Comparing Web Activity Measures for Demand Generation Vendors

I recently wanted to measure the relative popularity of several demand generation vendors, as part of a deciding how to expand the Raab Guide to Demand Generation Systems. This led to an interesting little journey which I think is worth sharing.

I started with a list of 23 marketing system vendors. A couple are fairly large but most are quite small. These were grouped into three categories: five demand generation vendors already in the Guide; four marketing automation vendors with significant demand generation market presence; and fourteen other demand generation vendors. (See http://www.raabguide.com/ for definitions of demand generation and marketing automation.)

My first thought was to look at their Web site traffic directly. The easiest way to do this is at Alexa.com, which tracks site visits of people who download its search toolbar. The number of users in this base is apparently a well-guarded secret, or at least well enough guarded that I would have had to look beyond the first Google search page for the answer. Alexa was originally classified by many experts as spyware, and is still somewhat controversial. But it was purchased by Amazon.com in 1999 and has since become more or less grudgingly accepted.

Be that as it may. I captured two statistics for each of my sites from Alexa: a ranking which basically reflects the number of pages viewed by unique visitors each month (the busiest site gets rank 1, next busiest gets rank 2, etc.); and a share figure that shows the percentage of total toolbar users who visit each site each month. (I think I have that correct; you can check the definitions at Alexa.com.) Ranking on either figure gives the same sequence (except for Pardot; I have no idea why). If you’re creating ratios or an index, the difference in the share figures is probably a better indicator of relative popularity, since a company with twice the share of another has twice as many visitors, but will not necessarily a rank number that is twice as low. (Lower rank means more traffic.)

Here are the ranks I came up with, broken into the three segments I mentioned earlier:

		Alexa - 3 mo average
Already in Guide:		rank	share
Eloqua		20,234	0.0070700
Silverpop		29,080	0.0030500
Marketo		68,088	0.0017000
Manticore Technology		213,546	0.0006100
Market2Lead		235,244	0.0004800
Vtrenz		295,636	0.0003600
Marketing Automation Vendors:
Unica / Affinium*		126,215	0.0008500
Alterian		345,543	0.0002500
Aprimo		416,446	0.0002200
Neolane		566,977	0.0001690
Other Demand Generation:
MarketBright	167,306		0.0005400
Pardot	211,309		0.0003600
Marqui *	211,767		0.0004400
ActiveConversion	257,058		0.0003400
Bulldog Solutions	338,337		0.0003200
OfficeAutoPilot	509,868		0.0002000
Lead Genesys	557,199		0.0001450
LoopFuse	734,098		0.0001090
PredictiveResponse	2,313,880		0.0000330
FirstWave Technologies	2,872,765		0.0000170
NurtureMyLeads	4,157,304		0.0000140
Customer Portfolios	5,097,525		0.0000090
Conversen*	6,062,462		0.0000070
FirstReef	11,688,817		0.0000010

These rankings were more or less as I expected. Within the first group, Eloqua is definitely the largest vendor, while Marketo is probably the most aggressive marketer at the moment. Vtrenz is the second-largest demand generation company, based on number of clients and almost certainly on revenue. But it is a subsidiary of Silverpop, so its traffic is split between Vtrenz.com and visits to Silverpop.com. This means that the Vtrenz.com ranking understates the company’s position, whie Silverpop ranking includes traffic unrelated to demand generation. I’ve therefore tracked both here. Manticore and Market2Lead get much less attention than the other three, so it makes sense that they have much less traffic.

Figures for the next group also seem to be ranked about correctly. Unica is certainly the most prominent of this group, with Alterian, Aprimo and Neolane trailing quite far behind. I would have expected a bit more traffic for Neolane, but it is definitely the new kid on this block and only entered the U.S. market about one year ago. The real surprise here is that this group as a whole ranks so far below the big demand generation vendors, even though the marketing automation firms are in fact larger and probably do more promotion. Perhaps the marketing automation vendors appeal to a smaller number of potential users (primarily, marketers in large companies with direct customer contact, such as financial services, retail, travel and telecommunications) and generate less traffic as a result.

