Showing posts with label Amazon Top 100 2014. Show all posts
Showing posts with label Amazon Top 100 2014. Show all posts

Monday, 16 October 2023

Kati of Terra Book 1 – Escape from the Drowned Planet

Kati of Terra Book 1 – Escape from the Drowned Planet

Amazon U.S: http://www.amazon.com/gp/product/B00811WVXO

Amazon U.K.: http://www.amazon.co.uk/gp/product/B00811WVXO

Amazon Canada: https://www.amazon.ca/dp/B00811WVXO

Amazon Germany: https://www.amazon.de/dp/B00811WVXO

Amazon Netherlands: https://www.amazon.nl/dp/B00811WVXO

Amazon Italy: https://www.amazon.it/dp/B00811WVXO

Amazon Australia: https://www.amazon.com.au/dp/B00811WVXO

Amazon Japan: https://www.amazon.co.jp/dp/B00811WVXO

Amazon India: https://www.amazon.in/dp/B00811WVXO

Amazon Spain: https://www.amazon.es/dp/B00811WVXO

Amazon France: https://www.amazon.fr/dp/B00811WVXO

Amazon Mexico: https://www.amazon.com.mx/dp/B00811WVXO

Amazon Brazil: https://www.amazon.com.br/dp/B00811WVXO

 

In saving her small son from alien abductors, a 24-year-old Earth woman, Katie, finds herself abducted instead. She awakens from a drug-induced coma on a spaceship, in a room full of children, both human and alien, and two other women, younger than she is. The young women adapt to the situation as best they can, keeping the youngsters calm and entertained. But, when a drugged alien man wearing a uniform is added to the captive cargo, it becomes clear that this is an intergalactic slave operation.

The slave traders implant their captives with “translation nodes” in order to allow communication among various groups. These are living entities, normally docile, merely enhancing certain brain functions, such as language acquisition. However, Katie discovers that she has accidentally received a very special “granda node”, a long-lived node with its own cantankerous personality, including a fondness for criminality and lethal weaponry. Fortunately for Katie, it also values its freedom. With its help, she escapes on a fringe planet, dragging the peace officer along—also at the granda’s suggestion.

She finds herself on a strange world, with a somewhat deranged personality, quite possibly a killer, in her head, and partnered with a man from an advanced civilization who abhors killing. He is a Federation Peace Officer, captured by the slavers while attempting to bring them to justice. His task is complicated by the fact that he has sworn to avoid the taking of sentient life during the performance of his duties. He can and does, however, make vigorous use of non-lethal weaponry. Since, before leaving the ship, Katie had promised to help her co-captives gain their liberty, she and the alien peace officer find that they have a common cause.

But first they must find their way off the primitive planet and get to the Federated Civilization, avoiding the slavers who have been left on the planet to re-capture them. Their flight is complicated by the fact that the planet has had a global warming catastrophe some centuries back – the locals refer to it as the Drowned World. This has forced the inhabitants to revert to a pre-industrial state of development; however, they are a wily and resourceful people, mostly helpful, but they can also be dangerous.

Kati (to mark her escape, she adopts a slight name change) and Mikal seek a Federation beacon, which had been hidden on this planet ages ago, to aid in situations such as this, (in accord with a longstanding Federation policy for fringe worlds). They must embark on an arduous trek across two continents and an ocean, seeking the temple that holds the beacon. They travel on foot, by cart, by riverboat, by tall sailing ship, and on pack animals, always pursued by the dangerous slavers.

They must rely on their wits, guile, charm and acting abilities to avoid recapture, while their chasers have advanced technology and ruthlessness on their side. Fortunately, they are able to make many friends who help them along the way, and their quest becomes a series of adventures, both frightening and funny, and involving a cast of engaging characters.

To complicate matters, Kati finds herself falling in love with Mikal, the strange, handsome and amusing alien. He seems to be reciprocating, though they both struggle against an untimely romantic entanglement.

Will Kati and Mikal escape from the Drowned Planet? Can they ultimately bring the slavers to justice, as Mikal has sworn to do? Can they free the remaining captives of the slavers, as Kati has promised to do? Read this book and the rest of the series to find out all.

At about 200,000 words (equivalent to a paperback of about 400 pages), the book is an excellent value.

