Separate Audiences

With each passing day, I become convinced we're approaching the concept of being "multichannel" in the wrong way.

Fifteen years ago, we had epic discussions at Lands' End about "incrementality". We had several catalog titles. Not surprisingly, the VP in charge of each catalog title kept her job if her catalog title increased sales and profit. So each executive mailed the "best" customers.

Which meant that the same customers received everything.

We'd test how much incremental business we received from this strategy. Standard reporting said a catalog generated $4,000,000 sales. Using test/control groups, some catalogs generates as little as $1,000,000 sales, meaning if you didn't mail the catalog, $3,000,000 would be reallocated to existing catalogs mailed previously.

Fast forward to today.

We're getting better at aligning merchandising and inventory management strategies.

From a marketing standpoint, we're still all about doing what we can to be everywhere our best customers are. We mail catalogs, lots of catalogs, to our best customers. We entice these customers to give us their e-mail address, so that we can send them e-mail marketing as well.

Then we make sure we are where our customers are. We have paid search programs to anticipate every possible want our customers have, not to mention portal advertising and shopping comparison marketing and affiliate marketing. If the customer wants to order online, great! If the customer wants to order in a store, great! If the customer wants to order over the phone, out of a catalog, great!

At the end of the day, we put all of our eggs in the best customer basket. Great!

The secret sauce seems to be in finding separate audiences. Yup, we should find unique customers who like e-mail marketing, then serve them with e-mail campaigns. Another subset loves catalogs, so we serve them with catalogs. The wild west of the internet is for customers who love "self-service", the do-it-yourselfer, so online marketing could focus on the self-service audience. We just re-allocate the marketing dollars we control to potential channels offering a unique audience (all of this assumes this can be done profitably, or else, all bets are off).

Separate audiences serve the same purpose as "diversification" in your stock portfolio. When one audience is unresponsive, the other audiences protect sales and profit.

I'm fearful (especially among catalogers) that we put all of our eggs in just one basket, focusing heavily on catalog mailings to drive online sales among a homogeneous customer audience. When sales for this one segment of customers slump, the entire business slumps.

The opportunity to diversify still exists today, but it is a long-term solution. Let's start diversifying, right now.

Multichannel Customers Are Not The Most Profitable Customers

This will probably stick in the craw of the multichannel establishment.

Have your data guru run this query.

• Step 1: Identify all customers who purchased via catalog or online channels during 2006.
• Step 2: Identify customers who were between the 30th and 40th percentile in 2006 spend. In my case, this is $200 - $300 in 2006 spend.
• Step 3: Split this audience into three groups, based on 2006 activity.
• Catalog-Only customers in 2006.
  
• Online-Only customers in 2006.
  
• Multichannel Customers (Catalog + Online) in 2006.
  
• Step 4: Measure repurchase rate, total sales, marketing cost, and profitability for each segment during 2007.

This is a forward-looking set of metrics, the type you need to run to really understand multichannel customer value, after controlling in some limited way for historical spend.

Take a look at this example from a catalog brand:

Future Twelve Month Value Of Last Year's Buyers: $200 - $300 Spend Last Year
Catalog	Web	Cases	Rebuy	Spend	Sales	Mktg	Profit
Yes	No	8,839	50.2%	$295.08	$148.13	$20.00	$24.44
No	Yes	6,217	43.6%	$288.39	$125.74	$9.00	$28.72
Yes	Yes	2,374	55.2%	$295.67	$163.21	$23.00	$25.96

Oh oh. Those vaunted multichannel customers are not the most profitable. Why?

One of the realities of multichannel marketing is that the "best" customers are most likely to be receptive to the "most" advertising channels. So in this case, the catalog brand bombs this customer --- saturating her with a veritable plethora of catalog and e-mail campaigns.

In addition, this customer does her own shopping, independent of catalog and e-mail marketing. She uses Google to search for merchandise. She utilizes affiliates, shopping comparison sites, portals, you name it. The cataloger spends money on all of those channels, so in essence, the customer is spending your marketing money on your behalf!

Your multichannel customer, the one you're focusing all this energy on in order to create a seamless multichannel experience, often end up being less profitable ... and we haven't even factored in systems integration costs yet.

We must find ways to reduce our investment in marketing to multichannel customers, so that self-directed customer investment (paid search, shopping comparison sites, affiliates, portals) doesn't cause an overall "over-investment". History is littered with ways to increase investment in multichannel customers, it is time to go the other way.

I'm guessing three out of four catalogers reading this blog will observe similar results, if this query is run at leading catalog brands.

Is this what you are observing when you measure the total profitability of customer segments?

Multichannel Forensics: Online/E-Commerce Startup Example For Venture Capitalists And Founders

My work is increasingly focused on internet startups, focusing on the challenge of forecasting long-term sales based on almost no customer purchase/usage history.

I try to keep things simple. Here's a business that has been in existence for less than a half year. By recency, here is the probability of a customer buying in any given month.

Recency	Custs.	Buyers	Resp.
1	1,239	88	7.1%
2	812	47	5.8%
3	522	26	5.0%
4	279	12	4.3%

Given the limited amount of information available here, let's take a wild guess at incremental response rates for twelve months of recency.

Recency	Custs.	Buyers	Resp.
1	1,239	88	7.1%
2	812	47	5.8%
3	522	26	5.0%
4	279	12	4.3%
5			3.7%
6			3.2%
7			2.8%
8			2.5%
9			2.3%
10			2.2%
11			2.1%
12			2.0%

Obviously, your guess is as good as mine. We have no idea what will really happen. But it is important to make a guess.

Given that guess, we can estimate a twelve month repurchase rate (using an extension of the life table as described in the Database Marketing book:

• 1 - ((1 - 0.071) * (1 - 0.058) * (1 - 0.050) * (1 - 0.043) * (1 - 0.037) * (1 - 0.032) * (1 - 0.028) * (1 - 0.025) * (1 - 0.023) * (1 - 0.022) * (1 - 0.021) * (1 - 0.020) = 35.5%.

An annual repurchase rate of 35.5% (and the actual rate will be lower, because we average all twelve month buyers, not just following a cohort through a twelve month period) puts this startup in "Acquisition Mode".

Now you might say, "duh, they're a startup, of course they are in Acquisition Mode". And you'd be right. But we're talking about the dynamics this business will need to deal with when it becomes mature, not what it needs to do for the next few years.

The dynamics suggest that if this business survives, its number one focus will always be to aggressively acquire new customers.

Fortunately, there's time to see how the business really evolves. And as the business evolves, these estimates are re-calibrated.

VCs like this information because it gives them tangible data about the long-term trajectory of the startup. If this startup can acquire an ever-increasing number of customers in a cost-effective manner, the startup has potential. Venture Capitalists get an early glimpse into the direction the startup is headed in.

Multichannel Forensics: How A Cataloger Becomes An Online Brand

Times change. Multichannel Forensics are a great way to illustrate how times change if you are a catalog brand.

Let's analyze the repurchase index for the catalog channel and the online channel for a catalog brand.

1999: Catalog to Online = 0.06, Online to Catalog = 0.59. Catalog customers are unwilling to shop online. Online customers transfer back to the catalog channel after purchasing online.

2000: Catalog to Online = 0.11, Online to Catalog = 0.48. To the catalog executive, things don't look dramatically different. Online customers still shift back to the catalog channel. For two consecutive years, it appears that the online channel depends upon the catalog for business.

2001: Catalog to Online = 0.17, Online to Catalog = 0.41. Same old same old, if you're the catalog executive. The online channel grows, but online customers switch back to traditional catalog shopping. However, pay attention to each index, one increasing, one decreasing.

2002: Catalog to Online = 0.23, Online to Catalog = 0.33. At this time, the catalog executive notices that catalog productivity is struggling, while the online channel continues to grow. Something seems fishy.

2003: Catalog to Online = 0.28, Online to Catalog = 0.29. The catalog seems to flounder, while the online folks gloat about their wonderful channel. This is when matchback analytics became popular, "proving" that online orders are driven by catalog orders.

2004: Catalog to Online = 0.32, Online to Catalog = 0.23. Here's where management changes occur. The catalog channel continues to flounder, now shrinking. Conversely, online purchasers are on the verge of becoming unlikely to buy over the telephone.

2005: Catalog to Online = 0.36, Online to Catalog = 0.15. We've passed a key inflection point, no longer able to return to "the good 'ole days". Catalog customers are willing to shop online. Online customers are no longer willing to pick up the phone and place an order. It is here that the traditional cataloger mails online customers like they are going out of style, using matchback analytics to "prove" that the catalog drives online orders.

2006: Catalog to Online = 0.40, Online to Catalog = 0.13. The cataloger doesn't realize it, but the online customer is in many ways becoming a fundamentally different customer, not needing catalog advertising to place orders. Yet, because the cataloger mails most online customers, the cataloger mistakenly "proves" that catalog mailings drive online orders. Multichannel Forensics metrics diverge from matchback analytics.

2007: Catalog to Online = 0.43, Online to Catalog = 0.12. The transition is basically complete. The catalog brand is now an online brand that uses catalog advertising. Online customers keep shopping online, while the remaining segment of catalog customers continue to leak into the online channel.

If a catalog executive sees this trend, then she knows that the days of catalog marketing are winding down.

Once again, the trend looks like this:

• 1999 Cat-Web = 0.06, Web-Cat = 0.59.
• 2000 Cat-Web = 0.11, Web-Cat = 0.48.
• 2001 Cat-Web = 0.17, Web-Cat = 0.41.
• 2002 Cat-Web = 0.23, Web-Cat = 0.33.
• 2003 Cat-Web = 0.28, Web-Cat = 0.29.
• 2004 Cat-Web = 0.32, Web-Cat = 0.23.
• 2005 Cat-Web = 0.36, Web-Cat = 0.15.
• 2006 Cat-Web = 0.40, Web-Cat = 0.13.
• 2007 Cat-Web = 0.43, Web-Cat = 0.12.

What does the relationship look like when the catalog business still matters?

• 1999 Cat-Web = 0.06, Web-Cat = 0.59.
• 2000 Cat-Web = 0.11, Web-Cat = 0.48.
• 2001 Cat-Web = 0.17, Web-Cat = 0.41.
• 2002 Cat-Web = 0.22, Web-Cat = 0.34.
• 2003 Cat-Web = 0.25, Web-Cat = 0.32.
• 2004 Cat-Web = 0.26, Web-Cat = 0.31.
• 2005 Cat-Web = 0.28, Web-Cat = 0.30.
• 2006 Cat-Web = 0.29, Web-Cat = 0.29.
• 2007 Cat-Web = 0.30, Web-Cat = 0.28.

If this is the relationship you see, you likely have three separate customer segments:

• Pure catalog customers who buy over the phone when catalogs are sent to them.
• Online customers who are inspired to purchase by catalogs.
• True online customers who do not respond to catalog marketing.

What does the relationship look like when the catalog business IS the business?

• 1999 Cat-Web = 0.06, Web-Cat = 0.59.
• 2000 Cat-Web = 0.08, Web-Cat = 0.48.
• 2001 Cat-Web = 0.11, Web-Cat = 0.41.
• 2002 Cat-Web = 0.14, Web-Cat = 0.35.
• 2003 Cat-Web = 0.16, Web-Cat = 0.32.
• 2004 Cat-Web = 0.17, Web-Cat = 0.31.
• 2005 Cat-Web = 0.17, Web-Cat = 0.30.
• 2006 Cat-Web = 0.18, Web-Cat = 0.30.
• 2007 Cat-Web = 0.18, Web-Cat = 0.30.

If this is the relationship you see, you are truly a cataloger. Your website basically serves the purpose of a glorified order form. Your survival depends upon the mailing of catalogs. Your future, however, depends upon your ability to cultivate a segment of customers who are willing to shop online, not needing catalogs to purchase merchandise.

The future is a bit murky for some catalogers. Many catalog companies have a head start building a segment of the customer base that shops online not because catalogs are mailed, but simply because the customer loves the brand.

Catalogers who do not have this "insurance policy" will struggle, because everything depends upon the cataloger mailing catalogs. Third parties like the USPS eat away at the brand from a profitability standpoint. Third parties like the DMA and Catalog Choice, via opt-out lists, facilitate customer backlash (though the backlash is ultimately our fault, not theirs), further lowering catalog productivity.

Catalogers who have this insurance policy, a segment of customers who buy exclusively online without the aid of marketing, are able to generate profit without the cost of advertising. This profit offsets the lowered profit from USPS cost increases, increases in paper cost, and customer backlash facilitated by Catalog Choice and the DMA.

So the goal for a cataloger is to understand where the customer is in the evolution from catalog marketing to online purchasing. By using Multichannel Forensics, executives are able to discern where a catalog brand stands on the evolutionary path to an online future. The executive then understands what must be done to protect the business, both short-term and long-term.

Multichannel Forensics: An E-Mail Example

Please click on the image to enlarge it.

I recently read a statement about a multichannel brand ... the statement sounded something like this:

"We boast an e-mail marketing file of over 2,000,000 addresses."

What does that really mean? Is it good to boast an e-mail marketing file of over 2,000,000 addresses?

How do I judge the importance of that number?

Ultimately, I want a healthy e-mail marketing file, not a big e-mail marketing file.

I want my e-mail file to exhibit at least two characteristics.

1. E-Mail recipients click-through content in an e-mail, and visit my website. At least I know these individuals are "active", or "engaged".
   
2. If the customer clicks-through to the website, the customer buys something.

Even more important, I want to know the long-term impact of these two activities. For instance, what is the long-term value of a customer who clicks-through an e-mail campaign, visiting a website, but chooses not to purchase anything?

Enter Multichannel Forensics.

The image at the start of this post features the behavior of an e-mail list of 2,000,000 names, but only looks at the names that are "active". In other words, we only look at folks who click-through e-mail campaigns, and those who purchase merchandise. This audience is considerably smaller, around 150,000 names. Your mileage may vary!

The free two-channel Multichannel Forensics spreadsheet can be used to analyze these cases.

In the example in this post, I can measure the five-year value of 1,000 e-mail customers who clicked-through to the website due to an e-mail campaign, but didn't buy anything last year. Here is what 1,000 clickers in 2007 are forecast to do over the next five years:

• Year 1: 480 Clickers-Only, 120 Purchasers, $30,000 sales.
• Year 2: 254 Clickers-Only, 118 Purchasers, $31,478 sales.
• Year 3: 145 Clickers-Only, 90 Purchasers, $24,404 sales.
• Year 4: 87 Clickers-Only, 63 Purchasers, $17,125 sales.
• Year 5: 54 Clickers-Only, 42 Purchasers, $11,512 sales.

Notice that there is long-term value in a customer who only clicked on campaigns last year, but didn't purchase anything. However, the value of this "clicker" decreases over time.

See, e-mail marketers view everything in the short-term ... a 22.4% open rate, a 6.3% click-through rate, a 3.9% conversion rate ... everything is measured within forty-eight hours, measured in repeated campaigns. There's no context in this type of measurement, there's just measurement!

What if I told you that each customer who clicked-through an e-mail campaign in 2007, but chose not to purchase anything, was worth $123 sales over the next five years?

Would you re-think the importance of getting your e-mail customer base actively involved in your campaigns? Mind you, they don't have to buy anything over the next forty-eight hours. Instead, they simply have to at least click-through one campaign in the next twelve months.

Multichannel Forensics are good to use when you're trying to make a case to truly invest in a smart e-mail marketing strategy, one that goes beyond 20% off your next order or buy-one-get-one-free, one that goes beyond free shipping, one that actually encourages engagement. Clicks are one way of measuring engagement, and engagement (as demonstrated here) yields tangible, long-term, quantifiable sales and profit.

If you're an e-mail marketer, it is a good time to challenge your e-mail vendor to help you move beyond short-term, campaign-based outcomes of $0.09 per e-mail. Partner with your e-mail vendor, or even your favorite e-mail blogger! Start using Multichannel Forensics (or any other analytical tool that demonstrates the long-term strategic impact of short-term decisions, by no means is Multichannel Forensics the only tool) to validate the long-term benefits of your craft, to get the funding you need to improve your e-mail marketing program.

Multichannel Forensics: PPC, SEO and Online Marketing Example

Please click on the images to enlarge them (you'll need to do this!).

From time to time, I'm asked to describe how Multichannel Forensics can be used by online marketers.

Honestly, the methodology is ideally suited for online marketers ... as long as the online marketer stores key information in a centralized customer database.

Take a look at this online ecosystem. The online marketer tracks source of order in the customer database, categorized as follows:

• Google, Paid Search
• Google, Natural Search
• Yahoo!, Paid Search
• Yahoo!, Natural Search
• MSN, Paid Search
• MSN, Natural Search
• Portal Advertising, All Sources
  
• Shopping Comparison Sites, All Sources
• Affiliate Marketing, All Sources
• E-Mail Marketing, All Campaigns
• Catalog Marketing via Catalog Key Code
• All Other Online Marketing Sources
• Organic Online Orders, No Marketing Attribution

Your first instinct might be to nitpick the categorizations, so feel free to use whatever categorizations make the most sense for your business when you apply the methodology. My purpose is to simply illustrate how the methodology might be used.

The second image shows the Migration Probability Table. This illustrates how customers who purchased by various sources last year migrated to different purchase sources this year.

The first image maps the ecosystem.

What can we learn from this analysis? Plenty!

• Google matters. It is a primary source for new customers. Customers who evolve to loyal status migrate from a newly acquired customer via Google to a Natural Search customer to finally placing Organic Online Orders. In fact, Google has a disproportionate influence over the direction of the brand, as other forms of advertising eventually feed back into Google.
  

• Customers are slowly migrating from Yahoo! and MSN to Google. Some customers who used to use Yahoo! and MSN to place orders last year now use Google this year. The Multichannel Forensics analyst should monitor this trend over time, leveraging it in the development of paid search budgets.
  

• Yahoo! and MSN purchasers migrate to the E-Mail Marketing channel. The E-Mail services offered by Yahoo! and Microsoft allow a unique multichannel element to occur. In essence, Yahoo! and Microsoft benefit because the customer shifts from purchasing via search to purchasing via E-Mail ... allowing Yahoo! and Microsoft to continue to stay active in the customer relationship with this brand.

• Customers who respond to Portal Advertising migrate to E-Mail and Catalog Marketing. Obviously, there is a subset of this customer base that takes matters into their own hands (search), and there is a subset responsive to advertising (Portals, E-Mail, Catalog).

• Shopping Comparison and Affiliate Marketing customers are least loyal in this example. These customers are apparently getting their needs met on a one-time basis, unlikely to shop again.

• Once a customer purchases without the aid of advertising or search, the customer is the most loyal of all customer types. These customers appear to order because the like the brand, not because they need search or marketing to drive them to the site. This is what a brand ultimately wants. However, the metrics indicate that these customers still use Natural Search via Google results to place orders in the future, clearly illustrating the importance of SEO, even among best customers.

• The most loyal customers are those who order without advertising, as well as those who order via E-Mail or Catalog Marketing.

• There is a hurdle for this brand to get over. Recall the disproportionate influence Google has over this brand. Notice that Google responders are not as loyal as are Organic, E-Mail or Catalog Marketing responders. In other words, an over-dependence upon Google results in a less loyal customer base. There is an opportunity to try to convert Google shoppers to other forms of advertising.

If the proper information can be stored in the customer database, really interesting Multichannel Forensics projects can be constructed, limited only by your imagination.

An analysis of this nature also illustrates the limitations of web analytics, where metrics are configured to allow analysts to focus on what a customer does within a single visit. An entire generation of professionals are being trained to believe that the customer relationship is best measured within a single visit. We need well-rounded web analytics professionals who understand how customer relationships evolve over time, folks who can measure the complex relationship between customers, advertising, products, brands and channels. This is the promise of Multichannel Forensics.

