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Find the optimal virtual phone numbers to buy for X customers area codes?

We have a unique problem that not even large telecom carriers have solved yet. I am hoping I could get some insights from the community on potential solutions since it is probably a weighted distribution problem with matrix multiplication or something else. Either way, I think it is a math question and there is no better place to look for math related programming solutions than here. :) Thank you ahead of time!

We need a mapping algorithm that takes X number (lets say 500,000) of Customer Contact Records and figures out the best X number (lets say 1,000) of Virtual Phone Numbers to buy and assign to the contacts.

The assignment follows this order of priority: area code, zip code, city, state and country.

The customer contacts have numbers from all types of area codes. Mainly the quantities follow city population for the cities in or close to the area codes.

So, of the 500k contacts, usually no single area code has more than 10% representation. Normally its only about 1-4% of any customer list is in a single area code.

So, the challenge is to create an algorithm that looks at the frequency of area codes in the 500k list and chooses the 1k best area codes to buy virtual numbers in.

Here is where it gets hairy.

No virtual number can be assigned to more than 250 contacts in the same area code. So, the algo needs to tell us how many of each area code we need to buy numbers in.

Area code: 951 may have 600 contacts in it. In that case, we need to buy 3 numbers in that area code. That means 3 of the 1,000 are a single area code. That type of thing has to be calculated in the 1k virtual number buy recommendation algo.

If there is no geo match possible and all contacts are in the same country, the contact is assigned to a random virtual number.

So, we need a buy algo and a mapping algo.

To make things more complicated, if there is an existing mapping but user buys more numbers, we need to allow the user to remap all contact to virtual number relationships in a way that maps the closest geo relationships.

As always, if you need a bounty to provide me a solution, please let me know. I have always paid immediately in the past.
POSTED BY: David Johnston
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CJ B
CJ B
Posted 8 years ago

Is this for optimizing "RoboCalling"? If so, I would propose that the thread be deleted from the forum by the moderators because while RoboCalling is legal, it probably should not be legal (and certainly should not be sanctioned by Wolfram or anyone else).
POSTED BY: CJ B

I think I understood the problem a bit better now, though not 100% clear yet. Imagine you have all the lat, lon coordinates (geo-positions) of your clients, whatever the amount. What I would do is:

  1. Create n number of random points inside the bounding box of these points. Where n is the number of virtual phone numbers.
  2. Calculate the Voronoi Tessellation of these points
  3. Perform Lloyd relaxation on this tessellation a few (10?) times to make the points uniform.
  4. Calculate the number of customers that fall inside each Voronoi cell. This will be the 'counts'.
  5. Now again, create random points but increase the chance of a point being somewhere depending on the number of counts.
  6. Perform 1 or 2 cycles of Lloyd relaxation to smooth it out a bit, and count again, Now the counts of each polygon should become similar.
  7. Repeat 3-5 as many times necessary...

Now you have some Voronoi cells left for which the counts should be roughly constant. i.e. each region for the phone-numbers is convex and local (what is wanted/required I presume) and each region has roughly the same number of customers.
POSTED BY: Sander Huisman

Also, this provides a huge benefit when it comes to analytics. We can now break down by geo as to what is working or not when it comes to both their advertising but customer service as well.

So, imagine there are 50 virtual numbers in the 951 area code. If each of those are grouped by zip first and city second, you can see how quickly the inbound/outbound call and text stats begin to provide a massive edge to the enterprise.

They can then narrow down and say x number of phone sales came from x location. They can also analyze angry customer service calls and pin point an approximate geo-location and take appropriate action to replace or retrain the manager in the geo.

They can additionally do geo-sentiment analysis on both inbound text messages as well as phone logs.

It's not a new idea but this type of segmentation technique has been coined "hyper-geo" and for good reason.

You may think "they could run these same analytics based on the address they have on file for the customer." This is true in some cases but many times the address on file is not accurate. Additionally, it also requires the enterprise to cross reference databases, etc. and it becomes an IT nightmare.