Friday, 5 February 2016

Part 2 of a Review of “Marketing Analytics – A Practical Guide to Real Marketing Science” (by Mike Grigsby Kogan)



Part 2 of a Review of “Marketing Analytics – A Practical Guide to Real Marketing Science” (by Mike Grigsby Kogan)

http://www.amazon.com/Marketing-Analytics-Practical-Guide-Science/dp/074947417

A while back, I got a book from my Skillsoft learning library, with the above title.  As a statistician/analyst at a university, I was curious about how the statistical techniques that I use on a routine basis are applied in the marketing world.  And as someone who is involved in a small publishing venture, I was also curious about the theory and practice of marketing in general, and how it might be used to sell more novels :).  So, I thought I would read the book and do a write-up for the blog, to help fix ideas in my own mind and inform blog readers as well.


Naturally, if the book interests you, you should go to the source.  The Amazon link is given above.  The book sells for about 20 bucks, in both e-book and paperback form.  Though the content gets somewhat technical, given the subject matter, the writer maintains a very readable style in my opinion.


Since the book is fairly long, a proper look at it will take at least two blogs, maybe three.  I previously did a blog on Part One of the book, which was concerned with some elementary statistical ideas, as well as some fundamental concepts and strategies within the marketing world.  What follows is my synopsis of Part Two of the book, in point form.    This section deals with some fairly advanced statistical techniques, in the “predictive analytics” realm, namely:


·         Multiple regression,



·          Logistic regression,



·         Survival analysis,



·         And econometric modelling.


In some cases, I have inserted an example of a given technique, from my own book related research, in italics.

Part Two – Dependent Variable Techniques

Chapter 3 – Modeling Dependent Variable Techniques - the things that drive demand

·         This chapter focuses on techniques that use equations that predict a dependent variable, based on the values of one or more independent variables.  These are generally known as regression techniques or general linear models.  The author gives a decent explanation of this, even mentioning a few subtle nuances, such as the use of dummy variables and price elasticity (there was a substantial technical section on this subject).  But this is a very brief review of a very large subject, so it is difficult to say whether a newcomer to the concepts would be able to really understand it well.

·         Here’s a quick example of a simple binary (two variable) regression relationship, from some book  publishing related data that I dug out of the Amazon website.  For a selection of 18 books on the “Alsobot” of one our books (Kati of Terra Book 1), I analyzed the book summaries with some text analysis software that counts the number of “hard words” in a sample of text.  Then, I did the same for a sample of reviews of each of the books (about 20 reviews for each book) and averaged them.  The graph below shows how the complexity of the book summary is related to  the complexity of the reviews for that book.

 A simple binary (dependent variable and one independent variable)  regression (using Excel) of the complexity of the book summary (called a blurb on the graph axis) and the complexity of the reviews for that book show that they are related, in an approximately linear fashion (the straight line). The graph shows the regression relation in equation form and the strength of the association in “R-square” form.  An R-square close to 1 indicates a very good positive linear relationship between the variables, and R-square close to 0 indicates no linear relationship, and an R-square close to -1 indicates a very good negative linear relationship.  In this case, the R-square is nearly 0.5, which indicates a reasonable strong relationship.

What does it tell you?  Briefly, I interpret it to mean that people read books that are written at the level with which they are comfortable, and that also corresponds to the level at which they generally write (reviews in this case).  More precisely, they read books that have summaries written at that level, but we can reasonably assume that  the summary is probably written at much the same level of complexity as the book.  The book’s summary signals to potential readers how difficult the book is likely to be, in a vocabulary sense.  Then, people respond (perhaps unconsciously) to that cue, and pick a book that corresponds to their vocabulary, and eventually review it with a similarly complex vocabulary.

This is a very simple model, since it only includes two variables and assumes a linear relationship.  Much more complicated models are possible, which could include dozens of variables (assuming a sufficiently rich dataset) as well as non-linear terms, interaction effects and other complexities.  But this simple case gets the idea across.


Chapter 4 – Who is Most Likely to Buy and How do I Target?

·         This continues the focus on methods that predict a dependent variable from one or more independent variables.  In this case, the independent variable is of the binary or yes/no variety, so the method under review is known as logistic regression.  In this case, the equation predicts the odds, or probability that a particular outcome will occur, namely that a person will buy the product or service in question.  Note that other outcome could be of interest, such as clicking a link or signing up on a mailing list.   The author goes into some of the nuances of this method, such as how to interpret logistic regression coefficients, the use of the prediction vs. outcome matrix and lift charts (used to determine which deciles of the population in question are the best prospects).  He also includes a useful sidebar on multicollinearity, when two independent variables are highly correlated with each other.