I didn’t have much sense of the relative positions of the other demand generation vendors, although I would have guessed that MarketBright and Pardot were near the top. Marqui has had little attention recently, perhaps because they’ve been through financial difficulties culminating in the purchase of their assets by a private investor group this past August. ActiveConversions I do know, only because I’ve spoken with them, and they rank about where I expected given their number of clients. The other names were somewhat familiar but the only one I’d ever spoken with was OfficeAutoPilot, which I knew to be small. Since I had no fully formed expectations, the rankings couldn’t surprise me.

In other words, the rankings provided by Alexa seemed generally reasonable given my knowledge of the companies concerned.

But Web traffic is just one measure. Where else could I look to confirm or challenge these impressions?

Well, there is another Web traffic site that is somewhat similar to Alexa, called Compete.com. I actually hadn’t heard of them before but they came up in my research. They apparently use their own toolbar but also some other Web traffic measures such as volumes reported by Internet Service Providers (ISPs). You’d expect them to pretty much match the Alexa figures. But do they? Here is a chart comparing the two, with the Alexa multiplied by 10 ^7 to make them more legible.

	Compete.com	Alexa.com
	unique visitors / month	share x 10^7
Already in Guide:
Eloqua	560,288	70,700
Silverpop	293,580	30,500
Marketo	34,244	17,000
Manticore Technology	15,789	6,100
Market2Lead	10,689	4,800
Vtrenz	5,313	3,600
Marketing Automation Vendors:
Unica / Affinium*	23,138	8,500
Alterian	4,497	2,500
Aprimo	5,131	2,200
Neolane	3,927	1,690
Other Demand Generation:
MarketBright	13,993	5,400
Pardot	7,339	3,600
Marqui *	3,282	4,400
ActiveConversion	1,503	3,400
Bulldog Solutions	6,408	3,200
OfficeAutoPilot	1,567	2,000
Lead Genesys	2,630	1,450
LoopFuse	1,930	1,090
PredictiveResponse	1,099	330
FirstWave Technologies	-	170
NurtureMyLeads	-	140
Customer Portfolios	-	90
Conversen*	-	70
FirstReef	-	10

You don’t need Sherlock Holmes to spot the problem: the Compete.com figures for Eloqua and Silverpop seem much too high compared with the others. I could concoct a theory that this reflects the difference between counting unique visitors in Compete.com and counting page views in Alexa, and throw in the fact that Eloqua and Silverpop/Vtrenz host landing pages for their clients. But the other demand generation vendors also host their clients’ pages, so this shouldn’t really matter. I suspect what really happens is that Compete measures low volumes differently from higher volumes (remember, they use a combination of techniques), and thus the figures for high-volume Eloqua and Silverpop are inconsistent with figures for the other, much lower-volume domains.

Anyway, if we throw away those two, the rest of the Compete figures seem more or less in line with the Alexa figures, apart from some small exceptions (Bulldog in particular ranks higher). All told, it doesn’t seem that Compete adds much value to what I already got from Alexis.

So much for Web traffic. How about search volume? Google Keywords will give that to me. Again, we’ll compare to Alexa as a reference:

	Google Keywords	Alexa
	avg search volume	share x 10^7
Already in Guide:
Eloqua	1,900	70,700
Silverpop	1,790	30,500
Marketo	839	17,000
Manticore Technology	113	6,100
Market2Lead	318	4,800
Vtrenz	752	3,600
Marketing Automation:		-
Unica / Affinium*	6,600	8,500
Alterian	861	2,500
Aprimo	1,600	2,200
Neolane	1,340	1,690
Other Demand Generation:		-
MarketBright	186	5,400
Pardot	210	3,600
Marqui *	1,300	4,400
ActiveConversion	46	3,400
Bulldog Solutions	442	3,200
OfficeAutoPilot	0	2,000
Lead Genesys	74	1,450
LoopFuse	260	1,090
PredictiveResponse	36	330
FirstWave Technologies	386	170
NurtureMyLeads	0	140
Customer Portfolios	0	90
Conversen*	170	70
FirstReef	12	10