We are skipping IT and providing hyper-geo analytics to their marketing and sales department without them having to get their IT involved.

Additionally, our carriers are going to start providing us with "Last Known Triangulation" of the cell phone. We will add that as an additional column and in priority order it will be before zip code. It will be lat and long and that is it.

Some say Google analytics or other products provide great geo-analytics. However, they are not monitoring billboards and other signage, etc etc.. They are also not showing the true keywords the user typed in and it shows in the dashboard as (Not Set).

No analytic platform that I have tried and tested works for massive hyper geo needs. With this system, they can use a different number for every ad and have cross platform and cross device tracking because their tracking is not based on the type of advertising they are showing people.

Here is an example of Hyper GEO Google Pay Per Click Software that we built. After watching these, you can see why hyper geo phone numbers are important for advertising. I think the customer service use case is a little more obvious.

1 Video:

https://www.youtube.com/watch?v=kSN5lWbwWzI

2 Video:

https://www.youtube.com/watch?v=QAVf5CI3vIs
POSTED BY: David Johnston

Geo-distribution is best because a single virtual number is not recommended to have more than 250 contacts assigned to each. So one number that covers tons of people is actually counter productive.

That is why we need one algo for generating the recommended buy list and a second for dealing with the mess of remapping based on closest geo.
POSTED BY: David Johnston

What makes a particular "number buy" best? One that covers the most number of the 500k people? One that has the most geographical distribution?
POSTED BY: Neil Singer

As a start, maybe K-means would get you a rough solution (assuming you can set the parameters to match your proximity objectives), although without some of the constraints you mention.

POSTED BY: David Cardinal

I think this question becomes much more clear if you provide a small (bogus) dataset and the expected outcome (with a cap of 5 instead of 250). Especially the priority part is not that clear...

POSTED BY: Sander Huisman

Yes. Large enterprise customer needs to be able to spread out their voice and sms traffic over a large pool of numbers. However, it is not good for their relationship with their customers to not provide a local number they can call and text to. The closer the geo-match, the more their customer engages with them. That equals better customer service experiences.

Despite what some telecoms may claim, it is impossible to handle millions of inbound calls and texts to a single phone number simultaneously. It is better to distribute the traffic over a pool of numbers. If that pool contained geo-clusters, that would be ideal.
POSTED BY: David Johnston

I'm not sure I entirely understand the question either, but it seems that the critical point is that you want to assign customers someone with the same area code so that they can make local calls. But does a smaller GeoDistance really matter, or is it just whether it is a local area code? If the virtual phone has an area code for the neighboring town, is there any advantage or is it just as good as having an area code from across the country?
POSTED BY: Christopher Wolfram

Yes. The closer the better. It's a psychological issue not a cost saving issue. When you get a call from an 800 number, you don't assume it's a local business and for some, this means they won't answer the phone. It also happens the other way around. You are advertising an 800 in Google Maps or somewhere else and the customer picks the ad with a local number instead of yours.

So, franchises and other multi-location businesses make a big mistake by forcing their stores to all share a corporate 800 phone number.

One of my start-ups is trying to solve that by providing a local-ish number for each of their locations. Some area codes are all used up and you can't buy numbers in that area code anymore. That is the pupose of getting closest geo match if there isn't a matching area code available.

Additionally, just because your customers live near your location, doesn't mean they themselves have that geo locations proper area code. People migrate and move around. It's impossible to get it perfect.

The API for buying the phone numbers only accepts a single buy request at a time. So, that means, we have to have a list of area codes we want to buy plus the counts for each. Then we can loop through the list and buy the numbers.

If the target area code isn't available for purchase, the API fortunately, allows us to buy the closest one as a substitute. However, we still have to match it to the contact list based on closest geo.

Like I mentioned before, the user may not buy enough of the right area codes to begin with. When they purchase more, the mapping needs to be allowed to be reset by the user. We have a simple check box "Remap Virtual Numbers By Area Code" and a button. That is why the buy list isn't the same as the mapping function.