·         A common use of logistic regression is related to attrition or retention.  Below is an exploratory example, using data that I have collected on the Top 100 Amazon ebooks for 2013 and 2014.  I took the book to be “retained” if it was still in the top 3200 rank by mid-2015.  The logistic regression then tests for variables that are significant in terms of predicting which books are in the retained group.  In this case, I just tested the effect of genre.  Note that this is a rather artificial example - normally a binary variable like this would indicate something like “did or didn’t drop out of school”, “did or didn’t die”, or some similarly stark yes/no result.  But we can imagine a case where a cut-off point in ranking could have that effect - for example a writer who fell below a given ranking might not have their next book accepted for publication.

The output below is from PSPP, an open source knock-off of SPSS.  The output shows that the only “statistically significant” genre effect is for Romance books, which are significantly less likely to be retained in the higher rankings than the reference genre, which was Thriller.  So, this result would imply that Romance writers have a shorter lifespan as an author, if rankings cutoffs are used to determine whether a writer continues to be published.
Through some mathematical calculations (involving exponentials, which “undo” the logistic regression, which is a form of regression analysis based on a logarithmic transformation of the basic regression equation) we can get a more understandable version of the result, namely that the probability of a book being retained in the higher ranking category, by genre is:

Thriller
65%
Lit Fic
66%
Other
54%
Romance
33%
SFF
79%

·  ══════╦══════════╤═════╤════╤═════╤══╤════╤══════╗
·                  B  │S.E.│ Wald│df│Sig.│Exp(B)║
·╠══════╬══════════╪═════╪════╪═════╪══╪════╪══════╣
·║Step 1║DG_LITFIC │  .04│ .49│  .01│ 1│ .94│  1.04║
·      ║DG_OTHER  │ -.44│ .62│  .52│ 1│ .47│   .64║
·      ║DG_ROMANCE│-1.31│ .35│13.84│ 1│ .00│   .27║
·      ║DG_SFF      .70│ .70│  .99│ 1│ .32│  2.02║
·      ║Constant    .60│ .27│ 5.07│ 1│ .02│  1.82║
·╚══════╩══════════╧═════╧════╧═════╧══╧════╧══════╝
·

Chapter 5 – When are Customers Most Likely to Buy?

·         This continues the focus on methods that predict a dependent variable from one or more independent variables. These moves on to a fairly new method, known as survival analysis. Survival analysis, frequently encountered in medical research, can be used in marketing to estimate when customers are most likely to buy, rather than just the yes/no question answered with logistic regression.  Ultimately, one derives survival curves, similar to the life tables of demography.  

One other advantage/complication of survival analysis over logistic regression is its ability to handle “censored data”.  That has nothing to do with risqué pictures, but rather refers to data about respondents who have gone missing e.g. have dropped out of a study, moved to a new unknown address, etc.).  It can also refer to information on subjects who have not yet “converted” at the time that the analysis was done, or the study was cut off.
Note that besides tracking “time to event”, survival analysis also enables the researcher to examine covariates that could impact this measure.  This, of course, is key information.  If you discover that females purchase quicker than males, for example, that would be very useful in how one might market by gender.  So, survival analysis has both descriptive and predictive aspects.

·         I haven’t actually had an opportunity to use this method, so I can’t add anything specific to survival analysis.  One might note another technique that is used to determine why some records fall on one side of a binary divide or the other, which is called decision trees.  It’s more of a “data science” method than a statistical modelling method, though.  The author prefers the latter methods, though many people are now using the decision trees method.  It has the virtue of being quite easy to understand by most people.  However, it can result in too much emphasis being placed on relationships that are actually the result of random chance, given enough variables (though the analyst always has to be mindful of this possibility, regardless of the chosen method of analysis).

