Omni-Channel Cost Shifting

One of the great benefits customer lifecycle programs brings to the party is unearthing cross-divisional or functional profitability opportunities that otherwise would fall into the cracks between units and not be addressed.  What I think most managers in the omni-channel space may not realize (yet) is how significant many of these issues can be.

To provide some context for those purely interested in the marketing side, this idea joins quite closely to the optimizing for worst customers and sales cannibalization discussions, but is more concerned with downstream operational issues and finance.  Cost shifting scenarios will become a lot more common as omnichannel concepts pick up speed.

Shifty Sales OK, Costs Not?

Why is cost shifting important to understand?  Many corporate cultures can easily tolerate sales shifting between channels because of the view that “any sale is good”.  On the ground, this means sourcing sales accurately in an omni-channel environment requires too much effort relative to the perceived benefits to be gained.  Fair enough; some corporate cultures simply believe any sale is a good sale even if they lose money on it!

Cost shifting  tends to be a different story though, because the outcomes show up as budget variances and have to be explained.  In many ways, cost shifting is also easier to measure, because the source is typically simple to capture once the issue surfaces.  And as a cultural issue, people are used to the concept of dealing with budget variances.

Here’s a common case:

  • Online campaigns are continually optimized against conversion / sales targets
  • These KPI’s typically ignore discounts and returns when measuring success
  • It’s not unusual to find products with high conversion rates that on a net basis, after campaign costs, markdowns, and returns, lose money on every unit sold
  • So as the campaign is “optimized” for conversion and units sold increase, it loses more and more money on a cumulative basis – but has great campaign stats!
  • Finally, when you look at the customers who buy these products, particularly as a first purchase, these customers tend not to purchase again

This last point is ironic, given the usual answer to questions about the financial impact of new customer discounts and returns is “we’ll make up the loss when they purchase again”.

Some companies will view the above from the “any sale is a good sale” perspective; they’re simply not concerned by losing money on every online sale of a product.

And I understand why.

Many of the people responsible for managing online actually come from the offline retail world, where managing against the considerations above is often not practical.  In the typical retail-only scenario, many of these costs are not directly controllable and are simply absorbed into the fixed cost structure of the business, often in the staffing area.

After all, store traffic is what it is, and products that generate poor customer interaction are dealt with in a time honored way – customer service desk, markdowns, liquidation, return to manufacturer.  The added overhead cost generated by dealing with these poorly-performing products is simply absorbed into the fixed cost structure of offline.

That’s the physical store model.  So be it.  Also not surprising: managers from retail would not worry too much about this, since in retail the whole scenario is largely uncontrollable.

What Changes with OmniChannel

When you stir pick-up / return-to store practices into the mix, much of the financial burden in money-losing online campaigns can now be shifted out of the online business model onto the retail store side – and you can measure this shift.  Since best customers tend to shop across all channels, the volume and related impact on profits can be substantial.

Initially this might be OK with the C-Level; just considered a part of doing business, right?

Except for one thing.

Online, the marketing investment in products with negative net margins / high return rates can easily be reduced or shut down.  Not only does this save upfront marketing costs, but also avoids pass-through of service costs to the retail units.  In other words, for online, unlike in retail,  much of this profit waste is controllable.

And that really changes the way this situation should be viewed.  It might be OK to generate profit-less sales, and it might be OK if the stores have to handle online returns.

But to intentionally spend money online knowing this will accelerate service and product handling costs on the retail side seems like it would be a  stretch for most C-Level folks. This would be especially true in privately / private equity held companies, where profits are more of an issue than in start-up and many publicly traded companies.

Think about what the above means in terms of an “omnichannel view”.  Does it make sense to take bad parts of a channel and force the other channel to participate in them?

Not likely.  And I suspect as retail store comps soften in the face of online sales increases, there will be pushback from the offline side on these most controllable issues first.

Going forward, as competition progressively intensifies, omnichannel structures will require a new management approach to grow profits, one that finds and addresses controllable profit-draining issues.  So despite all the progress on the tech / operational side, I think we’re still in the early days of optimizing omni-channel business outcomes.

What do you think?  Have you had any experience with  resolving channel-conflict issues?

Is any sale always a good sale at your company?

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Does Advertising Success = Business Success?

Digital Analytics / Business Alignment is Getting Better

I recently attended eMetrics Boston and was encouraged to hear a lot of presentations hitting on the idea of tying digital analytics reporting more directly to business outcomes, a topic we cover extensively in the Applying Digital Analytics class I taught after the show. This same kind of idea is also more popular lately in streams coming out of the eMetrics conferences in London and other conferences.  A good thing, given the most frequent C-Level complaint about digital analytics is not having a clear understanding of bottom-line digital impact (for background on this topic, see articles herehere, and here).

