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!

OMS: Up-Selling and Cross-Selling

Up-Selling and Cross-Selling merchandise to a customer is an established e-commerce best practice, right? Retailers love to capture the additional margin dollars at the end of a transaction by offering the customer the opportunity to add an item to her order.

With a standard Web Analytics software tool, we can identify how customers respond to a cross-sell or up-sell opportunity.

With the Online Marketing Simulation (OMS), we can see what the downstream impact is of a customer who responds to an up-sell or cross-sell opportunity.

Let's simulate 1,000 new customers who purchased three items for $50 each via paid search. In my dataset, I have eight merchandise divisions. Let's assume that the customer purchased three items from merchandise division #3. Here's a five year simulated run for this customer:

• 12 Month Repurchase Rate = 24%.
• Future Demand: Year 1 = $51,000, Year 2 = $35,000, Year 3 = $28,000, Year 4 = $25,000, Year 5 = $23,000.
• Merchandise Division #1 Buyers: Year 1 = 22, Year 2 = 15, Year 3 = 12, Year 4 = 11, Year 5 = 11.
• Merchandise Division #2 Buyers: Year 1 = 26, Year 2 = 18, Year 3 = 15, Year 4 = 13, Year 5 = 12.
• Merchandise Division #3 Buyers: Year 1 = 128, Year 2 = 72, Year 3 = 52, Year 4 = 45, Year 5 = 42.
• Merchandise Division #4 Buyers: Year 1 = 113, Year 2 = 65, Year 3 = 48, Year 4 = 41, Year 5 = 38.
• Merchandise Division #5 Buyers: Year 1 = 66, Year 2 = 42, Year 3 = 34, Year 4 = 30, Year 5 = 28.
• Merchandise Division #6 Buyers: Year 1 = 20, Year 2 = 15, Year 3 = 13, Year 4 = 12, Year 5 = 11.
• Merchandise Division #7 Buyers: Year 1 = 28, Year 2 = 19, Year 3 = 15, Year 4 = 13, Year 5 = 13.
• Merchandise Division #8 Buyers: Year 1 = 36, Year 2 = 25, Year 3 = 19, Year 4 = 17, Year 5 = 16.

Ok, those metrics don't have much meaning unless you have something to compare them to. Now let's assume that you, the online marketer, were able to cross-sell or up-sell this customer one additional item, a $50 item in Merchandise Division #7.

How does this one additional item, a $50 item in Merchandise Division #7, impact the future trajectory of this customer? We'll pop the results into the OMS ... let's see what the simulation tells us!

• 12 Month Repurchase Rate = 41%.
• Future Demand: Year 1 = $104,000, Year 2 = $68,000, Year 3 = $48,000, Year 4 = $38,000, Year 5 = $33,000.

Let's just stop right there. The simulation suggests that a new paid search customer with this one additional item from a different merchandise division is instantly worth between 50% and 100% more. Clearly, your mileage will vary, some of you will experience no incremental long-term value, some of you will experience double or triple this outcome. The goal, of course, is for you to strategically think whether this issue has applicability to your business.

Here's how customers purchased from the eight merchandise divisions in my dataset.

• Merchandise Division #1 Buyers: Year 1 = 37, Year 2 = 28, Year 3 = 21, Year 4 = 18, Year 5 = 16.
• Merchandise Division #2 Buyers: Year 1 = 39, Year 2 = 33, Year 3 = 25, Year 4 = 21, Year 5 = 19.
• Merchandise Division #3 Buyers: Year 1 = 227, Year 2 = 123, Year 3 = 79, Year 4 = 61, Year 5 = 53.
• Merchandise Division #4 Buyers: Year 1 = 225, Year 2 = 119, Year 3 = 78, Year 4 = 61, Year 5 = 52.
• Merchandise Division #5 Buyers: Year 1 = 86, Year 2 = 73, Year 3 = 55, Year 4 = 45, Year 5 = 40.
• Merchandise Division #6 Buyers: Year 1 = 36, Year 2 = 28, Year 3 = 22, Year 4 = 19, Year 5 = 17.
• Merchandise Division #7 Buyers: Year 1 = 44, Year 2 = 35, Year 3 = 25, Year 4 = 21, Year 5 = 18.
• Merchandise Division #8 Buyers: Year 1 = 55, Year 2 = 43, Year 3 = 32, Year 4 = 26, Year 5 = 23.