Chapter 6 – Modeling Dependent Variable Techniques (With More than One Equation)

·         This chapter goes into econometric modeling, using systems of simultaneous equations, basically supply and demand equations.  It talks about endogenous versus exogenous variables, in other words variables that are within the system (such as the price of the product) versus those outside the system (such as consumer incomes).  Even that is conceptually tricky. The seller doesn't have total control over pricing since incomes still have a huge influence over pricing pricing.  It is a complicated subject, and the author doesn't go into great detail – just enough to drive home the point that an enterprise has to look at the interaction and substitution effects that decisions on one product will have on other products, especially those that consumers consider to be close substitutes.
·         I haven’t had much to do with these methods, not being an economist.

In a later blog, I will go through some of the other statistical techniques that he explains, adding some of my own analytic experience, especially as it pertains to the book publishing domain.  Those methods are mostly of the classification and dimension reduction type (e.g. for market segmentation).

–------------------------------------------------------

And, since this is a book themed blog, here is your chance to buy a book.  This is a travelogue, featuring a statistician and a truck driver, on a long haul trip, taking lumber to Texas and oilfield equipment to Alberta.  So, you get content that alludes to the theme of the blog – statisticians and markets. :).
On the Road with Bronco Billy - A Trucking Journal
Kindle Edition
What follows is an account of a ten day journey through western North America during a working trip, delivering lumber from Edmonton Alberta to Dallas Texas, and returning with oilfield equipment. The writer had the opportunity to accompany a friend who is a professional truck driver, which he eagerly accepted. He works as a statistician for the University of Alberta, and is therefore is generally confined to desk, chair, and computer. The chance to see the world from the cab of a truck, and be immersed in the truck driving culture was intriguing. In early May 1997 they hit the road.

Some time has passed since this journal was written and many things have changed since the late 1990’s. That renders the journey as not just a geographical one, but also a historical account, which I think only increases its interest.

We were fortunate to have an eventful trip - a mechanical breakdown, a near miss from a tornado, and a large-scale flood were among these events. But even without these turns of fate, the drama of the landscape, the close-up view of the trucking lifestyle, and the opportunity to observe the cultural habits of a wide swath of western North America would have been sufficient to fill up an interesting journal.

The travelogue is about 20,000 words, about 60 to 90 minutes of reading, at typical reading speeds.



Wednesday, 30 December 2015

Amazon Top 100 (2013 and 2014) Retention Analysis


As regular readers of this blog know, I have kept data on the Amazon Top 100 list of books, for the years 2013 and 2014 and have written a number of blogs in which I analyzed that data. It will soon be time to update that database with the most popular new books of 2015. But before doing that, I thought it would be interesting to see just how the Top 100 of 2013 and 2014 did during the year 2015. How did their rankings change? How did their review numbers change? Which books held their rankings the best, by such important factors as genre, early ranking, writer sex, writer age, price and so on?
There are several ways to look at such data. The first and most obvious is via descriptive statistical analysis – i.e. just looking at measures such as average rank by category. Beyond that, more advanced techniques, such as logistic regression, can be used to determine the independent effect of each of these categories. That can help to predict the types of books that best hold their rankings and reviews (and therefore, sales) over time.


This blog will focus on the descriptive statistics, by looking at the average rank of these books, during each month in 2015. The graph above is an example. For the 2013 and 2014 Top 100 lists, the average rank by month in 2015 is given by the height of the bars. Higher average ranks are, of course, not desirable. In these graphs, lower numbers are better, just like in golf.
The books in the combined 2013 and 2014 Amazon Top 100 lists fell from an average rank of about 4000 in early 2015 to about 9000 by mid-December. One could fit a functional form to the data, but eye it appears to be quasi-linear, perhaps a gently sloped power law, over the 12 month period.
It is hard to be sure what that represents in terms of reduced sales or income, however, as the relationship between rank and sales is not linear. Furthermore, due to differences in pricing, the relationship between units sold and money earned is not straightforward either.
We can try to estimate the drop in sales via reviews. It turns out that this set of books “earned” about 3.3 reviews per day per book in the early part of 2015 versus about 1.2 per day in the last period. If we assume that a relatively constant percentage of books are reviewed by purchasers, that would indicate that sales of these books declined to about a bit over one-third of their early 2015 total, by the end of 2015. Note that in their “Top 100” year, these books averaged about 9.4 reviews per book per day. So, by the end of 2015, they were probably selling about one-eighth as many books as they were during their initial publishing year. This is shown in the graph below, along with data for each of the two Top 100 years, and best fit exponential decay curves.