Yes, we’ve largely moved beyond counting Visits, Clicks, Likes and Followers to more meaningful outcome-oriented measures like Conversions, Events, Downloads, Installs and so forth.  No doubt the C-Level put some gentle pressure on Marketing to get more specific about value creation, and analysts were more than happy to oblige!

Is Marketing Math the Same as C-Level Math?

Here’s the next thing we need to think about: the context used to define “success”.

In my experience, achieving a Marketing goal does not necessarily deliver results that C-Level folks would term a success.  And here’s what you need to know: C-Level folks absolutely know the difference between these two types of success and in many cases can translate between the two in their heads using simple business math.

Here’s an example.  Let’s say Marketing presents this campaign as a success story:

Continue reading Does Advertising Success = Business Success?

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Digital Customer Analysis Going Mainstream?

Is it possible the mainstream digital marketing space is about to finally move on from a focus on front-end measurement (campaigns, etc. ) to creating knowledge around how enterprise value as a whole is created?  And actually enabling action in this area?

Judging by the material coming out of the recent Martech conference in Boston, one would think so.  And it looks to me like I’m not the only one thinking “it’s about time”.

A couple of years ago I lamented:

It’s been very popular among marketing types to talk about “the customer” but seek metrics for affirmation other than those based on or derived from the customer. Digital analysts have followed their lead, and provided Marketers plenty of awareness, engagement, and campaign metrics.  As I’ve said in the past, this is a huge disconnect. Does it make sense (analytically) to have discussions about customer centricity, customer experience, customer service, the social customer, etc. and measure these effects at impression or visit level?

If you’d like to review some commentary on the conference, see a list of 5 posts here.  I found the list of tweets here particularly indicative of Martech’s potential, for example:

Continue reading Digital Customer Analysis Going Mainstream?

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Marketing Funnel Not Dead, Using Funnel Model for Attribution Is

It’s become fashionable to declare the “Marketing Funnel Model” dead.

For example, here is a post worth reading on this topic by Rok Hrastnik.  There are some very good points in this post on why using a funnel to attribute media value is really a troubled idea.  I was flagged on this post because it has a quote from me that seems to support Rok’s thesis about the death of the funnel model and the related idea, “Direct Response Measurement is a Wet Dream”.   The quote is from a comment I made on a post by Avinash where we were discussing the value of sequential attribution models:

There are simply limits on what can be “proven” given various constraints, and that’s where experience and a certain amount of gut feel based on knowledge of customer kick in.  If you can’t measure it properly, just say so. So much damage has been done in this area by creating false confidence, especially around the value of sequential attribution models where people sit around and assign gut values to the steps.  Acting on faulty models is worse than having no information at all.

But none of this means the Funnel Model is dead, or that Direct Response Measurement overall is a Wet Dream.  What’s (hopefully) dead is  people using the funnel model inappropriately for tasks it was never designed for, in this case multi-step attribution of media value to goal achievement.  On the other hand, if this specific funnel use case is what Rok was coming after, I agree, because it didn’t make any sense to use a funnel model for this idea in the first place.

Let’s unpack these ideas

Funnel thinking is based on a relatively reliable model of human behavior, AIDA.  This model from human psychology does not specify tools, channels, or media.  It simply says that there is a path to purchase most humans follow.  That is:

A – Attention: (Awareness): attract the attention of the customer
I – Interest:  (Intent) promote advantages and benefits
D – Desire: convince customers the product will satisfy their needs
A – Action: lead customers towards taking action / purchace

Example:  I’m Aware of tons of products I would never buy.  There are lots of products I think are Interesting but I have no Desire for.  There’s a short list of products I Desire but have not Acted on.  The list of products in my head worthy of purchase consideration gets smaller and smaller at each stage of the AIDA model.  This is the funnel.

The AIDA funnel has not changed and it’s not dead.

It’s a model of human behavior, not media consumption.

Continue reading Marketing Funnel Not Dead, Using Funnel Model for Attribution Is

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Marketing to Focus on Customer. Analytics?

It’s been very popular among marketing types to talk about “the customer” but seek metrics for affirmation other than those based on or derived from the customer.  Web analysts have followed their lead, and provided Marketers plenty of awareness, engagement, and campaign metrics.  As I’ve said in the past, this is a huge disconnect.  Does it make sense (analytically) to have discussions about customer centricity,  customer experience, customer service, the social customer, etc.  and measure these effects at the impression or visit level?

