Silverpop Announces Universal Behaviors to Provide Better Cross Channel Customer Experience

At their annual Amplify conference last week, Silverpop unveiled the culmination of a two year project that conveniently matches the Customer Data Platform (CDP) concept I’ve been describing for the past month. While the timing is just coincidental, Silverpop’s Universal Behaviors provide more evidence that a new breed of system is emerging.

Silverpop’s new features load customer behaviors from all sources into a central database, match identities to create a unified customer view, and make the resulting information available for real-time, automated interactions across all channels. The central database and cross-channel treatments are two of the three capabilities I’ve defined for a Customer Data Platform. Silverpop falls short on the third CDP function, which is integrated predictive modeling.  But it has partners who fill that gap.

Many CDPs have been quietly maturing for several years.  Silverpop's two-year gestation cycle is a good example.  I can't say precisely why so many are emerging more or less simultaneously, but suspect a combination of business conditions and ever-more-urgent marketer needs.  The long-term drivers are clear: more marketing channels make customer attention harder to attract, spread behavior across different media, and require coordinated contacts across channels. As a result, marketers need a unified customer database, unified campaigns, and way to deliver messages across whatever channels customers use now or in the future. This is what they get from a CDP.

It’s less surprising to see another CDP system than to see it coming from Silverpop.  After all, Silverpop’s twin heritages in B2C email and B2B marketing automation both use simple data models: flat lists for email and basic lead/contact/account tables for B2B marketing automation. Both types of systems traditionally merge customer data using only email address.  Neither build a company's primary marketing database or shares data with external systems. So its quite unexpected to see Silverpop ingest data from any source, cross-reference any set of individual identifiers, offer access to the data, and send messages for delivery by other systems.

So how do Universal Behaviors work?  Each Behavior is first defined in Silverpop with a fixed set of attributes. Source systems then capture Behaviors and post them via an API to Silverpop.  They are stored in MongoDB, a “NoSQL” database that supports high input volumes and multiple record structures.  This is another departure for Silverpop, which uses the Oracle database in its core systems.

Behaviors include whatever customer identifiers the source system can provide: email address, cookie ID, phone number, account number, etc. Silverpop uses matches from external systems to link all identifiers associated with an individual: for example, a Web transaction might include cookie ID and email address, while an email could contain email address and account number.  Silverpop could later take a Behavior with any one of those identifiers and associate it with the same individual.  But there are limits to Silverpop's customer integration powers: it doesn’t do “fuzzy” matching to merge similar identifiers or import third-party reference databases that contain such links.  I'm beginning to see those capabilities are specialties that are not necessarily core features of a CDP because they're best purchased from third party vendors.

The initial release of Universal Behaviors, set for July, will support predefined Behaviors from ArgyleSocial social listening, Webtrends Web site behaviors, Digby location-based marketing, Invodo video, and several as-yet unannounced vendors, as well as Silverpop’s own location-based and SMS offerings. It will later add more partners, provide a system development kit (SDK) for mobile apps, and eventually allow any company to build its own connectors.

Once Universal Behaviors are loaded into Silverpop, they become available within the system for queries, program triggers, rules within programs, dynamic content, personalization, scoring, and analysis – pretty much anything that could be done with standard Silverpop data.  Program outputs such as messages and lists can be pushed in real time to external systems to manage interactions.

Silverpop has also created native integrations with Adobe and Episerver Web content management systems.  These let those systems submit a visitor ID to Silverpop and receive Silverpop data to use in dynamic content and personalization. Connectors for other CMSs will be added as clients request them. Clients could also write their own integrations using a published Silverpop API or use tags to display Silverpop-generated content on any Web page. Currently, CMSs can access selected customer attributes but not the Universal Behavior database itself.  Silverpop plans to provide full data access in the future.

The mobile app SDK will go even further, allowing apps to execute Silverpop functions such as adding a customer to a program or sending an email.  This is in addition to the standard features of submitting Universal Behaviors, reading Silverpop data, and rendering Silverpop-generated content.

The critical point in all this is that Silverpop will integrate other customer-facing systems instead of only executing interactions itself.  The integration includes sending data to Silverpop, reading data within Silverpop, and receiving Silverpop marketing treatments.  In other words, the role of Silverpop shifts from delivering customer treatments to helping other systems find the best treatments to deliver. Of course, Silverpop still retains its original execution capabilities for email and some other channels.  But it’s perfectly conceivable that a client could hook the new Silverpop features to someone else's email delivery system (not that anyone at Silverpop mentioned the possibility).

