Raab Report: Financial Comparison of B2B Marketing Automation Vendors

I’ve been so busy analyzing the new VEST data that I missed the announcement that Eloqua’s would make its initial stock offering today. The valuation was a bit disappointing – $368 million, or just over four times revenue trailing 12-month revenue – but certainly a good return on its backers’ investment of about $41 million. And the stock did rise 12% on the first day. Good for them, and congratulations.

Coincidentally, I was already planning to write today about industry financials. I’ve been creeping in that direction with the previous two posts about revenues, growth rates, and market share. Now let’s plunge in with some more substantial analysis.

For companies like Eloqua and its competitors, there are really two big financial questions: how fast can they grow, and how can they become profitable? In a young industry like B2B marketing automation, the primary focus is growth, and I published some figures on that yesterday  (repeated below). As we saw, Eloqua’s client count is growing considerably slower* than all major competitors except Infusionsoft. This may be one reason the stock market gave it a relatively conservative valuation.

Revenue figures tell a similar story, as does revenue per client. We looked at those in Tuesday’s post;  I’ll repeat the caveat that figures for Eloqua and HubSpot are my own estimates based largely on client growth and (for HubSpot) changes in client mix. The standout performer in all these tables is Marketo, but bear in mind that they’ve also taken much more investment than any of the others ($107.5 million) and the $70 million in 2012 revenue hasn’t happened yet. Still, this suggests that Marketo might be able to fetch a higher price than Eloqua.

What about profitability? I’ll repeat that the financial markets care much less about profits than growth for early stage companies. Still, profits will have to matter eventually.  So they're worth a look.

Eloqua is the only company in this group with published financial statements, so any profitability analysis has to be speculative. One useful measure is employee counts, which are a reasonable proxy for expenses and operating efficiency. The table below presents clients, employees, and clients-per-employee ratios.

The first thing you’ll notice is the broad range in clients-per-employee ratios: from 40:1 for Infusionsoft to less than 4:1 for Eloqua. The main reason is the size of each company’s clients – Infusionsoft serves small businesses that take much less effort per client than the mid-size and large companies who buy Eloqua.

Still, Marketo, Pardot, SalesFusion and Net-Results all serve primarily mid-size companies, so they are somewhat comparable. (Act-On tends a bit smaller.) Given that assumption, the figures suggest that Pardot, Net-Results and SalesFusion are more efficient than the others. That’s probably true, perhaps because they are all self-funded. Net-Results also markets primarily through resellers, which also lowers its costs.  Act-On’s ratio is notably low, probably reflecting aggressive staffing as it prepares for rapid growth.

The second thing you’ll notice is the year-on-year trend. Infusionsoft, HubSpot, Act-On, and Net-Results all show a drop in the clients-per-employee ratio since last year, meaning they have become less efficient. We can probably attribute that to gearing up for growth. By contrast, Eloqua, Pardot and SalesFusion have become substantially more efficient. Eloqua’s gain is particularly impressive since it has the largest client base and relatively low growth – suggesting the company has been working hard to keep costs down in preparation for its public offering. It looks like Marketo has become just slightly more efficient, but we'll revise that opinion in a moment.

Since we do have revenue figures for the top four vendors, we can also look at their revenue per employee. This is a standard efficiency metric and more directly comparable across companies.  Here's that data, along with revenue per client.

These figures put the client-per-employee ratios in deeper perspective. They confirm that Eloqua has improved efficiency, and by far the highest revenue per employee in the industry.  The figures may be be overstated (see footnote) but even more conservative values would leave Eloqua in first place.  The figures also confirm that Infusionsoft’s cost structure is pretty much stable.

The news is better for HubSpot, whose apparent productivity decrease (measured in clients-per-employee) vanishes when you measure revenue per employee instead. The difference is the growth in revenue per client (which, I’ll remind you again, is only my personal estimate).

The story is even more dramatic for Marketo, whose 6% improvement in clients per employee becomes a 23% gain in revenue per employee, boosted by a 16% increase in revenue per client.  Impressive, but let's hold the applause until we see the actual results.


Whew, that’s a lot of numbers. Maybe only industry insiders will find them as interesting as I do. But other marketers should also find them helpful as they try to understand each vendor's business situation and determine how well it matches the marketer's own needs.

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* And that's using Eloqua-provided figures of 900 clients as of mid-2011 and 1,375 for mid-2012, which yield a 53% year-on-year growth rate.  The revised S-1 published in mid-July showed 42% year-on-year revenue growth.  A 42% growth rate would yield 2012 revenue of $101 million vs. my estimate of $110 million, and a 7% drop in revenue per customer to $73,455.  Ouch!  On the plus side, even the adjusted $288,571 revenue per employee is higher than anyone else, and a 16% improvement over 2011.

Raab Report: B2B Marketing Automation Industry Is Getting More, Not Less, Fragmented

I’ve gotten used to thinking of the B2B marketing automation industry as entering a consolidation phase, during which a handful of dominant vendors emerge and small vendors drop away. That’s why I was a bit surprised when yesterday’s blog post showed that the “big four” industry vendors (Infusionsoft, HubSpot, Marketo, and Eloqua) are actually growing slower than the “next four” largest (Pardot, Act-On Software, Net-Results, and SalesFusion)*. In other words, the industry is becoming less concentrated, at least for the moment.

In fact, this trend extends back for the past two years, which is as far as my VEST data goes. It likely extends still further, and, on reflection, this makes sense: at the start of a new industry, there are just one or two pioneering firms with no competition and, thus, 100% market share. This share can only drop over time as new entrants emerge. It's only in the later stages of consolidation – after crossing Geoffrey Moore’s chasm – that the dominant vendors really take control. For B2B marketing automation, we’re not there yet.

The table below shows all this in glorious detail: the big four vendors grew more slowly on a percentage basis than the next four, even though the big four added more clients in absolute terms. The figures for "other" are a bit misleading because the 2011 and 2012 figures include a few more companies than the 2010 data.  But they're directionally correct.

Share of clients somewhat overstates the dominance of the big four because Infusionsoft and Hubspot serve such huge numbers of small companies. Revenue would be a better measure but I don’t have reliable figures for the smaller vendors. I do have employee counts, at least for the top eight companies. They make the next four look more important: while the next four have just 12% of the clients, they have 18% of the employees.  This is up from 14% of employees a year ago, so the fundamental story is still the same: the next four are growing faster.

What all this means in concrete terms is that a new vendor can still challenge the current market leaders.  Both Pardot and Act-On are doing exactly that. Their success isn’t guaranteed and it’s not clear how much longer the window of opportunity will remain open. But, for now at least, the game isn’t over.

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* Actually, Genius should place in this group, since it has 900 clients.  But that information reached me the day after this post was written.  They're also growing much more slowly than the vendors listed here -- up from 700 clients a year prior (29% growth, vs. the 139% growth of the current "next four").  So I think the point about smaller vendors being able to grow quickly is better supported by keeping the existing data in place.

Raab Report: B2B Marketing Automation Revenues to Hit $525 Million in 2012

I’ve just released the latest edition of my B2B Marketing Automation Vendor Selection Tool (VEST), which contains detailed analysis of all 22 B2B marketing automation systems. Serious marketing of the new edition is yet to begin, but anyone considering purchase of a marketing automation system can buy the VEST now at the www.raabguide.com Web site.