Is someone who visits or purchases or comments one time really a customer, for the purposes of analyzing “centricity” ideas and concepts?  I think not.  Visit metrics simply don’t work for understanding these customer concepts, because by definition they unfold over time, not as single events.   Add in the fact most web activity is 1x in nature – even buyers – and you begin to realize that analyzing “traffic” yields very little in the way of “customer” insight.

From a Marketing perspective, hey, happy to have the 1x revenue, but these are interactions I’m not really excited about increasing spend on, knowing they will be a one-night stands.  This is especially true when you also know re-allocating some of the funds spent on the 90% 1x-ers to the other 10% could double company profits!

If you have followed my writings over the past 12 years, none of the above perspective is new.  What might be changing is this: more people in the online world are beginning to think the same way.

Continue reading Marketing to Focus on Customer. Analytics?

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“Missing” Social Media Value

I have no doubt there is some value in social beyond what can be measured, as this has been the case for all marketing since it began ;)  The problem is this value is often situational, not too mention not properly measured using an incremental basis (as you point out).
For example,  to small local businesses who do no other form of advertising, there is a huge amount of relative value to using social media, versus no advertising at all.  Some advertising is much better than none, and since it’s free, the incremental value created by (properly) using social is huge.
On the other hand, I wonder why social analysis seems to forget that people have to be aware of you to “Like” you in the first place.  Further, it seems unlikely a person would “Like” a brand or product if they have not already experienced it, and are already a fan.  If this is not true, if people “Like” a company even thought they do not (paid to Like?), then the problems with social go way beyond analysis…
But if true, , the number of “Likes” doesn’t have as much to do with awareness as it does with size of customer base, and is much more aligned with tracking customer issues (retention, loyalty) than anything to do with awareness / acquisition.
Add the fact many companies are running lots of advertising designed to create awareness, and the incremental value of social as a “media” may be close to zero, or at least less than the cost to analyze the true value of it.
And this last, really, is the core of the issue.  It’s simply not possible to measure “all” the value created by any kind of marketing, and there are hugely diminishing returns as you try to capture the last bits.  I think it’s quite possible the optimism for “value beyond what can be measured” is less than the cost of measuring it *if* people keep looking in the awareness / acquisition field.
Folks who want to find this “missing” social value should start doing customer analysis, and look in the “retention / loyalty” area, where the whole idea of social is a natural, rather than a forced, fit.

Has to be There

I find it really interesting that whenever there is a discussion of measuring the value of social media, there’s such a bias towards believing there is value in social beyond what can be properly measured.  See the comments following this post by Avinash for a good example.  Speculation is fine, but the confidence being expressed that a new tool or method will uncover a treasure trove of social media value seems un-scientific (as in scientific method) at best.

I don’t doubt there is some value in social media beyond what can be measured, as this has been the case for all marketing since marketing measurement began.  These measurement problems are not new to social either:  Marketing value created is often situational, it depends on the business model and environment.  What works in one situation may not work in another.

For example:

To small local businesses who do no other form of advertising, there is a huge amount of relative value to using social media versus no advertising at all.  Social advertising is much better than none, and since it’s free, the incremental value created by (properly) using social is huge.  It’s also really easy to measure the impact and true value, since the baseline control is “no advertising”.  Lift, or actual net marketing performance, can be pretty obvious in his case.

On the other hand, many companies are running lots of advertising designed to create awareness, and the incremental value of social as a “media” may be close to zero for these companies, or at least less than the cost to analyze the true value of it.  Possible explanation:  Social events such as “Likes” or comments are simply representations or affirmations of awareness already created by other media, so by themselves, create little value.  In other words, events such as Likes might track the value of other media spending, but may not create much additional marketing value.

Continue reading “Missing” Social Media Value

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Defining Behavioral Segments

The following is from the April 2011 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment and I’ll reply.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: I purchased your book and have a few questions you can hopefully help me out with.

A: Thanks for that, and sure!

Q: We have 4 product lines and 2 of them are seasonal. i.e we have customers that year in year out purchase these items consistently but seasonally, for example, every spring and summer.  Then they are dormant for Fall and Winter.  Should I include these customers along with everyone else when doing an RFM segmentation?

A: Well, it kind of depends what you will using the RF(M) model for, what kinds of marketing programs will be activated by using the scores. If you know you have seasonal customers and their habit is to buy each year, AND you wish to aim retention or reactivation programs at them, I would be tempted to divide the customer base so that seasonal customers are their own segment.  Then run two RF(M)  models – one for the seasonals, and one for everyone else.

