Thursday, August 14, 2014

Lots of Vendors Can Help You Find Leads on the Web

Few people would suggest you learn salesmanship from the play Glengarry Glen Ross,* but its central message rings true: good leads are the lifeblood of a sales organization.** That’s why scanning the Internet to find new  prospects is such an exciting opportunity. At least a dozen firms are now following that path.

These firms scan company Web sites, social media, news sites, directories, and other sources to identify companies, extract attributes like revenue, growth rates, and technologies used, and flag events that might indicate a sales opportunity, such as opening a new office, launching a new product, or hiring new management. Of course, there are plenty of important differences which impact which might make sense for you.  Some of the more important ones include:

• Specific data sources, scanning techniques, and analytical methods. Evaluating these in the abstract is interesting, but what works well for one purpose in one industry might work poorly for something else. So buyers really need to run their own tests to see what works for them.

• Types of predictive models available.  Some vendors only rank leads while others build multiple models for different purposes.

• Use of the client's internal data for model scoring, and whether this extends to sources beyond CRM.

• Whether the vendor sells prospect lists or only enhance names provided by the client.

• Whether the vendor provides lists of individuals as well as companies.  Since Web scanning is usually at the company level, the individual names usually come from other sources.

• Coverage outside the United States

• Information returned beyond names and lead scores, such as recommended treatments and social profiles.

• Whether the company maintains a permanent database on all businesses or only scans when clients request information about specified businesses or segments.  The permanent database costs more to maintain but stores history and trend information that is otherwise unavailable.

Here are brief profiles of the vendors I’ve identified in or near this space. There are probably others.  I’ve grouped them based on how much information I have available.  This correlates to some degree with market presence.

Vendors I’ve Reviewed

Mintigo both returns new prospects and applies scores to prospect lists provided by the client. It is currently stressing uses of predictive modeling beyond traditional lead scoring and making it easier for clients to set up new models on their own. I last reviewed them in June 2013.

Lattice Engines runs different types of models against names provided by the client. It provides recommendations for customer treatments in addition to scores. I wrote about them in April 2013.

Infer runs multiple models against leads provided by the client. It originally returned only lead scores, although they are now adding multiple applications that create different scores for different purposes. I wrote about them in August 2013.

Fliptop returns scores and some summary data on names provided by the client. It stresses quick model building. I reviewed them in June 2014.

LeadSpace scans for data on demand, rather than maintaining its own master database.  It can find new prospects in specified segments and enhance names provided by the client.  It returns individual names as well as companies. I wrote about them in June 2013.

Vendors I’ve Spoken with But Not Reviewed

Growth Intelligence is a relatively recent UK-based startup that provides lists of companies and associated contacts that are likely to become customers.  It draws from Web information, government lists, and similarity to the client’s current customer base.

Kemvi is just emerging from stealth and plans to launch formally late this year or early 2015. It expects to focus on finding trigger events and advising salespeople about the best ways to approach each prospect.

6Sense finds new prospects using behavioral data gathered from a network of "several thousand" Web publishers rather scanning public sources like others in this list. So it doesn’t quite belong here, but it’s interesting nevertheless.

Radius finds small business prospects that resemble current customers and deploys them to, along with key profile information and lead scores.

Vendors I’ve Only Seen on the Web

Avention (formerly OneSource) scans an eclectic collection of data sources to find prospect companies based on attributes and signals. It can rank companies with scoring but the scoring formulas are built manually.

Gagein sends alerts on trigger events in media, social or public Web sites. It can track companies named by the client or build prospects lists for client-specified segments. It’s primarily a sales tool, with other features such as social selling and apparently without any predictive modeling.

RealSociable is another sales-oriented product that tracks social media for trigger events related to target accounts. It appears to let users decide which events are important without using predictive models.  But it seems to have some clever technology to extract the trigger events from unstructured social streams. That (presumed) semantic filtering is the only reason to include it on this list -- otherwise, the limit to social sources and lack of predictive models would rule it out.


*and the one person who admitted to it now makes his living as an arts critic.

** Also, coffee really is for closers.

Wednesday, August 06, 2014

The Biggest Gap in Marketing Software Selection Isn't Product Information

There’s a reason I’m not a professional copy writer, which is that I’m bad at it. But, as with the press release I described yesterday, each new edition of the VEST report also requires me to write a promotional email for my house list. My solution today was:

Dear [First Name],

A friend of mine who is building one of those "wisdom of the crowd" software review sites tells me her research shows that what buyers want most is "apples to apples" comparisons of product features.