At any rate, this particular blog is more interested in comparing how well different categories of books did over time, rather than estimating sales figures (though I might try that in a later blog).


Overall Ranks in 2015, by “Top 100” Year

As you can see, the more recent books from 2014 held their ranks during 2015 better than the books from the 2013 Top 100 list. In both cases, though, the average rank of the books drifted upwards, throughout the year. The 2013 books started 2015 with an average rank of about 6000 at the end of January, and finished at about 12000 in mid-December. The 2014 books started the year at about rank 2000 on average, and ended at about 6000. Remember that these books were in the Top 100 lists in their respective years. However, the Top 100 lists were constructed relative to books published that year, and the ranks in 2015 were against all years, so the declines look worse than they really were.


Ranks in 2015, by Rank Quartile in Top 100 Year

The second set of graphs shows how book ranks changed in 2015, based on the books initial ranking in the Top 100 year. The first category, labelled 1, represents books that were in the first quartile of ranks in their initial year (i.e. the top 25%). The group of books labelled 2 was in the second quartile, and so on.
It is clear that books that were in the top quartile in their publishing year managed to hold their rank the best, and books in the bottom quartile did the worst, in that regard. That's not too surprising. There wasn't much difference in the two middle quartiles, though. So, it appears that the readers don't distinguish between books in the middle ranks all that much.
 
Looking at the data by year, the pattern repeats itself, or at least approximately so. Books that were in Quartile 1 during their publication year held their ranks the best, while those in Quartile 4 did the worst. Quartiles 2 and 3 reversed between the 2013 and 2014 list, however.

Ranks in 2015, by Sex of Writer

The third group of graphs gives book ranks during 2015, by the gender of the writer. It appears that gender didn't make much difference at the start of the year – both female and male writers were averaging about rank 4000. But as the year went on, books by females lost ground in the rankings more quickly than books by males, so that there was a substantial difference by year end.

As we will see later on, much of that is probably a reflection of the genre that the sexes tend to write in. Romances lost their rankings more quickly than other genres, and since women tend to write in the romance genre, their rankings suffered accordingly as 2015 progressed.
In this case, breaking out the data by year did reveal some differences. In the 2013 Top 100 books, there was little difference between males and females, in the ranks by 2015. However, the 2014 Top 100 books indicated an advantage for male writers. With this amount of data, we can't tell whether the male-female difference is real, but short lived, or whether it is a quirk of the datasets.

Ranks in 2015, by Educational Status of Writer

The graph of Rank by Writer Education is a bit counter-intuitive. At the start of the year, books by writers with graduate degrees held their rankings the best, followed by those with some university, then high school, then Bachelor's degree and Unknown. By the end of the year, it was writers with “some university” who held their rankings the best, though.
 

If we collapse these categories into “No degree or unknown status” versus “Has a degree”, things change somewhat. I collapsed those categories in that fashion, on the assumption that writers who weren't keen on disclosing their educational status, probably didn't have university degrees. But that could be wrong.
Using this re-categorization, the degree holders did somewhat better than the non-degree holders, though the difference was not all that great. Basically, they did better in the middle months of the year, but about the same at the beginning and end of the year. It seems fair to say that there is no clear trend evident. Breaking out the data by year (not shown) also shows no clear trend – in 2013 non-degreed writers seemed to do slightly better, while in 2014 the reverse was true.

Looking at the subject that the writer studied and/or worked in (besides writing), we see that the traditional subjects of English/History/Journalism and Law were most successful at holding their ranks through 2015.

Ranks in 2015, by Age Range of Writer

In this case, a clear trend was evident, in favour of older, more established writers. Generally speaking, as the writer was older, the books held their rankings better. This was probably a reflection of the older writers' longer tenure, and thus more established reputation with readers.

The exception was the first age group, which did somewhat better than the second. I should note that the difficulty that writers in the 35-44 age group had in holding their rank was probably related to genre – this tends to be the age group that writes a lot of Romance books, which don't hold their rank as well as other genres.
Looking at the data by year (not shown here) revealed a similar trend in both years, whereby older writers held their ranks better than younger writers.

Ranks in 2015, by Publisher Type

This graph also shows a very clear trend. Books published by Indie writer/publishers started off 2015 with much higher ranks, and lost ground from that point. Books published by the Big 5 publishers (BPH on the graph) did better, though not great. It was books that were published by the smaller traditional publishers that performed best, in terms of holding their ranks and starting off 2015 at a fairly desirable rank.
 
