast. Is the saga over for King?

Update 4 August 2015

The deterioration in search volume for King's main titles in Q2 2015 wasn't as bad as forecasted, thanks to an uptick in Candy Crush popularity in the last week. The consensus forecast for King's EPS for Q2 from Nasdaq puts EPS at $0.36. Based on the latest SVI data, I would expect King's revenue to be around $517 M. An EPS of $0.36 seems quite low. I would assume it to be close to $0.45.

The mobile game developer behind the Candy Crush Saga, King, had a record year in 2014, with revenue increasing 19% from the year before. But since then, things have changed. From the search volume for their top three titles, Candy Crush, Bubble Witch and Farm Heroes, we can see that none of the other titles have really taken off. What's worse, interest in their top title Candy Crush is going down.

What could this mean for revenues 2015? Revenue is already on a down trend. If we extrapolate out the search volume trend to the end of the year, the number of Google searches will have decreased by 43%.

The end of 2015 is of course half a year away, but if the trend continues, we should see a drop in King's revenue by 50% to $1105 M.

If we look a bit closer in time, ahead for Q2 2015, the same analysis puts revenue at $454 M. for the quarter. If we remove the first quarter 2013, that number goes up to $550, on par with the previous quarter.

The analysis is based on the assumption that Google searches equals general interest which translates into revenue. It's limited by the fact that Candy Crush accounts only for 50% of revenue, but is 95% of the search volume variation measure used here. A drop in new users wouldn't either translate into a direct drop in revenue, as existing users keep playing King's game.

The time of explosive growth looks to be over for King. If you have the analyst's revenue forecast for Q2, leave a note in the comments. How do you think the stock market will react to a continued revenue decline in Q2 2015?

Rovio forecasted revenue 2014 versus actuals

Back in March, I claimed that Rovio's revenue would decline from €153.5 M. to €152 M. The actuals are out, and it seems like I was only off by €4 M. Even better, the model could forecast a change in trend based on Google search data, which is very interesting to see.

The model used was slightly different from the one used for forecasting Supercell's revenue for the year. Previously I have worked with the direct correlation between revenue and search volume. This time, a log change model was instead used, and proved to be effective in this case.

Here's the previous post containing the forecast.

Financial ratio summary

Rovio Entertainment Oy	2010/12	2011/12	2012/12	2013/12	2014/12
Companys turnover (1000 EUR)	5232	75395	152171	153516	148332
Turnover change %	622.10	620.60	101.80	0.90	-3.40
Result of the financial period (1000 EUR)	2600	35356	55615	25898	7964
Operating profit %	56.60	62.10	50.50	22.80	6.70
Company personnel headcount	-	98	311	547	729

Rovio revenue estimate 2014

Is there a correlation between Rovio's revenue and the amount of Google searches for their most popular title Angry Birds? Admittedly, we only have three data points to go on, but they do line up nicely. The upper chart plots the log change in search volume (x-axis) against revenue (y-axis). Based on that correlation, Rovio's revenue should decline somewhat in 2014, to 152 million €.

Supercell revenue 2014 is 1.55 billion €, compared to forecasted 1.7 billion €

How powerful is Google Trends for predicting revenue of Internet compaines? This is just one data point, but my previous prediction for Supercell's 2014 revenue was not far off.

Supercell's revenue for 2014 was 1.55 billion €, compared to my prediction of 1.7 billion €.



The next prediction I have my eye on is for the Apple Watch. Google Trends data suggests that the Apple Watch will sell well below what market analysts expect. While the launch of the Apple Watch did create some buzz on search engines, that quickly died out.

Another mobile games company from Finland is Rovio. If would be interesting to see if the correlation holds up for them as well. It's not looking good.

Apple Watch sales prediction based on Google Trends data

Back in September 2014, I estimated that the unit sales of the Apple Watch will be 2700 000 in the first three months of sales. The number is based on the correlation between Google searches around the announcement for the iPhone and iPad. Later on in October I revised the number down to 400 000 based on low interest for the product.

When compared to the interest in the iPhone and iPad, the Apple Watch is still lagging behind. In fact, the iPod generates more Google Searches than the Apple Watch.

