December 21, 2010

Promotional Analysis: Profit Calculation

It may well be that discounts and promotions are what are needed to stimulate business.  Unfortunately, the tools needed to analyze whether a promotion is profitable or not aren't always available to the Google Analytics Generation.  The savvy Web Analyst needs to go a step further, in order to determine if a promotion is likely to generate profit.

In our example, we're going to pretend the following:
  • Our promotion is "Take 20% Off Of Your Order, Today Only".
  • Average order value = $100.
  • 35% of demand converts to profit.

Step 1 = Execute A Test:  Ok, I realize almost none of you are going to do this.  But if you had done this, you'd know exactly how much business would have have happened "organically", without the need of a promotion.

Step 2 = Talk To Finance:  Since you didn't execute a test, you'll need to guess how much demand would have happened.  Somebody in the Finance department has a forecast for total demand on the day of your promotion.  Let's pretend that amount is $100,000.

Step 3 = Measure Sales on Promotion Day:  Let's pretend that demand was $140,000 on the day of the promotion.

Step 4 = Calculate Incremental Profit:  Here, we measure the difference in profit between $140,000 at 20% off vs. $100,000 at full price.
  • The $140,000 demand yields $140,000 * 0.35 = $49,000 profit.  However, we gave up 20% of the $140,000 revenue, or $28,000, yielding $21,000 profit.
  • $100,000 demand yields $100,000 * 0.35 = $35,000 profit.
Now, honestly, the CFO folks are going to jump all over me, telling me that there are hundreds of subtleties involved in calculating profit.  Go ahead, jump all over me.  This is an example, folks, the idea here is to stimulate thought among the Google Analytics Generation.  Savvy Web Analysts will work with their CFO to do this analysis and calculate profit.

Another thing to note here.  In many cases, companies offer a promotion, and the customer chooses not to use it ... the customer fails to enter the promo code, for instance.  So the Savvy Web Analyst will apply a "utilization rate" here, saying that 88%, for instance, of customers utilized the promotion.

Step 5 = Calculate Incremental New Customers, And Incremental Existing Buyers:  This is important.  Let's pretend that our average order value was $100 in each case.  This means we had 1,400 customers purchase via discount, and we had $1,000 customers who would have purchased at full price.  Carefully measure how many customers are new vs. existing.
  • Discount Example:  400 new customers, 1,000 existing customers.
  • Full-Price Example:  100 new customers, 900 existing customers.
Step 6 = Know 12-Month Profit By Customer Type:  People have been arguing for lifetime value analyses for decades.  For the Google Analytics Generation, it's hard to use software to calculate lifetime value.  The savvy Web Analytics analyst exports data out of existing Web Analytics platforms and analyzes long-term value.  I like to use 12-month profit.  You use whatever you want to use.
  • Discount Newbies = $10 of 12-month profit.
  • Discount Existing Buyers = $15 of incremental, additional 12-month profit.  This is the profit you get by converting, say, a three-time buyer into a four-time buyer.
  • Full-Price Newbies = $15 of 12-month profit.
  • Full-Price Existing Buyers = $17 of incremental, additional 12-month profit.  This is the profit you get by converting, say, a three-time buyer into a four-time buyer.
Step 7 = Calculate Expected Long-Term Profit:
  • Discount Newbies = 400 * $10 = $4,000.
  • Discount Existing Buyers = 1,000 * $15 = $15,000.
  • Full-Price Newbies = 100 * $15 = $1,500.
  • Full-Price Existing Buyers = 900 * $17 = $15,300.
  • Discount Long-Term Profit = $19,000.
  • Full-Price Long-Term Profit = $16,800.
Step 8 = Calculate Short-Term + Long-Term Profit:
  • Discount Strategy = $21,000 short-term + $19,000 long-term = $40,000.
  • Full-Price Strategy = $35,000 short-term + $16,800 long-term = $51,800.
In this case, the discount/promotion strategy yielded less short-term profit, more long-term profit, but not enough total profit.

Again, there are countless experts out there who will take exception with the methodology outlined here.  That's ok, those experts should publish their take on this, letting everybody see how they would approach the topic.  I'm trying to create a framework here for the Google Analytics Generation to see how one might measure whether discounts and promotions yield profitable outcomes.  In this case, there's no denying that the promotion yielded a significant sales increase, but does not appear to generate enough profit, short-term or long-term, to pay for the promotion.

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