I'll spare you my rant on why crowd sourced recommendations are a bad idea (hint: when you're sick, do you go to a doctor or ask a bunch of random strangers which treatments worked for them?) Suffice it to say that Raab Associates' B2B Marketing Automation Vendor Selection Tool (VEST) is written by a professional analyst (me) who has assembled 200 rigorously defined points of comparison on 25 marketing automation systems, allowing buyers to quickly find vendors who meet their needs. At a time when the marketing automation industry is more confusing than ever -- and when 25% of marketing automation buyers are unhappy with their results*-- it's essential to have solid, detailed information to make a sound decision.

That’s not terrible, at least by my pitifully low personal standards.  But it did leave me feeling a bit uncomfortable about bashing the crowd-sourced software review sites. The problem was the doctor analogy: although it does express my fundamental objection accurately, it doesn’t tell quite the whole story. It’s true that random strangers can’t accurately diagnose you or prescribe a treatment. But random strangers can indeed provide useful information about whether a doctor is good to deal with and how well they their recommendations worked out. Similarly, crowd-sourced sites can provide valid information on how easy it is to use a piece of software and how well the company does at customer support. This is much closer with the kind of information you’d get from consumer view sites like Yelp.  Customers don’t need to be technical experts to tell you whether they’re happy.

You’ll note that I haven’t criticized the crowd-sourced sites for the typical review site problems of fake reviews and biased reviewers. Companies like g2crowd (my main point of reference here, although not my friend’s business) do a reasonably good job at controlling for these by requiring users to verify their identity by logging in through LinkedIn. Of course, smart vendors will still game the system by encouraging satisfied users to post reviews, so the relative rankings will reflect the vendors’ marketing skills at least as much as their actual product quality. There’s nothing unethical about that, but it does undermine the notion that the resulting ranking accurately reflect the relative quality of the products and not just the relative skills of each company’s marketers.

On the other hand, good crowd sourcing sites let users see reviews from companies similar to their own in terms of size, industry, etc., and ask specific enough questions to get meaningful answers. And even the general comments they gather are somewhat useful as indicators of what a (highly biased) sample of users think.

But, now that I’m on the subject, I’ll let you know what I really think: which is that feature comparisons, whether prepared by a not-so-wise crowd or a professional analyst like Yours Truly, are not really the problem. What stops marketers from choosing the right software isn’t a lack of information about product features.  It's a lack of understanding which features each marketer needs. Figuring out their feature needs requires crossing the gap between their business objectives, which most marketers do understand, and the features needed to support those objectives, which most marketers do not.  Making that translation is where industry experts really add value, even more than in familiarity with the details of individual products. I’ve recently been working on a very interesting project to close that gap…but that’s a topic for another day.

Tuesday, August 05, 2014

VEST Report: Analytics Tops List of Upgraded Marketing Automation Features

I finished the latest release of the B2B Marketing Automation Vendor Selection Tool (VEST)  yesterday, which is always a great relief. But the elation lasted about two minutes, since I then had to write a press release announcing it. The challenge with that is you need a “news hook”, meaning something that gives reporters a reason to write about your story. For the January release, that’s always easy, since I have a new estimate of industry revenues and the press loves that sort of thing. But I can’t repeat that for the mid-year release.  That meant I had to dive back into the VEST data and find something interesting to say about it.

Of course, that isn’t all bad, since rolling around in industry data makes me as happy as a pig in mud.* But finding clever insights on demand is still tough. Happily, I did find something intriguing, at least to my obviously-biased eyes. You can read the headline in the press release or – lucky you – get even more details below.

What I did for my analysis was look at changes in vendor scores for the 200 items that go into the VEST data. That gives an interesting view of where vendors are improving their products. I had no particular expectation of what I’d find.  But when I looked at the most common items (those which had been upgraded by three or more vendors), it immediately became clear that changes related to analytics were heavily represented. In fact, if you count lead scoring and content testing as part of analytics, seven of the dozen items fell into that category. Who knew?

Looking deeper, I expanded my analysis to include items upgraded by two or more vendors, which included 43 of the 200 total. By golly, the results were similar – 19 of the items fell into analytics, compared with just four each in the next most common groups (campaign management, content marketing, and CRM integration). Houston, we have a pattern.

As I say, this result was totally unexpected, but it can still be explained with 20/20 hindsight. I might have expected more development of features for social, mobile, and content marketing, which are top-of-mind for many marketers today. But social and content marketing are mostly managed outside of marketing automation and mobile is mostly limited to ensuring messages are viewable on mobile devices.  By contrast, analytics is something most marketers do want from their marketing automation system and an area where great improvements are still possible. So a clear-eyed understanding of how marketing automation is actually used, as opposed to what people are talking about, would have predicted analytics as the focus of vendor attention.