The new report contains a rich trove of industry information. The one item that people usually find most interesting is the size of the industry. I put this at $325 million for 2011, a 50% increase from 2010. With 2012 half finished, I can now make a reasonably solid estimate for this year. I find the growth rate has actually accelerated to 60%, for a total of about $525 million.

I come at these figures in two ways.

Installations by industry sector. Vendors in the VEST are asked for estimates of their client counts by company size. We distinguish four segments: micro-business with under $5 million revenue; small business with $5 to $20 million revenue, mid-size business with $20 to $500 million revenue, and large business with over $500 million revenue. Most vendors do provide the sector breakdown, although some are pretty rough estimates.  For a couple of vendors, I’ve used my own estimate based on past data.

Using the sector counts plus estimated revenue per client for each sector, I can calculate the revenue by sector and for the industry as a whole. Since the client counts are mid-year figures, they should roughly equal the full-year average. I’ve only included figures for vendors who specialize in B2B systems; none of the other vendors (Neolane, Oracle, Silverpop, Aprimo, MarketingPilot) are provided estimates of the B2B portion of their client base.  The table below shows my calculations:

The total comes to $362 million estimated 2012 revenue. I estimate the non-B2B specialists and other marketing automation vendors (IBM, SAS, SAP, etc.) who are not listed in the VEST will have another $165 million in B2B revenue, for a total of $527 million.

Revenue estimates for individual vendors. The second approach starts with the four largest B2B specialists: Infusionsoft, HubSpot, Marketo, and Eloqua. Each has announced revenue for 2011 (formally or in press interviews) and two, Infusionsoft* and Marketo**, have made forecasts for 2012. I estimated 2012 revenues for HubSpot and Eloqua based on their client counts and revenue per client.  I then estimated revenue for the other specialist vendors by combining results from two methods: estimated revenue per employee and estimated revenue per client. Finally, I’ve added figures for the non-specialist vendors, using the same assumptions as before. The table below shows the results.

As you see – and I swear I didn’t cook these numbers – this gives $525 million, almost exactly matching the other method.  

Of course, there's more to these figures than just the industry size.  One interesting point is that the “other specialist” vendors are actually growing faster than the big four vendors. This is a bit of a surprise, since we’d expect the industry to consolidate and squeeze out the smaller players. Still, remember that the big four control 75% of the revenue. 

The difference is client growth actually larger than the revenue estimates suggest.  The table below shows that the client base of the “other specialists” grew by 80%, which is faster than any of the big four.

One caveat is that a number of the smaller vendors didn’t provide updated client counts, and they may be vendors who were not growing much. But the reality is that the next three largest vendors (Pardot, Act-On, and Net-Results) did provide data, and each grew by well over 100%.  So the missing vendors don't have enough volume to affect the big picture.


I’ll share one final set of data that also points to industry strength. The table below shows revenue per client for the big four vendors over the past two years. These are actuals except for the 2012 figures for HubSpot and Eloqua, and I consider those to be educated, conservative guesses.

This table shows a consistent increase in revenue per customer across all vendors and all years. Given the intense competition within the industry, that’s pretty impressive: it shows that the big four vendors are managing to increase their revenue per client, which all must do to become profitable.  I suspect the increase is less the result of firmer pricing than of broader product lines that let the vendors sell more to each customer.  Nor does this mean that industry prices are rising: it’s possible – in fact, likely – that the smaller vendors are selling for less than their larger competitors, and that the average price in the industry is still dropping.

All told, this paints the picture of a healthy industry: still growing rapidly, still open to competition, and supporting sustainable prices.   It's a cheery bit of news.

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*Infusionsoft "expected revenue of $40 million in 2012" (Customer Experience Matrix, April 14, 2012)

**Marketo "revenues last year grew 140% over the year to $35 million. Management expects revenues to double during this year" (Sramana Mitra blog interview with Phil Fernandez, July 21, 2012)

The Marketing Funnel Is Dead. Let's Have Dessert.

Last week’s post on lead scoring attracted more positive attention than I expected. This was doubly surprising because first, I didn’t think lead scoring was such a hot topic and second, I don’t really agree with the approaches I described.

To clarify that second point, I’m not saying what I wrote was wrong or insincere. Rather, I consider it an accurate description of an approach I find problematic. The approach was using lead scoring as a way to define lead stages. My problem is the concept of lead stages themselves.

This verges on heresy, but I’m having an increasingly hard time with lead stages as a way to organize a marketing program. Of course, stages make perfect intuitive sense, and they’re ultimately based on the AIDA (Awareness, Interest, Desire, Action) model of the sales process that has been around for more than 100 years.*

But we all know in our heart of hearts that real buyers don’t follow such an orderly sequence. Indeed, there has been a fair amount of research questioning the validity of AIDA and similar “hierarchy of effects” models. The fundamental criticism is that decision making isn’t as rational as AIDA suggests because emotions play a much stronger part than AIDA allows. I’d also add – without a shred of empirical proof, thanks for asking – that B2B decision processes flit among stages in no particular sequence, depending on who asks what questions at any given moment. This randomness is abetted by the Internet, which makes information appropriate to all stages equally accessible on demand. But I suspect the process was always more chaotic than marketers cared to admit.

I’d further argue that buyers’ interests are especially fluid early in the purchase process, which is where marketers are involved. It may be more structured towards the end where salespeople can shepherd buyers through a defined set of stages. No, I don’t have any evidence for this either.

The point is this: if buyers don’t move through a fixed set of stages, then it doesn’t make sense to use lead scoring to determine which stage a buyer is at. Nor, for that matter, does it make sense to structure lead nurturing programs to lead (or follow) buyers from one stage to the next. As I said, heresy.

But any jackass can kick down a barn.** I wouldn't discard the funnel model without offering a better alternative – and by better, I specifically mean more effective at producing productive leads. Here’s my two-part modest proposal:

- within nurture programs, leads should be offered whatever materials they are most likely to select next, based on their recent behavior. This is exactly the same as offering customers the products they are most likely to buy (think Amazon book recommendation or Netflix’s movie suggestions) and it can be based on similar advanced predictive modeling technology. And, just as Amazon and Netflix offer more than one option, nurture programs should also offer several items – within limits, since too many choices can depress response. There’s an important humility in offering choices: it recognizes how poor we are at predicting what people want.

- for lead scoring, the goal is to predict which leads the sales force will like. I chose that word carefully – it’s not a question of whether sales will accept a lead, but whether they’ll decide it’s worth sustained effort. Yes, there could be a “like” button that lets sales rate the leads, but don't be so literal-minded.  It would be simpler and more effective to check how much activity sales has invested in the lead within, say, thirty days after they received it. Leads that sales is working are, by definition, leads that sales thinks is worthwhile. Leads they don’t work should never have been sent to them. This approach doesn’t magically solve the problem of connecting marketing leads to sales results, but it’s easier than tying leads to actual revenue.