If we limit ourselves to the first two groups, the search numbers look mostly plausible. The low figure for Manticore could have to do with checking specifically for “Manticore Technology”, since a looser “Manticore” would incorporate an unrelated company and references to the mythical beast. The high value for Unica probably reflects some unrelated uses of the word in other languages or as an acronym. I have no particular explanation for the relatively low value for Alterian or the substantial flattening of the range between Eloqua and its competitors. Perhaps Eloqua’s traffic is less search-driven than other vendors’. Or not. In any event, I think the implicit rankings here are about as plausible as the Alexa rankings.

But things get crazier in the Other Demand Generation vendor segment. I understand the Marqui number, which is high because Marqui can be a misspelling of other words (marquis, marque, marquee) and has some unrelated non-English meanings. Similarly, Conversen is a verb form in Spanish. I think that Bulldog Solutions, FirstWave and LoopFuse also gain some hits because of their component words, even though I tried to keep them out of the search results. The bottom line here is you have to throw away so many terms that the remaining rankings don’t signify much. So, in general, search keyword rankings need close consideration before you can accept them as a meaningful measure of importance.

How about Google hits? I’ll show them alongside the Google Keywords as well as Alexa rank.

	Google hits	Google Keywords	Alexa
		avg search volume	share x 10^7
Already in Guide:
Eloqua	118,000	1,900	70,700
Silverpop	111,000	1,790	30,500
Marketo	103,000	839	17,000
Manticore Technology	9,620	113	6,100
Market2Lead	25,900	318	4,800
Vtrenz	35,200	752	3,600
Marketing Automation:			-
Unica / Affinium*	7,750	6,600	8,500
Alterian	262,000	861	2,500
Aprimo	161,000	1,600	2,200
Neolane	40,200	1,340	1,690
Other Demand Generation:			-
MarketBright	34,500	186	5,400
Pardot	27,600	210	3,600
Marqui *	1,370,000	1,300	4,400
ActiveConversion	16,800	46/p>	3,400
Bulldog Solutions	9,340	442	3,200
OfficeAutoPilot	777	0	2,000
Lead Genesys	5,880	74	1,450
LoopFuse	95,400	260	1,090
PredictiveResponse	21,800	36	330
FirstWave Technologies	13,400	386	170
NurtureMyLeads	1,050	0	140
Customer Portfolios	12,200	0	90
Conversen*	2,790	170	70
FirstReef	18,100	12	10

Here the impact of limiting Manticore to “Manticore Technology” shows up even more clearly (although Manticore truly doesn’t get much Web attention). I limited the Unica test to “Unica Affinium” since the number of hits is otherwise over 100 million; but this seems to excessively depress the results. Note that the low ranking for Alterian has now been reversed; in fact, Alterian has the most hits of all, and the marketing automation group in general shows more activity than the demand generation vendors. That could be true – those vendors have been around longer. Or it could be a fluke.

Once again, the Other Demand Generation group has a big problem with Marqui and perhaps smaller problems with LoopFuse and FirstReef. Even excluding those, the numbers jump around a great deal. As with keywords, these figures don’t seem to be a reliable measure of anything.

Let’s try one more measure: the blogosphere. Here I tried three different services: Technorati, BlogPulse and Ice Rocket.