Needless to say, this analysis is really just a byproduct of the primary purpose of the VEST, which is to assemble apples-to-apples comparisons of B2B marketing automation vendors so that buyers have an easier time finding the right system. I’ll probably circle back and write a bit more about the latest data in another post. In the meantime, if you’re actually in the process of making a purchase, or just want to understand the industry better, you can buy your very own copy at the Raab Guide Web site.


* Does anyone know whether pigs really like to roll in mud? It’s a great cliché and all, but I am not a farm boy.

Friday, July 25, 2014

LinkedIn Buys Bizo and Oracle Adds Database Services: Everything Is Going According To Plan

The past week brought two industry announcements: acquisition of Bizo by LinkedIn and new “Data as a Service” offerings from Oracle. Both illustrate the continuing evolution of marketing technology towards a data-centric world.

The Bizo purchase, priced at $175 million, makes perfect sense.  It gives LinkedIn more tools to expand its marketing offerings and lets Bizo use LinkedIn data to improve targeting within its own products. Some eyebrows were raised by a statement on LinkedIn’s blog that it will sell off Bizo’s Data Solutions business, which markets Bizo’s 120 million name database of business contacts. But LinkedIn doesn't need that data: it already has vastly better information in its own files.  Retaining Bizo's data business would only have raised questions about whether LinkedIn data was somehow leaking into the marketplace through Bizo. Many LinkedIn customers would have considered this unacceptable use of their profiles, regardless of whether LinkedIn’s privacy policy actually allows it (which my quick reading suggests it does). The more interesting question is who, if anyone, will buy the business from Bizo.

The Oracle announcement provided unintentional symmetry with Bizo: as LinkedIn was leaving the customer data sales business, Oracle was expanding its offerings. Arguably Oracle’s announcement was little more than relabeling of the BlueKai data management platform it purchased in February. But Oracle presented it in terms that make clear it sees a new, central role for data in the marketing technology stack – a view I share wholeheartedly.

In fact, Oracle’s discussion made almost exactly the same points I’ve been making about Customer Data Platforms: that marketers need a shared customer database which integrates information about each individual and makes the consolidated information easily available to analysis and execution systems. The key notion is that this consolidated database has its own very high value, apart from the value of any applications that use it. Oracle is supporting this vision by ingesting data from hundreds of partners; doing advanced quality assurance, identity matching, and “signal extraction” from unstructured data (i.e., intent, sentiment, themes, topics, entities, etc.); and providing connectors to dozens of ad targeting, site customization, testing, and analysis systems. It also highlights functions to manage data access rights in compliance with privacy, regulatory, and contractual obligations, something that's also important even though I haven’t given it quite as much attention.

While this is quite similar to what BlueKai did before Oracle bought them, it’s a big difference to have Oracle’s muscle behind the vision of making it easy for marketers to access to a rich, powerful customer database. Among other things, the Oracle product will set a benchmark for pricing of similar services by other vendors.  I didn't see a price announcement, but if Oracle prices aggressively and executes well, it will be much harder for smaller vendors to compete. The likely result is to switch the focus of competition from assembling data and providing a database to making clever use of the data through things like advanced analytics. That’s really where smaller vendors can shine and, from some lofty cosmic viewpoint, the world is better off if the smart people focus their creative energies on that rather than on duplicating the basic data assembly capabilities.

Back to that question of who will buy Bizo’s data business: I wouldn’t be at all surprised to see take it over, since it would supplement their existing business and give an advertising-oriented data management platform to balance against Oracle/BlueKai. In the on-going tit-for-tat competition between Salesforce and Oracle, that is probably reason enough for Salesforce to do the deal.

Friday, July 18, 2014

Are Millennial Marketers More Analytical?

I had an interesting conversation this week with a vendor of marketing measurement systems on the question of why more marketers won’t buy his type of software. After all, surveys often show that marketers and CEOs alike rate better measurement as a high priority. Yet actual measurement techniques don’t improve much from year to year: to cite the most recent report to cross my desk, the 2014 State of Marketing Measurement Survey Report from Ifbyphone found that 45% of marketers are measuring Return on Investment in 2014 vs. 40% in 2013 -- a gain that is probably within the survey's margin of error.  Other, simpler measures are more common and growing more quickly, but that’s exactly the point: marketers don’t invest in meaningful performance measures like ROI.

My vendor friend’s suspicion was that marketers don’t buy better measurement because, whatever they say in surveys, they really don’t want to be measured. My own opinion, based on comments from marketers over the years, is they don’t have time to put advanced measurement systems in place.