Of these two proposals, the first one is the more radical since it implies a change in the structure of nurture campaigns. Today, sequential campaigns are the gold standard and complex branching structure are the mark of sophistication. A campaign that just presented the most relevant materials would have a vastly simpler structure – essentially a big loop that kept coming back with more messages, which would only differ in which offers they included. The sophistication would lie in the offer selection, not the campaign logic. Lead scoring's only role would be to run in the background and continuously assess whether a lead is ready to send to sales.

Even this choice-based approach doesn’t fully discard a sequential model. You need something to help decide what kinds of content to create, and the most logical tool is the content matrix that marketers already use to ensure they have content for all personas at all buying stages. But while you’re still cooking a full range of dishes, you’re offering them as a buffet rather than a fixed-course dinner. If a customer wants to eat dessert first, why argue?


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* Usually attributed to Elias St. Elmo Lewis in 1898, although there is some controversy.

** Sam Rayburn, although I bet he didn't originate it.

3 Ways to Use Lead Scoring Within Your Marketing Automation Programs

I wrote last week about the difficulty of linking marketing leads to sales results. One reason the topic was on my mind is I’m also thinking a lot these days about lead scoring. The practical use of lead scoring is to decide which leads to pass from marketing automation to sales, or, even more pragmatically, to predict which leads will be accepted by sales.* But the ultimate goal is to identify the leads most likely to generate revenue. Building an accurate scoring model therefore requires an accurate view of how leads and revenue are connected.

For all the reasons I discussed last week, that lead-to-revenue connection is hard to make. This is one reason that most lead scoring projects focus instead on the criteria that salespeople use in judging which leads to accept. The other reason is that salespeople can decide which leads they’ll work on – so giving them what they want, regardless of whether it’s what they really need, is the key to lead scoring being considered a success.

Many companies today have inserted a phone call between marketing automation and the sales department, screening every plausible lead before sending them to actual salespeople. This reduces the need for scoring accuracy because the phone call will clarify whether the lead is sales ready.  Since the cost of a missed opportunity is much higher than the cost of a wasted phone call, scoring in this situation must simply find all leads with a reasonable chance of success.

In short, scoring programs face two scenarios:

- for scores that directly determine which leads are sent to sales, accuracy is needed but data on past results (necessary to build a good model) is scarce

- for scores that determine which leads get a screening call, accuracy isn’t very important.

Perhaps this is why so few companies use lead scoring (just 19% in a recent MarketingSherpa study) and why the scoring models tend to be simplistic. Investment in more sophisticated techniques, such as statistically-based predictive models, is rarely worth the cost.

There is, however, another use for lead scoring: assigning leads to stages as they move through the marketing funnel.**

Conceptually, assigning leads to funnel stages is quite different from calculating their probability of making a purchase. A funnel stage is defined by meeting specific criteria such as BANT (budget, authority, need and timing) and engagement (downloading a paper or providing contact information). This is more like a checklist than a numeric score, although items like the number of specified behaviors may be calculated. Still, it's sometimes convenient to use score ranges as stage definitions.

In this context, scoring can be used in three ways.

- assign points  to directly to stage criteria.  For example, imagine a three-stage funnel of Respondent (replied to an email), Qualified Respondent (meets BANT conditions) and Sales Ready Lead (demonstrates engagement). If the scoring rules give 100 points for a response, 100 points for meeting BANT criteria, and 100 points for demonstrating sufficient engagement, then people with 100 points are Respondents, people with 200 points are Qualified Respondents, and people with 300 points are Sales Ready Leads. This is a common approach, although it’s not much different from applying the same rules to classify leads directly.


- treat the score as a probability estimate of reaching the final goal (sales readiness, sales acceptance, or revenue). Under this approach, a Respondent might be someone with a goal probability of under 10%; a Qualified Respondent might have a goal probability of 10% to 50%, and Sales Ready Lead might have a goal probability above 50%. This method avoids the need to define specific lead stage criteria, replacing them with objective predictive modeling methods that are likely to be more accurate.

- treat the score as a probability estimate of reaching the next stage (Respondent, Qualified Respondent, etc.). This retains the explicit stage criteria, which may help marketers visualize who is in each stage and how best to treat them. The predictive model provides additional segmentation within each stage, so marketers can focus their efforts on the most promising leads. Since linking leads to stage movement is easier than linking them to revenue, these predictive models are easier to build.

Today, most companies probably do a hybrid of the first and second options. That is, they assign points based on specified criteria (first option) but assign stages based on point ranges (second option). This combines the familiarity of criteria-based scoring rules with the convenience of numerical stage definitions, making it the easiest method available. But it is also doubly arbitrary, since neither the point values nor the range boundaries can be measured against an objective standard.

I’d suggest that marketers move towards a purer version of the second method, building statistical models that predict the final goal (revenue if available; sales acceptance or sales-ready lead criteria if not). Stage definitions can be arbitrary ranges but correlated against existing stage criteria. Eventually, marketers may want to move toward the third method, with separate models for each stage. This makes it easier to focus on advancing leads from one stage to the next while retaining the rigor of a statistically based approach.


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* For example, Marketo’s Definitive Guide to Lead Scoring defines lead scoring as “a shared sales and marketing methodology for ranking leads in order to determine their sales-readiness.”

**Eloqua’s Grande Guide to Lead Scoring puts it nicely: lead scoring “helps marketing and sales professionals identify where each prospect is in the buying process.”

Dell To Resell Pardot Marketing Automation

Dell announced today that it has added  Pardot  marketing automation to its list of Dell Cloud Business Software applications.  Other products in the suite include Salesforce.com for sales automation and customer service, Adobe EchoSign for e-signatures, AppExtremes Conga Composer for proposal creation, Dell’s own Boomi for application integration, and a Dell-built analytics platform.  That is some pretty good company to keep.

Beyond the Pardot system itself, the Dell offering includes pre-built integration with the other Dell products and with Microsoft Dynamics CRM, and fixed-price implementation packages (from free to $5,000) including training, campaign development, site search setup, CRM integration, and Google AdWords integration.  The Pardot system costs from $1,000 to $3,000 per month depending on the email volume, file size, and numbers of forms, landing pages, programs, and automation rules.  This is the same as Pardot’s direct-sold prices.  Dell also offers a 30 day free trial of Pardot.

The details of the deal are probably less important than its potential for market penetration.  Dell hasn’t been on my list of potential entrants into the marketing automation space, but it certainly has a huge presence among small and mid-size businesses.  This gives it the capability to add thousands of clients to Pardot’s existing base, which has just recently passed 1,000.  Like Intuit’s acquisition last month of local marketing vendor Demandforce,  a well-executed rollout could quickly establish a firm whose market share dwarfs existing competitors.  In some ways, the Dell/Pardot deal is even more interesting than Intuit/Demandforce, because it touches the small to mid-size businesses that form the core of the B2B marketing automation client base.  Intuit/Demandforce will serve many micro-businesses, while other recent deals (FICO/Entiera and Experian/Conversen) are aimed at larger, business-to-consumer marketers.

This doesn't mean an effective Dell/Pardot rollout is guaranteed.  These sorts of relationships often fizzle quickly, typically because the larger company’s sales force can’t be bothered to sell the new partner’s product.  That seems a bit less likely to happen in this case, since Dell’s cloud business group offers just a handful of applications and Dell has traditionally been a very effective marketer – although its recent performance has been spotty.