Pay close attention to Merchandise Division #7. This was the division where the up-sell / cross-sell item was purchased from. There is some improvement in the number of buyers in this division. Now pay attention to Merchandise Divisions #3 and #4 --- these divisions are expected to get a significant bump in customers.

Yes folks, your actions in one area of your business cause unexpected changes in the business performance of another area of your business. In this example, the cross-sell of one item from Merchandise Division #7 causes Merchandise Division #4 to experience a significant improvement in performance within this customer segment (in fact, almost all divisions experience improvement). Merchandise Division #4, of course, had no role in either simulated transaction --- it simply benefits from something that happens elsewhere in your business.

Would you make different business decisions if you knew how this dynamic impacted your e-commerce business?

This is what Advanced Web Analytics, specifically, the Online Marketing Simulation (OMS) is all about. We're looking to see how the decisions we make today impact the future of our business. Most Web Analytics applications look backward, measuring what happened in the past. The OMS environment looks forward.

OMS: Nuts And Bolts, The Algorithm!

I promised some details!

Here's what I like to do. I'll take client data, and create a set of attributes that are of interest to the management team. In the dataset I'm currently working on, here are the attributes:

• Recency: Months Since Last Purchase.


• Demand12: Demand Spent In Past 12 Months.


• Demand99: Demand Spent 13+ Months Ago.


• Price_Item: Average Price Per Item Purchased.


• Items_Order: Average Number Of Items Per Order.


• 24 Channel Attributes: This business has twelve purchase channels (i.e. paid search, affiliates, etc.). I create 1/0 (1=yes, 0=no) indicators that tell if the customer purchased from that channel in the past 12 months, and if the customer ever purchased from that channel 13+ months ago. Dollar values can also be used instead of 1/0 indicators.


• 16 Merchandise Attributes: This business has eight tabs, if you will, across the top of the homepage, representing eight merchandise divisions. I create 1/0 (1=yes, 0=no) indicators that tell if the customer purchased from that merchandise division in the past 12 months, and if the customer ever purchased from that channel 13+ months ago. Dollar values can alos be used instead of 1/0 indicators.

So, this dataset has 45 variables, one row per customer. The file contains data through today.

Next, I need to reduce the dimensionality of the database. There's literally an infinite number of ways to combine the 45 variables, right? Somebody could have last purchased 1 month ago, spending $64.95, whereas another customer could have last purchased 1 month ago, spending $61.95.

I do this via a combination of Logistic Regression (Response), Ordinary Least Squares Regression (Spend), and "Factor Analysis" (Merchandise and Channels). Yes, I realize this is geeky.

The combination of Logistic Regression, Ordinary Least Squares Regression, and Factor Analysis result in a series of "strategic segments". Each segment is a combination of customer quality, channel preference, and merchandise preference. Some of the segments are very responsive, some are not responsive. Some buy from all merchandise divisions, some only buy from one merchandise division, and have a specific channel preference.

For smaller companies, I limit the number of segments to 100 or less. For bigger companies, I'll use 1,000 or more segments ... it all depends upon how many customers end up in each segment.

Once I determine what segment a customer resides in, I create a brand new dataset, replicating every variable for every customer as the customer looked exactly one year earlier (in social media, you might use a week timeframe instead of the yearly timeframe we use in e-commerce). I create the same segmentation strategy, and assign a segment id to each customer based on the way the customer looked last year.