This again was at least partially a reflection of genre, since Indies are largely found in the Romance genre. However, the extra marketing push of traditional publishing might also be playing a role.
Looking at the data by Top 100 year shows that this effect was very similar for both sets of books. For the Indie books in the 2013 dataset, though, we see that the 2015 average ranks have not seen a clear trend during the year – they have more or less stabilized in the 10,000 to 15,000 range, though with a fair bit of variance.

Ranks in 2015, by Publishing Month

This graph was very interesting, though probably no surprise to anyone with experience in the traditional publishing industry. Clearly, you want to be published in the 11th month, November. Those books started off with very good ranks and held their position. Books published in September also did fairly well. But books published in October were clobbered. That appears to be the no-mans-land of publishing, at least in this dataset.
I imagine that the November effect is related to the most popular and established writers being published in that month, timed carefully to benefit from Christmas gift book buying. It would appear that October books are too far from Christmas to hit that sweet spot. As for September, it seems likely that is a “return-to-school” effect. March also seemed to be a good month, perhaps a “nearing-end-of-term” effect.
This result held true for both the 2013 and 2014 Top 100 lists (graph not shown).
 

Ranks in 2015, by Original Price Range

This graph shows how well books held their rank in 2015, by the price range that they were originally published at. Those ranges were Low = under $4, Moderate $4 to $7.99, high $8 and up.
As you can see, the high priced books started 2015 at a lower rank, on average, and held the lower rank better than the other groups. The moderately priced books were next, though by the end of the year there was little difference between them and the high priced books. Low priced books entered the year with the least desirable rankings, and got worse from there.
This effect was also similar for the two years.


Ranks in 2015, by Ebook vs Pbook Price in 2015

During 2015, traditional publishers began increasing book prices, and notably often priced ebooks higher than pbooks (print books). This is an effort to maintain the print book market, and the print book stores that sell those books. Traditional publishers can thereby use their advantage in getting into the big print book stores as a selling point to both readers and writers.
The graph below shows how that worked out, in terms of holding rankings during 2015. One can see that books where the ebook was priced higher than the pbook lost ground in the latter part of 2015, about when that pricing strategy took hold. So, it definitely hurt those books. The other books, where the ebooks were priced lower than the pbooks held their rankings better. The “NA” books are those that were only available as ebooks.

 
The graph with data split out by Top 100 year shows how this effect was more pronounced in the 2014 set of books, so recency seemed to play a role in this. Note the big jump in ranks for the 2014 books whose ebook was priced higher than the pbook, beginning in Sept 2015.


 

Ranks in 2015, by Genre

This graph was also very interesting. It is clear that Romance books had the shortest shelf life. They tended to start 2015 at the highest ranks and lost rank from there. Next were the “Other” books, a mix of hard to categorize fiction and non-fiction. Thriller/Suspense/Crime started off at about the same point at Literary Fiction, but lost more ground as the year progressed. Interestingly, it was Science Fiction and Fantasy that started the year with the lowest ranks and lost the least ground as the year progressed.
The graph with 2013 and 2014 books broken out separately shows that this trend was very similar across both years, though in the 2014 set, Science Fiction held its rank better than Literary Fiction.


To summarize the ability of books to hold their sales rank over time:
  • Newer books did better (2014 publishing vs 2013).
  • Books that were originally better ranked did better.
  • Books by male writers did better, but the effect was fairly small.
  • Books by writers with university degrees did better, but the effect was small.
  • Books by older writers did better.
  • Books by traditional publishers did better.
  • Books published in November did much better, books published in October did much worse.
  • Books that were originally high priced did better.
  • Pricing a book's ebook version higher than its pbook version seemed to hurt its ranking.
  • Romances had the shortest shelf life, literary fiction and Science Fiction the longest.
In later blogs, I intend to look at how reviews held up, and also do some multivariate analysis, to see what the most important predictors of a long shelf life were.

=========================================================
After all these stats, you might want to read some less quantitative. So, try a road trip through North America in an 18 wheeler, with “On the Road with Bronco Billy”:




 
Or even better, try a spaceship and planet-side road trip (escaping from slavers), with our gal Kati of Terra: