Millions Dream of Megamillions

Two weeks ago, a coworker and I decided to try our luck at the record-breaking megamillions jackpot game. We made a pact to buy tickets and split the winnings. If everything had gone according to plan, we would currently be working remotely from New Zealand, sipping daiquiris and chilling with Kiwis (the bird, not the fruit). Sadly, this dream did not come to fruition, as two other people walked away with our $390 million prize.

After doing a little research in the Compete data files, I soon learned that we were not alone in our misfortune. Traffic to steadily rose as the jackpot climbed, peaking for the week of March 4 at 1,014,610 people. Intrigued by these numbers, I decided to investigate the relationship between the jackpot size and site traffic.

The green bars represent the dollar amount (in millions) of the megamillions jackpot for each drawing dating back to February 2. The blue line represents the daily share of visitors for (Daily share of visitors shows the percent of people who visited a site versus everyone surfing the web that day.) Traffic to the megamillions site jumps the day after each drawing and then drops back down until the next drawing. The larger the jackpot, the larger the spike in traffic.

The growth from peak to peak is even more astounding. The following graph shows the velocity of change in both values since the February 2 drawing.

Jackpot value and share of visitors grew at similar rates for the first three weeks, but on March 3, share of visitors surpasses jackpot value. On March 7, the day after the $390 Million drawing, share of visitor was more than 750% larger than it was on March 3. So even if you didn’t strike it rich a few weeks ago, at least you can take comfort in knowing that a lot of people out there can empathize with you. Better luck next time lottery player!

About Debra Miller Arbesman:
Debra Miller Arbesman is senior associate, retailer and consumer products at Compete, a Kantar Media company that helps brands improve their marketing based on the online behavior of millions of consumers.

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