	Technorati	Blogpulse	Ice Rocket	Alexa
	blog posts	blog posts	all posts	share x 10^7
Already in Guide:
Eloqua	130	267	286	70,700
Silverpop	70	119	188	30,500
Marketo	3	179	229	17,000
Manticore Technology	0	12	56	6,100
Market2Lead	0	7	25	4,800
Vtrenz	0	30	53	3,600
Marketing Automation:				-
Unica / Affinium*	0	6	43	8,500
Alterian	8	119	145	2,500
Aprimo	0	118	139	2,200
Neolane	0	33	64	1,690
Other Demand Generation:				-
MarketBright	1	23	33	5,400
Pardot	0	32	33	3,600
Marqui software*	5	15	19	4,400
ActiveConversion	0	6	12	3,400
Bulldog Solutions	0	30	43	3,200
OfficeAutoPilot	0	5	5	2,000
Lead Genesys	0	1	5	1,450
LoopFuse	4	48	43	1,090
PredictiveResponse	0	0	0	330
FirstWave Technologies	0	5	11	170
NurtureMyLeads	0	1	5	140
Customer Portfolios	0	0	3	90
Conversen*	0	2	0	70
FirstReef	0	0	0	10

Results for all three services are roughly consistent, although Technorati gets many fewer hits and Ice Rocket finds a few more than Blogpulse. The major anomaly is the low value for Unica, but that happens because I actually searched on Unica Affinium, to avoid all the irrelevant hits on Unica alone. Similarly, I searched on Marqui Software to avoid unrelated hits on Marqui. The high values for Bulldog Solutions and Loopfuse are valid (I scanned the actual hits); these two vendors just managed to snag a relatively high number of blog mentions. Remember we are looking at very small numbers here: it doesn’t take much to get 40 blog mentions. Nor, if we trust the Alexa, do they translate into much Web traffic. However, the blog hits might explain the relatively high keyword search counts for those two vendors.

Well, I hope you enjoyed the trip. This is far from an exhaustive analysis of the issue, but based on the information available, I’d say that Alexa Web traffic is the most useful measure for assessing the market presence of different demand generation vendors, and blog mentions have at least some value. Google hits and keyword searches capture too many unrelated items to be reliable.

No Silver Bullets for Social Media Measurement

The editor of my forthcoming book on marketing measurement asked me to add something on social media, which led to several days of research. Although there are many smart and articulate people writing on the topic, the bottom line is, well, you can’t really measure the bottom line.

There are plenty of activity measures such as numbers of page views, comments and subscribers. Sometimes there are specific benefits such as reduced costs if technical questions are answered through a user forum instead of company staff. Sometimes you can compare behavior of social media participants vs. non-participants, although that raises a self-selection problem – obviously those people are more engaged to begin with.

But measuring the impact of social media on attitudes in the population as a whole—that is, on brand value—is even harder than measuring the impact of traditional marketing and advertising methods because the audience size is so small. Measuring the impact of brand value on actual sales is already a problem, what you have with social media could be considered the brand value problem, squared.

In fact, the closest analogy is measuring the value of traditional public relations, which is notoriously difficult. Social media is more like a subset of public relations than anything else, although it feels odd to describe it that way because social media is so much larger and more complicated than traditional PR. Maybe we'll need to think of PR as a subset of social media.

The best advice I saw boiled down to setting targets for something measurable, and then watching whether you reach them. This is pretty much the best practice for measuring public relations and other marketing programs without a direct impact on sales. I guess there’s nothing surprising about this, although I was still a bit disappointed.

Still, as I say, there is plenty of interesting material available if you want to learn about concrete measurements and how people use them. Just about every hit on the first two pages of a Google search on “social media marketing measurement” was valuable. In particular, I kept tripping across Jeremiah Owyang, currently an analyst with Forrester Research, who has created many useful lists on his Web Strategy by Jeremiah blog. For example, the post Social Media FAQ #3: How Do I Measure ROI? provides a good overview of the subject. You can also search his category of Social Media Measurement. Another post I found helpful was What Is The ROI For Social Media? from Jason Falls’ Social Media Explorer blog.