If there are numbers in the contact list that do not have a proper area code match, right now we are randomly assigning them. However, we need it to be a close geo match assignment instead.

Its not hard to tally the customer list based on the first 3 digits or 4 if they start with a 1. It's not hard to divide those tallies by the total to get their % of total and create a weight.

It's also not hard to find the GEO info for each number. See here: http://reference.wolfram.com/language/ref/interpreter/PhoneNumber.html

The hard part so far is taking the two imperfect list and matching them without assigning too many customer contact records to a single virtual number. Right now we have some customers with virtual numbers not assigned and we have others with too many assigned. This happened because they don't buy the full recommended numbers that our system advises them to buy. They want to save money so they buy less, but then they get better results from the geo-matched so they buy more and want to remap again.

I can't share the actual contacts of this customer, but here is the resulting table in the systems back end dashboard. In the "Numbers" column, the first number is the amount of actual virtual numbers assigned to contacts in that area code. The second number is how many virtual numbers are recommended. So the first line shows 9/22. This means the system assigned 9 numbers but it should have assigned 22.

You can see that because some of the customer records did not have a matching area code and also they are missing zip and city so our system failed to find them a match. So some have 0/ and no numbers are assigned.

You can also see issues like the 209 and other area codes where more numbers were assigned than recommended.