This separation between a central data-and-decision platform and multiple independent execution systems is the core concept underlying the Customer Data Platform. I’m increasingly convinced it is the only way that marketers will be able to keep up with ever-expanding channels and customer expectations.  By developing a structure that fits the CDP model, Silverpop has responded to a pressing client need and established itself in an important new category.

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Moving On: Lessons from the B2B Marketing Trenches

 
I’ve just ended my six month tour as VP Optimization at LeftBrain DGA, and am now returning full time to my usual consulting, writing, and general shenanigans. It was fun to work again as a hands-on marketer. Here are some insights based on the experience.

- lots of content. We all know that content is king, but sometimes forget the king has a voracious appetite. A serious demand generation program might move contacts through half dozen stages with several levels within each stage and several messages within each level. This could easily come to forty or fifty messages, each offering a different downloadable asset.  The numbers go even higher when you start to create separate streams for different personas. Building these materials is major undertaking, first to understand what’s appropriate and then to create it. But deploying the initial content is just the start: you then have to monitor performance, test alternatives, and periodically refresh the whole stream. Finding efficient ways to do this is critical to keeping costs and schedules within reason. (Note that I’m talking here about email programs to nurture known contacts, not acquisition programs to attract new names. That takes another massive content collection.)

- content isn’t everything. It’s an old saw among direct marketers that the list determines most of your response rate and the offer controls for most of the rest.  Actual creative execution (copy, graphics, format, etc.) accounts for maybe 10% of the result. We proved this repeatedly with tests that used the different content at the same stage in the campaign flow: basically, results were similar even with content originally designed for different purposes. Conversely, the same piece of content had hugely different results at different places in the flow. What this meant in both cases was that response was primarily driven by the people at each stage, not by the specifics of the materials presented.

- simplicity helps. That results are primarily driven by audience doesn’t mean that content doesn’t matter. We did a fascinating (to me, at least) analysis of 100 emails, logging specific features such as number of words and readability scores and then comparing these against open, click-through, and form submit rates. A clear pattern emerged: simpler emails (shorter, fewer graphics, easier to read) performed better. In fact, the pattern was so clear that there's a danger of over-reaction: at some point, a message can be too short to be effective (think of the mayor in The Simpsons, who just repeats “Vote for Me”). So the real trick is to find an optimal length, and even then to recognize that some messages truly need to be longer than others.

- simplicity isn’t everything, either. We did a lot of testing – it was my favorite part of my job – but the content tests were often inconclusive: sometimes shorter won, sometimes longer won, most often the difference was too small to matter. Given that we were starting with competently-created materials, that’s not too surprising. On the other hand, we consistently found that forms with fewer questions yielded better results, typically by a ratio of 3:1. This is one example of a non-content item with major impact; another was contact frequency (more is better, but, as with simplicity, only up to a point). There were other aspects of program structure that I would have tested had time and resources permitted; the goal was to focus on variables with the potential for a substantial impact on over-all results. This generally meant moving beyond individual content tests to items with larger and more global impact.

- test themes, not details. Don’t misinterpret that last sentence: I’m not against content tests. What I'm against is tests that only teach one small, random lesson, such as whether subject line A is better than subject line B. The way to build more powerful tests is to build them around a hypothesis and then try several simultaneous changes that support or refute that hypothesis. (I’ve shamelessly stolen this insight from Marketing Experiments, whose methodology I hugely admire and highly recommend.) So, if you think simplicity is an issue, create one test with shorter subject line and less copy and fewer graphics and a simpler call to action, and run that against your control. This is exactly the opposite of conventional testing advice of changing just one thing at a time.  That approach made sense back in the days of direct mail when you were running a handful of versions per year, but isn’t an option in the content-intensive environment of modern online marketing. And even if you had the resources to run a gazillion separate tests, you’d still need to see larger patterns to guide your future content creation.

- multivariate tests work. As if the infinite number of potential tests were not enough of a challenge, most B2B marketers also have relatively small program quantities to work with. We multiplied our test volume by applying multivariate test designs, which let us use the same contacts in several different test cells simultaneously. This probably needs a post of its own, but here's a quick example: Let’s say you need 10,000 names per test cell and have 20,000 names total. Traditionally, you could just run one test comparing two choices. But with a multivariate design, you’d create four cells of 5,000 each. Cells 1 and 2 would get the first version of the first test, while cells 3 and 4 would get the second version. But – and here’s the magic – cells 1 and 3 would also get the first version of the second test, while cells 2 and 4 would get the second version of the second test. Thus, each test gets the required 10,000 names, but you can still see the impact of each test separately. (Here’s a random article that seems to do a good job of explaining this more fully.). We generally limited ourselves to two or three tests at a time. More complicated structures are possible but I was always concerned about keeping execution relatively simple since we were doing all our splitting manually.