Cognos Papers Propose Sales and Marketing Metrics

I’ve always felt that defining a standard set of marketing measures is like prescribing medicine without first examining the patient. But people love those sorts of lists, and they offer a starting point for a more tailored analysis. So I guess they have some value.

Based on that somewhat crotchety premise, I’ll call your attention to a pair of papers from Cognos on “Delivering the reports, plans, & metrics Sales needs” and “Delivering reports, plans, and metrics for better Marketing” (idiosyncratic capitalization in the original). These are widely available on the Internet; you can find both easily if you run this search at IT Toolbox.

Since the whole point of standard measures is to be broadly applicable, I suppose it’s a compliment to say that the measures in this paper are reasonable if not particularly exciting. One point they do illustrate is the difference between marketing and sales, which are often conflated into a single entity but are in fact quite distinct. Let’s look at the metric categories for each:

- Sales: sales results; customer/product profitability; sales tactics; sales pipeline; and sales plan variance.

- Marketing: market opportunities; competitive positioning; product life cycle management; pricing; and demand generation.

It’s surely a cliché, but these measures suggest that marketing is strategic while sales is almost exclusively tactical. That’s a bit blunt but it sounds about right to me.

Given my admittedly parochial focus on demand generation these days (see www.raabguide.com), I couldn’t avoid noticing that Cognos gave demand generation just one of its five marketing slots. That seems a bit underweighted, given that it probably accounts for the bulk of most marketing budgets. But I do have to agree that strategically, marketing should be spending its time on those other topics too.

The papers list specific measures within each category. It’s going to as boring to type these as you’ll find it to read them, but I guess it’s worth the trouble to have them readily available for future reference. So here goes:

Sales metrics:

Sales results
- new customer sales
- sales growth
- sales orders

Customer/product profitability
- average customer profit, lifetime profit and net profit
- net sales
- gross profit
- customer acquisition and retention cost
- sales revenue
- units sold

Sales tactics
- average selling price
- direct cost (of sales efforts)
- discount
- sales calls and sales rep days
- sales orders
- units quoted

Sales pipeline
- pipeline ratio (they don’t define this; I’m not sure what they mean. Maybe distribution by sales stage)
- pipeline revenue
- sales orders and conversions
- cancelled order count
- active and inactive customers
- inquiries
- new customers and lost business

Sales plan variance
- sales order variance
- sales plan variance
- sales growth rate variance
- units ordered and sold variance

You’ll notice a bit of overlap across groups, and I’m not sure why “Sales plan variance” is a separate area: I would expect to measure variances against plan for everything. The list is also missing a few common measures such as profit margin (which shows the net impact of decisions regarding product mix, pricing and discounts), actual vs. potential sales (hard to measure but critical), lead-to-customer conversion rates, and win ratios in competitive deals.

Marketing metrics:

Market opportunities
- company share
- market growth
- market revenue
- profit
- sales

Competitive positioning
- competitor growth
- competitor price change
- competitor share
- competitor sales
- market growth
- market revenue and profit
- sales

Product life cycle management
- new products developed
- new product growth, share, & profit
- new competitor product sales & growth
- market growth
- brand equity score
- new product share of revenue

Pricing
- price change
- sales
- price segment share and growth
- discount ($)
- discount spread (%)
- list price, net price, & average price
- price elasticity factor
- price segment sales and value
Demand generation
- marketing campaigns (#)
- marketing spend
- marketing spend per lead
- qualified leads (#)
- promotions ROI
- baseline and incremental sales

If these weren’t two separate papers, I’d say the author had gotten tired by the time she wrote this one. We see even more redundancy (sales appears in three of the five lists) and “brand equity score” sticks out like a moose at a Sarah Palin rally. (Now there’s a joke that will age quickly.) It’s interesting that the competitive measures provide some of the relative performance information that was lacking in the sales metrics, and that reporting on profit addresses to some degree my earlier question about margins. Is the author implicitly suggesting that sales shouldn’t be held accountable for such things? I disagree. On the other hand, measures of customer value or quality are all assigned to sales. I think marketing is primarily responsible for that one.