Whatever the result of this particular deal, it is more evidence that the B2B marketing automation industry is rapidly approaching consolidation.  As deep-pocketed outside companies become active, they battle each other on a grand scale and little firms get crushed almost accidentally.  It may be some time before a single victor emerges – if ever – but it’s hard to imagine many of today’s small companies remaining successful as elephants stampede all around them.

3 Ways to Connect Marketing Activity to Revenue

Discussions of revenue attribution often remind me of the famous recipe* that begins “First, catch your hare”.  Specifically, they assume that marketers know which marketing-generated lead is associated with each bit of revenue, and then go on like medieval theologians to debate how credit should be shared among promotions to that lead.  The missing hare is that marketers often can’t link leads to revenue in the first place.

The issues will be painfully familiar to anyone who’s ever tried this. For those who haven’t, let’s start with the mechanics.  In most configurations, leads are created in marketing automation and later transferred to Sales, which creates an opportunity that eventually becomes a closed sale with revenue attached.  If all goes smoothly, the original marketing campaign and marketing-generated lead are named on the opportunity to provide the lead-to-revenue connection. 

But – spoiler alert! – things don’t always go smoothly.  When Sales creates the opportunity, it often links it to a contact record other than the original marketing lead.  Perhaps the salesperson was already working with someone else, perhaps the marketing lead wasn’t the real decision maker, or perhaps Sales just doesn’t want to give acknowledge Marketing’s contribution.  The original marketing campaign is often lost for similar reasons.


All those beautiful attribution recipes are moot if you don’t know which lead is linked to which revenue.  So let’s put down the cooking pots and go hare hunting.



The first approach is simply to get Sales to retain the marketing information when it creates the opportunity.  Let’s not dismiss this out of hand – yes, salespeople can be uncooperative, but appropriate training and management support can convince them it’s important to retain the information.  So it’s worth a try.

But let’s say you don’t have time to wait for better data or can’t get Sales to do what you need.  Now you’ll need to work a bit harder with the data on hand. 

One approach is to look for matches at the account level: build a list of marketing-generated leads, find the accounts associated with them, find the revenues associated with those accounts, and assume there’s a connection.  This could hugely overstate marketing-related revenue, since it potentially takes credit for sales that had nothing to do with marketing activity.  So you’ll probably want to put some parameters on the matches such as only including accounts with no pre-existing contacts, leads that Sales followed up on, and opportunities created soon after the marketing lead was submitted.  Setting these rules may take some serious discussion between Sales and Marketing, but that’s a good thing.

Unfortunately, there’s no guarantee that Sales will retain the leads sent by marketing or attach them to the correct accounts.  Nor is it certain that the companies listed in the marketing automation system will match the accounts listed by Sales.  In this case, you may need to build an even looser relationship, looking at company names in both systems – or even ignoring the Sales system altogether and taking data from accounting records.  Because the same company may be listed differently in different systems, this sort of matching requires either knowledgeable people or comprehensive reference databases that can make the non-obvious connections.  Fortunately, this is a well understood problem and plenty of resources are available to help.

Company-to-company matching casts an even wider net than lead-to-account matching, so it’s correspondingly harder to give marketing credit for every connection.  But you can rate how likely it was that marketing played a role in a given opportunity by looking at factors like timing, pre-existing relationships, and amount of marketing activity.  This could translate into allocating a fraction of the revenue to marketing, ultimately a more realistic, if less satisfying, approach than taking full credit for some deals and no credit for others. 

If fractional allocation strikes you as too complicated, you can also start with a much simpler question: did companies that interacted with marketing programs show more sales than similar companies that didn’t interact with marketing programs?  You won’t be able to prove that any particular contact generated any particular deal, but a strong correlation between marketing programs and revenue growth is good evidence that marketing had an impact.  Once you’ve captured that hare, you can think about the details of how you’ll cook it.

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*Jugged Hare in Hannah Grasse’s The Art of Cookery Made Plain and Easy, although it apparently  doesn’t include the “catch your hare” part.

Salesforce.com and Oracle Buy Social Marketing Systems: Not the End of Marketing As We Know It

Salesforce.com yesterday announced agreement to buy social media publishing vendor Buddy Media for $689 million, thereby adding another big fluffy piece to its “marketing cloud”. Oracle followed suit this morning  with an acquisition of social media monitoring and semantic analysis vendor Collective Intellect. This followed Oracle’s $300 million acquisition last month  of social publishing system Vitrue. Just for symmetry, it’s worth pointing out that Salesforce.com acquired its own social monitoring system, Radian6, in March 2011.

What are marketers to make of all this activity, not to mention Marketo’s acquisition in April  of social marketing vendor Cloud Factory? Is this the death of marketing as we know it?

In a word, no. Social media are certainly a new way to hear what buyers are saying and send them marketing messages. But only the most besotted booster would argue that it will replace, rather than supplement, traditional methods. Every serious marketer already recognizes this, so I’m not even being boldly contrarian by saying it out loud.

The more interesting question is whether social media can be the foundation of a company’s marketing infrastructure. Both Marketo and Oracle already offer robust marketing platforms, so they presumably see social media as a supplement rather than a replacement. (It’s possible that Marketo hopes to reinvent itself as a social media specialist, which is surely more attractive to investors than marketing automation. The key positions taken by Cloud Factory executives might even support the theory.  But Marketo hasn’t hinted at this approach.)

Salesforce.com is another story.  They’ve always avoided traditional marketing automation, so perhaps they feel a complete “marketing cloud” can be built without it.

The gaps in this approach are obvious to anyone familiar with standard marketing automation systems: no Web behavior tracking, no multi-step nurture campaigns, no marketing resource management. But Salesforce.com could close those gaps by gradually extending its existing products. This might actually be easier than acquiring a separate marketing automation system and shoe horning it into other Salesforce.com components.

One thing I don’t see is social media systems themselves expanding to be marketing automation platforms. So far as I know, the data structures within the social media systems are simple contact profiles – little more than flat files – which can’t easily be extended to store and analyze detailed activity histories across multiple channels. Nor does a standard social media publishing or monitoring platform have the multi-step, branching campaign flows that are the heart of marketing automation. It’s probably easier to add social marketing functions to a marketing automation platform than the other way around. Indeed, many marketing automation vendors have already started.

So, back to the original question: what do these acquisitions mean? I’d say they’re good news for marketers, who will increasingly find social marketing functions available within core marketing platforms, ending the need to integrate separate products. The acquisitions are more problematic for marketing automation vendors, who now need to build, buy, or connect with social marketing systems to remain competitive. This will make it still harder for smaller vendors to compete, hastening the industry consolidation we all know is coming anyway. Nothing boldly contrarian about that prediction either, but it’s still worth bearing in mind.

Social and Mobile Features Head the List of New Marketing Automation Capabilities

I’m getting ready for the next edition of the B2B Marketing Automation Vendor Selection Tool (VEST). This is based on nearly 200 questions to vendors, mostly about product features. The first step in the process is to update the list of questions. This is based on a review of recent vendor announcements plus my own feeling for what’s important. What emerges is an interesting portrait of industry trends in product development.