- metrics matter. As it happens, most of the programs we executed rely heavily on form submissions to move people to the next stage. This meant that form fills were the key success metric, not opens or click-throughs. Although these generally correlate with each other, the relationship is weaker than you might expect.  Some exceptions were due to obvious factors such as differences in form length, but the reasons for others were unknown.  (I often suspected but could never prove reporting or data capture issues.)  Of course, most email marketers are used to looking at open and click rates, so it took some gentle reminding to keep everyone focused on the form fill statistics. The good news is we prevented some pretty serious mistakes by using the right measure.  Note that form fills are especially important in acquisition programs responders are lost altogether if don't complete a form that let you add them to your database.

- test results need selling. As you’ve probably guessed by now, I spent much of time lovingly crafting our tests and analyzing the results. But others were not so engaged: more than once, I was asked what we found in a test whose results I had published weeks before. This wasn’t a complete surprise, since other people had many other items on their mind. But we did eventually conclude that simply publishing the results was not enough, and started to go through the results in person during weekly and monthly status meetings. We also found that reviewing individual results was not enough; when we found larger patterns worth reporting, we had to present them explicitly as well. Again, there’s no surprise in this, but it does bear directly on expectations that managers will find important data if reporting systems simply make it available. Most will not: the systems have to go beyond reporting to highlight what’s new, what it means, why it matters, and what to do next. Although some parts of that analysis can be automated, most of it still relies on skilled human effort.

- reports need context. Reporting was another of my responsibilities, and we made great strides in delivering clearer and more actionable data to our clients. One of the things I already knew but was reminded really matters was the importance of putting data in context. It wasn’t enough just to show cumulative quantities or conversion statistics; we needed to compare this data with previous results, targets, and other programs to give a sense of what it meant. To take one example, we reported the winner of a series of email package tests, without realizing until late in the analysis that the response rate for the test as a whole was much lower than previous results. This was a more important issue that the tests themselves. We had other instances where entire waves were missing from reports; we only uncovered this because someone noticed they were missing – whereas, a proper comparison against plan would have highlighted it automatically. Again, such comparisons are widely acknowledged as a best practice: my point here is they have immediate practical value, so they shouldn't just be relegated to the list of “nice but not necessary” things that no one ever quite gets around to doing.

- survival is more important than conversion. That phrase has a vaguely religious ring to it, and I suppose it’s also true in a theological sense. But right now I’m talking about reporting of survival rates (how many people who enter a nurture program actually end up as customers) vs. conversion rates (how many people move from one program stage to the next). Marketers tend to focus on conversion rates, and of course it’s true that the survival rate is mathematically the product of the individual conversion rates. But we repeatedly saw changes in program structure or even individual treatments that caused large swings in a single conversion rate, which was often balanced by opposite changes in the following stage. Looking at conversion rates in isolation, it was hard to see those patterns.  This was an even bigger problem when each rates was calculated cumulatively, so the impact of a specific change was masked by being merged into a larger average. More important, even when there was an obviously related change in two successive rates, the net combined impact wasn’t self-evident. This is where survival rates come in, since they directly report the cumulative result of all preceding stages. Of course, conversion rates and survival rates are both useful: I'm arguing you need to report them both, not just conversion rates alone.

- throughput matters. Survival and conversion rates show the shape of the funnel, but not the dimension of time. We did report how long it took contacts to move through our programs – in fact, a sophisticated and detailed approach was in place before I arrived – but the information was largely ignored. That was a pity, because it contained some important insights about contact behaviors, opportunities for improvement, and results of particular tests. A greater focus on comparing expected vs. actual results would have helped, since calculating the expectations would have probably required a closer focus on how long it took leads to move through the funnel.

- acceleration is hard. A greater focus on timing would have also forced a harder look at the fundamental premise of many B2B campaigns, which is that they can speed movement of prospects through the sales funnel. The more I think about this, the more doubts I have: B2B purchases move according to their own internal rhythms, driven by things like budget cycles, contract expirations, and management changes. Nurture programs can educate potential buyers and build a favorable attitude towards the seller, thereby increasing the likelihood of making a sale once the buyer is ready. They can also track, through lead scoring, when a buyer seems ready to act and is thus ripe for contact by sales. That’s all good and valuable and should more than justify the nurture program’s existence. But expectations of acceleration are dangerous because they may not be met, and could unfairly make a successful program look like a failure.