Country Area Code   Count (Percent)   Numbers
+1 (US) 216 (Ohio)  5148 (2.1%)  9 / 22
+1 (US) 215 (Pennsylvania)  5096 (2.08%) 22 / 21
+1 (US) 214 (Texas) 3926 (1.6%) 15 / 17
+1 (US) 217 (Illinois)  3233 (1.32%) 7 / 14
+1 (US) 209 (California)    3162 (1.29%)   22 / 14
+1 (US) 210 (San Antonio, TX)   3037 (1.24%)  21 / 13
+1 (US) 404 (Georgia)   2743 (1.12%)  2 / 12
+1 (US) 347 (New York)  2471 (1.01%) 0 / 11
+1 (US) 205 (Alabama)   2442 (1%) 19 / 11
+1 (US) 832 (Texas) 2308 (0.94%)    61 / 10
+1 (US) 678 (Georgia)   2189 (0.89%)  0 / 10
+1 (US) 314 (Missouri)  2037 (0.83%) 10 / 9
+1 (US) 313 (Michigan)  2013 (0.82%) 2 / 9
+1 (US) 239 (Florida)   1944 (0.79%)  2 / 9
+1 (US) 407 (Florida)   1914 (0.78%)  1 / 9
+1 (US) 317 (Indiana)   1876 (0.77%)  2 / 9
+1 (US) 330 (Ohio)  1861 (0.76%) 1 / 8
+1 (US) 702 (Nevada)    1643 (0.67%)   0 / 8
+1 (US) 202 (Washington D.C.)   1640 (0.67%)  7 / 8
+1 (US) 208 (Idaho) 1624 (0.66%)    10 / 7
+1 (US) 901 (Tennessee) 1616 (0.66%)    10 / 7
+1 (US) 240 (Frederick, MD) 1611 (0.66%)    3 / 7
+1 (US) 443 (Maryland)  1587 (0.65%) 0 / 7
+1 (US) 757 (Virginia)  1547 (0.63%) 0 / 7
+1 (US) 954 (Florida)   1546 (0.63%)  14 / 7
+1 (US) 803 (South Carolina)    1540 (0.63%)   15 / 7
+1 (US) 267 (Pennsylvania)  1514 (0.62%) 2 / 7
+1 (US) 917 (New York)  1505 (0.61%) 0 / 7
+1 (US) 405 (Oklahoma)  1495 (0.61%) 1 / 7
+1 (US) 909 (California)    1489 (0.61%)   86 / 7
+1 (US) 502 (Louisville, KY)    1450 (0.59%)   0 / 7
+1 (US) 816 (Missouri)  1446 (0.59%) 37 / 7
+1 (US) 248 (Michigan)  1433 (0.59%) 3 / 7
+1 (US) 414 (Wisconsin) 1427 (0.58%)    0 / 7
+1 (US) 336 (North Carolina)    1404 (0.57%)   1 / 7
+1 (US) 786 (Florida)   1380 (0.56%)  0 / 7
+1 (US) 323 (California)    1376 (0.56%)   4 / 7
+1 (US) 843 (South Carolina)    1376 (0.56%)   0 / 7
+1 (US) 817 (Texas) 1375 (0.56%)    0 / 7
+1 (US) 304 (West Virginia) 1372 (0.56%)    0 / 6
+1 (US) 706 (Georgia)   1368 (0.56%)  0 / 6
+1 (US) 225 (Louisiana) 1359 (0.56%)    5 / 6
+1 (US) 501 (Arkansas)  1336 (0.55%) 0 / 6
+1 (US) 904 (Florida)   1330 (0.54%)  34 / 6
+1 (US) 480 (Arizona)   1326 (0.54%)  0 / 6
+1 (US) 503 (Oregon)    1324 (0.54%)   0 / 6
+1 (US) 513 (Ohio)  1319 (0.54%) 0 / 6
+1 (US) 704 (North Carolina)    1308 (0.53%)   0 / 6
+1 (US) 615 (Nashville, TN) 1294 (0.53%)    0 / 6
+1 (US) 619 (California)    1292 (0.53%)   0 / 6
+1 (US) 801 (Utah)  1290 (0.53%) 0 / 6
+1 (US) 720 (Colorado)  1288 (0.53%) 0 / 6
+1 (US) 219 (Indiana)   1278 (0.52%)  3 / 6
+1 (US) 813 (Florida)   1269 (0.52%)  43 / 6
+1 (US) 602 (Arizona)   1264 (0.52%)  0 / 6
+1 (US) 601 (Mississippi)   1244 (0.51%)  0 / 6
+1 (US) 201 (New Jersey)    1240 (0.51%)   11 / 6
+1 (US) 916 (California)    1235 (0.5%)    0 / 6
