Showing posts with label Comp Store Sales. Show all posts
Showing posts with label Comp Store Sales. Show all posts

## November 09, 2007

### Diagnosing The Challenge

It looks like 75% of retailers tracked by Chain Store Age had October comp store sales under three percent.

Many nervous executives are going to ask what you can do to help lift business out of this slump.

A good place to start is to diagnose how various segments of customers are performing, year-over-year.

For instance, let's assume you have a segment of customers that is considered "best". During October 2007, you observed this level of performance:
• Number of Households On 10/1/2007 = 100,000.
• Percent Who Purchased In October 2007 = 15.0%.
• Spend Per Purchaser, October 2007 = \$100.00.
• Net Sales Per Household, October 2007 = 0.15 * 100 = \$15.00.
• Total Sales = 100,000 * 15.00 = \$1,500,000.
Now, re-run the analysis for October 2006, using a comparable group of customers.
• Number of Households On 10/1/2006 = 105,000.
• Percent Who Purchased In October 2006 = 16.0%.
• Spend Per Purchaser, October 2006 = \$97.00.
• Net Sales Per Household, October 2006 = 0.16 * 97 = \$15.52.
• Total Sales = 105,000 * 15.52 = \$1,629,600.
Compare each metric, year-over-year:
• Number of Households = -4.8%.
• Percent Who Purchased In October = -6.2%.
• Spend Per Purchaser In October = +3.0%.
• Net Sales Per Household = -3.3%.
• Total Sales = -8.0%.
What caused total sales to be down by 8.0%?
• More than half the problem occurred because there are fewer "best" customers compared with last year.
• The rate at which best customers purchased in October dropped by 6.2%. That's not good.
• Sales were helped some by spend levels that actually increased by 3%.
This analysis should be done for "best" customers, "average" customers,
"marginal" customers, and new/reactivated customers.

You want to identify where your shortfall is occurring. If you see the same trends across all segments, you have brand weakness or overall economic pressures suppressing your performance.

But if some customers are impacted, while others continue to perform well, you have unique challenges. If you're best customers are performing well, while marginal, new and reactivated customers are under-performing, you may have a portion of your customer base "trading down" to other brands they can afford, as an example.

If business stinks long enough, you'll notice a trend where all metrics look good, but there aren't enough customers to fuel growth. Called "file momentum", this is one of the more frustrating challenges merchants face --- they fix business problems, but have to wait to see the fruits of their labor.

As business conditions erode, be sure to stay on top of the metrics that tell you which customers are under-performing.

## September 24, 2007

### Does A Website Drive Sales In Retail Stores?

Please click on the image to enlarge it.

One of the more flummoxing issues facing multichannel retailers is quantification of the sales that a website drives to a retail store.

There aren't a lot of "best practices" for quantifying this issue.

Some folks want to give the website credit for any purchase initiated online. For instance, if a customer ordered online, and picked-up the item in-store, some folks want to give the website "credit" for that order. Some feel that any retail purchase that was researched online deserves to be credited, at least in-part, to the online channel.

Retail folks will claim that without stores, the website never would have existed, and therefore, the stores should get credit for all orders, even orders that happen online. Many retailers include online sales in their comp store sales calculation --- artificially propping up tepid retail comps with +25% online growth. Read the 10-K statements issued by retailers, you'll see that this is a common practice.

Multichannel Forensics can be used to simulate what might happen if a website no longer existed, was no longer there to support store sales.

Using this free spreadsheet, the analyst can plug in retail/online metrics, view the forecast, then zero-out the online portion of the business.

In the attached example, retail sales were growing at about four percent per year. Once the website is shut down, the simulation suggests that retail sales stop growing. Of course, all web sales disappear as well.

Theoretically, some assumptions can be made for perceived cannibalization between retail and online sales. In lieu of the actual, quantifiable metrics, this example shows that the website probably has a real, long-term impact on retail sales.

Plug the dynamics of your business into the spreadsheet. You can get an introductory view into the impact your website might have on retail growth.