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:

Spent $20,000, created 1000 new customers who bought $50,000 worth of product
Sales generated = 2.5 x ad spend

It’s very likely at least the CEO and CFO, if not all the C-Level players,  know:

  • Cost of product averages 55% of revenue
  • To process credit card / pick, pack, and ship costs another 5% of revenue
  • Meaning, of each sale, only 40% is available for business use
  • 40% of revenue = $20,000, the same as was spent on the advertising

So this campaign actually generated no positive financial benefit for the company at all – ignoring other costs (like cost of returns) that will make this case even worse.

C-Level thinks: this is a waste of time and money, don’t let them spend too much

This is how a Marketing success can become a Business failure, and why there’s more work to do on this idea of providing clarity on the impact of digital to the C-Level or whatever senior management level you deal with.  Sure, very senior level people probably don’t review individual campaign results, but at some point a roll-up of all the individual campaign results will be presented to Senior management, right?

Now, the above example is a very simple one and perhaps for many marketers / analysts in the commerce area a “duh” example (want more complex examples? see the Control Group Series).  After all, we’re not talking about the most robust measure of marketing success – incremental value creation.  But given how often we see ROAS and similar marketing metrics used to define success in case studies, seems like a good place to start!

And for those of you thinking this example doesn’t apply to you because you’re not in the commerce category, trust me, you’re mistaken about that.  Every business has costs related to generating revenues, providing services, and so forth.  For this reason, basing a financial success metric on “sales” is frequently not telling the relevant C-Level story.

Also, many of you are thinking, “But Jim, because these sales were to new customers, and some of those will purchase again, and some of those will become best customers (or donors, or subscribers, or whatever appropriate to your business model), don’t you have to include these ideas in the story?”  And you’d be absolutely correct about that.

Getting Down to Business

Here’s what is important to realize:

1.  The downstream revenue ideas above are not included in the original success story
2.  More importantly, the C-Level folks are likely aware of these concepts but don’t know specifics so can’t easily run the math in their heads to create their “need to know” number

Which is pretty much the point of this post: why would you ever want the C or V-level people to have to run these numbers in their heads?  Why not just give them the information they need to know to validate the outcome from their perspective?

For example:

– On average, this batch of new customers will each generate $20 in sales beyond the first purchase transaction in the next 12 months (based on past customer behavior)
– This adds 1000 x $20 = $20,000 in sales to the campaign = $70,000 on a 12 month basis

Here, you can leave the C/V-Level folks to do their kind of math and determine business outcome.  Or, if you’re a bit more tuned into the thought process at this level, you add:

– Taking into account 40% of sales flows to the business, $70,000 x .4 = $28,000
– Since we spent $20,000 on the campaign, we created $8,000 for business use

More than Metrics at Stake

C-Level people now run a completely different kind of math in their heads with this information, math that can have a much greater impact on your team’s future.

Looks something like this:

  • Campaign will generate $8,000 in business benefit in 1 year on $20,000 in spend
  • Company could invest the $20,000 at 3% risk-free and create $600 in 1 year benefit
  • Or, could take unknown risk and invest in software project with projected 20% return
  • Above is crap compared to proven $8,000 return on $20,000 = 40% (on annual basis)
  • This gain is so huge it should easily cover any related costs like product returns

C-Level thinks: do these people have enough budget, I wonder?

Obviously, a completely different C-Level story line.

The exact formulas and numbers the C-Level will use for above may differ by type of business, stage of growth, and so on, but the general thought process is the same – they’re looking for real financial return on the effort to compare with other cash on hand options.

Perhaps it would be worth a chat with someone in Finance to discover the specific approach your C-Level uses to evaluate options for cash on hand?

Enabling this simple bit of additional business math – especially if you are in a Marketing or Analytics management role – does 2 things:

1.  Demonstrates all the way up the chain that the people doing this work really understand the business – so perhaps the work they do should be given more weight
2.  Begins to set a measurement standard that is anchored in the reality of business and can be applied to most marketing and many other types of customer-centric investments

Now, perhaps you’re thinking nobody asks you for those kind of metrics; it’s not your job to get into the business side of measuring outcomes.  Fair enough!

But I can tell you this: there are people in the company who very much care about this approach to success measurement, and those same people are likely to be C/V-Level folks who are deciding about allocating resources for salaries, software, and hardware.  Would it hurt to provide some additional numbers that clarify tangible financial impact?

If you can prove your efforts are creating real business money, it’s easier for management to say yes to spending more on budget, staff, resources, whatever it is that you need.

The marketing success math is good enough for marketing folks to decide which campaigns should be allocated more resources within the budget for marketing.  But it’s not good enough for management decisions to be made on allocations between budgets for several different functional areas or divisions of the company.

My advice: don’t let the C/V-Level folks run their own math, and maybe honestly misinterpret the real financial contribution your team is making to the business.

Do the (business) math.

