Complimenting Web Analytics With Customer Insights

Your CEO makes a rare and unexpected appearance at your cube. Her appearance causes you pulse to increase, and causes your mouth to become dry. You quickly close out of the browser that is looking at The MineThatData Blog, and spin your chair in the direction of the inquiring executive who earns twenty times your salary.

Your CEO asks a simple question. "Why is our business missing expectations by ten percent?"

Here is where incomplete knowledge hinders the humble web analyst. The CEO might want to hear an answer like this: "Customer acquisition activities are meeting expectations. However, existing customers are not visiting the site as often as last year, not repurchasing at the same rate as last year, and are purchasing merchandise at lower price points than last year, contributing to our shortfall. We believe lower e-mail open rates are contributing to lower levels of site visitation."

Your CEO does not want to hear this: "We are seeing a fourteen percent decrease in organic traffic, partially offset by a seven percent increase in traffic from paid search. Conversion rates have actually improved, from 4.07% last year to 4.13% this year. This increase occurred because the mix of new and existing customers skewed toward existing customers, coupled with minor improvements in shopping cart abandonment. Our CPA improved, from $29.48 last year to $28.97 this year, illustrating improvements in response to portal advertising and favorable changes in the mix of partners in our affiliate program. E-mail open rates continue to decrease, in accordance with well-documented industry trends, resulting in a significant decrease in overall productivity."

The latter response actually demonstrates superior knowledge of the business than the first response. The first response tells a story that is easier for the executive to digest and act upon.

All too often, web analytics folks have an incomplete view of the business, caused by a failure of web analytics tools to view customer behavior across time. Web analytics tools are great at demonstrating what happens within a visit. These tools do a terrible job of illustrating what happens to one customer over time. Couple an incomplete view of the customer with an overly technical dissertation of business results, and you have trouble.

Web analytics gurus would be well served to forge partnerships with customer insight analysts. You know who these people are. They are the folks who sit on a different floor of your building, writing bizarre programming code in some obscure language called "SAS" or "SPSS". These are the people the CEO visited back in 1994 when a catalog wasn't meeting expectations.

The goal of this partnership is to combine metrics across platforms, so that all analytics individuals have a unified understanding of customer behavior, and can develop a more complete story about customer behavior.

There are a series of metrics that could be generated by this forged partnership. Let's explore some of these metrics. The following list is by no means exhaustive. Feel free to add metrics in the comments section of this discussion.

Probability Of Visiting Site, Existing Customers: Compare the rate at which last year's customers are re-visiting the site this year. If 94.2% of last year's customers are visiting the site this year, compared with 92.4% last year, you know that you are getting people to at least visit your website at better rates that last year.

Visits Per Existing Customer: Among those who do come back to your website, how many times are they visiting this year, verses last year? If existing customers who do come back to the website visit 7.42 times this year verses 9.82 times last year, you might have a problem with marketing efforts to drive customers back to the website.

Retention Rate: Web analytics tools are not well-suited to illustrate customer behavior across time. Work with the customer insight team to measure the difference in retention rate. For instance, assume that year-to-date, 63.2% of last year's buyers have purchased again, whereas last year, 68.8% of prior year buyers purchased again. This tells you that your existing customers are less loyal than last year. You can compare this metric with visits per existing customer. In this case, customer visit rates may be causing the reduction in retention rate.

Orders Per Buyer: Analyze how many times each of your retained customers purchased this year, compared with last year. If retained customers purchased 3.26 times this year, verses 3.09 times last year, you know that the customers who are purchasing are actually increasing their loyalty.

Units Per Order: Measure how many items a customer purchases, when they buy something. As an example, assume that this year, customers are purchasing 2.48 items per order, whereas last year, customers were purchasing 2.31 items per order. This suggests that customers are willing to purchase more merchandise, a good thing!

Price Per Item Purchased: The price of each item is an important metric to measure. If the price of an average item purchased is $38.42 this year, and was $42.99 last year, you know that customers are purchasing less-expensive items, offsetting the fact that they are purchasing more units per order.

Average Order Size: This is a simple multiplication of Units Per Order by Price Per Item Purchased. In our example, the AOS is $95.28, verses $99.31 last year. Customers are spending less per order, because they are purchasing less-expensive items than last year.

New and Reactivated Customers: This is a simple count of the number of first time customers, and number of reactivated customers (those who haven't purchased in several years. Assume that this year, 125,000 new customers and 35,000 reactivated customers purchased, whereas last year, 85,000 new customers and 25,000 reactivated customers purchased. This tells you that there are significant improvements in getting new and infrequent customers to purchase from the website. The analyst needs to take this a step further, measuring the orders per buyer, units per order, price per item purchased and average order size for these two audiences.

