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Optimizing End of LifeCycle (Bands 6 – 8)

Wednesday, June 25th, 2008

In the Band 5 Optimization for HSN, we looked for high ROMI special situations in the database. This is really classic database marketing stuff, you’re looking for segments, and you’re looking for ways to Optimize those segments. You could spend the rest of a career doing this kind of thing; there are always new segments like FIPS being revealed if you have an active analytical staff.

There were other programs in Band 5 based primarily on product-related transition phases in the LifeCycle; I won’t go into these here. If you are interested in these ideas, I wrote one detailed example, which combines Customer Experience Management / Band 3 – Customer Comment Analysis / Math / Product / Marketing right here.

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Tags: Marketing Bands
Posted in DataBase Marketing, Marketing thru Operations, Measuring Engagement | No Comments »

Acting on Desirability

Tuesday, April 29th, 2008

Now that we know how to Measure Desirability, we need to act on what we learn.

Many web Analysts and Marketers are pretty hip to Optimizing for Actions. What they have a hard time thinking about is Optimizing Against in-Action. It’s the mirror image of what people usually pay attention to. If you’re having a hard time wrapping your head around this idea, try this analogy:

In the early days of web site funnel analysis, most people focused on the Active traffic, that is, the traffic making it to the next step, ”step conversion”. The focus was always on optimizing “for Action”, on getting people who made it to Step 2 to Step 3, etc.

One problem with this mindset, of course, is that the percentage of traffic making it through the funnel steps is often quite small. So by optimizing “for Action” you are dealing with a small, probably biased group and the potential impact versus total traffic is going to be relatively small.

At some point, people began to realize the tremendous value of the mirror-image question – this traffic that is falling out of the funnel, where did it go? Because if you could optimize against in-Action you would hit a much larger cross-section of the population and have a larger total impact.

In other words, the most important question to ask is not “Why is this small group of visitors converting”, it’s “Why is this huge group of visitors not converting?” Further, if you knew what non-converting traffic did just before the in-Action, you could infer from this “Previous Action” why they were not converting.

This thought process is what convinced the web analytics vendors to start creating the “leak diagram” version of the Funnel, where you can see exit paths by funnel step. This functionality allows you to target efforts not based on what people were doing, but what they were not doing, and infer why they were not doing it by looking at the Previous Action (funnel Exit path).

I challenge anyone to argue it’s easier or more effective to optimize a funnel by Action rather than by in-Action with Previous Action. Previous Action shouts “why”. Knowing that 80% of the Funnel Abandonment at Step 2 goes to the “Shipping Policies” page is like all those visitors screaming at you “I need more info on Shipping!”

It just makes too much sense.

Likewise, when we see dis-Engagement we should read un-Desirability. And we should look to the Previous Action for clues on what is un-Desirable. Previous Action Clues such as:

a. They bought the same product or products
b. Products bought were from the same vendor or category
c. Responded to same campaign / traffic from same source
d. They talked to the same salesperson or service agent
e. They were formally Engaged with the same kind of content

and on and on. Find the dis-Engaging visitors or customers, then cross-tab by Previous Action. Just like a Funnel Analysis with Exit Paths. Attack the high volume ones first. If you need help starting, perhaps you should ask Customer Service for a whole list of un-Desirability opportunities.

Here’s what needs to be understood. Interactivity demands that these issues are somebody’s problem. For as great as Interactivity is as attracting customers, it tends to be quite weak at holding them. There is a tremendous ramp in Engagement early on in the cycle, which drops off just as fast on the other side for most participants except the very hard core. Why? Interactivity drives very high expectations on the visitor / customer side, and it doesn’t take much to screw up that relationship.

Interactivity is relentless like that.

So somebody has to do this job: finding the root cause of dis-Engagement and fixing it. Why? Because even more than with the typical web site optimization, very small changes can produce enormous increases in Profits. Why? Because you are dealing with much larger populations – those who did not Act, as opposed to those that tool Action of some kind.

What does this all mean on the ground level?

For Web Analysts: There is an exciting and challenging world waiting for you in this dis-Engagement data. You may or may not be able to access this data through your web analytics tool. If you can’t, find out where it is – in the customer service systems, help desk systems, commerce systems – and start exploring. If you have a BI unit, find somebody in BI who wants to work on these ideas with you.

For any given free cycle, you should resist the natural tendency to “Go Deep” in your own world, spending your precious time probing the inaccurate. Instead, “Go Broad”, and try to start connecting some of these un-Desirability ideas. This can be hard work, but I know you’ll enjoy it, and the payoffs in terms of profitability are huge.