Well, that’s interesting: I hadn’t really planned to criticize these measures when I sat down to write this, but now that I look more closely, I do have some objections. It honestly doesn’t seem fair to be harsh, since any list can be criticized. Maybe I’m just crotchety after all. In any event, you can add this list to your personal inventory of metrics to consider for your own business. Maybe something in it will prove useful.

Is Measuring Brand Value Worth the Effort?

Hello, blogosphere! Did you miss me?

Probably not, but, whatever. Launch of the new Raab Guide to Demand Generation Systems http://www.raabguide.com/ is largely complete, so I can now find some time for this blog. Also, the publisher of my MPM Toolkit book seems to have settled on a January publication date, so I need to pay more attention to this side of the industry.

I’ll ease back into this blog with a survey on brand value from the Association of National Advertisers (ANA) and Interbrand consultancy. The press release is available here .

Key findings of the survey were that 55% of senior marketers “lack a quantitative understanding of brand value” and that 64% said “brands do not influence decisions made at their organizations.” Bear in mind that these are ANA members, who tend to be large media consumers. If they can’t measure or use brand value, nobody can.

Taken together, these two figures mean that brands don’t influence decisions even at some companies which are able to measure their value. The survey explored this a bit, and found that at companies where brands lack influence, the most common reason (cited by 51%) was that “incentives do not support importance of brand”. In other words, if I interpret that correctly, people are not rewarded for increasing brand value—so they don’t work to do that, even if they do have the ability to measure it. The next most common reason, at 49%, was the more expected “inability to prove brand’s financial benefit”. Other answers ranked at 40% or below.

This wouldn’t matter to anyone who's not a brand valuation consultant, except for one thing: 80% of the responders report that “demands from the C-suite and boardroom were steadily increasing” to demonstrate that branding initiatives add profit. That means even existing branding budgets are at risk.

If you accept, as I do, that branding programs do add value, then not being able to justify them is a serious problem. But there’s a difference between knowing something has value and knowing what that value is. As I’ve pointed out prevously and the good people at MarketingNPV recently wrote at more length, different brand valuation methodologies give widely varying results, and even the same methodology gives different results from year to year.

This has important practical implications: specifically, brand measurements are not precise enough to guide tactical decisions. Yet that is exactly what the ANA survey says marketers want: 93% felt a quantified understanding would allow “more focused investment in marketing” and 82% felt it would provide “an opportunity to custom out underperforming initiatives”. Frankly, I’d say those are unrealistic expectations.

The MarketingNPV paper argues that marketers should not attempt to measure brand value by itself, and instead focus on “quantifying the impact of marketing on cash flows”. That may seem like begging the question: after all, the value of a brand is precisely its impact on future cash flows. But I think of brand value as a residual factor which accounts for future cash flows that cannot be attributed to more direct influences such as promotions, distribution and pricing. So it does make sense to say, first let’s do a better job of predicting the impact on cash flows of those directly measurable items. Once we've taken that as far as we can--and I'd say most firms are nowhere near--then we can spend energy on brand value to explain the rest.

I'm Taking a Break

The sad truth is that even my mother doesn't read this blog on a regular basis. (Not that I blame her--she has important water aerobics and Free Cell games to attend to.) But in case you've noticed the lack of activity, I'm taking a break right now to concentrate on finishing the Guide to Demand Generation Systems by end of the month. I hope to get back to posting regularly once that's done.

And also in case you're wondering about my planned book on marketing performance measurement: publication date is now set for January, 2009. The book was actually finished months ago, but the production process is backed up at the publisher. Apologies to those of you who thought you had found the perfect holiday gift for the Certain Someone.

Measuring the Value of a Marketing Measurement Project - Part 3 of 3

So far this series of posts has described generic inputs to calculate the value of a marketing measurement project. Ordinarily an internal expert or a consultant would help define the values for those inputs. But let’s assume we’re in the self-service world where non-experts provide those values for themselves. What advice can we give them?