+1 (US) 469 (Texas) 1226 (0.5%) 0 / 6
+1 (US) 504 (Louisiana) 1203 (0.49%)    0 / 6
+1 (US) 770 (Georgia)   1199 (0.49%)  0 / 6
+1 (US) 773 (Chicago, IL)   1196 (0.49%)  28 / 6
+1 (US) 918 (Oklahoma)  1190 (0.49%) 0 / 6
+1 (US) 760 (California)    1184 (0.48%)   0 / 6
+1 (US) 419 (Ohio)  1179 (0.48%) 0 / 6
+1 (US) 951 (California)    1166 (0.48%)   18 / 6
+1 (US) 559 (California)    1157 (0.47%)   0 / 6
+1 (US) 203 (Connecticut)   1155 (0.47%)  10 / 6
+1 (US) 919 (North Carolina)    1150 (0.47%)   0 / 6
+1 (US) 910 (North Carolina)    1149 (0.47%)   0 / 6
+1 (US) 251 (Alabama)   1145 (0.47%)  1 / 6
+1 (US) 334 (Alabama)   1136 (0.46%)  0 / 6
+1 (US) 646 (New York)  1135 (0.46%) 0 / 6
+1 (US) 318 (Louisiana) 1135 (0.46%)    1 / 6
+1 (US) 505 (Albuquerque, NM)   1132 (0.46%)  0 / 6
+1 (US) 412 (Pennsylvania)  1125 (0.46%) 0 / 6
+1 (US) 614 (Ohio)  1122 (0.46%) 0 / 5
+1 (US) 512 (Texas) 1119 (0.46%)    0 / 5
+1 (US) 864 (South Carolina)    1115 (0.46%)   0 / 5
+1 (US) 662 (Mississippi)   1100 (0.45%)  0 / 5
+1 (US) 510 (California)    1075 (0.44%)   0 / 5
+1 (US) 423 (Tennessee) 1047 (0.43%)    0 / 5
+1 (US) 352 (Florida)   1040 (0.42%)  1 / 5
+1 (US) 256 (Alabama)   1036 (0.42%)  0 / 5
+1 (US) 714 (California)    1031 (0.42%)   0 / 5
+1 (US) 818 (California)    1027 (0.42%)   39 / 5
+1 (US) 812 (Bloomington, IN)   998 (0.41%)   0 / 5
+1 (US) 603 (New Hampshire) 987 (0.4%)  0 / 5
+1 (US) 402 (Nebraska)  984 (0.4%)   0 / 5
+1 (US) 305 (Florida)   982 (0.4%)    2 / 5
+1 (US) 717 (Pennsylvania)  981 (0.4%)   0 / 5
+1 (US) 310 (Los Angeles, CA)   978 (0.4%)    0 / 5
+1 (US) 850 (Florida)   974 (0.4%)    0 / 5
+1 (US) 765 (Indiana)   973 (0.4%)    0 / 5
+1 (US) 206 (Washington)    964 (0.39%)    12 / 5
+1 (US) 810 (Michigan)  959 (0.39%)  0 / 5
+1 (US) 281 (Texas) 956 (0.39%) 0 / 5
+1 (US) 520 (Arizona)   947 (0.39%)   0 / 5
+1 (US) 903 (Texas) 944 (0.39%) 0 / 5
+1 (US) 804 (Virginia)  944 (0.39%)  0 / 5
+1 (US) 661 (California)    938 (0.38%)    2 / 5
+1 (US) 270 (Kentucky)  932 (0.38%)  0 / 5
+1 (US) 213 (Los Angeles, CA)   927 (0.38%)   10 / 5
+1 (US) 207 (Biddeford, ME) 927 (0.38%) 6 / 5
+1 (US) 937 (Ohio)  927 (0.38%)  0 / 5
+1 (US) 561 (Florida)   908 (0.37%)   0 / 5
+1 (US) 562 (California)    880 (0.36%)    0 / 5
+1 (US) 229 (Georgia)   877 (0.36%)   0 / 5
+1 (US) 231 (Michigan)  873 (0.36%)  2 / 4
+1 (US) 541 (Oregon)    872 (0.36%)    0 / 4
+1 (US) 315 (New York)  863 (0.35%)  1 / 4
+1 (US) 808 (Hawaii)    860 (0.35%)    0 / 4
+1 (US) 518 (New York)  856 (0.35%)  0 / 4
+1 (US) 727 (Florida)   853 (0.35%)   0 / 4
+1 (US) 360 (Vancouver, WA) 850 (0.35%) 1 / 4
+1 (US) 252 (North Carolina)    849 (0.35%)    1 / 4
