1 00:00:00,790 --> 00:00:03,980 What steps does it take for someone using your product or 2 00:00:03,980 --> 00:00:06,230 service to get to their goal? 3 00:00:06,230 --> 00:00:10,880 Having these illustrated and tracked using an analytic service can help you 4 00:00:10,880 --> 00:00:14,990 identify critical breaking points in this flow and address them strategically. 5 00:00:16,120 --> 00:00:19,910 Let's continue to use Amazon.com as an example. 6 00:00:19,910 --> 00:00:22,700 First, we need to outline the steps and 7 00:00:22,700 --> 00:00:24,720 the funnel for making a purchase on Amazon. 8 00:00:26,050 --> 00:00:29,080 Aim for about 5 to 10 steps in your funnel. 9 00:00:29,080 --> 00:00:30,910 Here's ours. 10 00:00:30,910 --> 00:00:33,674 1, go to Amazon.com. 11 00:00:33,674 --> 00:00:37,245 2, search for a desired product. 12 00:00:37,245 --> 00:00:40,375 3, look at product pages. 13 00:00:40,375 --> 00:00:43,400 4, add product to cart. 14 00:00:43,400 --> 00:00:45,516 5, proceed to checkout. 15 00:00:45,516 --> 00:00:48,538 6, sign in. 16 00:00:48,538 --> 00:00:51,732 7, review check out page. 17 00:00:51,732 --> 00:00:54,120 And 8, place your order. 18 00:00:55,390 --> 00:00:57,710 Now, as you can guess, fewer and 19 00:00:57,710 --> 00:01:01,590 fewer people will go on from one step to the next. 20 00:01:01,590 --> 00:01:05,230 So really, it all begins to look like a funnel. 21 00:01:06,370 --> 00:01:11,320 If this visualization becomes hard to read, you can also use a simple bar chart. 22 00:01:12,550 --> 00:01:15,760 You'll find that one advantage of using a bar chart 23 00:01:15,760 --> 00:01:19,090 to visualize this flow instead of the more traditional funnel, 24 00:01:19,090 --> 00:01:23,190 is that it's easier to map the data while maintaining readability and scale. 25 00:01:24,600 --> 00:01:28,860 One thing that defines all funnels is they are linear. 26 00:01:28,860 --> 00:01:33,180 In our example where step number 3 is someone looking at product pages, 27 00:01:33,180 --> 00:01:36,750 it doesn't matter how many product pages someone looked at 28 00:01:36,750 --> 00:01:40,070 since it's all just one step in getting to the conversion. 29 00:01:41,100 --> 00:01:45,660 The fact that people may look at 3 products or 30 can be significant, but 30 00:01:45,660 --> 00:01:47,580 within a different research context. 31 00:01:48,830 --> 00:01:53,040 Now comes the real useful part of going through this exercise. 32 00:01:53,040 --> 00:01:58,090 If somewhere in the funnel you noticed a particularly steep drop-off, 33 00:01:58,090 --> 00:02:00,670 you know it's a problem area that you need to look into. 34 00:02:01,980 --> 00:02:06,230 As you can see here, a steep drop has appeared between adding something to 35 00:02:06,230 --> 00:02:09,240 the cart and proceeding to checkout. 36 00:02:09,240 --> 00:02:12,450 People appear to be putting your product in their shopping carts, and 37 00:02:12,450 --> 00:02:13,450 then leaving the site. 38 00:02:14,560 --> 00:02:17,645 It's quite possible that this drop-off is normal. 39 00:02:17,645 --> 00:02:22,755 So before you assume something is wrong, you'll need to gain some context. 40 00:02:22,755 --> 00:02:26,905 Understanding the historical trends for your product and your industry 41 00:02:26,905 --> 00:02:31,645 will allow you to decide if this drop-off is worrisome or completely normal. 42 00:02:33,030 --> 00:02:37,610 If in fact you find that a drop-off is unusually high, 43 00:02:37,610 --> 00:02:42,950 run a qualitative study, such as a usability test, to understand why. 44 00:02:44,130 --> 00:02:46,740 Once you think you've found a design solution and 45 00:02:46,740 --> 00:02:48,770 have tested it with a few users. 46 00:02:48,770 --> 00:02:51,630 Consider introducing it as part of an AB test 47 00:02:51,630 --> 00:02:54,220 to make sure it improves the conversation rate. 48 00:02:54,220 --> 00:02:57,090 And if the test is successful, launch to everyone.