For Strategic Marketers: Somebody has to do this job. Will it be you or the “Chief Customer Officer? Given Marketing causes a lot of these un-Desirability problems in the first place, it seems to me the Root Cause folks should be in charge of this effort, rather than those catching the flack.

Now I know what you’re thinking - this Desirability thing ain’t my job. I Push. I generate Sales, Awareness, etc. “Desirability” is not on the List. Poor service? Not on the list. Faulty products?

Please, not my area.

OK. Let me ask you something. When you lose a customer, what needs to happen on your side? You have to replace that customer just to stay even, right? So, to grow sales – which I think you are in charge of - you have to not only replace the lost customer but also add another customer. That means, for a fixed budget, that you’re not going to be able to grow sales as fast as you could if you were better at keeping customers, if you were attacking un-Desirability.

Do you think your sales goals for this year are “cake”? That you have “easy” targets? That you are absolutely confident you’re going to hit the numbers? If you answered Yes, then fine, you don’t need to care about Desirability. Just churn ‘em and burn ‘em, my friend. I guess you’re not the kind of person who would like to absolutely smash your sales targets to bits.

For Both Analysts and Strategic Marketers: If you are going to talk the customer experience talk, please start walking the walk.

A couple of suggestions:

1. Yes, this un-Desirability work often requires (demands?) cross-functional teams, because un-Desirability problems often start in one silo (Sales, Marketing, Product) and end in another (Service). Is that an impossible barrier to overcome? Start looking for partners. Better yet, start formalizing the idea of a Business SWAT team. More real world examples here, here, and here.

2. Wikipedia defines Experience as “observation of some thing or some event gained through involvement in or exposure to that thing or event”. Event. Behavior. Stop with the demographic segmentation already, it’s just obscuring everything that’s important to customer experience. Save the demographics for the Push end of the funnel where they mean something. Once you get to Action and move over into Pull mode, you’re now into Behavior.

Desirability is about Behavior, not Age and Income.

So that’s the whole model, front-end to back-end. This model incorporates many of the ideas floating around out there right now – Customer Centricity and Experience, Engagement, Reputation Management – into a single Data-Driven, Optimization-friendly, Customer-Aware, Accountable Marketing process.

In short, Measure Customers, not Campaigns. That’s the secret to unlocking the power of Interactivity and making it work for you. Otherwise, Interactivity can work against you.

Your Comments and Questions are appreciated. Your challenges as well – why can’t you do this? What will it take to change that?

Example: At HSN, I started by forming the Business Swat Team at the Director Level – IT, TeleCom, Customer Service, Marketing (me), Merchandising / Presentation, Fulfillment, Finance, and (of course) BI

Our first mission was this one.

Tags: Desirability
Posted in DataBase Marketing, Marketing thru Operations, Measuring Engagement, Web Analytics | No Comments »

Measuring Desirability

Saturday, April 26th, 2008

Why do we want to do a 2-Step acquisition? Because the conversion rate is going to be higher per dollar of media spend. It’s the equivalent in Online of the difference between buying single words and buying phrases in PPC. The former generates a lot of traffic, but the latter gets higher conversion and is much more Productive.

In other words, a 2-step customer comes into the Relationship with higher Potential Value and higher Momentum. And that’s important, because it means you spend less in Marketing over the longer term as the customer will, on average, keep interacting for a longer time.

If you’re not sure what that all means, perhaps it will become clearer as we dissect Desirability (Satisfaction), the last component of the AIDAS model. Here’s the core issue:

Offline, we know people come back to Brands or Businesses “by themselves” because they like the Product or Experience. We also do Advertising to these same people, as well as those less likely to come back or not likely to come back at all.

So how do we know what percent of the resulting activity is due to people just coming back because they enjoy the business, and how much is due to the Advertising? How do you calculate ROI?

A Very difficult task. Even if you could identify the “likelies”, you generally can’t exclude them from offline media. So this whole issue of “likelihood to come back” offline has been completely ignored, because there’s no way to act on it.

Online, and in much of Offline Database Marketing, we don’t have this problem. It’s a pretty straightforward and common analytical task.

We can measure quite accurately how much of “coming back” is from Advertising and how much is from “Experience” or the more global concept of what Forrester calls Desirability - the fact the customer simply enjoys interacting with the business, and wants to interact again. And, online we can target specific individuals with specific messages based on their likelihood to come back.

But, most people in Online marketing are not acting on this intelligence or targeting capability; they’re ignoring the idea largely because it didn’t matter offline. Are these the same people that keep saying “Interactivity is Different”?