Now each customer is assigned to a segment from one year ago, and a segment today. I aggregate this dataset down to every last-year / this-year segment combination (100 x 100 = 10,000 segment combinations). When simulated over five years, there ends up being 100 x 100 x 100 x 100 x 100 = lots of combinations!

Once I have the 10,000 segment combinations, I can take a sample customer (i.e. first time buyer purchasing an iPod via paid search), and simulate how that customer will migrate and evolve over the next five years. I can see what merchandise that customer will buy in the future, I can see the channels the customer will purchase from in the future, and I can calculate the incremental demand and profit the customer will generate.

Best of all, I can compare this customer vs. any other customer, to see how customers will evolve. Will a paid search iPod buyer evolve differently from an e-mail inkjet printer buyer? Will either customer use a retail channel in the future? Am I unwittingly altering the future trajectory of my business by optimizing for inexpensive keywords?

These are the problems that CEOs are asking me to solve for them, problems not easily answered by a typical Web Analytics toolset.

At a 30,000 foot level, that's the nuts and bolts behind the Online Marketing Simulation (OMS) that I've developed.

As we work through future examples, consider the following questions:

1. Can my Web Analytics software tool do this analysis?


2. Can my Web Analytics analyst do this analysis for me?


3. Can my Web Analytics software vendor do this analysis for me?


4. Can the leading Web Analytics consultants / bloggers do this analysis for me?

If the answer to each question is "no", then the Online Marketing Simulation (OMS) is something you'll want to investigate.

Gliebers Dresses: Marketing Candidate Interview Schedule

The Gliebers Dresses Executive Meeting will focus on candidates for the open Chief Marketing Officer position.

Glenn Glieber (Owner): "... it really is amazing that Sarah Wheldon is telling the world that Anna Carter is going to shut down their catalog division in 2010. Are they stupid? Is she stupid? Was she stupid when she worked here? She was the biggest catalog advocate in this whole company. She added all of those catalog in-home dates in order to grow sales. Now, she's being interviewed on CNBC, bragging about how Anna Carter will save all of this money, telling the world that there are enough online channels to more than make up for the sales lost by a paper catalog. I even heard that Catalog Decide, the leading opt-out vendor, is thinking of naming her their Executive of the Year. Cripes, all of this publicity will cause their outcome to be positive. But don't people understand that the catalog is the backbone of the brand? She's going to kill their business, I'm telling you. Their web sales will plummet by 40% or 50%, they won't have any telephone sales, and Anna Carter is going to simply 'go off'. Maybe Anna hired her to be the fall person for when this strategy fails."

Meredith Thompson (Chief Merchandising Officer): "Kevin, is that you?"

Kevin: "Yup, it's me".

Candi Layton (HR and Chief Customer Officer): "Well, Pepper and I combed through the 275 resumes that we received, and we've narrowed the field down to three highly qualified individuals. During the next two weeks, we will fly the candidates to New Hampshire."

Meredith Thompson: "Will we continue our long-standing tradition of group C-Suite interviews?"

Candi Layton: "Absolutely. There's no reason to mess with tradition here at Gliebers Dresses."

Roger Morgan (IT and Operations): "Oh, I'm so excited! It is so much fun to cross-examine potential candidates so that we can evaluate their skills and group dynamics. Lois, remember what I asked you when you interviewed with us last year?"

Lois Gladstone (Chief Financial Officer): "You asked me to tell you the capital of Nebraska."

Roger Morgan: "And the look on your face when you answered 'Lincoln' told me that you could handle pressure. Those dot.com folks at Google and Microsoft ask goofy questions like that, and look at what kind of candidates they recruit ... they get the best of the best, folks, while we all get the best of the rest. We need to compete with them, to use their tools to our benefit!"