You won’t be surprised to learn that most of the changes involve social and mobile marketing, today's two hottest areas in marketing in general. We’ll get back to those in a bit. But first, I’d argue the single most important result is just how few changes there really were. B2B marketing automation is far from mature in terms of market penetration, but the mix of product features is pretty well set. Most of vendor announcements I reviewed were about common features that particular vendors had been lacking or were enhancing.  Social and mobile are the exceptions, but both are still very small contributors to most B2B marketing programs. I saw much more activity around features that were new last year, such as dynamic content and integration with Webinar systems and with Microsoft Dynamics CRM.

So exactly what new social and mobile features are now on my list? The previous report already included basic social capabilities including sharing marketing content to social media, tracking responses generated from social media, and monitoring social media activity. The new VEST expands that list to include:

- track social media influence: individual-level tracking mechanism that can identify the number of times a recipient has shared a promotion to social media and the number of responses generated the shared promotions. This information is part of the contact profile of the individual.

- create social media posts: deliver messages through social media, such as Twitter posts and Facebook updates. These messages can be created and then scheduled for future delivery.

- create social forms: create forms that are delivered within a third-party social media system such as Facebook.

- create social promotions: create social promotions such as contests, polls, ratings, etc.

- social sign-on and data capture: recipients can register using third-party social credentials, such as their Facebook ID. This gives access to information stored within the third-party social media system and allows communication through that system.

- build social profile: capture information about a specified individual by searching public information across multiple social media systems. This information includes social media handles and social activity such as posts, comments, and questions answered. The information is added to the individual profile and activity history.

The broad range of these features represents both a maturation of B2B social marketing and uncertainty about what will ultimately prove useful. We can expect more social features in the near future, although I suspect some will later be abandoned when it turns out they’re not especially effective in a B2B context.

On to mobile.  My previous list of mobile features was limited to text messaging. I’ve expanded that to add:

- mobile formats: generate Web and email versions in formats tailored to delivery on mobile devices such as smartphones and tablets.

- mobile CRM: salespeople can access the system on mobile platforms such as smartphones and tablets.

- mobile reporting: users can access reports on mobile platforms such as smartphones and tablets.

- mobile administration: users can set up campaigns and create content on mobile platforms such as smartphones and tablets.

Only the first of these, mobile formats, is about delivering marketing messages. The others are all about marketers and salespeople accessing the system on their own mobile devices. That’s clearly the current focus on mobile marketing automation, although it’s safe to expect more mobile marketing in the future – such as location-based promotions, which are notably absent so far.

I also added three entries in other categories. These were:

- app marketplace: the vendor has a formal app marketplace that lets third party applications connect to its product without custom integration.

- real time recommendations: rules and/or predictive models can recommend the best treatment for a customer as an interaction takes place within system-managed content such as a Web page.

- real time interactions: rules and/or predictive models can recommend the best treatment for a customer as an interaction takes place within an external platform such as a call center or Web site. This requires features to collect information about the interaction from the external platform, to match this information against the system's own database of contacts profiles and history, to make recommendation using the available information, and to deliver the recommendation to the external platform. .

These features all expand the scope of B2B marketing automation, mostly be connecting it with other systems. In one sense that's the opposite of the previous new entries, which were about adding features to marketing automation itself.  But both approaches aim to place marketing automation at the center of a company’s customer management infrastructure. Since other products, including CRM and Web sites, are also reaching for that position, we’ll see how widely these features get adopted. My sense is they’ll be more successful at small companies, where the labor savings of a unified system are most important because technology resources are most constrained.

None of the features I’ve added are currently available in more than a handful of systems.  Some may not yet be present in any. Few marketers this year will choose a system primarily because these particular features are present.  But we'll find over time which are really important.

6 Key Marketing Measures That Don't Include Revenue

Ask most marketers how they measure performance, and they’ll tell you they look at results: incremental revenue or return on investment if they’re available, or response rates if they're not. Industry experts take a similar approach, focusing largely on the need for better revenue measures. The situation – and barely concealed frustration – is captured perfectly in the headline from a recent Forrester Consulting study sponsored by Silverpop: “Response Metrics Are Used To Evaluate Success, Leaving Customer Or Business Impact Metrics Largely Ignored”.

I agree that revenue is important, but humbly suggest that there’s more to marketing measurement than ROI. Marketers need several types of information – and shouldn’t let the quest for performance measures prevent them from meeting other requirements.

Here are six non-value measures that marketers should build into their reporting systems.

• Benchmarks. Sure, marketing’s job is to generate revenue, but is generating $1 million good or bad? The only way to know is by placing the number in context, which could be this year’s marketing plan or last year’s actual results. Even if marketers can’t measure revenue, they can  set benchmarks for metrics like number of leads generated, funnel conversion rates, or cost per order. In fact, measuring components that contribute to results gives better insights into marketing performance than reporting on the results themselves. For this reason, marketers who can’t directly measure marketing-generated revenue should think twice before creating complex indirect estimates that are hard to understand and have limited credibility. The money would probably be better spent on reports that provide a clearer picture of what’s actually happening.

• Projections. Past results are interesting but the future is more important. Again, the real need is to understand the factors that determine future results, such as response rates and funnel velocity. Changes in these can give early warning of risks and opportunities.  The trick is to distinguish real trends from random variations, so marketers react quickly without chasing too many false alarms.

• Operations. It’s easy to make mistakes in setting up a marketing program, especially one with multiple stages, lead scoring models, and decision rules. Even careful testing can’t always capture all program steps or contingencies. Marketers need reports on the number of people in each program stage and receiving each message, and they need a model that lets them know whether those numbers are reasonable. Reports should show both program-to-date and weekly or daily results: cumulative data show major errors such as bad program logic, and short-term results capture small problems, such as a missed processing step, that could get lost in a program-to-date aggregate.

• Exceptions. Projections and benchmarks put data in context, but marketers don't have time to comb through every figure.  They need exception reports to highlight the most important variations, both positive and negative.  Marketers also need tools to drill into the exceptions so they can understand what happened and identify new opportunities.

• Testing.  Formal tests are the most certain way to understand the impact of marketing projects, but they often require special reporting tools such as ways to compare results for different customer groups over time.  Incremental revenue is the ultimate measure for test evaluation, but often other metrics such as response rates or velocity are easier to capture and more directly relevant.  Reaping the full benefit of tests also requires systems to distribute results and catalog findings for future reference.

• Strategic Goals. Marketing plans should be based on corporate strategy, but long-term goals fade into the background once marketers start making tactical choices based on day-to-day results. The reporting system should provide direct measures of strategic objectives – things like penetration of new market segments and exploration of new channels – so marketers can see the cumulative impact of deviations from the original plans. Many strategic goals, such as process change, staff training, and systems deployment, are not measured in revenue at all.

The types of measures I’ve just described don’t replace revenue and ROI reporting.  Rather, they meet needs that revenue reports alone cannot. Ideally, all these types of information will be combined in a marketing dashboard that provides a quick overview of critical information and allows drilling into details when necessary. Marketers should realize that the contents of this dashboard will change over time as their focus shifts to different programs and strategic goals. They should also recognize that good reporting will generate new questions as it uncovers risks and opportunities that would otherwise have gone undetected.  The system should make those questions easier to answer, but marketers shouldn't expect their total work to decrease.  What they can expect is that better reporting will increase the value created by their efforts: yet another new metric, Return on Reporting, should go up.