+1 (US) 337 (Louisiana) 844 (0.34%) 1 / 4
+1 (US) 740 (Ohio)  842 (0.34%)  0 / 4
+1 (US) 716 (New York)  837 (0.34%)  0 / 4
+1 (US) 870 (Arkansas)  835 (0.34%)  0 / 4
+1 (US) 859 (Kentucky)  830 (0.34%)  0 / 4
+1 (US) 540 (Virginia)  829 (0.34%)  0 / 4
+1 (US) 912 (Georgia)   825 (0.34%)   0 / 4
+1 (US) 609 (New Jersey)    823 (0.34%)    0 / 4
+1 (US) 417 (Missouri)  818 (0.33%)  0 / 4
+1 (US) 484 (Pennsylvania)  807 (0.33%)  0 / 4
+1 (US) 321 (Florida)   806 (0.33%)   1 / 4
+1 (US) 570 (Pennsylvania)  802 (0.33%)  0 / 4
+1 (US) 956 (Texas) 791 (0.32%) 0 / 4
+1 (US) 260 (Indiana)   778 (0.32%)   2 / 4
+1 (US) 573 (Charleston, MO)    774 (0.32%)    0 / 4
+1 (US) 719 (Colorado)  768 (0.31%)  0 / 4
+1 (US) 713 (Texas) 759 (0.31%) 0 / 4
+1 (US) 517 (Michigan)  758 (0.31%)  0 / 4
+1 (US) 973 (New Jersey)    758 (0.31%)    0 / 4
+1 (US) 708 (Illinois)  757 (0.31%)  0 / 4
+1 (US) 254 (Texas) 753 (0.31%) 1 / 4
+1 (US) 724 (Pennsylvania)  750 (0.31%)  0 / 4
+1 (US) 408 (California)    746 (0.3%) 6 / 4
+1 (US) 440 (Ohio)  741 (0.3%)   0 / 4
+1 (US) 301 (Maryland)  739 (0.3%)   2 / 4
+1 (US) 626 (California)    736 (0.3%) 0 / 4
+1 (US) 734 (Michigan)  735 (0.3%)   0 / 4
+1 (US) 509 (Washington)    734 (0.3%) 0 / 4
+1 (US) 616 (Michigan)  734 (0.3%)   0 / 4
+1 (US) 612 (Minnesota) 726 (0.3%)  0 / 4
+1 (US) 732 (New Jersey)    711 (0.29%)    0 / 4
+1 (US) 574 (Indiana)   690 (0.28%)   0 / 4
+1 (US) 478 (Georgia)   687 (0.28%)   0 / 4
+1 (US) 516 (New York)  678 (0.28%)  0 / 4
+1 (US) 386 (Florida)   674 (0.28%)   0 / 4
+1 (US) 586 (Michigan)  669 (0.27%)  0 / 4
+1 (US) 224 (Illinois)  668 (0.27%)  0 / 4
+1 (US) 814 (Pennsylvania)  668 (0.27%)  0 / 4
+1 (US) 631 (New York)  655 (0.27%)  0 / 4
+1 (US) 860 (Connecticut)   649 (0.27%)   0 / 4
+1 (US) 913 (Kansas)    649 (0.27%)    0 / 4
+1 (US) 410 (Maryland)  648 (0.26%)  0 / 4
+1 (US) 303 (Colorado)  647 (0.26%)  0 / 4
+1 (US) 618 (Illinois)  647 (0.26%)  0 / 4
+1 (US) 989 (Owosso, MI)    646 (0.26%)    0 / 4
+1 (US) 805 (California)    637 (0.26%)    0 / 4
+1 (US) 401 (Rhode Island)  629 (0.26%)  1 / 4
+1 (US) 915 (Texas) 629 (0.26%) 0 / 4
+1 (US) 585 (New York)  626 (0.26%)  0 / 4
+1 (US) 415 (San Francisco, CA) 614 (0.25%) 0 / 3
+1 (US) 269 (Michigan)  611 (0.25%)  2 / 3
+1 (US) 228 (Mississippi)   610 (0.25%)   0 / 3
+1 (US) 707 (California)    609 (0.25%)    0 / 3
+1 (US) 931 (Tennessee) 605 (0.25%) 0 / 3
+1 (US) 361 (Texas) 604 (0.25%) 1 / 3
+1 (US) 972 (McKinney, TX)  602 (0.25%)  0 / 3
+1 (US) 316 (Kansas)    596 (0.24%)    1 / 3
+1 (US) 978 (Massachusetts) 593 (0.24%) 0 / 3
+1 (US) 623 (Arizona)   592 (0.24%)   0 / 3
+1 (US) 856 (New Jersey)    590 (0.24%)    0 / 3
+1 (US) 718 (Jamaica, NY)   589 (0.24%)   0 / 3
