Here‘s my presentation on where Uber should open next. Too busy for a presentation? Here’s a map I created showing the top cities where Uber will open next:
Made using R packages:ggmap, maps, ggplot2 and edited in Adobe Photoshop
Now that I’ve done both Uber and Ola, maybe I’ll predict where certain banks will open next. Stay tuned for what’s to come.
Ola Cabs—India’s largest taxi service provider—are responsible for my travel to and from basketball practice, friends’ houses and school (when I miss the bus), so it’s only fair that we give Ola due respect. Last time we analyzed where Domino’s pizza should open stores. Let’s now run our recommender algorithm and see where Ola Cabs should (and hopefully, will) start services next.
Here is my presentation detailing my model’s top recommendations for where Ola cabs should open.
If you don’t have the time to go through a 15-slide presentation, here’s the skinny on my model’s recommendations. These are the cities where I predict Ola cabs will open next:
Made using R packages:ggmap, ggplot2, ggmap; Edited using Adobe Photoshop
Next, I’ll be doing the same for Ola’s top competitor—Uber. And of course, I’ll write about how these ‘predictions’ fare next quarter.
I’ve worked with recommender systems to predict where Bajaj Finance and Capital First would open next. You can see those posts here and here.
So what about where Domino’s pizza?—the chain that really started my obsession with recommender systems and business analytics. When I just started my project, I ran a small scale prediction for Domino’s pizza.
Quoting from an earlier blog post “I took store location data for Domino’s Pizza that I’d collected back in 2015 and ran my model. It told me that Bharatpur, Chittorgarh and Palakkad were the top 3 towns Domino’s should open in. I went onto the Domino’s locations web page to see if I’d struck gold. And I had. One year later, Domino’s now has stores open in Chittorgarh and Palakkad.”
Let’s bring out the big guns for Domino’s pizza now, shall we? Let’s run the full-fledged model and see if my predictions prove true one more time. Here are my model’s mapped predictions for the next 10 cities where Domino’s will open stores:
Made with R packages:ggmap, ggplot2, maps and edited in Adobe Photoshop
If you’re interested, here are the next 10 recommendations for where Domino’s pizza will open: Malegaon, Khammam, Agartala, Kumbakonam, Srinagar, Chittoor, Muktsar, Raichur, Tarn Taran, Akola.
Why don’t these cities look familiar? Because Domino’s pizza has already opened in 292 cities; they are now targeting the smaller towns.
I’ll keep you updated on how my predictions fare. I’ll also post predictions for more franchises’ store openings. Stay tuned.
Update: Kottayam has a Domino’s store. I collected the data to build these predictions in September 2016. So it looks like one of our ten predictions has proved true in only two months. Let’s see what happens to the other 9.
Which cities should Capital first open in next? These recommendations are based on the same model and same code as my recommendations for Bajaj Finance (with a few Capital First specific tweaks).
Here is my presentation on where Capital First should open branches next. Here is the code behind it.
I’ll be uploading recommendations for new franchises soon. I will also be talking about more technical matters – gathering, cleaning the data behind the model, User-based collaborative filtering, analyzing and building the model
and tracking how my ‘predictions’ perform over the new few months. Stay tuned for what’s to come.
Which cities should Bajaj Finance open in next? These are my recommendations for where Bajaj Finance should start offering financial products next.
Now that my ACT and SAT exams are over, I’m back to blogging. Before my work on predicting the FM Auctions using a regression model, I left off talking about building a model that uses recommender systems to predict where franchises should open next – suggest to them the cities which are more suitable for their business. I spent the most of the last two months building and fine-tuning this same model.
It took many weeks to collect this data – 26 franchises and 1476 cities worth of data – and many more weeks to refine it – optimizing the data for each franchise, removing duplicate entries (a city like Tiruchirappalli, I learnt, can have up to 8 different spellings), cross-checking that data against Annual reports and Earnings Presentations, deciding on a resolution for each franchise (Should a store in Thane be counted as a store in Mumbai?) – that sort of thing.
After all this work, I’m proud to say that I can now deliver: Here is my presentation on where Bajaj Finance should open branches next. Here is the code behind it.
I will be uploading recommendations like this one for every franchise I can get my hands on over the next few weeks. I will also be talking about more technical matters – gathering, cleaning the data behind the model, analyzing and building the model and tracking how my ‘predictions’ perform over the new few months. Stay tuned for what’s to come.
FM Companies: Here’s the updated cheat sheet for bidding at phase 3 of the FM auctions.
This updated model has tighter ranges and is, in my opinion, more accurate. I used Facebook’s ad reach potential for each city as a predictor of its license fee to build a linear regression model. Predictions here are based on that model. If you’re interested, here‘s the code for that model.
This Facebook fueled model beats the franchise based model I built a week ago for two reasons. First, Facebook’s ad reach potential is a better proxy for a city’s advertising potential than franchise data. Advertising potential is directly related to how much companies will spend on advertising their products on radio, which, in turn, is directly related to the price at which an FM station gets sold.
Second, the Facebook ad potential helped my predictions get more granular. Two cities, no matter how similar they might be, rarely get sold at the same price. Using franchises as a predictor didn’t provide my data with this price uniqueness. I only ended up with many buckets of similarly priced cities. If a city had 1 Cafe Coffee Day and 1 Domino’s pizza store, my model predicted it to sell at the exact same price as every other city with 1 Cafe Coffee Day and 1 Domino’s pizza store. That’s still a good approximation, but my facebook fueled model allows me to go beyond that approximation.
For a 60% chance of winning the auction for a city, bid the amount in the second column. For an 80% chance, bid the amount in the third column. For an almost-certain 95% chance, bid the amount in the fourth column.
A third of the cities up for auction in the last round of Phase 3 of the auctions went unsold. The cities highlighted in the table below are my predictions for which cities are likely to go unsold in this round.
Ask yourself how badly you want the frequency of a particular city. Refer to the table to find the value that matches your priority for that city. Bid that amount. (All figures in lakhs of rupees.)
| City | 60% chance | 80% chance | 95% chance | Reserve Price |
| Achalpur | 143 | 249 | 416 | 171 |
| Agartala | 279 | 383 | 546 | 16 |
| Aizwal | 308 | 411 | 573 | 12 |
| Akola | 213 | 318 | 483 | 30 |
| Alappuzha | 351 | 454 | 615 | 702 |
| Amravati | 259 | 363 | 527 | 351 |
| Asansol | 331 | 433 | 596 | 194 |
| Barshi | 152 | 258 | 424 | 171 |
| Belgaum | 323 | 426 | 588 | 702 |
| Bellary | 213 | 318 | 483 | 702 |
Click here to view and download the table.
If you’re very eager on buying the frequency for Achalpur, you’re best off bidding the amount in the 95% chance column. Bidding at 416 lakhs gives you an almost certain chance of clearing the round with the frequency in hand.
Suppose you’re not as confident about the frequency for Agartala; you’re not as desperate to go out and get it. You decide that you want to bid an amount that will give you a 60% chance of winning the auction for Agartala’s FM Station. Bidding at 279 lakh is your best bet.
A quick note about reserve prices: the higher it is compared to the 60% chance bid, the lower the probability of the frequency selling. For Achalpur, the reserve price (171 lakh) is above the 60% bid (143 lakh). For Agartala, however, there’s some distance between the reserve price (16 lakh) and the 60% bid (279 lakh). This means that the FM station for Agartala will sell like hot cakes but that for Achalpur won’t. Cities highlighted in the table are likely go unsold.
I hope this table was valuable to you. Good luck at the auctions!
Priyank Aranke 