Experian Buys Conversen Marketing Automation to Strengthen Its Offerings

Experian Marketing Services yesterday announced its acquisition
 of marketing automation vendor Conversen. This is the third marketing automation acquisition in the past month, following Intuit’s purchase of Demandforce and FICO’s purchase of Entiera.

Conversen is somewhat similar to Entiera in offering sophisticated multi-step campaigns, although its main differentiator is multi-channel dynamic content that makes it relatively easy to deliver different messages to different segments across multiple channels. See my review from February 2010 for a more detailed explanation. But while Entiera was selling directly to marketers, Conversen's clients were mostly ad agencies and marketing service providers who offered it to their own customers. When I last spoke with Conversen about a year ago, they had more than 35 partners with more than 150 end clients. That’s a respectable installed base for this type of product.

Experian’s stated rationale for the acquisition is to allow more sophisticated cross channel marketing dialogues than its existing tools provided. The transaction is part of a larger drive for Experian to revitalize its Marketing Services group, which has been investing in new people and technologies over the past couple of years.

Like the Entiera acquisition, the Conversen deal removes another independent player from the ranks of large scale consumer marketing automation systems. Marketing service providers looking for a system to license will be particularly unhappy, since they already had few choices and won't like licensing from a competitor. Experian hasn’t said it will withdraw Conversen from existing partners, but its main goal is clearly to use the system for its own massive client base.

Conversen will be a key component in a larger digital marketing suite from Experian. If there’s a real trend in the recent acquisitions, it’s that a handful of large companies are building digital marketing suites and either offering them as software (IBM, Teradata, SAS, Oracle, SAP) or combined with marketing services (FICO, Experian, maybe Harte-Hanks). I’ve argued forever that big integrated suites will ultimately swamp products that specialize in one field such as email, Web content management, or online advertising. The scope of the suites makes it hard for specialists to compete, especially as ever-tighter integration becomes necessary to meet customer expectations for seamless service across channels. Experian, which already has huge businesses in online advertising, email and social marketing, is one of the few service vendors with the resources to build and update its own systems rather than purchasing from third-party vendors.

As someone who views things mostly from a marketers’ perspective, I'm not thrilled at this development. Fewer competitors make it harder for buyers to get a good deal and big suites tend to be less innovative than small specialists. Of course, there are still plenty of small marketing automation vendors, but the gorillas will  attract many of their clients and scare away potential investors. It’s probably no coincidence that the recent acquisitions have been small companies, not the better known marketing automation players: the buyers have wanted technology, which is hard to develop, more than market position, which they already have. Nor is it surprising that acquisitions have been consumer marketing systems rather than B2B marketing automation: there’s ultimately more money in consumer marketing and the vicious dogfight in B2B marketing automation in the past few years has made profitability almost impossible for anyone. There’s little reason for an outsider to buy in such an unpleasant neighborhood.

FICO Buys Entiera Marketing Automation: Another Independent Option Gone

Three weeks ago, Intuit shook up the low end of the marketing automation universe by purchasing small business marketing shooting star Demandforce. Last week the action shifted to the high end, where FICO announced its purchase of Entiera, one of the few remaining enterprise class products.

FICO, the company formerly known as Fair Isaac and originator of the influential FICO credit score, first dipped its toe into marketing automation services and software when it acquired DynaMark in 1992. Since then, the company has continued to grow its marketing offerings, through acquisition and internal development.  But it sell these largely as add-ons to its core predictive analytics products. FICO statements make clear that Entiera will continue this strategy, both by providing new capabilities for event-triggered to existing clients and by making the full set of FICO products available to smaller companies.

FICO’s backing will certainly allow Entiera to sell to more companies. But, in sharp contrast to the Intuit/Demandforce deal, I see this acquisition as shrinking rather than increasing competition in the relevant market segment. Entiera was one of the few independent vendors still chasing the business of mid-size and enterprise marketing automation buyers. This group had already been reduced with the acquisition of Alterian by SDL last December and of SmartFocus by eMailVision the previous April. Of the firms on my list of B2C options from last September, only a handful (Neolane, Decision Software Inc, RedPoint and ClickSquared are primarily selling marketing automation software. The others are either more oriented to email services (ExactTarget, and I should add Responsys and Silverpop) or have minimal industry presence (MarketingPilot, Pitney Bowes' Portrait Software, Conversen, SmartSource Online, etc.).

In theory, FICO could finance a significant expansion of Entiera’s independent business. With $620 million in 2011 revenue and over $100 million operating cash flow, the company could certainly afford it. But marketing services are clearly just a sideline for FICO. So it’s likely they’ll use Entiera’s technology to support sales of their core analytical products to current customers and perhaps to deliver them more cost-effectively to new customers. That’s great for FICO and for Entiera’s founders. But in a segment where most of the major products are already owned by giant corporations (IBM, Teradata, SAS), marketers now have one less young vendor hungry for their business.

What Brain Research Teaches about Selecting Marketing Automation Software

I’m spending more time on airplanes these days, which means more time browsing airport bookshops. Since spy stories and soft core porn are neither to my taste, the pickings are pretty slim. But I did recently stumble across Jonah Lehrer’s How We Decide, one of several recent books that explain the latest scientific research into human decision-making.

Lehrer’s book shuttles between commonly-known irrationalities in human behavior – things like assigning a higher value to avoiding loss than achieving gain – and the less known (to me, at least) brain mechanisms that drive them. He makes a few key points, including the importance of non-conscious learning to drive everyday decisions (it turns out that people who can only make conscious, rational decisions are pretty much incapable of functioning), the powerful influence of irrelevant facts (for example, being exposed to a random number influences the price you’re willing to pay for an unrelated object), and the need to suppress emotion when faced with a truly unprecedented problem (because your previous experience is irrelevant).

These are all  relevant to marketing, since they give powerful insights into ways to get people to do things. Indeed, it’s frightening to recognize how much this research can help people manipulate others to act against their interests. But good marketers, politicians, and poker players have always used these methods intuitively, so exposing them may not really make the world a more dangerous place.

In any event, my own dopamine receptors were most excited by research related to formal decision making, such as picking a new car, new house, or strawberry jam. Selecting software (or marketing approaches) falls into the same category. Apparently the research shows that carefully analyzing such choices actually leads to worse decisions than making a less considered judgment. The mechanism seems to be that people consider every factor they list, even the ones that are unimportant or totally irrelevant.

It's not that snap judgments are inherently better. The most effective approach is to gather all the data but then let your mind work on it subconsciously – what we normal folks call “mulling things over” – since the emotional parts of the brain are better at balancing the different factors than the rational brain. (I’m being horribly imprecise with terms like “emotional” and “rational”, which are shorthand for different processes in different brain regions. Apologies to Lehrer.)

As someone who has spent many years preparing detailed vendor analyses, I found this intriguing if unwelcome news. Since one main point of the book is that people rationalize opinions they’ve formed in advance, I’m quite aware that “deciding” whether to accept this view is not an objective process. But I also know that first impressions, at least where software is concerned, can’t possibly uncover all the important facts about a product. So the lesson I’m taking is the need to defer judgment until all factors have been identified and then to carefully and formally weight them so the irrelevant ones don’t distort the final choice.