- drip needs attention.  Like that leaky faucet you never quite get around to fixing, drip programs often don't get the attention they deserve.  In practice, the vast majority of people who enter a nurture program will not move quickly to the purchase stage; most will stall somewhere along the way. This is where the drip program must work hard to keep them engaged. Again, every marketer knows this, but it’s easy to focus attention on the fascinating and complicated stage progressions (remember all that content?) and relegate the drip campaigns to a simple newsletter. Big mistake. Put as much effort into segmenting your drip communications and encouraging response as you put into stage conversions. If you want a practical reason for this, look at your mail quantities: chances are, you’re actually sending more drip emails than all your active stages combined.

- proving value is the ultimate challenge. It’s relatively easy to track contacts as they move through the marketing funnel, but it’s much harder to connect them to actual revenue in the sales or accounting systems. I whined about this at length in June, so I won’t repeat the discussion. Suffice it to say that some sort of revenue measurement, however imperfect, is necessary for your testing, reporting, and program execution to be complete.

Whew, it’s good to have all that out of my system. As I said at the beginning, I did enjoy my little visit to the marketing trenches. Now, it’s goodbye to that world and hello to what’s next.

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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.

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True Influence's LeadPAC Offers Pay-Per-Click Email. Think About It.

Summary: LeadPAC lets marketers pay for email responses as easily as they pay for search responses. It’s a major improvement over traditional lead generation.

I can’t recall a vendor with the same business model as LeadPAC from marketing automation vendor True Influence. That's pretty rare in itself, but what really matters is that LeadPAC's model offers some powerful benefits. That's worth some excitement.

So what, exactly, makes LeadPAC so special?

LeadPAC lets marketers order prospect lists based on segmentation criteria such as title, industry and company size. Nothing new there. The system will also send emails to those names without the marketer loading them into a separate system: a little harder to find but still far from unique. But here's the new part: users only pay for responses.

I’ve seen marketing agencies and direct response media that work on a cost-per-lead basis. But I’ve never seen it baked into the email engine of a marketing automation system. If you're aware of a similar product, please let me know.

Of course, the classic pay-per-click medium is paid search, and above all Google AdWords. It's no accident that LeadPAC resembles AdWords in both function and appearance. True Influence CEO Brian Giese said the goal with LeadPAC is to give marketers a way to create real leads quickly, using AdWords as a model.

Like AdWords, LeadPAC lets clients set a target cost per name and a weekly budget for their spending. Again like AdWords, the system keeps sending promotions – in this case, emails – until the budget is reached. The system further resembles AdWords in having some automated intelligence: in the case of LeadPAC, this means spacing the emails, limiting any name to one contact per week, and taking into account different response rates based on time of day and day of week. One thing it doesn't do – yet – is build predictive models to select the most responsive names within the specified universe. Nor is pricing based on AdWords-style bidding: clients pay a fixed fee ranging from $10 to $30 per name depending on the level (senior executives cost more than department managers). Just to be clear, that's all they pay: there's no fee for the marketing automation system itself.

Setting up a campaign in LeadPAC involves three basic steps.

- Select the audience by choosing from personal and company attributes including title, department, level, location, company size and ownership. The prospects come through LeadPAC’s partnerships with major consumer and business list vendors.

- Define the email to send, starting either with vendor-provided templates or by uploading a client's own template. LeadPAC provides a typical editor and standard features such as previewing the email and sending test messages.

- Define the campaign start date and weekly spending limit. Once clients submit their campaign, LeadPAC reviews it for content, reasonableness and compliance with anti-spam regulations.

Clients receive lists of responders on a regular basis. They can load these into any marketing automation system or True Influence's own marketing automation product, which lets them run multi-step nurture campaigns, apply lead scores, and synchronize data with Salesforce.com.

The beauty of all this, as with AdWords, is simplicity. Clients still need to specify their audience and create their email offer. But the cost-per-response model saves them the effort of managing details such as importing and refreshing lists, spacing their mailings over time, and tracking which segments respond best. This takes usability beyond the interface, by actually eliminating tasks rather than just making them easier to do. It makes email lead generation possible for companies that lack even basic skills in managing such programs.

Indeed, clients paying only for responses have little incentive to optimize their list selections or their copy. The vendor alone bears the cost of low response rates. This is probably part of the reason that True Influence reviews the campaigns for reasonableness.

Interestingly, one cure for this problem is to have clients do even less. If TrueInfluence deployed automated response modeling, it could avoid having anyone define target segments and still improve its response rates. Add some automated copy testing and marketers would be about as close to push-button lead generation as I can imagine.

Of course, email is just one part of lead generation and an even smaller part of full-scale marketing automation. So marketers will have plenty of work whether or not they use LeadPAC. But as an example of ways to really make marketing easier, LeadPAC is food for thought.

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