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!

An Open Letter To E-Mail Marketers: Shopping Cart Abandoment E-Mail Campaigns

There appears to be some criticism about my view of shopping cart abandonment e-mail marketing programs.

So my fellow e-mail marketers, the vast majority of which act in an honest manner, marketing opt-in campaigns with integrity, let's consider the following:

Let's pretend that 100 customers abandon a shopping cart on Monday. On Tuesday, you send a targeted e-mail campaign, and you observe the following statistics:

• 30 customers click-through the e-mail campaign, 50% of those individuals buy something, meaning that 15% of the customers purchased because of the campaign.

So these are good numbers, right?! I mean, who in their right mind would ever complain about an e-mail campaign that delivers a 15% response rate?

One of the challenges of e-mail marketing is that e-mail marketers like you and I are used to measuring "positives". We are driven to measure positive outcomes. Our metrics are calibrated to highlight anything we do that is good.

But what about the 85% that did not purchase? What if we angered 25 of the 85 customers, and they don't ever come back and buy from us again, because of our marketing program? Are we measuring this important KPI? Probably not ... because it is truly hard to measure negatives, isn't it?

There are three things we can to do prove that shopping cart abandonment e-mail campaigns are good for us, and good for the customer.

1. Execute e-mail campaign mail/holdout groups. If 15 of 100 customers purchase in the shopping cart abandonment e-mail campaign, and 11 of 100 customers purchase in the holdout group, then we got an incremental 4 customers to purchase. 4 is still better than 0, right? But we do need to measure the incrementality of our marketing activities, don't we? We cannot take credit for orders that would have happened anyway.
   
2. Follow the mail/holdout group for a year. See if, at the end of twelve months (or even three months), the group that received these type of marketing campaigns spent any additional money. If so, good, it means that as a whole, the campaigns are working. But what if the groups have equal performance, when measured over the long-term? If this happens, then we are simply shifting demand, we're not actually creating demand.
3. Quickly identify customers who do not interact with these campaigns, and create a field in your database, so that we don't necessarily send these campaigns to that audience.

If any marketing campaign works, e-mail or otherwise, then we'll observe an improvement in at least one of the following metrics/KPIs:

• An increase in the annual customer retention rate, maybe from 44% to say 47%.
  
• An increase in the annual customer reactivation rate, maybe from 13% to say 15%.
  
• An increase in orders per retained/reactivated customer, from 2.25 to 2.35 as an example, measured annually.
  
• An increase in average order value, from $125 to maybe $132, measured annually.
  
• An increase in new customers, measured on an annual basis.
  
• An increase in customer profitability, measured on an annual basis.
  

As an e-mail marketing community, we need to demonstrate to others that shopping cart abandonment e-mail marketing programs increase one or all six of the metrics I just listed, while not angering other customers. Given the tools listed in this blog post, that's not hard to do, is it?

And guess what? The long-term testing is just as likely to prove that the value of this marketing program is more than what is illustrated by traditional metrics as it is likely to prove that the value is less. When you convert a customer to a purchase, their future value is significantly increased --- so the testing may show that this style of marketing is essential.

Let's have a balanced perspective ... marketing works positively for some, works negatively for others. The sum of the two can be measured via testing. This is what I'm advocating in the article --- summing the positive, negative, and incremental outcomes. To only focus on half of the metric set is misleading.

We can do this kind of testing!

Shopping Cart Abandonment: A Flawed Metric

One of the strategies we're asked to review is shopping cart abandonment. In the past two weeks, I noticed a number of trade journals, bloggers, and consultants promoting shopping cart improvement strategies.

There's no doubt that the shopping experience can be improved ... we all agree with that.

But shopping cart abandonment is an inherently flawed metric, a metric that does not do a good job of measuring longitudinal customer behavior.

If a shopping cart abandonment project succeeds, you will observe improvements in the following customer metrics --- answer these questions if you recently worked on a shopping cart abandonment project:

1. In the six months after shopping cart abandonment improved, did the number of new customers increase at a rate faster than planned?
2. In the six months after shopping cart abandonment improved, did the number of reactivated customers increase at a rate faster than planned?
3. In the six months after shopping cart abandonment improved, did the retention rate of your twelve month buyer file improve?
4. In the six months after shopping cart abandonment improved, did the orders per retained customer improve?

If the answer to these four questions is "no", and yet your shopping cart abandonment metric improved, then you did not fundamentally change customer behavior.

Ask the consultant you are working with to verify that these four metrics were improved when the consultant worked on prior shopping cart abandonment projects. Good consultants can provide you with this data, or if they are under NDA, they can at least verify the magnitude of improvement in customer metrics.

Shopping Cart Abandonment And Conversion Rate

1) Customer visits on 11/1, places item in shopping cart.

2) Customer visits on 11/5, browses, leaves item in shopping cart.

3) Customer visits on 11/7, deletes item from shopping cart, calls over the phone, and buys an item after talking to a customer service rep.

Question #1: Do you have a conversion rate or shopping cart abandonment rate problem?

Question #2: If your answer to question #1 is "No", are you accounting for this behavior in your web analytics tool, so that you can clearly see any real shopping cart abandonment rate issues?

Question #3: If the customer in (3) purchased online, do you have a conversion rate or shopping cart abandonment problem?

Discuss!

Hawking Shopping Cart Abandonment

Yesterday, I witnessed an interaction between a relative newbie to the direct marketing industry, and a vendor.

The discussion was about "Shopping Cart Abandonment". The newbie wanted to know if 'x' percent was a good abandonment rate.

The vendor did a reasonably good job of being unbiased, then offered solutions like promotional e-mails offering a discount or free shipping for merchandise abandoned in the cart within the past seven days.

At the end of the discussion, business cards were exchanged, and if the vendor is lucky, a relationship will be proffered.

Here's a metric that may be more appropriate than the one that measures shopping cart abandonment:

Multichannel Volume Per Monthly Unique Visitor = (Catalog Demand + Online Demand + Retail Net Sales) / (Monthly Unique Visitors).

The numerator sums total monthly volume across channels for each unique website visitor during a thirty day period of time.

By using this metric, we avoid some of the pitfalls that cause us to mistakenly hire vendors hawking shopping cart abandonment solutions.

For instance, take the classic multichannel situation where a customer visits the website, puts two items in the shopping cart, then purchases merchandise in the store three days later.

In this situation, your business enjoys a true "conversion".

Web pundits will tell you that you have a "problem".

Give this multichannel metric a try, and compare like months, year-over-year, to see if your website is being used as an effective multichannel marketing vehicle.