As it happens, that sort of weighting is exactly what I’ve always insisted is important in making a sound selection. My process has been to have clients first list the items to consider and then assign them weights that add to 100%. This forces trade-offs to decide what’s most important. The next step is to score each vendor on each item.  I always score one item at a time across all vendors, since the scores are inherently relative. Finally, I use the weights to build a single composite score for vendor ranking.

In theory, the weighting reduces the impact of unimportant factors, setting the weights separately from the scoring avoids weights that favor a particular vendor, and calculating composite scores prevents undue influence by the first or last item reviewed. Whether things work as well as I’d like to believe, I can’t really say. But I can report three common patterns that seem relevant.

- the final winner often differs from one I originally expected. This is the “horse race” aspect of the process and I think it means we’re successfully avoiding being stuck with premature conclusions.

- when the composite scores don’t match intuitive expectations, there’s usually a problem with the weights. I interpret this to mean that we’re listening to the emotional part of the brain and taking advantage of its insights.

- as scoring proceeds, one vendor often emerges as the consistent winner, essentially “building momentum” as we move towards a conclusion. I’ve always enjoyed this, since it makes for an easy final decision. But now I’m wondering whether we're making the common error of seeing patterns that don’t exist.  Oh well, two out of three isn’t bad.

Perhaps I could reduce the momentum effect by hiding the previous scores when each new item is assessed. In any event, I’ve always felt the real value of this process was in the discussions surrounding the scoring rather than the scores themselves. As I said, the scores are usually irrelevant because the winner is apparent before we finish.

Still, having a clear winner doesn’t mean we made the right choice. The best I can say is that clients have rarely reported unpleasant surprises after deployment. We may not have made the best choice, but at least we understood what we were getting into.

I guess it’s no surprise that I’d conclude my process is a good one. Indeed, research warns that people see what they want to see (the technical term is “confirmation bias”; the colloquial term is “pride”). But I honestly don’t see much of an alternative. Making quick judgments on incomplete information is surely less effective, and gathering data without any formal integration seems hopelessly subjective. Perhaps the latter approach is what Lehrer’s research points to, but I’d (self-servingly) argue that software choices fall into the category of unfamiliar problems, which the brain hasn’t trained itself to solve through intuition alone.

Intuit Buys Small Business Local Marketing Vendor Demandforce: There's a New Gorilla in Town

Intuit  last week announced an agreement to acquire local business marketing vendor Demandforce  for $423.5 million. That’s a hefty sum for a company with a reported revenue of $37.5 million, although it has been growing at a blistering pace – more than doubling last year – and now has over 35,000 customers. Still, the move makes perfect strategic sense, giving Intuit a stronger foothold in the marketing side of its small business customer base. (Intuit dominates the market for small business accounting with five million Quickbooks users.)

Demandforce focuses on local service businesses like dentists and auto body shops. It’s not a typical marketing automation vendor, since it provides appointment management, referrals, reviews, social campaigns, local advertising, post cards, and search marketing in addition to email. Its most direct competitors in the marketing automation world would be Infusionsoft and HubSpot, although neither has the same depth of appointment-related features. The price point of $200 to $300 per month is also similar to those systems.

Because the micro-business segment is pretty distinct from the rest of marketing automation, the impact of the Demandforce acquisition will initially be limited to its direct competitors. Within that group, Intuit’s penetration and clout should make it an immediate superpower – with the caveat that the accountants who are Intuit’s primary connection with its customers are not likely to sell them marketing services. Still, Intuit should be able to expand its network of channel partners fairly quickly. The local marketing business is a huge opportunity that hasn’t had a dominant player. I can claim some bragging rights for having suggested nearly two years ago that Intuit might take this role.


The more interesting question for the rest of the marketing automation industry is whether Intuit will move beyond Demandforce’s current target customers. In the short term, probably not: the opportunity is large enough to keep them busy for quite some time. But local marketing involves more than small businesses, so Demandforce has a natural growth path in that direction. A modest functional expansion could also make Demandforce competitive at the lower end of the standard B2B marketing automation world, where products like Act-On, Marketo Spark, and Genius contend. From there, it could creep upwards. Again, that’s down the road but it does put a ceiling on the pricing and growth prospects of companies currently serving those segments.

It's also worth considering the financial aspects of the deal. On the one hand, the $400+ million price – more than 10x revenue – has to be heartening for other marketing automation vendors contemplating an exit. But Demandforce is far from typical. It is already larger than all but a handful of marketing automation companies, is growing faster than anyone of similar size, and has made all this progress on a mere $11.8 million of investment. It also offers benefits that are more clearly defined and easier to deliver than marketing automation provides to larger companies. And the very fact that Intuit is now playing in the industry will make it harder for others to grow – especially if Demandforce moves quickly. Given all these advantages, it’s not clear other vendors will come close to duplicating the terms that Demandforce received.

Marketo Buys Crowd Factory, Silverpop Buys CoreMotives, and Other News from Pardot, Neolane, Act-On and OfficeAutoPilot

With its usual fanfare, Marketo today announced the acquisition of social marketing campaign company Crowd Factory.

Crowd Factory is a certified cool product, which is probably reason enough for Marketo to buy them.  But what I find intriguing is how little the two businesses overlap.  Marketo is primarily focused on business marketing, and in particular lead generation, nurturing and analytics. Crowd Factory has some B2B clients but is clearly aimed at large-scale consumer marketing. Campaign types listed on its Web site include refer a friend, social sweepstakes, polls and voting, flash deals, group offers, and intelligent share buttons. Few would be considered relevant for most B2B campaigns.

Indeed, Marketo already had a reasonable set of B2B social marketing features. Here’s the chart I built last December in a post comparing social marketing features across the industry.

The yellow boxes represent capabilities added by Crowd Factory (although actual integration of the two products will probably take some time). As you see, Crowd Factory doesn’t fill many gaps. Rather, it adds B2C features that might will enable Marketo to penetrate a new set of accounts.


But it’s not fair to let Marketo get all the attention. Other vendors have also extended their products recently. These include:

Silverpop announced it had purchased CoreMotives, which adds marketing automation capabilities within Microsoft Dynamics CRM.  The company also announced plans to integrate Silverpop’s flagship Engage system with Dynamics CRM. The CoreMotives acquisition is quite interesting, since CoreMotives creates a merger of CRM and marketing automation (which I’ve long predicted) rather than the now-standard model of separate systems for each. The deal might be seen bet-hedging by Silverpop, although I suspect it’s more a way to penetrate accounts too small to buy a separate, sophisticated marketing automation or email product.

Pardot added features for search marketing.  These are keyword monitoring, which tracks the user’s site rank in Google and Bing, and competitor monitoring, which captures metrics such as Google PageRank, inbound links, and indexed pages. Data comes from several sources. This is an important enhancement and part of larger trend for marketing automation vendors to move beyond email and landing pages.

Neolane announced new features to identify anonymous Web visitors based on prior email interactions and to use external catalog data in dynamic offers.  Not as sexy as a social marketing acquisition but useful nevertheless. 