I hope not, because they’re certainly not acting like it is!

Why should this concept of “likelihood to come back” really matter to Online Marketers? Because it is much, much more powerful than you think it is. Orders of magnitude larger. However, once you screw up, the downside is also quite powerful – “not likely to come back”. This brings up two important and powerful areas to consider:

1. Over-spending to get people to come back who would have come back anyway
2. Under-spending to get people to come back who are less likely or unlikely to come back

In most cases, you will find the budget mis-allocated in this way. To optimize, you will want to reallocate budget from #1 into #2.

Online, there is a powerful ”Pull” that brings people back, over and over – without needing to provide incentives or begging them. This Pull is the very fabric of Interactivity.

What’s more, you can measure this Pull quite precisely and take action where appropriate. Here is how:

1. If you don’t try anything else new this year, do a controlled test with your e-mail program. This is the simplest, most direct way to prove to people you’re not (I’m not?) crazy about how powerful this Pull idea is. Please do not use whatever demo / product segmentation you normally use with e-mail for this test. If you want to analyze this Pull behavior, you have to segment using behavior.

Most of the big e-mail vendors can do this for you, tell them you want to do a “Recency Test with 30-day segments and a Control Group for each segment”. The most universal “last interaction” (the base for Recency) for many folks will be “last open”. You could also use “last click-through”, but of course you will have smaller active base. If you’re in commerce, use “last purchase date” if you can, since that is what really matters. Just send whatever your default creative is so you keep a baseline with prior campaigns. You will probably end up with results that look like this.

If you want to know more about these ideas or set the test up yourself, there are detailed explanations in this series and this series. Questions? Just comment below.

2. Perhaps more importantly, you can measure the decline of Pull, the absence of Pull, and take action on that as well. Pull is your measurement of Desirability. Where you find lack of Pull, you will find un-Desirable experiences you can take action on.

Now, a lot of people talk about being “customer-centric” and customer experience and all that. Makes perfect sense, and has made sense since probably the first barter transactions, right?

What you don’t hear people talk about is how to measure the profitability of a customer experience or Desirability effort. How to identify Desirability problems – even if the customer doesn’t say a word about them. How to isolate and fix these Desirability problems. And how to measure the increased profitability directly attributable to fixing these Desirability problems. Wouldn’t you like to identify these un-Desirability problems before they go Social on you? Why be reactive when you can be proactive?

That would be a pretty neat trick, don’t you think?

Here’s how you do it.

Once you have proven how powerful this Pull (come back by themselves) concept is with your own data – and it is especially powerful among your best, most Engaged customers (is that a surprise to you?), start asking why, for other groups, Pull is declining or absent. What is the commonality among visitors or customers with the lowest “”likelihood to come back”, where Pull is declining or absent?

Here’s what you will find:

and on and on. Behavioral segments.

Visitors or customers who “did the same thing”.

Basically, you will find out where Desirability is lacking, literally, what you are doing every day in Sales, Marketing / Product, Service, or Operations to drive away customers and prospects.

And then you can decide what you are going to do about it. That’s a whole other challenge I will address in the next post.

Your feedback and questions are appreciated.

Tags: Desirability, Pull Marketing
Posted in DataBase Marketing, Marketing thru Operations, Measuring Engagement, Web Analytics | 1 Comment »

Push, then Pull

Thursday, April 24th, 2008

To summarize, there are significant forces in play that require Marketing folks to realize that optimizing Marketing goes far beyond media, message, response, and all the traditional MarCom stuff.

To take advantage of these changes, there has to be a Strategic admission that Sales, Marketing, and Service are all parts of a customer-centric whole. Interactivity forces this on you; it’s a Relationship Marketing environment.

CMO’s have an opportunity to step up and take control of this situation. If they don’t, the job of integrating these disciplines will be handed to a Chief Customer Officer, Chief Experience Officer, or some other needless C-Level fabrication. And that’s not really going to work, it’s a partial solution.

For those of you with Brand as your current primary focus, it should be easy to make the argument about why this integration matters and why you should be in charge of it. If you don’t do something about really integrating all the customer facing disciplines, examples abound of the Brand damage that can occur.

No amount of “Advertising” can fix Brand rot, you have to get to the Root Cause, which is probably cross-functional in nature. It really doesn’t make sense to ignore excellence in execution and then react to the problems caused when you can discover, address, and fix these issues before they happen.