Candi Layton: "So we have three candidates. Maria Garcia is the Online Marketing Executive at BlueDotRedDotGreen.com, an online brand that allows users to make personalized gifts. Duncan Berkshire is the Divisional Vice President of Brand Marketing for Blast Candy Bars, and Stan Klepsky was previously the Executive Vice President of Marketing for the Bentley catalog, they sell jewelry. He was downsized in December. I guess Bentley chose to promote the Online Marketing Director to the EVP/Marketing position."

Roger Morgan: "Just think of the questions I can ask, questions that will get these candidates out of their comfort zone!"

Meredith Thompson: "Were there any candidates that had fashion apparel experience?"

Candi Layton: "Well, Pepper, obviously. But otherwise, no, there were no candidates that were at a Sr. Management level with fashion apparel experience."

Meredith Thompson: "Why interview these folks, why not just put Pepper in the job?"

Roger Morgan: "Can we interview Pepper first? Pepper, quick, what is the capital of Idaho?"

Candi Layton: "Kevin, are there things that you look for when you interview marketing leaders?"

Kevin: "Regardless of the position, I like to create a 'quiz'. Since all interviews require some level of interpersonal banter, it is very likely that each candidate fails to receive equal treatment. The quiz has maybe five or ten open-ended questions. You're trying to learn how the candidate approaches certain issues."

Lois Gladstone: "What type of questions would you ask?"

Kevin: "I'd ask technical questions. For instance, if a catalog had 96 pages and was going to generate $3 million in sales, and you increase the catalog to 124 pages, what do you think the sales estimate is for this catalog?"

Roger Morgan: "$3.6 million?"

Kevin: "I'd ask the candidate how s/he would grow sales from catalog marketing, knowing that sales from catalog marketing have been in decline for more than a decade, just to hear how the candidate thinks about the problem."

Roger Morgan: "Mail more!"

Kevin: "I'd ask the candidate questions about the future of marketing. How would the candidate implement a mobile marketing strategy? How would the candidate measure whether social media is worth spending time on, assuming that social media will never be responsible for more than 1% or 2% of sales? How would the candidate decide which widgets to implement on the homepage? How would the candidate develop a 'pull marketing strategy'? How would the candidate integrate e-mail and social media? How many versions of an e-mail marketing campaign maximize sales? What would happen to the sales of e-mail subscribers if you stopped sending e-mail campaigns to them?"

Roger Morgan: "Do we have answers to any of those questions?"

Kevin: "I'd give the candidate a series of metrics from a recent pay-per-click campaign, and ask the candidate to calculate profit-per-new-customer. I'd give the candidate a series of events, you mail a catalog on June 1, the customer receives an e-mail on June 3, the customer orders on June 5 after clicking through the e-mail ... and then I'd ask the candidate to determine the percentage of the order that was driven by the catalog, vs. the percentage of the order driven by the e-mail campaign. You're looking to see how the candidate thinks, and you can objectively compare the answers of each candidate. And I'd ask the candidate theoretical questions about how the candidate would deal with promotions and firings and determining bonus payouts and conflicts with the owner."

Candi Layton: "Kevin, can you come up with the list of questions, and then we'll let each candidate know that there will be a one hour quiz?"

Kevin: "Sure."

Roger Morgan: "I'm really looking forward to this!"

Glenn Glieber: "Ok folks, on to the next topic. Lilly Benson in Accounts Payable says that a deer stands outside her window every day, eating our flowers and, in general, the deer stares at her and freaks her out. She wants the deer shot. She says it is an eight pointer, so it would be quite the prize for somebody. Jennifer Tillman at the Call Center heard about this, and thinks that is an inhumane way to deal with a simple problem. Jennifer wants to spray the plants with a formula that discourages deer from eating the plants, and then Jennifer wants to trap the deer, and haul the deer to Maine. But Pat Thorson in design lives in Maine, and thinks it is wrong to transport animals across state lines. Roger, can you develop a plan to deal with this deer situation by early this afternoon?"

OMS: The Online Marketing Simulation

This is the fourth part of our series on Advanced Web Analytics and Online Marketing Simulations (OMS).