Act-On Software added dynamic content, progressive profiling, new survey components, search engine optimization meta-tags and source tracking, and performance reports. These are mostly catch-up features but the search engine optimization piece again shows the industry’s movement in that direction.

OfficeAutoPilot expanded its professional services offerings – another industry trend – and improved its tools for building forms and creating emails. They also announced the ability to purchase sky banners – you know, those things towed by airplanes – but that was just an April Fool’s joke. Pity.

B2B Email Benchmarks: Answers Vary Widely

One of the things I’m enjoying about my new role as head of analytics at Left Brain DGA is being closer to hands-on marketing than I was as a consultant. This leads to different questions than I used to get, including the ever-popular “what’s a reasonable response rate for our emails?” That one came up last week and led me to review my files on industry benchmarks. Without giving away any deep secrets, I thought I’d share the results.

I found five relevant studies dating back to 2009. Taking the oldest first:


Silverpop International Email Marketing Benchmark Study, 2009


This one doesn’t break out results by mailer type, so it’s probably dominated by business-to- consumer marketers. But it does distinguish gross opens from unique opens, which are significantly different. It also shows median as well as average results, in addition to top and bottom quartiles.  This is a good reminder that there's a very substantial range of variation in different marketers' performance.  The difference between the medians and averages is also something to bear in mind when looking at the other surveys, which only report averages.

Silverpop is also the only study to give both bounce and unsubscribe rates – the others give one or the other.

Here is the U.S. data from the Silverpop report.

MailerMailer Email Marketing Metrics Report, 2011


I just discovered this one and am impressed.  It goes beyond simple reporting to analyze the impact of delivery day and time, number of links, subject line length, and personalization (some surprises here). The repoort breaks out results for several categories, of which the most relevant are probably Computer, Consulting, Large Business and Small Business. (I’ve added a simple average to use later.)

In general, this study shows substantially lower open rates than other studies, somewhat higher click rates, and higher bounce rates. I’ve calculated the click-to-open rate, which isn’t necessarily the result you’d get if you looked at the actual average. But it’s worth having as a point of reference.

Here’s some detail from the study itself (you'll have to click on this to make it legible).  The business categories account for two of the four highest open rates and are all in top half of the click rates. 

Eloqua Marketing Metrics Outlook 2011


You’d expect Eloqua to put out a good study on this topic, and they deliver. The best-in-class, average, and laggard classifications illustrate the huge gap between even average performers and best-in-class. They also reinforce the point, illustrated in the Silverpop data, that medians are significantly below averages because of high-end outliers.  Not to go all stat-geeky on you, but that really matters if you're looking for a benchmark that reflects "typical" performance.

Arguably all Eloqua clients would be relevant to marketing automation users, but the most relevant for true B2B would include Manufacturing, High Tech, and Business Services:

Across all categories, Manufacturing has the highest open rate and is tied for second highest click-through, but the two other "true" B2B categories rank at the bottom.  The combined averages for the three are just slightly below the average for all categories.

Epsilon Email Trends and Benchmarks Q42011


Epsilon is another industry stalwart, publishing regular quarterly reports. But they only provide one category for B2B Products and Services, plus another for Business Publishing. The open rate for that category seems pretty high compared with other studies, although the click rate is largely in line.

Comparing Business Products with other categories, both the open rate and click rate are in the middle of the pack, each ranking sixth highest of 13 categories.

Epsilon also provides an intriguing breakdown within each industry of results by email type (acquisition, editorial, marketing, research, and other).  The figures for marketing emails in the Business Products category (19.2% open rate, 2.7% click rate)  are considerably lower than the category total (27% and 4.4%), but I can’t make sense of the numbers: marketing accounts for 88% of the industry volume, so they just shouldn't be that far apart.  (More formally: if you combine the message type figures in a weighted average, the result does not equal the category total.)  I’ll assume the group totals are more reliable than the detail.

Signup.to The UK Email Marketing Benchmark Report 2012


Finally, we have a study from Signup.to in the UK. I’d question its relevance to the U.S. market, but the 2009 Silverpop study showed similar figures for both. It includes figures for B2B Sales, B2B Service, Industrial/Manufacturing, and IT. These vary pretty widely, especially for open rates.

Compared with other categories, the business emails get somewhat above-average response:

  
What Does It All Mean?

Within each report, open and click rates B2B categories tend to be in the middle or  above average.  But the over-all ranges vary substantially from one report to another: at the extremes, MailerMailer open rates range from 7.1% to 17.6%, while Epsilon ranges from 14.2% to 35.6%.  Without understanding the reasons for these variations, it's hard to select a single reference point as a benchmark.  The best I can suggest is to throw out the outliers, which would leave Eloqua and Signup.to.  I'd also tend to favor the Eloqua figures because they are based on the "average" performers, and therefore are closer to a median rate.  (You'll remember that averages tend to be higher than medians, because a handful of very high performers distort the results).

That said, the table below shows the average figures for each survey (which, you'll remember, themselves hide significant variations within each report). I’ve calculated an average of averages, excluding Silverpop since it didn’t break out B2B from B2C. As it happens, the averages fall somewhere between the Signup.to and Eloqua figures.  So, if you forced me to propose benchmarks for B2B email performance, I'd say those numbers are as good as any.

Infusionsoft Revamps Its Interface, Adds New Campaign Builder, Web Analytics and Lead Scoring

Infusionsoft introduced its latest release earlier this month. This included a full revamp of its customer interface, a new campaign builder and shopping cart, and new capabilities for Web analytics, lead scoring, and lead source reporting.

Stated so plainly, this doesn’t sound like much. But Infusionsoft says it’s the biggest release in company history and I've no reason to doubt.  A new interface and campaign builder are big projects.

Both represent significant improvements over previous Infusionsoft editions. The interface is cleaner, organized around tasks rather than data objects, and lets users customize their menu of top-level functions. The campaign builder now supports branching flows, timers, and multi-step sequences with a smooth drag-and-drop interface. The shopping cart is also significantly more powerful. Lead scoring (nicely executed), Web analytics, and lead source reporting all fill major gaps in the product.

That said, this is still an evolutionary release for Infusionsoft. In part, this is because the company is careful not to overwhelm clients with too many changes. More fundamentally, it reflects Infusionsoft's steady focus on solving the same problem for the same customers: helping businesses with under 25 employees run their marketing, sales and e-commerce more efficiently.  This lets Infusionsoft understand its customers' needs deeply and build systems that meet them.  The company conducted more than 500 interviews in preparing the new release.        

Infusionsoft still has a ways to go.  The new release adds some missing marketing features but doesn’t incorporate social media, blogging, or Web site management. These are common needs for small businesses, so I’d consider them gaps in the product. The system also lacks dynamic content, custom data tables, and sophisticated user rights management – but those are more relevant to larger firms than Infusionsoft’s target customers.  Split testing is also missing – and even though few small businesses do it, I'd argue it belongs in the product, because they should.

Infusionsoft also reported that it continues to grow nicely.  The company now has 8,500 customers, 30,000 users, and expected revenue of $40 million in 2012, up 50% over $26 million in 2011. It is expanding its service offerings, app marketplace, and network of consulting partners – a critical resource for small businesses that often have little in-house marketing talent.  Pricing on the new release remains unchanged, with full-featured versions starting at $299 per month.