Of course, time is a matter of prioritization, so this really means that marketers think the time spent on an advanced measurement project will produce less value than if that time were spent on something else.  In other words, marketers don’t invest in advanced measurement because they don’t think the resulting information will drive enough improvement in their marketing results.  That's not an unreasonable belief: much ROI information is in fact interesting but not actionable and, therefore, adds no business value.  Further evidence: the advanced measurement techniques that have been widely adopted, like marketing mix models and multi-touch attribution, all have proven bottom-line impact. The impact of marketing ROI, on the other hand, is often less clear.

Then our conversation took an unexpected turn: the vendor speculated that younger marketers might be more analytical and hence more inclined to ROI measurement.  This was a new thought to me and offered the cheery prospect of an actual change from the long-term status quo. But neither of us had seen any research on the topic, so we couldn’t judge whether it was likely to be true.  End of discussion.

I’ve since had time to look into this more deeply. There’s plenty of research on millennials’ (currently 19-34 years old) in general and a fair amount on their behavior in the workplace. Most of it reinforces familiar stereotypes: millenials are collaborative, tech-savvy, results-focused, fast-working, multi-tasking, anti-hierarchical, socially-conscious, company-disloyal, and of course digitally connected. But none of the research shed much light on whether they’re more or less analytical than older generations: since they’re skeptical of authority, you can expect them to be more open to challenging past assumptions, but this doesn’t necessarily mean they rely on data to resolve those challenges. They could just as easily rely on what feels right to them, even though they’ve had little time to sharpen their intuitions on the stone of reality.  Even their presumed affinity for digital media, which is certainly more measurable than traditional media, doesn’t necessarily translate to an interest in ROI measurement. Indeed, most digital measurements such as Web traffic and social media interactions have almost nothing to do with ROI.  Finding that millennials rely heavy on them would bode poorly for advanced measurement methods.

But all of this is just speculation, and I am definitely a fact-based kinda guy. Has anyone seen any information on how marketers’ behaviors differ by generation? If not, would you find it an interesting topic for a survey?

Thursday, July 03, 2014

StrongView Moves Beyond Email to Real-Time, Contextual Marketing

Today's email vendors face an interesting business challenge. On one hand, building a high-volume email engine is a lot harder than you’d think, so business is strong despite near-commodity status. But marketers want to integrate email with other messaging channels, so a stand-alone email platform is increasingly unattractive. The obvious solution is to add other channels and, even more important, features to control the decisions of when, how, and to whom messages are sent. This puts vendor at the center of the marketing operations, helping them to retain clients and charge higher fees.

Indeed, this strategy has succeeded for many vendors: ExactTarget, Responsys, Silverpop, and Neolane all grew from mostly-email to broader systems that were purchased by larger vendors as the foundation of an all-compassing marketing suite. Other email providers have remained independent but still expanded their scope to remain competitive and grow.

StrongView, which was original email specialist StrongMail, has followed this course. Rotating banners on the company Web site position StrongView as a “product platform” and “marketing cloud” as well as mentioning “cross-channel lifecycle marketing”, “present tense marketing”, “true one-to-one communication” and “the first customer insight solution supporting unlimited cross-channel interaction data”. This may set a world record for buzzword intensity, but that’s okay so long as the underlying product matches the implied promises. On the whole, I’d say it does.

The key to all this is the one unfamiliar phrase on the previous list: “present tense marketing”. This is StrongView’s own coinage, intended to describe their proprietary view of context-based marketing (which strikes me as plenty buzzy all by itself, but then I have a low tolerance for such things). The gist of contextual marketing, in StrongView's definition, is to tailor customer treatments to the current situation (location, device, environment, past behaviors, etc.), so those treatments lead the customer in a profitable direction. StrongView sees this as the future of marketing and has defined its strategy as helping marketers make the transition by providing the necessary technology and supporting services.

StrongView earns considerable credit in my book for articulating a proper strategy: one that defines not just a goal (helping marketers make the transition) but also the method for achieving that goal (by providing the necessary technology and services). Still, articulation is just a first step. The next is even more important: implementing the method effectively.

StrongView has identified several key implementation requirements. Necessary technologies include real-time analytics to select best treatments; dynamic message assembly to construct those treatments; and multi-channel routing to deliver the treatments via email, SMS, mobile apps, Web pages, social media, and display ads.

Of these technologies, content creation, dynamic assembly and delivery are extensions of the company’s original email functions. StrongView has supplemented them by building an impressive campaign flow designer that handles complex, multi-step programs.  Predictive analytics rely on external modeling tools, but the vendor will compensate with prebuilt models and with services to create custom models. There’s also a methodology to guide creation of campaigns and models.

