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.

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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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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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.

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.

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

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")

Creating daily search volume data from weekly and daily data

Risteski & Davcev (2014) refers to my method for getting Google Trends data for more than 90 days in their recent paper. For those interested, I've outlined the key principle in this post.

This post illustrates a method for combining daily and weekly search data from Google Trends in order to create a daily time series for over a period longer than the 90 days of daily data provided by Google Trends. For the sake of simplicity, I have only included two weeks, but the general principle can easily be extended to longer time series. SVI in the tables refers to Google Trends' search volume index.

For an example of how this can be done using R, look here.

Step 1: Collect daily search data from Google Trends and combine it into one array.

Date	Daily SVI
1.1.2014	72
2.1.2014	96
3.1.2014	16
4.1.2014	70
5.1.2014	61
6.1.2014	97
7.1.2014	44
8.1.2014	32
9.1.2014	8
10.1.2014	13
11.1.2014	67
12.1.2014	9
13.1.2014	63
14.1.2014	91

Step 2: Collect weekly search data over the same time period

Date	Daily SVI	Weekly SVI
1.1.2014	72	20
2.1.2014	96
3.1.2014	16
4.1.2014	70
5.1.2014	61
6.1.2014	97
7.1.2014	44
8.1.2014	32	30
9.1.2014	8
10.1.2014	13
11.1.2014	67
12.1.2014	9
13.1.2014	63
14.1.2014	91

Step 3: Adjust the daily data based on the weekly data

The key here is the adjustment factor. It is the weekly SVI divided by the daily SVI for those dates where there are values for both. For other data points, the last available adjustment factor is applied.

Date	Daily SVI	Weekly SVI	Adjustment factor	Adjusted values
1.1.2014	72	20	0,3	20,0
2.1.2014	96		0,3	26,7
3.1.2014	16		0,3	4,4
4.1.2014	70		0,3	19,4
5.1.2014	61		0,3	16,9
6.1.2014	97		0,3	26,9
7.1.2014	44		0,3	12,2
8.1.2014	32	30	0,9	30,0
9.1.2014	8		0,9	7,5
10.1.2014	13		0,9	12,2
11.1.2014	67		0,9	62,8
12.1.2014	9		0,9	8,4
13.1.2014	63		0,9	59,1
14.1.2014	91		0,9	85,3

Time series plot

As can be seen from the graph below, the inter-week changes remain the same, but is adjusted down or up based on the weekly search volumes. This way, the relative volumes between weeks is unchanged and the data is comparable over periods longer than the 90 days provided by Google Trends.

Data artifacts in daily Google Trends data

An important consideration when merging daily Google Trends data together is that the first and last day of a month sometime are shown as zero in the data. This can create serious inconsistencies in the data, as we cannot make a percentage adjustment to zero values. To account for this, I recommend that the the first and last 30 days from the time series is stripped out before merging the data.

To illustrate the issue, I've copied the daily Google Trends data for the search term "CAC40" below. The value for 1 January 2014 is zero in the upper graph, while it's 77 in the upper graph.

January - February 2014

December 2013 - February 2014

David Leinweber writes more on the topic here.