### OMS: Shopping Cart Abandonment

Web Analytics software and practitioners do a very nice job of illustrating the rate that various segments of customers abandon shopping carts.

The next step we can take on this journey is to understand what the consequences are of an abandoned shopping cart. In other words, given that a customer abandoned a shopping cart, we need to measure what happens next, and then quantify the sales and profit impact of what happens next.

Recall the OMS framework authored over the past few weeks ago. We can add variables to this framework. Add dummy variables that tell whether the customer abandoned a shopping cart in the last week (1 = yes, 0 = no), last month (1 = yes, 0 = no), or even last year (1 = yes, 0 = no). Add these variables to your pre-post datasets, and run them through your factor analysis, integrating shopping cart abandonment with channel and merchandise preferences. Enter the variables into your regression models, too, while you're at it. Essentially, you make shopping cart abandonment part of the 100 to 1,000 segments that forecast future customer behavior.

Or define the information however you like ... no rules here!

At this point, you'll be able to run simulations that show how customers evolve, based on past shopping cart abandonment activity.

For instance, we can compare four customers. We'll assume that prior to last month, all other attributes are equal:
1. Customer purchased merchandise online last month.
2. Customer abandoned a shopping cart last month.
3. Customer visited website last month, didn't put anything in shopping cart.
4. Customer didn't bother to visit website last month.

For each of the four customers, we run a five year sales simulation, based off of past customer behavior. For 1,000 simulated customers, you might find an outcome that looks like this (your mileage will vary):

1. Purchaser: Year 1 = \$100,000, Year 2 = \$70,000, Year 3 = \$50,000, Year 4 = \$35,000, Year 5 = \$25,000, Total = \$280,000.
2. Shopping Cart Abandoner: Year 1 = \$85,000, Year 2 = \$65,000, Year 3 = \$45,000, Year 4 = \$35,000, Year 5 = \$25,000, Total = \$255,000.
3. Visitor, No Cart: Year 1 = \$60,000, Year 2 = \$40,000, Year 3 = \$30,000, Year 4 = \$20,000, Year 5 = \$15,000. Total = \$165,000.
4. No Visit: Year 1 = \$40,000, Year 2 = \$25,000, Year 3 = \$15,000, Year 4 = \$12,000, Year 5 = \$10,000. Total = \$102,000.

See, within the OMS context, we get a different level of business intelligence. In this example, an abandoned shopping cart costs us \$25,000 of demand per 1,000 customers ... a loss of \$25 per customer, over five years.

But worse, look at the website visitor who doesn't even make it to the shopping cart. In this case, the unconverted customer (no shopping cart, no purchase) spends \$115,000 less per 1,000 customers ... a loss of \$115 per customer, over five years.

Of course, even the unconverted visit is worth something. We get an incremental \$63,000 per 1,000 simulated customers when a customer visits, vs. no visit at all. In other words, OMS is projecting in this instance that there is a downstream value to every action on a website, and that value can be calculated via five-year simulations. In this case (and remember, your mileage will vary --- use the principals here to estimate the numbers for your own business).

• An visit is worth an incremental \$63,000 per 1,000 customers over five years, \$63 per customer.
• An incremental item into the shopping cart adds \$90,000 per 1,000 customers over five years, \$90 per customer.
• An incremental purchase adds \$25,000 per 1,000 customers over five years, \$25 per customer.

In Web Analytics, we look back in time to report what happened. In OMS, we look forward, simulating a likely future outcome based on what happened in the past. Combined, Web Analytics and OMS make good business sense, and provide the answers a CEO frequently looks for.

If this style of shopping cart abandonment analysis makes sense to you, contact me for details on an OMS project!