Judging the Value of Marketing Data

Last week’s post on ranking demand generation vendors highlighted a fundamental challenge in marketing measurement: the data you want often isn’t available. So a great deal of marketing measurement comes down to deciding which of the available data best suits your needs, and ultimately whether that data is better than nothing.

It’s probably obvious why using bad data can be worse than doing nothing, but in case this is read by, say, a creature from Mars: we humans tend to assume others are telling the truth unless we have a specific reason to question them. This innate optimism is probably a good thing for society as a whole. But it also means we’ll use bad data to make decisions which we would approach more cautiously if we had no data at all.

But how do you judge a piece of data? Here is a list of criteria presented in my book The MPM Toolkit, due in late January.

· Existence. Ok, this is pretty basic, but the information does have to exist. Let’s avoid the deeper philosophical issues and just say that data exists if it is recorded somewhere, or can be derived from something that’s recorded. So the color of your customers’ eyes only exists as data if you’ve stored it on their records or can look it up somewhere else. If the data doesn’t exist, you may be able to capture it. Then you have to compare the cost of capturing it with its value. But that’s a topic for another day.

· Accessibility. Can you actually access the data? To get back to last week’s post, we’d love to know the revenue of each demand generation vendor. This data certainly exists in their accounting systems, but they haven’t shared it with us so we can’t use it. Again, it’s often possible to gain access to information if you’re willing to pay the price, and you must once more compare the price with the value. In fact, the price / value tradeoff will apply to every factor in this list, so I won’t bother to mention it from here on out.

· Coverage. What portion of the universe is covered by the data? In the case of demand generation vendors, the number of blog posts was a poor measure of market attention because the available sources clearly didn’t capture all the posts. In itself, this isn’t necessarily fatal flaw, since a fair sample could still give a useful relative ranking. But we can’t judge whether the coverage was a fair sample because we don’t know why it was incomplete. This is a critical issue when assessing whether, or more precisely how, to use incomplete data. (In the demand generation case, the very small numbers of blog posts added another issue, which is that the statistical noise of a few random posts could distort the results. This is also something to consider, although hopefully most of your marketing data deals with larger quantities.)

· Accuracy. Data may not have been accurate to begin with or it may be outdated. Data can be inaccurate because someone purposely provided false information or because the mechanism is inherently flawed. Survey replies can have both problems: people lie for various reasons and they may not actually know the correct answers. Even seemingly objective data can be incorrect: a simple temperature reading may inaccurate because the thermometer was miscalibrated, someone read it wrong, or the scale was Celsius rather than Fahrenheit. Errors can also be introduced after the data is captured, such as incorrect conversions (e.g., inflation adjustments used to create “constant dollar” values) or incorrect aggregation (e.g., customer value statistics that do not associate transactions with the correct customers). In our demand generation example, statistics on search volume were highly inaccurate because the counts for some terms included results that were clearly irrelevant. As with other factors listed here, you need to determine the level of accuracy that’s required for your specific purpose and assess whether the particular source is adequate.

· Consistency. Individually accurate items can be collectively incorrect. To continue with the thermometer example, readings from some stations may be in Celsius and others in Fahrenheit, or readings from a single station may have changed from Fahrenheit to Celsius over time. This particular difference would be obvious to anyone examining the data, although it could easily be overlooked in a large data set that combined information from many sources. Other inconsistencies are much more subtle, such as changes in wording of survey questions or the collection mechanism (e.g., media consumption diaries vs. automated “people meters”). As with coverage, it’s important to understand any bias introduced by these factors. In our demand generation analysis, Compete.com used several different techniques to measure Web traffic, and it appeared that these yielded inconsistent results for sites with different traffic levels.

· Timeliness. The primary issue with timeliness is how quickly data becomes available. In the past, it often took weeks or months to gather marketing information. Today, data in general moves much more quickly, although some information still take months to assemble. There is a danger that quickly available data will overwhelm higher-quality data that appears later. For example, initial response rate to a promotion is immediately available, but the value of those responses can only be measured over time. Decisions based only on gross response often turn out to be incorrect once the later performance is included in the analysis. Still, timely data can be extremely important when it can lead to adjustments that improve results, such as moving funds from one promotion to another. Online marketing in particular often allows for such reactions because changes can be made in hours or minutes, rather than the weeks and months needed for traditional marketing programs.