The goal of an Online Marketing Simulation (OMS) is to help us see how decisions that are made today influence the long-term health of our business. We're going to use an analysis process that is not commonly, if ever, used in Web Analytics.

We manage the Online Marketing Simulation (OMS) by linking conditional probabilities, simulating how a group of customers are likely to evolve over the next five years (or if you're analyzing social media, maybe the next five days!).

Let's look at a very simple example. You have three micro-channels in your online business.

1. Online Orders via E-Mail Marketing
2. Online Orders via Paid Search
3. All Other Online Orders

In this simple example, 10,000 customers purchased in 2007 via paid search, not purchasing via e-mail marketing or via any other method of generating an online order. We follow the 10,000 customers to see how they evolve during 2008. Let's assume that the 10,000 customers migrated as follows in 2008:

• 6,000 did not purchase during 2008.
• 1,500 purchased via all other online orders.
• 1,200 purchased via paid search.
• 200 purchased via paid search and all other online orders.
• 700 purchased via e-mail marketing.
• 100 purchased via e-mail marketing and all other online orders.
• 200 purchased via e-mail marketing and paid search.
• 100 purchased via e-mail marketing, paid search, and all other online orders.

Since we are analyzing three micro-channels in this example, all via yes/no indicators, we have 2*2*2=8 possible future outcomes.

The majority of customers (6,000 of the 10,000, 60%) did not purchase during 2008.

Notice that 1,700 customers purchased via paid search during 2008. This is one of the interesting things that we don't take into account when using the web analytics tools from the leading paid and free vendors to measure conversion rates --- we don't factor in how today's actions influence tomorrow's business. In this example, 10,000 paid search customers in 2007 yield 1,700 paid search customers in 2008.

Are you budgeting for future paid search activity that you are causing because of today's paid search optimization activities?

This is what the Online Marketing Simulation (OMS) environment does. We look at the future trajectory of all customers. Instead of looking at three dimensions (paid search, e-mail, all other), we look at a dozen or two dozen or more dimensions. We look at many combinations of prior activity, measuring the percentage of customers who migrate to a future state of activity. And we don't have to look only at advertising micro-channels, we can fold in the merchandise categories the customer purchases from (or views online if you wish). Once each customer is placed in his/her future state, we replicate the process, showing where the customer will migrate in year two, then year three, then year four, then year five.

In the example above, we can estimate how much paid search expense we will incur over the next five years because of today's paid search and conversion rate optimization practices. We can estimate how many customers will purchase via e-mail marketing over the next five years because of today's paid search and conversion rate optimization practices. We can see how one merchandise category will grow or shrink if we change our e-mail marketing strategies. We can sum demand, expense, and profit, short-term and long-term.

In the next OMS post, we'll begin to work through an actual dataset with numerous dimensions, so that you can see how the Online Marketing Simulation (OMS) environment really works. The example will be representative of the type of consulting I do for clients, helping them understand how the online channel will evolve based on today's decisions.

As we work through examples over the next month, ask yourself four questions after each post:

1. Can my Web Analytics software tool do this analysis?
2. Can my Web Analytics analyst do this analysis for me?
3. Can my Web Analytics software vendor do this analysis for me?
4. Can the leading Web Analytics consultants / bloggers do this analysis for me?

If the answer to each question is "no", then the Online Marketing Simulation (OMS) is something you'll want to investigate.

Gliebers Dresses: Winners And Order Starters

It's time for the Tuesday Gliebers Dresses Executive Meeting.

Glenn Glieber (Owner): "So what song do all of you think we should try to license for our annual Homecoming celebration? I'm partial to 'I'm Still Standing' by Elton John."

Meredith Thompson (Chief Merchant): "Kevin, is that you?"

Kevin: "Yup, it's me."

Pepper Morgan (Interim Chief Marketing Officer): "Here's our problem, Kevin. We want to know what we should advertise to our customers."