Q: If I include seasonal customers, and I run RFM say on a monthly basis, these seasonal customers will climb / fall drastically with time depending on the season, so it seems like it may complicate the scoring process.

A: Sure, and you could segment as I said above.  Or, you could run across a longer time frame, say across 2 – 3 years worth of data. This would “normalize” the two segments into one and take account of the seasonality in the scoring – perhaps be more representative of the business model.  However, the scores would become less sensitive due to the long time frame so the actions of customers less accurately predicted by the model.

Q: Can you provide me with some examples as to how segmentation is carried out?  Let’s say I being with RFM and all my customers are rated 5-5, 5-4, 4-5 etc.  What are the next steps, do we overlay with other characteristics like age, gender, etc?  Or are the 5-3 etc. our actual segments?

A: This goes back to what you want to use the RF(M) model for.  In the standard usage, each score will have roughly the same number of customers in it, those with higher scores will be more likely to respond to marketing and purchase, lower scores less likely.

Continue reading Defining Behavioral Segments

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Increase Profit Using Customer State

The following is from the March 2011 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment and I’ll reply.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: We’ve been playing around with Recency / Frequency scoring in our customer email campaigns as described in your book.  To start, we’re targeting best customers who have stopped interacting with us.  I have just completed a piece of analysis that shows after one of these targeted emails:

1. Purchasers increased 22.9%
2. Transactions increased 69%
3. Revenue increased 71%

A: There you go!

Q: My concern is that what I am seeing is merely a seasonal effect – our revenue peaks in July and August.  So what I should have done is use a control group as you described in the book – which is what I am doing for the October Email.

A: Yep, that’s exactly what control groups are for – to strain out the noise of seasonality, other promotions, etc.  But don’t beat yourself up over it, nothing wrong with poking around and trying to figure out where the levers are first.

Q: Two questions:

1.  What statistical test do I use to demonstrate that the observed changes are not down to chance

2.  How big should my control group be – typically our cohort is 500-800 individuals

A: Good questions…

Continue reading Increase Profit Using Customer State

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All Talk, No #Measure

Hypocrisy in Web Analytics?

Before every eMetrics (I’ll be in San Fran teaching Basecamp, at the Gala, etc.), I try to ask myself, what is the most critical issue facing the web analyst community right now?  Then, at the show, I ask everyone I run into what they think about this issue.

There’s lots of issues to choose from.  Career path I think is a big area of discussion, given the mergers in the space and trend towards outsourcing.  Then there’s the “we don’t get no respect” thing; senior management doesn’t seem to listen / understand / act on the information provided.  And one of my favorites from the past is still out there, data torture - people being pressured to manipulate data to reach a predetermined analytical outcome.

But seems to me, more important at this juncture is trying to resolve why there is so much written about the importance of “the customer” but very little measurement at the customer level.  Think about it.  Customer experience, customer centricity, the entire social thing, it’s all about customers.

But when folks wants to trot out “proof” that this or that approach is the road to the promised land, they analyze impressions, visits, clicks, etc.  Visitor-level stuff.  Does that seem like the correct approach to you?  Seems to me, if you want to provide knowledge about customers, you should measure customers.

Continue reading All Talk, No #Measure

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But What is an Impression Worth?

Seems like coming up with a value for social media has become a cottage industry, for example, $3.60 Facebook Fan Valuation Is Just the Tip of the Iceberg.  These values are often derived from what is paid for online media.  So you have to ask, if someone is basing the value of a Facebook fan on the value of impressions generated, what is the real value of those impressions?  Because unless this is known, the whole framework is faulty.

Just because you pay $5 / CPM for impressions, does not mean they are worth $5 / CPM, does it?  Do people really still have that kind of mentality?  Is the price of the media equivalent to its value?

For example, I’m sure you have heard of multi-million dollar campaigns that generate very little lift in sales.  Happens frequently in fast food, for example.  What is the value of that media?  Is it the millions paid?

What really blows my mind about this approach is it’s so offline, so old school PR. Do the folks who put forth these kinds of models believe nothing has changed in 50 years?  What happened to the whole rap of online being “different”, that you can’t measure it like offline, blah blah.

Except when it’s convenient to do so?

If you want to know the value of a Facebook fan, why not measure the value of a Facebook fan?  Because it’s hard, and would require organizational discipline?  Too bad.   Substituting the kind of models used in the example above for actually measuring the value of a Facebook fan is misleading at the very best.

Continue reading But What is an Impression Worth?

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