I haven’t listed cost as a separates consideration only because there are often incremental investments that can made to change a data element’s existence, accessibility, coverage, etc. Those investments would change its value as well. But you will ultimately still need to assess the total cost and value of a particular element, and then compare it with the cost and value of other elements that could serve a similar purpose. This assessment will often be fairly informal, as it was in last week’s blog post. But you still need to do it: while an unexamined life may or not be worth living, unexamined marketing data will get you in trouble for sure.

Twitter Volume for Demand Generation Vendors

A comment on my previous post suggested Twitter mentions as a possible measure of vendor market presence. That had in fact occurred to me, but I hadn't bothered to check because I assumed the volume would be too low. But since the topic had been raised, I figured I'd take a peek.

The first two Twitter monitoring sites I looked at, Twitscoop and Twitterment, seemed to confirm my suspicion: of the three most popular vendors, Eloqua had 6 Twitscoop hits and 3 Twitterment hits; Silverpop had 2 on each; and Marketo had 3 on Twitscoop and none on the other. No point in looking further here.

But then I checked Twitstat. In addition to having a slightly less childish name, it seems to either do a more thorough search or look back further in time: for whatever reason, it found 152 hits for Eloqua, 65 for Silverpop, and 133 for Marketo. Much more interesting.

Alas, the numbers dropped down considerably after that, as you can see in the table below. Everything else is in single digits except for two anomalies LoopFuse with 22 mentions and Bulldog Solutions with a whopping 217. Interestingly, both those sites also had exceptionally high blog hit numbers on IceRocket. The root cause is probably the same: one or two active bloggers or Twitter users (which seems to be the accepted term; I guess we can't call them Twits) are enough to skew the figures when volumes are so low. More particularly, LoopFuse gets a lot of attention because some of its founders are closely tied to the open source community. Bulldog Solutions just seems to have a group of employees who are serious into Twitter. In fact, I now know more about their lives than I really care to (although there was nothing indiscreet in the posts, I'm pleased to report).

A couple of side notes:

- the very short length of the messages does make them easy to read, which paradoxically means you can actually gather more information from Twitter than by scanning blog posts, because reading the blog posts takes too much time. Of course, when we're dealing with such tiny volumes, there is no way to generalize from what you read: Twitter is strictly anecdotal evidence, and perhaps even dangerous for that reason.

- there seemed to be several Tweets that were purposely sent for marketing purposes. Nothing wrong with that, and they were quite open about it. Just interesting how quickly some firms have picked up on this. (OK, not so quickly: Twitter has been around since 2006 and very popular for about a year now.)

Still, the bottom line for the purposes of measuring demand generation vendors is still the same as for blogs: too little volume to be a reliable measure of relative market interest.

	Twitterment	Ice Rocket	Alexa	Alexa
	twitter mentions	blog posts	rank	share x 10^7
Already in Guide:
Eloqua	152	286	20,234	70,700
Silverpop	65	188	29,080	30,500
Marketo	122	229	68,088	17,000
Manticore Technology	0	56	213,546	6,100
Market2Lead	5	5	235,244	4,800
Vtrenz	8	53	295,636	3,600
Marketing Automation:				-
Unica Affinium	6	43	126,215	8,500
Alterian	5	145	345,543	2,500
Aprimo	6	139	416,446	2,200
Neolane	5	64	566,977	1,690
Other Demand Generation:				-
Marketbright	9		167,306	5,400
Pardot	4	33	211,309	3,600
Marqui Software	2	19	211,767	4,400
ActiveConversion	2	12	257,058	3,400
Bulldog Solutions	219	43	338,337	3,200
OfficeAutoPilot	2	5	509,868	2,000
Lead Genesys	1	5	557,199	1,450
LoopFuse	22	43	734,098	1,090
eTrigue	1		1,510,207	430
PredictiveResponse	1	0	2,313,880	330
FirstWave Technologies	0	11	2,872,765	170
NurtureMyLeads	0	5	4,157,304	140
Customer Portfolios	0	3	5,097,525	90
Conversen	1	0	6,062,462	70
FirstReef	0	0	11,688,817	10

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.