Meredith Thompson: "See, I think it is important to ride winners, you know, items that work year-in and year-out. I think we should beat those puppies into the ground, extracting as much profit as is humanly possible out of them."

Pepper Morgan: "And I think our brand needs a breath of fresh air. We're all about fashion, and fashion changes, all the time."

Meredith Thompson: "But we're lucky if we hit on three out of ten new items that we introduce. New merchandise is risky. You merchandise pages four and five of a catalog with new merchandise, and if that merchandise fails, we're sunk."

Pepper Morgan: "If we don't feature the new merchandise, then the customer perceives that our brand is stale. Reese Witherspoon didn't wear one of our dresses because we ran it on the homepage for seventeen consecutive months."

Meredith Thompson: "I think we need to protect profit, right Lois? We're not in a position where we can just feature risky items at the front of a catalog, on the homepage or key landing pages, or in e-mail marketing campaigns."

Kevin: "There's a few things we do know. We know that the product that has always worked best has less risk associated with it, and as a result, has better productivity. We also know that the items that work best in each catalog, on average, are new products that go absolutely crazy. We also know that the items that are dogs in each catalog are, on average, new products. Our 2010 contact strategy employs a strategy to capitalize on this issue. Recall that the small page count catalogs will feature only the best products. Because we can count on the productivity to be high, and because the page counts are small, we can mail very deep into the customer file and prospect list. With new products, the risk is greater, so we only advertise new products to the best customers, thereby mitigating the risk of offering a poor-performing product to a poor-performing customer."

Meredith Thompson: "But what do you feature in a catalog or e-mail campaign or landing page? In other words, even in one of our smaller catalogs, do we feature newer products, or time-tested winners?"

Kevin: "Have you run an 'order starter' analysis?"

Pepper Morgan: "What is that?"

Kevin: "If your order entry system captures the first item a customer asks for in an order, followed by the second item, then the third item, and you assume that this is the order of purchase intent for the customer, then you can record the items that cause customers to 'start' an order. Roger, does the order entry system record information in this manner, and then feed the customer database in this manner?"

Lois Gladstone (Chief Financial Officer): "Roger is out of the office today, he's speaking at an e-commerce conference about multichannel marketing integration. But I believe the databases are populated that way."

Meredith Thompson: "What does Roger know about multichannel marketing integration?"

Kevin: "Any item that appears first in an order is given a value of '1'. The item that appears second is given a value of '2', and so on. Take all of your new and existing items featured in catalogs and e-mail campaigns, and see which items 'start' orders. In theory, those are the items that could be merchandised at the front of a catalog, or featured in an e-mail campaign. Typically, but not always, you'll see that the first twenty pages in a catalog should have a decent number of order starters featured. You'll often see that e-mail campaigns work well when order starters are featured in the creative. Your mileage may vary, but at least do the analysis to find out."

Candi Layton (HR and Chief Customer Officer): "I'll ask my Twitter followers to weigh in on the topic, ok?"

Lois Gladstone: "We'd be better off having Reese Witherspoon in the first twenty pages of every catalog, and in every e-mail campaign. She can start some orders for us!!"

Pepper Morgan: "We did ask her PR team if she'd be willing to accept compensation in exchange for a series of catalog and e-mail marketing and homepage appearances. Her PR team turned down our request."

Lois Gladstone: "How about Morgan Fairchild? Is she available?

Candi Layton: "Who?"

Lois Gladstone: "Or what about Susan Sarandon? Didn't she wear a Gliebers Dress in Bull Durham?"

Meredith Thompson: "No, that was an Anna Carter dress."

Lois Gladstone: "Rats."

Glenn Glieber: "I think we've exhausted this topic. Thanks Kevin, we'll have Bow Tie Guy run the order starter analysis for us. Now let's get back to the theme song for the annual Homecoming celebration."

Candi Layton: "What about 'Every Morning' by Sugar Ray? I mean, we come in here and work every single morning, don't we?"

Lois Gladstone: "What about 'Every Day Of The Week' by Jade? We come in here and work every day of the week, don't we?"

Meredith Thompson: "Rainy Days And Mondays Always Get Me Down?"

Lois Gladstone: "Wasted Days And Wasted Nights by Freddy Fender?"

Glenn Glieber: "PEOPLE, I'm serious! I need a theme song."

From Conversion Rate to Repurchase Rate to Multiple Probabilities

We're up to the third part in our series on Web Analytics and Online Marketing Simulations (OMS).

My central thesis is that by emphasizing conversion rates, we optimize our business based on the advertising sources that cause a customer to purchase now. By doing this, we create an inefficiency. We overlook customers who yield a positive outcome in what we deem an inefficient manner.

Here's an example.

• We all know that pay-per-click customers convert at less-than-thrilling rates.
• We know that pay-per-click customers can be expensive, maybe costing us $0.10 per click, or $0.40 per click, or $0.70 per click.
• Over time, pay-per-click customers can become e-mail subscribers.
• And e-mail subscribers often have higher-than-average conversion rates if they click-through an e-mail campaign.
• And e-mail marketing is really close to free, having virtually zero variable cost.

If we want to optimize conversion rates, we'll steer ourselves away from expensive pay-per-click programs with low conversion rates, right? At the same time, we'll want to maximize our e-mail marketing program, with low costs and high conversion rates.

If we want to optimize repurchase rates, we'll take a different action. We want pay-per-click customers, because pay-per-click customers become e-mail subscribers. We want to optimize the multi-year process of acquiring an expensive pay-per-click customer who becomes a profitable e-mail customer.

If we optimize via conversion rate, we won't "seed" our business with the pay-per-click customers who become e-mail subscribers with high conversion rates. We optimize our business in the short-term, but create a long-term inefficiency that limits our ability to grow over time.

We have an opportunity to add to our responsibilities. We have an opportunity to measure what are called "conditional probabilities". Here are examples of conditional probabilities:

• What is the probability of a customer becoming an e-mail subscriber, given that she last purchased via pay-per-click?
• What is the probability of a customer becoming a loyal customer, given that she has become an e-mail subscriber?
• What is the probability of a customer buying from multiple channels, given that she has become a loyal customer?

By linking each of these conditional probabilities, we arrive at a customer that generates an optimal amount of profit, over time. Within each step, we may have numerous instances of sub-optimal conversion rates, but those sub-optimal situations result in a customer that is optimally profitable. We combine conditional probabilities with demand and profit calculations, allowing us to simulate the future based on the actions we manage today.

From an Advanced Web Analytics standpoint, we create a table that records customer actions in a prior period of time, and in a future period of time. In the future period of time, we also tag the amount of demand the customer generated in the future period of time. The list of variables below is not exhaustive, and variables can be combined (receive catalog, buy via pay-per-click), creating what I call "micro-channels".

Prior Period of Time (1 = yes, 0 = no).

• Did customer visit the website?
• Did customer put merchandise in a shopping cart?
• Did customer purchase from the website?
• Did customer purchase multiple times from the website?
• Did customer purchase via e-mail?
• Did customer purchase via pay-per-click?
• Did customer purchase via affiliates?
• Did customer purchase via offline catalog marketing?
• Did customer purchase via display ads?
• Did customer purchase via social media?

The same set of variables are replicated for a future period of time, along with demand and profitability (if available) metrics. Obviously, the same customer will not have the same attributes, as customer behavior changes.

The prior timeframe is usually defined as a year, the future timeframe is usually defined as a year. That being said, there's no reason you cannot explore different timeframes, weeks, months, seasons, etc. However, you identify more inefficiencies, more opportunities for profit, when you lengthen the timeframe.

When the dataset is created, the analysis begins. We begin to link the conditional probabilities together, finding customer behavior that leads to long-term sales and profit. In our next blog post in this series, we'll begin to explore how this information comes to life.