Industry analysts expect Apple to sell between 10-30 million watches in the first year, or 4-7.5 million per quarter. Even if 400 000 is way too low, the low search interest for the watch indicates that sales will be lower than what analysts predict.

Google Trends data is always two days behind, so we will have to wait until Wednesday to see how the Apple Watch launch compares to the iPad and iPhone. So far, it doesn't look great.

More on the methodology

TransferWise's hockey stick growth according to Google Trends

To get a free transfer from TransferWise, use this link.

Supercell revenue estimate 2014

I've previously used Google Trends to estimate revenues, and I will give it another go here for the Finnish gaming company Supercell. I've gathered the combined search volume for Hay Day, Clash of Clans, and Boom Beach.

Based on the search volume, the new game Boom Beach has not yet exceeded the interest in Supercell's first game Hay Day, and is still a long way off from matching the success of Clash of Clans. The trend is however positive. The interest in Clash of Clans has continued increasing.

The total search volume for all three games has increase 330 % as compared to 2013. Based on the past correlation between revenue and search volume, I estimate Supercell's revenue to be 1.7 billions 2014.




The graph above plots the weekly search volume index summed by year against the official yearly revenue figures. The green dot represents the estimate for 2014.

Most googled words 2014

These are the five search terms with the biggest increase in interest during 2014. The new iPhone tops the list, followed by Disney's Frozen, the emerging markets classifieds company OLX, India's Flipkart and Google Drive.

Top 5 search terms with biggest increase 2014 from 321k

Out of these search terms, OLX is has the highest search volume, while the new iPhone was by far the most Googled word out of the list during the month of the launch.

Creating daily search volume data from weekly and daily data using R

In my previous post, I explained the general principle behind using Google Trends' weekly and daily data to create daily time series longer than 90 days. Here, I provide the steps to take in R to achive the same reuslts.


#Start by copying these functions in R. 
#Then run the following code:
#NB! In order for the code to run properly, you will have to specify the download directory of your default browser (downloadDir)

downloadDir="C:/downloads"

url=vector()
filePath=vector()
adjustedWeekly=data.frame()
keyword="google trends"


#Create URLs to daily data
for(i in 1:12){
    url[i]=URL_GT(keyword, year=2013, month=i, length=1)
}

#Download
for(i in 1:length(url)){
    filePath[i]=downloadGT(url[i], downloadDir)
}

dailyData=readGT(filePath)
dailyData=dailyData[order(dailyData$Date),]

#Get weekly data
url=URL_GT(keyword, year=2013, month=1, length=12)
filePath=downloadGT(url, downloadDir)
weeklyData=readGT(filePath)

adjustedDaily=dailyData[1:2]
adjustedDaily=merge(adjustedDaily, weeklyData[1:2], by="Date", all=T)
adjustedDaily[4:5]=NA
names(adjustedDaily)=c("Date", "Daily", "Weekly", "Adjustment_factor", "Adjusted_daily")

#Adjust for date missmatch
for(i in 1:nrow(adjustedDaily)){
    if(is.na(adjustedDaily$Daily[i])) adjustedDaily$Daily[i]=adjustedDaily$Daily[i-1]
}

#Create adjustment factor
adjustedDaily$Adjustment_factor=adjustedDaily$Weekly/adjustedDaily$Daily

#Remove data before first available adjustment factor
start=which(is.finite(adjustedDaily$Adjustment_factor))[1]
stop=nrow(adjustedDaily)
adjustedDaily=adjustedDaily[start:stop,]

#Fill in missing adjustment factors
for(i in 1:nrow(adjustedDaily)){
    if(is.na(adjustedDaily$Adjustment_factor[i])) adjustedDaily$Adjustment_factor[i]=adjustedDaily$Adjustment_factor[i-1]
}

#Calculated adjusted daily values
adjustedDaily$Adjusted_daily=adjustedDaily$Daily*adjustedDaily$Adjustment_factor

#Plot the results
library(ggplot2)
ggplot(adjustedDaily, aes(x=Date, y=Adjusted_daily))+geom_line(col="blue")+ggtitle("SVI for Google Trends")