Here are some ideas to think about on the Tactical side:

1. Don’t use Mass Media to try and build / close Relationships; that’s a waste of time. Use Mass Media for what it’s very efficient at - creating Awareness and Intent. The first step of the 2-step, it’s the Push part. If Push sounds like it’s intrusive, remember people expect Push from Mass Media to begin with. You have the proper context; that’s why Mass Media can be effective for Push.

Use unique taglines and phrases in the execution, knowing a search on the web is a high probability next step. Google just released a study on what this looks like for newspapers, complete with a neat PDF diagram (see page 2). Make sure the web team is prepped for the Mass Media, that they have optimized the unique taglines and phrases for Search, both Paid and Organic.

2. Make sure the copy directly implies you are open for the Brand Promise to be tested in an interactive environment, where Brand Proof will take place. This will usually be the web, but it could be a call center or other venue. Invite those with Intent to convert this Intent to Desire through Interaction with you; this is Pull.

Focus on driving curiosity and peaking Interest rather than selling, e.g. “Want to Know More? Here’s our web site…”

3. Pull is self-service, it’s about proper execution - consistency with the Mass message, ease of use, transparent, Relationship building. Potential customer is now driving, you are awaiting response. Answer the questions raised by the Brand Promise (on a web site or in the call center), allow them to be tested.

Don’t simply repeat the Promise – that job has already been done, it’s a waste of time, it’s redundant, not respectful.

Instead, fully and completely Expose the Brand Promise, let it stand for testimony. Allow Brand Proof to take place. This is not the time to be Intrusive; that’s out of context. Make it easy for the prospect to feed back the experience, and be ready for the dialogue. Relationship Marketing is an Exchange, a dance, two-way, back and forth.

React and Respond. Be “Social”, if you want to call it that.

This portion of the program – which might consist of many different campaigns driving traffic into it – is where failure most often occurs, and where you get into this whole “customer is in control” thing.

Like that’s a negative? What they are in control of is their own process, and what’s the matter with that? It’s enabling, empowering for the customer; it builds the Relationship. Hopefully, what you have done here is given control; as opposed to having it taken from you. There is a very big difference between the two.

If the customer has to “take control”, you’re doing something wrong. You have broken processes, you have cross-functional chaos, you’re not enabling a dialog. Or you’ve inflated promises, created false expectations, at worst, told half-truths. You’re creating frustration.

That’s when customers feel like they have to take control from you.

That covers the Tactics for Aquisition (AIDA), I’ll tackle Retention (S) in the next post. As always, Comments on are appreciated.

Tags: Desirability, Display Advertising
Posted in Brand Management, Marketing thru Operations, Web Analytics | No Comments »

Want Engagement? Get Desirability

Thursday, April 10th, 2008

Forrester’s Marketing Forum this year covered Engagement, but not the kind of Engagement so often discussed in web analytics.

Nope, Engagement from a Marketing perspective, you know, surprise and delight leads to better customer experiences leads to better customer retention and higher profits.

The presentation came complete with some nifty offline Engagement examples, e.g. the more a patient is Engaged in their healthcare the better the result. The improved results came from, get this, “improving doctor usability”. And yes, there was a test on this business optimization effort with tangible results generated.

You can get a good feel for where this conversation is headed from Jeremiah Owyang’s blog by listening to the 2 Forrester keynotes, each about an hour long. For those short on time, pick one, depending on your interest:

Strategic Level: platforms, frameworks, etc. from Brian Haven

Tactical Level: examples, “how to” etc. from Kerry Bodine

No time for a video?

For a bulleted list of the key points you need to understand in order to optimize your Marketing model, see the “Five Fundamentals of Integrated Marketing” ClickZ article here.

I’ll have more to say on why these ideas are so important in the next couple of days. For now, I will leave you with this:

If the customer is taking control, it’s only because you’re using the wrong Marketing model, maybe one like this one. No customer wants to have to “take control” in the first place.

The more Engaging you are, the less old-school “pray and spray” Marketing – online or offline - you should have to do.

That’s the whole point of Engagement.

Comments on the videos or article? Anything ring a bell for you?

Tags: Desirability, Forrester
Posted in Analytical Culture, Brand Management, Marketing thru Operations, Measuring Engagement, Web Analytics | 2 Comments »

Interview-Podcast w/ Jim Novo

Friday, February 1st, 2008

Friend and fellow blogger Alan Rimm-Kaufman spent some of his valuable time asking my opinion on various online marketing issues in a far-ranging interview and podcast.

We met in person for the first time doing a presentation together at the DMA show in Chicago this fall, and because he used to work at Crutchfield – a truly customer-driven remote retailer – we share some experiences and beliefs.

For those of you who might be wondering where a lot of the Marketing Productivity ideas I post here come from, this interview-podcast is probably a pretty good backgrounder. We talk about a lot of stuff, including:

Monetizing customer experience

Importance of Control Groups / Source Attribution

Multichannel Marketing Strategy

LifeCycle Contact Strategy versus Calendar-based

Retail Business Models / Lab Store

Search box or not? / Serendipity

How to tell if online customers are really engaged – without web analytics

Here’s another link to the Interview-Podcast. Enjoy!

That was lots of fun, thanks Allen!

Posted in Analytical Culture, Analytics Education, Brand Management, DataBase Marketing, Lab Store, Marketing / Tech Interface, Marketing Research, Marketing thru Operations, Measuring Engagement, Web Analytics | No Comments »

Marketing through Operations

Wednesday, January 9th, 2008

OK, so to review, here’s the premise. Customer-centricity is something companies want to embrace more than ever. Company can do this through a Chief Customer Officer, but why isn’t a Marketing exec taking the reins on this issue? In direct marketing companies – where customer-centricity is not just a fad, but has a decades-long history – the Marketing folks know that Operations typically contains a goldmine of customer-centric Marketing opportunities they can take advantage of. Many of these opportunities come from problems with empathy and context - or for the more technical folks out there, “Usability”.

Yes, you can optimize the service side of a business just like you can optimize a web site. Here is how:

1. Do you have a relationship with a peer in customer service? If not, that’s really short-sighted for a marketing person who wants to be viewed as a strategic thinker – find someone, OK?

2. Does customer service record the reason for each call? If not, that’s nuts. Most every call center system provides this capability, but you do have to turn the damn module on and populate it with the reasons people call. So if the center is not using this functionality, get talking about how to get it turned on.

3. You and your customer service peer need a list of the reasons people call. Get this by talking, of course, with the agents. If such a list does not exist, create it. If such a list does exist, review it – it’s probably filled with crap or default reasons that don’t really have much to do with your business. This is the most common mistake I see made in the “customer centric” area – using default call reasons not customized for the business.

4. Once you have the module running and the call reasons right, make sure the agents know how important it is to status every call correctly. Tell them by statusing calls, you plan to make their jobs easier by reducing routine problem calls, allowing them to spend more time on quality of call and resolving complex issues.

5. Determine how to report on compliance with correct statusing. If you don’t do this, all your effort will be subject to failure. Hint: Do not provide agents with a giant ”other reason” bucket; force accurate call accounting by providing a full and complete call reason set that only allows a very small percentage of “other reason” ticks.

6. Find out from Customer Service or Finance what the internally acceptable “cost per call” calculation is; what does Finance think it costs to take a customer service call?

7. In conjunction with customer service, study the reasons people call and think about how to reduce the need for those callers to call. This project is about reducing or eliminating the triggers for a call. Why do they call? FYI, most really customer-centric companies have a meeting on this topic every week. At HSN, we had this meeting every day. Why? Because we could react in real time. If you are in an interactive business, perhaps you can too.

8. In many cases, you will find they call because of things marketing does or could affect, for example:

Confusing language or other problems with marketing materials / advertising – this is a huge category which includes all kinds of bad Marketing execution – wrong or expired coupon codes, collateral distribution problems, etc.
Incomplete or confusing instructions or product packaging
Incomplete or confusing installation process or procedures
Pricing or bundling logic issues – the options don’t make sense to the customer
Problems with call center script language or logic
Illogical touch-tone trees or branching problems
All kinds of similar problems with the web site too numerous to mention here

Note to web analysts reading this:

Sound familar? After you optimize the web site, find out if they will let you join the BI unit and optimize the business. Idea: Optimizing a VRU / IVR is really no different than optimizing a web site using path analysis – think about it. Traffic sources, the funnel, leaky bucket, pogo-sticking. Same thing.

9. Get off your GRP-lovin’ ass and fix the operational problems Marketing is causing or can affect.

If you are saying to yourself, “But I don’t have control over a lot of the items on this list” then ask yourself why that is. All this stuff is about copy and presentation, and heck, you’re the expert in those areas, right? So why don’t you have control over these issues? Did you ever ask for this control? If not, why? That’s what a strategic thinker would do, because all these customer contact issues directly affect customer value and retention.

This stuff is marketing. It directly affects the value of the customer and customer retention, not to mention word-of-mouth. You want that new fangled social media thingie you bought to boost sales, right? How about optimizing the customer experience with your company?

Oh, I forgot, less than 30% of you said increasing customer LifeTime Value is a top marketing objective. So I guess less than 30% of you should move to the next step.

10. Measure the reduction in phone calls for these problem areas you have fixed, calculate the cost savings, present to senior management.

Extra credit: measure the increase in customer satisfaction, if that’s all you can do. Better than nothing. Hopefully you have some kind of statistically correct, longitudinal study going and can measure satisfaction properly.

Super extra credit: measure the actual reduction in customer defection and monetary value of this reduction. That’s the right thing to do and will boost the monetary value of your actions tremendously.

11. Pitch strategic seat at the table / Chief Customer Officer responsibilities using knowledge from “why they call” study and resulting operational modifications. You will have no shortage of future issues to work on. Somebody has to do it, might as well be you.

12. Convene cross-functional team, you will need it. Get best and brightest from every area of the company or unit. At minimum: Marketing / Sales, Customer Service, Finance, IT, Distribution

13. Start fixing more stuff that pisses the customer off, generates calls, and truncates customer value. Achieve customer centricity. After all, they tell you every single day what pisses them off.

Why don’t you fix this stuff?

Any takers? Anybody doing this? Any Marketers think they will get resistance if they start poking their nose into customer service land?

Posted in Analytical Culture, Brand Management, DataBase Marketing, Marketing thru Operations | 5 Comments »

Measuring Customer Experience ROMI #2: Lab Store – New Customer Kits

Monday, March 12th, 2007

Here’s another Customer Experience kind of test that proves you can generate incremental profit by improving the Experience. You just have to make sure customers want the experience “improved”. This example is from the Lab Store and the ROMI on this little program is a real eye popper.

Back in the old days (meaning the 80’s), what I guess is now called WOW was referred to as “surprise and delight”. Essentially, this 2-step idea works like this: when you surprise the customer, you really get their attention. If you can get their attention by surprise and delight them at the same time (instead of pissing them off with your surprise), then you are going to have a more loyal customer. The trick, of course, is to somehow make more money doing it…

New Customer Kits are a very simple way to do this, and in my remote retailing experience, it works every time. First impressions, in case you didn’t know, are really important – and especially so in remote retailing, where there is no way for the customer to get any tangible “feeling” for the company. Sure, you have copy on the web site that paints a picture. But how many times have people read all this wonderful copy only to be screwed when delivered the tangible experience?

The challenge is to design a kit that is relatively inexpensive yet packs an emotional delight. Lots of people toss extra stuff for the customer in the first order, but that stuff is usually company-centric, for example, “Here is a magnet with our URL on it” or “Here is a catalog of our other products”. That’s fine, but it’s neither surprising nor delightful.

Here is what makes up a good New Customer Kit, based on years of testing:

1. A letter or other message from the company that Welcomes the customer, talks about the people and philosophy behind the company, and reinforces any guarantees or promises that are part of the Brand. This piece must be written carefully, and from a customer-centric point of view. No “we we” stuff.

2. A free gift. This gift must be related to the merchandise or general category being purchased, and must not be discards, seconds, or defective merch. Giving a new customer something that is dented or discolored is not a gift, it’s an insult. Giving a new customer something that is promotional (magnet) may be a gift, but it is expected and not particularly delightful. Giving a new customer a “gift” because they made a first purchase (Buy today and we’ll include a…) might be delightful but sure is not surprising. Ignore the above cautions at your own peril.

3. Free Samples, if relevant to the business. Anything that is consumable and generates repeat purchase is ideal.

Anyway, I suppose you’re expecting some kind of numbers to go along with all the fuzzy-wuzzy “Oh, if we just make their experience better, they will be more loyal” drivel you hear all the time online. This is the Marketing Productivity Blog, after all, right? OK, here are the stats on this technique from the Lab Store. As usual, this promotion was tested versus control (new customers who did not receive a New Customer Kit are control) and we compare sales activity of both test and control over the next 90 days. Why 90 days? Well, if it makes money at 90 days, it sure makes money at 120…

Average cost of New Member Kit (there are several versions) – $.74

Increase in 90-day second purchase rate, test versus control – over 30%

90-day ROMI – 4,891% ($36.68 in net profit for every $.75 spent)

Surprised and Delighted Customers – Priceless

Now that the bottom line has been presented, the black box folks simply interested in the “what happens” can skip the next part. If you want to know why it works and maybe learn something useful you can port elsewhere, read on.

New Customer Kits are a great way to shape Theatre of the Mind.