By combining information from a web analytics tool with information from the customer insights warehouse, a more complete story can be told regarding customer behavior. In this case, the problem seems to exist among getting existing customers to come back and visit the site on a frequent basis. The web analytics team and the customer insights team can work together to understand the reasons that are causing this problem.

American Girl and Molly's Blog: A Great Example of Customer Engagement

American Girl is a respected multichannel retailer, with stores, catalogs and a website.

American Girl customers can purchase a doll named Molly McIntire. Molly was conceived in books, and moved onto the big screen. To support these mediums, American Girl publishes a blog about Molly.

Look at the engagement Molly's fans have with American Girl. The post from December 4, entitled "Plan The Next Movie", encouraged 374 comments in just three days. American Girl gets free advice from their most avid fans, advice that helps shape future marketing activities. What a spectacular way to solicit feedback!

I'd give American Girl an A+ for finding unique ways to have a conversation with customers. Are you aware of any other companies that achieve this level of engagement with customers?

P.S.: I'd love to measure how this blog impacts e-commerce.

Do You Like Your Web Analytics Software Package?

From the LunaMetrics blog (a must-read if you are into web analytics), a discussion from various retailers about the shortcomings of web analytics packages. Sad .... very sad. Do web analytics tools fail to meet your expectations? (FYI, I use Google Analytics, MyBlogLog and Sitemeter on my site).

Best Buy Verses Circuit City

Best Buy and Circuit City are both leading multichannel retailers. Each chain features hundreds of stores with the latest electronics and entertainment. Each chain features websites with above-average functionality. Both chains lead the multichannel industry with buy-online, pickup-in-store functionality, thereby cancelling out any possible multichannel advantage. Both chains feature a similar merchandise assortment. Both chains have similar pricing on common items.

So why do you, the loyal reader and avid multichannel consumer, choose one of these retailers over the other? When everything between the chains is essentially the same, what causes you to pick one over the other?

If Best Buy verses Circuit City were an election, I'd give the nod to Best Buy, by a 51-49 margin. I don't dislike Circuit City. I just ever-so-slightly prefer the layout of a Best Buy store, I prefer the lighting, I prefer the signage.

In a world where everything is essentially the same, the experience becomes so vitally important, because it is the only thing that differentiates one business from another.

What do you think? Which chain do you prefer, and what is your reason for your preference? Is there anything that Best Buy or Circuit City does that places one ahead of the other in your mind?

A/B Test Design And Incremental Multichannel Campaign Performance

Never before has the traditional "A/B" test been as important as it is in our multichannel ecosystem. Such a simple concept, the "A/B" test is uniquely designed to measure the incremental performance of marketing activities.

As an example, assume a multichannel organization mails a catalog to a housefile list of 1,000,000 names. The database marketer chooses the best 1,100,000 households, and randomly splits them into two groups. The "A" portion of the test are the 1,000,000 households who receive the catalog. The "B" portion of the test are 100,000 households who will not receive the catalog.

Maybe a month after the in-home date, the database marketing analyst is prompted to analyze the results. Within each group, the 1,000,000 who received the catalog, and the 100,000 who didn't receive it, the analyst calculates the average net sales within the catalog/telephone channel, the online channel, and the retail channel.

Here are sample results:

	Quantity	Telephone	Online	Retail	Totals
Received Catalog	1,000,000	$6.00	$8.00	$21.00	$35.00
Did Not Receive Catalog	100,000	$2.50	$7.00	$19.50	$29.00
Incremental Lift		$3.50	$1.00	$1.50	$6.00

In this example, the catalog drove an incremental $3.50 per customer to the catalog/telephone channel, $1.00 per customer to the online channel, and $1.50 per customer to the retail channel, for a total of $6.00 incremental sales per customer.

Because we mailed 1,000,000 households, the total net sales attributed to this mailing is 1,000,000 * $6.00 = $6,000,000.

Some vendors advocate a different methodology --- they advocate allocating any online and retail order generated during the time the catalog was active to the mailing of the catalog. This results in a gross over-estimation of the importance of the catalog. Please don't go down this path.

A similar methodology can be used to test multiple marketing activities at the same time. Assume an e-mail campaign was mailed to the opt-in portion of this audience. Within this audience, you randomly assign customers to one of four test segments. Here are some sample results.

