## January 15, 2008

### Standardized Multichannel Performance (SMP)

Sometimes, it is difficult for a CEO/Owner to understand whether catalog productivity is increasing, or decreasing.

Let's look at a Spring catalog, over the past three years, plus the forecast for 2008 (sales volume in 000s):

 Four Year Performance For The Spring Catalog Year Pages Circ Phone Online Stores Total 2005 124 800 \$1,900 \$800 \$500 \$3,200 2006 116 1,000 \$1,950 \$850 \$624 \$3,424 2007 132 1,200 \$2,175 \$1,100 \$663 \$3,938 2008(p) 140 1,300 \$2,425 \$1,350 \$675 \$4,450

In 2008, the circulation team wants to increase pages and increase circulation, citing increased volume across all channels.

Sound good?

Let's find out if the plan is good.

I introduce a metric called "Standardized Multichannel Performance", or "SMP".

For this business, we standardize the performance of each catalog to a 100 page catalog circulated to 1,000 households. By doing this, we can theoretically compare each catalog on a "comparable" basis. We do this adjustment if we don't have any good "comp segment" information to compare.

Step 1: Calculate an adjustment factor as follows:
• ((100 pages / actual pages) ^ 0.5) * ((1,000 circ / actual circ) ^ 0.5).
• 2004 example: ((100 / 124) ^ 0.5) * ((1,000 / 800) ^ 0.5).
• 2004 example: (0.806 ^ 0.5) * (1.25^ 0.5).
• 2004 example: (0.898) * (1.118) = 1.004.
Step 2: Multiply the adjustment factor by actual multichannel sales volume.
• Adjustment Factor * Multichannel Sales Volume
• 1.004 * \$3,200 = \$3,213.
In this catalog were 100 pages, mailed to 1,000 households, it would have generated an SMP of \$3,213, just over \$3.2 million dollars.

Now, we repeat this process for every catalog in the series.

 Four Year Performance For The Spring Catalog Year Pages Circ Phone Online Stores Total SMP 2005 124 800 \$1,900 \$800 \$500 \$3,200 \$3,213 2006 116 1,000 \$1,950 \$850 \$624 \$3,424 \$3,179 2007 132 1,200 \$2,175 \$1,100 \$663 \$3,938 \$3,129 2008(p) 140 1,300 \$2,425 \$1,350 \$675 \$4,450 \$3,299

If we mailed a similarly sized catalog to a similar audience, the "SMP" suggests that this catalog would have decreased in performance, from \$3,213,000 in 2005 to \$3,179,000 in 2006 to \$3,129,000 in 2007.

In other words, this catalog is performing worse, year over year.

And in 2008, your circulation team is forecasting a productivity increase of ((3,299 / 3,129) - 1) = 5.4%.

Unless your circulation team found lists that are expected to perform exceedingly well, or your merchants found product that is significantly better than in prior years, you may have a catalog forecast that is not achievable.

Catalog execs should have this little formula tucked-away in a spreadsheet. Choose a page count that is "average" for your business. Choose a circulation depth that is "average" for your business. Once you identify these metrics, calculate "SMP" for each catalog, as well as the catalogs you have planned for this year. You should be able to identify productivity trends and forecast errors easily, using this methodology.

1. Good timely topic.. The author points to a number of factors that very important.

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2. Anonymous10:20 AM

How does one go about validating the adjustment factor, the square root law or in general power law of this diminishing returns or marginals?

3. It's harder to figure out for the number of pages in a catalog, since it is very expensive to test that metric.

For circulation depth, it's not too challenging. You probably have RFM segments that you rank from best to worst. Just look at the dropoff in performance of your RFM segments ... if you have the tools, do a regression equation --- test different functions (like the square root function).

Overall, having done maybe fifty different models over the past fifteen years, I find that a power function anywhere between 0.3 and 0.7 (with 0.5 = square root) is appropriate for direct marketing data.

4. Anonymous1:52 PM

Thank you for the explanation.

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