What you have with a remote retailing customer is a ”theatre of the mind” scenario, much like you have in radio advertising. Customers can’t see or touch you, so “Cues” become extremely important; if you don’t populate the theatre of the mind for the customer, the customer will go ahead and populate it themselves. If you want some control over the image of your company people create in their head, you need to be proactive. Theatre of the mind, folks. Very powerful stuff.

Our New Customer Kit generates absolutely tons of “Thank You” e-mails from new customers who want to tell us all about how great the experience was purchasing from the Lab Store. Now, I think you’d agree that purchasing from a web site isn’t a particularly thrilling experience in any way, but if you really listen (and understand a bit of Consumer Psychology) these customers are not really talking about the web site, or even our company.

What they really are saying is they are very happy with themselves for making a first purchase from us; our actions have confirmed they made a good decision. Remember, this is remote retailing. There is risk to the customer, especially on that first purchase; they have no idea if their expectations based on the web site copy are going to match the reality of delivery. They are concerned about what might happen – will they be proven smart or dumb for taking this risk?

When we deliver the products they ordered in a timely way we meet expectations. When we deliver these products carefully packed in a pristine new box packed with fresh blank newspaper, we probably exceed expectations by a bit. But when these new customers get to the Welcome letter, the free gift, and the samples, we blow out their expectations.

The picture these new customers had in their mind of our company based on the web site experience is then permanently altered; we’re doing brain surgery for 74 cents a head.

Now, I have a question for you – is this program Marketing or Customer Experience Management?

Posted in Analytics Education, DataBase Marketing, Lab Store, Marketing thru Operations | 2 Comments »

Measuring Customer Experience ROMI #1: Nice to New Customers

Friday, March 9th, 2007

I’m going to preface this piece by saying I don’t really think “Customer Experience Management” is anything different from smart, integrated Marketing and Customer Service. If there isn’t an actionable framework for it, like Ron, I’m not sure CEM has a future, other than to create something for people to talk about, and maybe sell some software…

Whichever direction you believe in, here is an interesting case that makes several points about this area of discussion.

The Nice to New Customers test was conducted at Home Shopping Network in 1994. The idea came from the annual survey of all customers that indicated that the “average” customer felt the “new customer experience” was “as expected”. Given the high percentage of 1x buyers we were experiencing (as do all interactive remote retailers), I thought, “Hmm, maybe if we deliver a customized first purchase experience and process, these new customers will be more likely to make a second purchase”. Sounds logical, right? This was a Business SWAT case since it involved Marketing, Customer Service, IT, and Telecommunications, all working together to set it up, determine the metrics, make sure Management understood the impact of the test on existing silo Scorecards, etc. In other words, I sold my soul to get this test to happen.

We set up a pretty elaborate test where a random sample of new customers (about 100,000, a solid test group) were shunted to our “best agents” and given a new ”Welcome Treatment”. Instead of the general “get them off the phone as fast as you can” attitude prevalent in the network, these reps had permission to spend as much time with the customer as the customer wanted and generally customize the experience. There was a lot of role play and monitoring connected to this effort, and the service managers on the project were convinced these new customers were in fact treated to a much better initial experience than the average new customer. In fact, the customers seemed thrilled. So far, so good.

Problem was, this test group of new customers exposed to a better “Customer Experience” ended up generating no incremental sales versus control. Well, there you go. We lost a ton of money on this test, a stellar -118% ROMI, because we literally had to pay back customer service out of the marketing budget for the lost productivity in the network due to the test. Hey, that was the deal I cut to get this test done. You win some, you lose some.

But it gets worse. When we started dicing the post-analysis of the test down to behavioral groups based on the details of the first transaction, we found there was actually some incremental sales lift among new customers with “light buyer” initial profiles. This is good. Problem was (and you know what is coming, don’t you?), new customers with heavy buyer profiles were negatively impacted, and because the Potential Value of this group was so huge, the losses versus control in this relatively small number of folks far outweighed the gains in light buyers, causing the net effect of the promotion to be negative.

Isn’t that a fine kettle of fish? Being Nice to potential Best Customers killed the test.

When we surveyed these customers in the test after we knew their behavioral profiles (to make sure we knew the behavioral context of their answers) they basically told us this: they were expecting a very operationally efficient transaction and we provided them a customer-centric one. Cognitively, they were making an impulse purchase and they wanted an impulse transaction, not an empathetic one. This disconnect caused post-purchase dissonance and reduced intent to purchase. Using today’s language, we were basically “spamming” them; we were overstepping any Permission we had to engage them at a more personal level. And this negative effect was most pronounced among new customers with high Potential Value. In hindsight, knowing what we knew about the psychological profile of Best Buyers, this made all the sense in the world and was an interesting confirmation of the test results.