	Quantity	Telephone	Online	Retail	Totals
Catalog + E-Mail	400,000	$5.50	$8.50	$21.25	$35.25
Catalog Only	50,000	$6.00	$8.00	$21.00	$35.00
E-Mail Only	50,000	$3.00	$8.10	$18.65	$29.75
No Catalog, No E-Mail	50,000	$3.50	$7.00	$18.50	$29.00

Tests like these yield interesting and intriguing results. Notice that the best strategy for the catalog/telephone channel was to mail only a catalog. The best strategy for the online channel was to mail a catalog and an e-mail. The best strategy for the retail channel was to mail both a catalog and an e-mail.

Statisticians can assist with significance tests, if you feel that is appropriate. It is more important to simply execute tests of this nature, and learn how all of your marketing activities interact with each other. What you learn about how marketing activities and channels interact with each other within our multichannel ecosystem may surprise you.

Multichannel Retailing: A Complex Marketing Ecosystem

Last month, I had an e-mail exchange with Jim Fulton, author of two of the most popular posts in MineThatData history. We were discussing whether the online channel was more similar to the catalog channel, or the retail channel. Earlier today, I had an e-mail exchange with fellow Green Bay Packers fan Jeffrey Hassemer about the ecosystem our catalog, online and retail channels currently reside in.

A similar theme from each conversation arose, the idea that our multichannel retailing environment has become a gigantic ecosystem, full of inter-dependencies that executives did not have to deal with a decade ago.

In 1994, a catalog executive had the illusion of control. She decided that she would have 17 in-home dates. She decided how many pages she would have in each catalog, she decided the merchandising assortment, she determined the creative treatment. Her decisions about when catalogs were mailed, circulation depth and list, page counts, and merchandise assortment yielded a predictable amount of demand, so predictable that a call center and distribution center could be reliably staffed on the forecast of volume generated from the catalogs. If the executive wanted her business to be a $100,000,000 business, only cash, the availability of quality lists, and the ability to forecast and acquire inventory stood in her way.

Let's assume that in 2006, the catalog executive is still in charge of what is now a business that is at least 50% catalog and 50% online, if not skewed more online than catalog.

A giant marketing ecosystem now controls her business. Our executive manages a catalog housefile, an online housefile, and an e-mail subscriber list. Twelve years ago, the catalog executive mailed one catalog that drove $5,000,000 in net sales. Today, she mails two catalogs, eight e-mail campaigns, one-hundred affiliate programs, two portal deals and various search programs to drive $5,000,000 in net sales. Couple that with another $3,000,000 in net sales that are organically generated via the website, and it is obvious that our former catalog executive is living in an out-of-control marketing ecosystem.

Maybe the most challenging transition that direct marketing executives have had to make is the trasition from being "in-control", to allowing the marketing ecosystem to be in-control. Twelve years ago, the catalog executive made decisions, and saw the cause-and-effect of her decisions within a few days of the catalog in-home date. Today, that same executive makes twenty times as many decisions across a veritable plethora of marketing vehicles, and achieves far less control over the outcome.

In the Pacific Northwest, we closely monitor the health of our Orca Whale population. These whales live in a complex and dynamic ecosystem. Their health is impacted by pollution, the quantity of salmon, weather, proximity of tour boats, the number of times our military hurt their hearing system with sonar, the number of transient Orcas passing by. By managing these factors, we can influence the health of the Orcas.

In a similar manner, we direct marketers now "influence" the health of our business. The marketing ecosystem is made up of numerous factors that interact with each other, causing unusual and unpredictable outcomes. No longer is a decomposition of each strategy reasonable. The combination of strategies cause interactions that impact our business. Executives who think in a linear, cause-and-effect manner might struggle in this multidimensional ecosystem.

Over the next several months, I plan on exploring the interaction of products, brands, channels and marketing strategies. Let's have a lively discussion about how direct marketing has changed, and what can be done to better understand our direct marketing ecosystem.

Casey Kasem's American Top 40

If you subscribe to DirecTV, you probably receive several music channels from XM Satellite Radio. Each Wednesday evening at 7:00pm PDT, on Channel 804, they replay an old episode of Casey Kasum's American Top 40. Tonight, they are playing the forty biggest hits from December, 1979, including the long-distance dedications.

The diversity of the artists is surprising. From Kenny Rogers to John "Cougar", Prince to Kenny Loggins, Isaac Hayes to Fleetwood Mac, Andy Gibb to "M", country music to disco, a lot of ground is covered during the airing of forty songs.

Interesting fact: I can record any television show I want, and burn it onto a DVD, using my HD-DVR. I can rewind, pause, fast forward as well. When it comes to the music channels, no-go, those functions are disabled. I can make a copy of the movie "Titanic", but I cannot make a copy of The Eagles "Heartache Tonight", which dived eleven notches to number twenty in the countdown.