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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:

2 obstacles to marketing change in organizations: accountability and complacency. @paulroetzer #MarTech

Marketing CMOs now own the whole customer experience in the most progressive companies, from product to retention. @lauramclellan #MarTech

The best marketers can think on a strategic level, then execute on a tactical level & get hands dirty. @paulroetzer #MarTech

On average, marketers depend on data for only 11% of customer-focused decisions. @ceb_news #MarTech

What makes a marketing technologist? Curiosity. Leadership. Creativity. Risk-taker. Can start as tech or marketer. @lauramclellan #MarTech

Marketing: right brain. Technology: left brain. #MarTech needs people strong in BOTH brains. Our greatest challenge is very rare talent. @cspenn

While the technology stack for achieving customer-focused value measurement has certainly improved over the past several years, the cultural support for a  movement like this is may be still lacking.  There are of course exceptions, but in general I find most corporate structures in digital have a hard time finding and dealing with the knowledge created from introspection at the customer level.  That thing  called “customer” typically touches a lot of different parts of the business, so the determination of value would also.

The DAA courses and workshops have always taken the “both brained” approach referred to above by MarTech, attempting to create a “view from the middle” that could help address and wrestle these kinds of issues to the ground.   The course material is primarily targeted to folks working in the trenches though; the corporate culture needs to also get in the game by enabling people to easily cross the both-brain boundary.  Some part of greasing this path is also up to the analyst, for sure.

What’s the core challenge to making this work?  Consider a couple of client examples:

Company A spent millions on front-end infrastructure and advertising.  The business does OK, but growth is lacking and the market is specialized, so potential customers are not just out there lurking behind every ad impression.  A very simple customer analysis found best customers to be leaving the company at a fairly rapid rate, and the reason was clear: best customer defection centered around service issues that were not on the radar of the unfortunate marketing folks being held accountable for poor company performance.

Company B also spends a ton of money on front-end campaign-oriented systems and acquisition efforts; they are in all the latest ad formats / platforms and are very sophisticated users of these technologies.  They generate tons of new customers but have a poor customer retention profile for this type of business, so profits were being chewed up by all the ad spend.  A relatively simple Classic LifeCycle analysis showed their campaign spending was very strongly weighted towards generating low value customers, while leaving a lot of high value customer potential on the table.  In this case, there was no centralized assessment of “after the sale” net corporate performance, so the company lacked the ability to allocate marketing resources to highest and best use.

I should also add the people involved were aware of, understood, and cared about the potential for these kinds of issues to be present in the business model, but had not pursued them at their own company due to various forms of “resistance”.

So, what happened, how did these situations evolve?

In both cases the eventual conclusion was simply that nobody was responsible for looking at or understanding these ideas.  In these cross-silo challenges, the silo acts as a barrier to making progress against the business plan.   The pairing of cause and effect bridged institutional boundaries and there was no incentive or negative incentive to analyze across boundaries, since the primary concern was performance within each silo itself.

Meanwhile, from the non-siloed corporate perspective, each business simply walked away from huge piles of free cash flow without even realizing it was there for the taking.

How is it possible companies can afford to spend millions on front-end customer acquisition efforts but do not take the time to foster cross-boundary collaboration and accompanying measurement?  Especially when there’s so much yakking out there on how important “the customer” is, and “customer” is by definition a cross-boundary entity?

Perhaps most companies will need a “business analyst”-style position, similar to the role in classic IT, to cross these silo boundaries between digital operations, marketing, and analytics.  Someone who has a formal responsibility to speak all languages and question all sides.  Or, will Marketing Operations Management as a separate entity be responsible for resolving these kinds of boundary issues?  Catchy name, but name alone will not allow transparent silo boundary crossing and accountability.  Enabling culture required.

Martech / Marketing Operations Management might be able to help with the technical barriers that remain, but let’s hope they also keep an eye on and actively promote addressing the corporate analytical culture issues as well.

Your thoughts on any of the above are most welcome.  When you have encountered resistance to cross-silo optimization efforts, were you able to move the ball forward, or was it more prudent to just leave the opportunity on the table?

If you did move forward with your idea, what approach did you take with manic-ment?

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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.

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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.

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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.

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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.

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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…

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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.

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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.

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Optimizing for Customer Value

The following is from the February 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: Thank you for creating this useful website!

A: You’re welcome!

Q: When figuring out retention rate for an annual or a 8 months life time cycle period, how do I pick the starting period?  Do I look at their first orders on a date?  Or I pick a time frame such as one month?

A: It depends on:

1. What kind of “retention” you are talking about, the definition, which is probably impacted by the audience for the data

2.  What you will do with the retention data, what kind of decisions will be made and actions be taken because of the data

You should always ask these questions above  when someone requests “retention data” – or any other kind of analysis, for that matter!  For example, there probably is a huge difference in what you would provide to the Board of Directors for an annual benchmark and what you would provide to Marketing people for executing campaigns.

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