The CFO, well, he didn’t think this result was so interesting…but did applaud the idea that we would step up to the plate and actually pay back customer service for the losses related to decreased productivity in the network out of the Marketing budget. It was the first time anybody had done this kind of intra-silo payment and really paved the way for tighter integration between Marketing and Service.

You might consider this test result when evaluating your e-mail contact strategy, at least for new customers. Are you sure you are generating maximum revenue? What if the half percent or so that unsubscribe each month are future Best Customers with high Potential Value? Do you use control groups, do you know the answer to this question?

Interactive behavior provides a very special backdrop for Marketing and Service; be careful what you ask for.

I’m not saying if you did this test you would get the same results. What I am saying is you cannot assume all the stuff you read about “Customer Experience” online is going to work with your customers. You simply have to test these ideas with real customers and measure the results. And if you are dealing with interactive customers, keep in mind that “Customer in Control” is something you might not want to mess with. In other words, sometimes Control is the Experience, particularly if the general Marketing / Brand backdrop is Operational Efficiency.

It’s one thing to start a company saying you are going to deliver some kind of superior Customer Experience and embed this idea in your service delivery model. We all know these kinds of companies. It’s a completely different idea to think that you are going to improve the current experience at your company, and this effort is going to have positive effects for both the customer and the company because it sounds logical to you.

Lessons learned:

1. The bottom line lesson here really was about a poorly constructed test based on a faulty customer survey methodology. Without the customer opinion first tied to an actual behavior, we had no option other than to use the opinion of the “average customer” as a base to act against. Because of this, the only action we could take was against ”all new customers”, and ended up shooting ourselves in the foot. Based on the post test dicing, we later retested and found (surprise, surpirse) a program like this could be extremely profitable when we treated targeted new customers differently based on their Potential Value.

If we had this behavioral information (the initial Light Buyer / Best Buyer profiles) tied to the survey responses from the beginning, we would have understood these segments were different and designed the test accordingly. Make sure if you are going to take some kind of action on a survey, you first understand a behavior and then survey the people with that behavior. To do it the other way around, trying to “back into the behavior”, wastes a lot of time and money just in the data gathering and processing itself, never mind in the “re-testing” we had to go through once we knew what was really going on.

2. It doesn’t always pay out to be Nice to New Customers. Sometimes they simply want what they expect.

Posted in Analytical Culture, DataBase Marketing, Marketing thru Operations | 2 Comments »

Lab Store: The Next Inspector

Wednesday, January 31st, 2007

This is a great B2B example of a Marketing / Customer Service program operating in Fulfillment from a vendor of ours. It drives profitability on the vendor side as well as increased satisfaction on the customer side. Simple as a rock, effective on a number of levels, and measurable.

When we open a carton (usually 6 or 12 items in a carton) from this vendor, the first thing we see printed on the inside lid of the box is this message:

Remember
Our Customer is the
Next Inspector

Think about that message. If you are packing the vendor boxes, you see this message every time you start to seal the box. Every time. How much “training” would it take to have the same effect? In addition to the more direct message it sends to a packer about quality control at the carton level, it also sends a broader message to employees concerning customer experience and care. After all, the employees know customers see the same message.

Simple, direct, impactful.

As a customer, when we opened these cartons for the first time, we thought, “Wow, that is pretty neat. These guys really give a crap about what they do.” Whether they really do care or not, of course, is up for speculation, but that is not the point, is it? We think they care. In fact, my wife’s response to this message was to cut off the carton flap with the message on it and put it over the packing station. Not sure they planned for something like that, but a nice “halo effect”.

And, unlike most of the vendors we deal with, we have never received a mis-packed box from these folks in 6 years.

I talked with the vendor about this and he filled me in. The idea came out of Marketing as a potential solution to a packing error problem that was causing nasty-gram traffic in Customer Service and the hard loss of customers. An Operational defect that had a direct and trackable negative effect on both Customer Service and Marketing – as these process problems almost always do. He doesn’t know what the ROI is because it’s silly to even calculate it – the incremental profit generated by decreased packing errors (cost reduction in “make good” shipments and returns processing) since program implementation is so large relative to the cost of printing the message on the box that the ROMI would have 8 or 9 figures to the left of the decimal point.

That’s not including any customer metrics like slowing of customer defection rate and halo effects, because those are obvious to them. Customers simply stopped defecting due to mispacked packages.

Period. Do you need to run a lot of math on a result like that to figure out if it’s profitable?

Posted in Analytical Culture, Lab Store, Marketing thru Operations | No Comments »

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