The Role Of A Website Inspired Store Purchase (WISP) And Multichannel Forensics

Industry pundits like to point to Circuit City and their "buy online, pickup in store" strategy as a glowing example of customer-friendly multichannel synergy. Surveys of hundreds of consumers seem to validate these statements.

But how do you know if this strategy is right for your business? In other words, is this really the way a multichannel customer shops your brand?

In reality, multichannel retail store leaders have to answer fundamental questions.

1. Do customers use my website primarily for research purposes, or for E-Commerce?
2. If customers prefer to use my website to facilitate store purchases, will they ever use the website for E-Commerce?
   

A few retailers do a nice job of linking website visitation behavior with store purchases. Those retailers are able to see a new channel ... "website inspired store purchase". Customers who purchase merchandise in a store within seven days of researching a product online are classified as a "website inspired store purchase" buyer.

In this framework, you have three channels:

• E-Commerce.
• Website Inspired Store Purchase.
• Retail/Store Channel.

The intriguing channel, of course, is the "website inspired store purchase" channel. We want to understand what these customers do after buying from this new channel. Do these customers become loyal e-commerce buyers? Do these customers shop exclusively in stores? Or do these customers stay in this "new" channel?

Multichannel Forensics are ideally suited for answering this question.

Let's take a look at a retailer that manages these three channels:

Migration Probability Table
		E-Commerce	WISP	Retail
Repurchase Rate:	Total	38.0%	65.0%	53.0%
	E-Comm.	32.0%	18.0%	4.0%
	WISP	11.0%	43.0%	9.0%
	Retail	2.0%	24.0%	49.0%
Repurchase Index:	E-Comm		27.7%	7.5%
	WISP	28.9%		17.0%
	Retail	5.3%	36.9%
Classification:	E-Comm		Acquisition / Equilibrium
	Web/Store		Retention / Equilibrium
	Retail		Hybrid / Isolation

Let's review this study, one channel at a time.

The E-Commerce channel is in "Acquisition / Equilibrium" mode. This means that for the E-Commerce channel to grow, new customers have to constantly be recruited. E-Commerce customers are not likely to shop retail on their own. Instead, the E-Commerce customer is willing to use the website to research product, then buy it in the store. The logical path to get this customer to buy in stores is to use the website to educate the customer.

The "website inspired store purchase" channel (WISP) has interesting dynamics. These customers are the most loyal to the brand, and therefore, are most important to management. Pay close attention to the repurchase index information. WISP customers are more likely to migrate to retail purchases than they are to migrate to E-Commerce purchases. Over time (and this will take a long time, given index information in this example), this segment of customers will become self-sufficient retail customers who become less and less likely to use the website.

Now take a look at the retail customer who doesn't use the website. This customer is in "Hybrid / Isolation" mode. This customer does not want to use the website, for research purposes, or for E-Commerce. This segment either represents an opportunity, or a strategic inflection point. Management might view this as an opportunity to encourage more customers to research the website, might view this as an opportunity to improve the website. Or management might believe that a large group of customers are simply unwilling to use the website, allowing the website to facilitate E-Commerce transactions as a first priority.

What we can see is an (at this time) irreversible path that customer follow:

• E-Commerce -----> WISP -----> Stores

E-Commerce loses customers to WISP status.

WISP customers will shop E-Commerce, and really like shopping Stores.

Store customers do not go back.

So, the natural customer progression results in customers becoming store customers. Leadership can choose to enable this evolutionary behavior. Leadership can try to change the marketing and website strategy to change customer behavior.

The next step is to run five year simulations on different business strategies, understanding which strategies yield positive outcomes.

We spend too little time talking about the role of a website in a retail business. We think customers use a website in a "multichannel" manner, using the website to buy in stores. In reality, life isn't this clean. Each retailer that chooses to run this analysis will obtain different results, results that often buck conventional wisdom. But by and large, Multichannel Forensics teaches retailers that the primary objective of a website in a retail brand is to educate customers. The secondary objective of a website in a retail brand is to facilitate E-Commerce. And increasingly, a third objective of a website is to entertain a customer. Use Multichannel Forensics to allow your customer to help you decide what the primary objective is of your website.