+1 (US) 610 (Pennsylvania)  587 (0.24%)  0 / 3
+1 (US) 815 (Joliet, IL)    586 (0.24%)    0 / 3
+1 (US) 253 (Washington)    581 (0.24%)    2 / 3
+1 (US) 508 (Massachusetts) 579 (0.24%) 0 / 3
+1 (US) 865 (Tennessee) 568 (0.23%) 0 / 3
+1 (US) 920 (Neenah, WI)    562 (0.23%)    0 / 3
+1 (US) 617 (Massachusetts) 562 (0.23%) 0 / 3
+1 (US) 218 (Minnesota) 556 (0.23%) 1 / 3
+1 (US) 580 (Oklahoma)  549 (0.22%)  0 / 3
+1 (US) 479 (Arkansas)  547 (0.22%)  0 / 3
+1 (US) 785 (Kansas)    546 (0.22%)    0 / 3
+1 (US) 530 (California)    538 (0.22%)    0 / 3
+1 (US) 863 (Florida)   530 (0.22%)   0 / 3
+1 (US) 302 (Delaware)  530 (0.22%)  1 / 3
+1 (US) 928 (Arizona)   527 (0.22%)   0 / 3
+1 (US) 806 (Texas) 524 (0.21%) 0 / 3
+1 (US) 608 (Wisconsin) 524 (0.21%) 0 / 3
+1 (US) 908 (New Jersey)    521 (0.21%)    0 / 3
+1 (US) 636 (Missouri)  517 (0.21%)  0 / 3
+1 (US) 606 (Kentucky)  517 (0.21%)  0 / 3
+1 (US) 703 (Virginia)  516 (0.21%)  0 / 3
+1 (US) 651 (Minnesota) 510 (0.21%) 0 / 3
+1 (US) 409 (Texas) 510 (0.21%) 0 / 3
+1 (US) 970 (Durango, CO)   508 (0.21%)   0 / 3
+1 (US) 715 (Wisconsin) 506 (0.21%) 0 / 3
+1 (US) 630 (Illinois)  505 (0.21%)  0 / 3
+1 (US) 406 (Billings, MT)  505 (0.21%)  0 / 3
+1 (US) 828 (North Carolina)    505 (0.21%)    0 / 3
+1 (US) 262 (Jackson, WI)   504 (0.21%)   0 / 3
+1 (US) 434 (Virginia)  497 (0.2%)   0 / 3
+1 (US) 845 (New York)  495 (0.2%)   0 / 3
+1 (US) 941 (Florida)   486 (0.2%)    0 / 3
+1 (US) 413 (Massachusetts) 484 (0.2%)  0 / 3
+1 (US) 312 (Chicago, IL)   481 (0.2%)    2 / 3
+1 (US) 985 (Louisiana) 465 (0.19%) 0 / 3
+1 (US) 914 (New York)  460 (0.19%)  0 / 3
+1 (US) 775 (Nevada)    435 (0.18%)    0 / 3
+1 (US) 319 (Iowa)  433 (0.18%)  1 / 3
+1 (US) 925 (California)    429 (0.18%)    0 / 3
+1 (US) 309 (Illinois)  426 (0.17%)  0 / 3
+1 (US) 571 (Virginia)  426 (0.17%)  0 / 3
+1 (US) 607 (New York)  421 (0.17%)  0 / 3
+1 (US) 515 (Iowa)  408 (0.17%)  0 / 3
+1 (US) 949 (California)    407 (0.17%)    0 / 3
+1 (US) 425 (Washington)    404 (0.17%)    0 / 3
+1 (US) 731 (Scotts Hill, TN)   396 (0.16%)   0 / 3
+1 (US) 605 (South Dakota)  393 (0.16%)  0 / 3
+1 (US) 507 (Minnesota) 385 (0.16%) 0 / 3
+1 (US) 575 (New Mexico)    384 (0.16%)    0 / 3
+1 (US) 936 (Texas) 374 (0.15%) 0 / 2
+1 (US) 847 (Illinois)  373 (0.15%)  0 / 2
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+1 (US) 846 ()  1 (0%)   0 / 1 Please note that contact summary is updated every 15 mins.
POSTED BY: David Johnston

I don't entirely understand the question. Why do you need virtual phone numbers for the contacts? Is it for them to use for some reason?
POSTED BY: David Cardinal
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