All these functions require a much larger, more flexible data environment than a traditional email system.  StrongView has stepped up to the challenge by building a data store using Amazon RedShift.  This closes a critical gap faced by email vendors trying to reach the next level.  StrongView has also knitted together everything from content creation and campaign design to execution and reporting in a tightly integrated user interface, another requirement in providing the speed and efficiency needed for a “contextual” approach.

Finally, we come to services.  StrongView recognizes that many marketers will need help in making the transition towards more advanced marketing techniques, so it is offering marketing strategy, analytics, technical development, campaign design, creative, production and delivery.  These are sold on project or retainer basis as appropriate. StrongView is clear that these services are intended to help marketers supplement their own resources, not to convert the business into a service agency.

All told, this is a pretty complete package. Although StrongView’s vision is far from unique, they have carefully worked through the implications to define and deliver a complete solution.  This should be enough to get their customers started.  Results will determine what happens next.

Friday, June 27, 2014

Fliptop: A Customer Data Platform for Predictive Lead Scoring, Pure and Simple

It’s been a while since I wrote about Customer Data Platforms, but only because I’ve been distracted by other topics. The CDP industry has been moving along nicely without my attention: new CDPs keep emerging and the existing vendors are growing.

Fliptop wasn’t on my original list of CDPs, having launched its relevant product just after the initial CDP report was published. But it fits perfectly into the “data enhancement” category, joining Infer, Lattice Engines, Mintigo, Growth Intelligence (which I’ve also yet to review) and ReachForce. Like all the others except ReachForce, the company builds a master database of information about businesses and individuals by scanning the social networks, company Web pages, job sites, paid search spend, search engine page rank, and other sources. When it gets a new client, it loads that company’s own customer list and sales from its CRM system, finds those companies and individuals in the Fliptop database, enhances their records with Fliptop data, and uses the combined information to build a predictive model that identifies the likelihood of someone making a purchase. This model can score new leads and classify existing opportunities in the sales pipeline.

So what makes Fliptop different from its competitors? The one objective distinction is that Fliptop is publicly listed on the App Exchange, meaning it has passed the security reviews. Not surprisingly, the company’s Salesforce connector is very efficient, automatically pulling down leads, contacts, accounts, and opportunities through the Salesforce API and feeding them into the modeling system. New clients who import only Salesforce data can have a model ready within 24 hours, which is faster than most competitors. But data from other sources may require custom connectors, slowing the process.  Fliptop is also able to model quickly because it defaults to predicting revenue: in other systems, part of the set-up time is devoted to deciding what to model against.

Once the model is built, Fliptop scores the client’s entire database and assigns contacts, accounts, and opportunities into classes based on expected results. A typical scheme would create A, B, C, and D lead classes, where A leads are best. Reports show the percentage of records in each group and the expected win rate, which in turn relates to expected revenue. A typical result might find that the top 10% of contacts account for 40% of the expected revenue or that the top 40% of contacts account for 95% of the revenue. Clients can adjust the breakpoints to create custom performance ranges. Reports also show which categories of data are contributing the most to the scoring models: this is more information than some systems provide and is presented quite understandably.  (Incidentally, Fliptop reports it has generally found that "fit" data, such as company size and industry, is more powerful than behavioral data such as email clicks and content downloads.)

Fliptop scores are loaded into a CRM or marketing automation system where they can be used to prioritize sales efforts and guide campaign segmentation. There are existing connectors for, Marketo, and Eloqua and it’s fairly easy to connect with others. New leads can be scored in under one minute or in a few seconds if the system is directly connected to a lead capture form. Clients can build separate models for different products or segments and receive a score for each model. The system automatically checks for new sales results at regular intervals and adjusts the models when needed.

At present, Fliptop only sends scores to other systems. (Infer takes a similar approach.) The next release of its integration is expected to add top positive and negative factors on individual records.  The company is considering future applications including campaign optimization, pipeline forecasting, and account-level targeting. But it does not plan to match competitors who offer treatment recommendations, sell lists of new prospects, or provide their own behavior tracking pixels.

Pricing for Fliptop is based on data volume and starts at $2,500 per month. The company offers a free 30 day trial – unusual in this segment and possible because set-up is so automated. After the trial, clients are required to sign a one-year contract. The system currently has about three dozen paying clients and a larger number of active trials.

Bottom line: Fliptop does a very good job with predictive lead scoring. Marketers looking for a broader range of applications may find other CDPs are a better fit.