The first is to define the project scope. That is, you want to limit the analysis to the customers, revenues and costs that will actually be affected by the project. For example, inputs related to a Web analytics project should only include the number of Web customers, Web revenues and Web costs. But even this case isn’t so simple: if the project is measuring something specific, such as paid search results, then it may affect just a subset of all Web customers. It’s easy to overestimate the scope of a project’s impact, which in turn overstates the expected benefit. The more precise you can be in your understanding of the actual customers affected by a project, the more you’ll be able to build a realistic estimate of its impact.

A second useful consideration is the actual mechanism that will provide the expected change. Because we are talking about marketing measurement projects, all the project provides is better information about business results. Unless this information can be used to change something, it won’t have any impact. So, for example, a system to uncover immediate opportunities is worthless if you can’t react in less than a week. Similarly, a model that shows the value of changes in the marketing mix has no value if the mix won’t be changed for political reasons. A close look at how the new information will be used will allow a more precise estimate of the expected changes in value.

A third consideration is your ability to project the impact of your changes. Most marketing measurement projects can show the relative performance of current marketing programs, but only some can estimate the impact of incremental investments in those programs. This can be a particular issue where there are constraints on program expansion, such as limited advertising inventory. Where projections of incremental returns are not immediately available, you may be able to conduct additional research or use standards rules of thumb such as the square root rule to make reasonable estimates.

Finally, you need to consider the time horizon of your analysis. Most MPM projects will require a significant initial investment followed by lower, recurring operating costs. The benefits will probably follow a very different pattern, starting slowly and then growing over time. The formulas I’ve presented can be extended to create this sort of projection by doing separate calculations for a sequence of time periods. But in many cases a much simpler approach is acceptable, basically of estimating the benefit for a “mature” program for a fixed time period such as one year. This can be compared with program investment to calculate annual ROI or pay-back period.

Note that even a “simple” calculation may need to consider long-term impacts. For example, a program that acquires new customers more effectively will benefit from the full lifetime value of those customers, not simply their first-year revenue. This is where the idea of “mature” value comes in. It implies looking at a slice of results across all customer groups after the program has been in place for some time. Doing this math correctly can be fairly complex. But, again, you may be able to save effort by making some simplifying assumptions. In the case of a program that let’s you spend more efficiently on acquisitions, this might mean assuming the number of new customers will remain the same because you reduce your acquisition budget. Since the number of new customers doesn’t change, the later values also remain the same. The value of the program thus equals the savings in acquisition cost—a much simpler item to estimate. (Of course, this will underestimate the actual program value if you will in fact be maintaining your acquisition budget and acquiring more customers. Whether this matters depends on the situation.)

There are certainly other considerations that could apply in a given situation. Identifying them all may not be possible. But even if you could list them, the resulting document would be too long to be practical in a self-service situation. So we have to hope that our self-service consumers are able to complete the process on their own, or at least recognize when it’s time to call for expert help.

Measuring the Value of a Marketing Measurement Project - Part 2

The first post in this series explained why I might create a standard spreadsheet to measure the value of a marketing performance measurement (MPM) project. In a traditional consulting engagement, this measurement would be a custom analysis tailored specifically to the project at hand. In the self-service world, this is an unavailable luxury. I’m starting with value measurement because I’m incurably linear and the first question to answer about a project is what value the client hopes to receive. The answer drives everything else.

In creating a generic project value form, the trick is to define a set of categories that are specific enough to be useful yet broad enough to cover all the possible cases. My inner Platonist wants to start with a general value formula such as value=revenue – costs, and then subdivide each element. But how would you know if the subcomponents corresponded to the value drivers of actual MPM projects? It’s better to start with a sample of MPM projects, identify their value drivers, and then see if these can be joined as components of a single formula. (Philosophy majors everywhere will recognize the difference between deductive and inductive reasoning. But I digress.)

A reasonable list of typical MPM projects would include marketing mix models, brand value studies, response measurements, Web analytics, social media measurement, and operational process measurements. Except for the final category, these all help allocate marketing resources to the most effective use. In contrast, operational processes help the department perform its internal functions more efficiently. This distinction immediately suggests breaking the value formula into two primary components: value received and marketing operations.

Of course, value received is the more important of the two, particularly if the calculation includes non-overhead marketing costs such as advertising, discounts and channel promotions. One way to subdivide value is to consider that a typical marketing plan will be divided among customer acquisition, development and retention programs. Of these, acquisition and retention focus on number of customers, while development focuses on value per customer. It therefore makes sense to calculate value as the product of these factors (i.e., value= number of customers x value per customer). Since many companies are more product-oriented than customer-oriented, value per customer could further be divided into value per unit and units per customer. Value per unit, in turn, could be split into revenue per unit, product cost per unit (cost of goods sold, shipping, etc.), and marketing cost per unit.

A single value for “number of customers” doesn’t really capture the dynamic between acquisition and retention rates, so it too must be broken into pieces. The basic formula is number of customers = (existing customers + customers added – customers lost).

The result of all this is a value formula with the following elements:

net value = value received – marketing operations cost

value received = number of customers x units per customer x value per unit

number of customers = existing customers + customers added - customers lost
units per customer (possibly broken down by product mix)
value per unit =revenue per unit – product cost per unit – marketing cost per unit

Now let’s do a reality check against our list of MPM projects:

- marketing mix models include product mix, pricing, advertising, and channel promotions as their major components.

- product mix is covered by revenue per unit and/or units per customer.

- pricing is covered by revenue per unit and/or marketing cost per unit, depending on how you treat discounts, coupons, etc.

- advertising is covered by marketing cost per unit

- channel promotions are covered by product cost per unit and/or marketing cost per unit

- brand value studies measure the relation of consumer attitudes to behaviors such as trial, retention and consumption rates. These are covered by existing customers, customers added, customers lost, and possibly by units per customer. A more formal sales funnel could easily fit into this section of the formula if appropriate.

- response measurements are covered by customers added and marketing cost per unit.

- Web analytics projects encompass a range of objectives such as lower cost per order, improved conversion rates and higher revenue per visitor. These are covered respectively by marketing cost per unit, number of new customers, and a combination of units per customer and revenue per unit. Other objectives could also probably be covered by the formula components.

- social media measurements are like brand value measurements: they relate messages to attitudes to behaviors. They would also be covered by changes in customer numbers and units per customer.

- operational process measurements are covered directly by marketing operations cost

So it looks like the proposed set of variables lines up reasonably well with the value drivers of typical MPM projects. This means that project planners should be able to define the expected benefits in terms of these variables without too many intermediate calculations. Once they’ve done this, the actual value calculation is purely mechanical.

The final set of inputs would look like this (with apologies for poor formatting):

inputs..................current value....expected value......change

Number of Customers:
existing customers........xxxx............xxxx...................xxxx
+ customers added........xxxx............xxxx...................xxxx
- customers lost.............xxxx............xxxx...................xxxx

Units per customer:......xxxx............xxxx...................xxxx

Value per Unit:
revenue per unit............xxxx............xxxx...................xxxx
- product cost per unit...xxxx............xxxx...................xxxx
- marketing cost/unit.....xxxx............xxxx...................xxxx

Marketing Ops Cost.......xxxx............xxxx...................xxxx

Based on those inputs, the final calculation is:

Value Received (= Number of Customers x Units per Customer x Value per Unit)
- Marketing Ops Cost
= Net Value

OK, so now we have a formula that calculates business value using inputs relevant to marketing measurement projects. But the real work is deciding what values to apply to those inputs. Part 3 of this sequence will talk about that.