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Confessions of a Data Voyeur: From Curiosity to Better Decisions

Confessions of a Data Voyeur: From Curiosity to Better Decisions. Bundle.com is a NYC based start-up that uses data to help people make smarter money decisions. Using data from billions of financial transactions, Bundle is able to show people decisions that others like them have made.

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Confessions of a Data Voyeur: From Curiosity to Better Decisions

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  1. Confessions of a Data Voyeur:From Curiosity to Better Decisions

  2. Bundle.com is a NYC based start-up that uses data to help people make smarter money decisions. Using data from billions of financial transactions, Bundle is able to show people decisions that others like them have made. We believe that this data can lead to better recommendations and better decision-making. 2

  3. Further, we believe that decisions based purely on qualitative measures are leading to mistakes and bad decisions 3

  4. Today people are exposed to more new information in one day than someone in the middle ages would see in their entire lifetime!

  5. What we heard… “There is loads of information out there, but it doesn’t seem relevant to people like me..” 5

  6. So how do people make decisions when there is just too much information…

  7. They often turn to their networks and the environment surrounding them. Whether it be

  8. …and they do so because peer comparison is part of human nature…for better…and for worse.

  9. …and while social media has had some success ….building relationships ….and building products

  10. In the end, does it only add to the information overload problem?

  11. Are we past the point where people can make good decisions about where to spend their money, and more importantly, their time?

  12. What we heard.. “what I really want to know is what…not just anyone, but people like me are spending their [time and] money on… 12

  13. So we built a product that allowed people to see what others like them, their neighbors, were spending their money on.. 13

  14. Bundle’s Everybody’s Money Data 14

  15. But we took it a step further and asked ourselves “ What if you could see peoples actual experiences with merchants by analyzing their spending patterns?” 15

  16. So we did a little experiment….we were hungry… And someone recommended Takashi…a restaurant on Hudson street not far from Bundle

  17. It was rated 4.5 stars on Yelp and 4 stars on Citysearch….it must be good….

  18. ..and, of course, individual ratings were on both ends of the spectrum

  19. Restaurant Relationship Map Balthazar Daniel Rosa Mexicana Saint Ambroeus Spice Market Smith & Wollensky Standard NY

  20. Merchant Intelligence Data – Customer Demo Actual Analysis for one NYC Restaurant (name not shown for competitive purposes)

  21. ..and asked ourselves a question.. “What if we can see how often people actually go back to Takashi.. ..what if we could give every merchant a score based on how loyal their customers are to them?” 21

  22. We created a Loyalty Score for Takashi 100

  23. As before, we took it one step further.. We scored every merchant in New York based on how loyal their customers are to them

  24. Numbers for illustrative purposes only

  25. Numbers for illustrative purpose only

  26. Getting to this point, however, has not been trivial Transaction -> something that makes sense -> something that is useful 26

  27. Recommendations on optimizing your spending Transactions level (aggregated and anonymized) financial data US Government Data (Bureau of Labor Statistics, Dept of Labor, Census Bureau etc.) Third party data (e.g. demographics) 27

  28. Transactions are not easy to decipher… Mar 19 Mar 23 Mar 22 Mar 20 Mar 20 Mar 20 TARGET T-2380 2380 NEW YORK NY COSTCO WHSE #1062 00NEW YORK NY MIYAKO SUSHI NEW YORK NY RYE HOUSE 0000000000NEW YORK NY NYC TAXI MED 7M36 09LONG ISLAND C NY ATTM*512015043468MNY ALPHARETTA GA $62.94 $48.90 $142.55 $159.00 $17.70 $39.29 ??? Do you recognize which merchants these relate to? Keep in mind that there are millions of merchants across the U.S.

  29. Analyzing and extracting relationships from billions of records is not easy… $a =~ s{[\W\s]}{}g;   $b =~ s{[\W\s]}{}g;   if ($a eq $b) { return 1;}  $a =~ s{^(THE|AN?)\s*}{}g;   $b =~ s{^(THE|AN?)\s*}{}g;   my $common_regex = $common_words{$cat};   if (defined $common_regex) {       $a =~ s{$common_regex}{}g;  $a =~ s{[\W\s]}{}g;   $b =~ s{[\W\s]}{}g;   if ($a eq $b) { return 1;}  $a =~ s{^(THE|AN?)\s*}{}g;   $b =~ s{^(THE|AN?)\s*}{}g;   my $common_regex = $common_words{$cat};   if (defined $common_regex) {       $a =~ s{$common_regex}{}g;   $a =~ s{[\W\s]}{}g;   $b =~ s{[\W\s]}{}g;   if ($a eq $b) { return 1;}  $a =~ s{^(THE|AN?)\s*}{}g;   $b =~ s{^(THE|AN?)\s*}{}g;   my $common_regex = $common_words{$cat};   if (defined $common_regex) {       $a =~ s{$common_regex}{}g; $a =~ s{[\W\s]}{}g;   $b =~ s{[\W\s]}{}g;   if ($a eq $b) { return 1;}  $a =~ s{^(THE|AN?)\s*}{}g;   $b =~ s{^(THE|AN?)\s*}{}g;   my $common_regex = $common_words{$cat};   if (defined $common_regex) {       $a =~ s{$common_regex}{}g;   

  30. We continue to develop data oriented products that can help people make better decisions with their money and are eager to hear your thoughts Email us at jaidev@bundle.comphil@bundle.com To access the alpha version of our recommender, please sign-up at www.bundle.com Thank You! 30

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