idio Does Sophisticated Content Recommendation

Systems in our new Guide to Customer Data Platforms range from B2B data enhancement to campaign managers to audience platforms. This may lead you to wonder whether there’s anything we actually left out.  In fact, there was: although the final choices were admittedly a bit subjective, I tried to ensure the report only included systems that met specific critieria including a persistent database, customer-level data, marketer control, and marketing-related outputs to external systems. In most cases, I could judge whether a system fit before doing a lot of detailed research. But a few systems were so close to the border that I only made the final call after I had evaluated them in depth.

idio was one of those. The company positions itself as a tool to deliver “personalized and relevant multi-channel communications”, which sure sounds like a CDP.  Indeed, it meets almost all the critieria listed above, including the most important one of building and maintaining a persistent customer database. But I ultimately excluded idio because it is tightly focused on identifying the content that customers are most likely to select, a function I felt was too narrow for a proper CDP. The folks at idio didn’t necessarily agree with this judgment, and pointed to planned developments that could indeed change the verdict (more about that later).  But, for now, let’s not worry about CDPs and take idio on its own terms.

The full description on idio's home page reads “idio understands your customer’s interests and intent through the content they consume and uses this to deliver personalized and relevant multi-channel communications” and that pretty much says it all. What idio does is ingest content – typically from a publisher such as ESPN, Virgin Media, Guardian Media, or eConsultancy (all clients) – but also from brands with large content stores such as Diageo, Unilever, and C Spire (also all clients). It uses advanced natural language processing to extract entities and concepts from this content, classifying it with the vendor’s own 23 million item taxonomy.

The system then monitors the content selected by its clients’ customers in emails, Web pages, mobile platforms, and some social platforms and builds an interest profile for each customer.  This in turn lets the system recommend which existing content the customer is most likely to select next. The recommendations are typically fed back to execution systems, such as email generators or Web content managers, which insert links to the recommended content into Web pages, emails, or newsletters.  Reports show selection rates by content, segment, or campaign, and can also show the most common topics published and the most commonly selected. Pricing is based on recommendation volume and starts around $60,000 per year for ten million recommendations.

Describing idio’s basic functions makes it sound similar to other recommendation systems, which doesn’t really do it justice. What sets idio apart are the details and technology.

• Content can include ads, offers and products as well as conventional articles.
• The natural language system classifies content without users tagging each item, a huge labor savings where massive volumes are involved, and can handle most European languages.
• idio's largest client ingests more than 1,000 items per day and stores more than one million items, a scale far beyond the reach of systems designed to choose among a couple hundred offers or products.
• Interest profiles take into account the recency of each selection and give different weights to different types of selections – e.g., more weight to sharing something than just reading it.
• Users can apply rules that limit the set of contents available in a particular situation.
• The system returns recommendations in under 50 milliseconds, which is fast enough to support online advertising selection.
• It stores customer data in a schema-less system that can make any type of input available for segmentation and reporting, although not to help with recommendations.
• It can build a master list of identifiers for each individual, allowing systems to submit any identifier and access a unified customer profile.
• It can return a content abstract, full text, images, or HTML, or simply a pointer to content stored elsewhere.
• It captures responses directly as the content is presented.

Most of these capabilities are exceptional and the combination is almost surely unique. The ultimate goal is to increase engagement by offering content people want, and idio reports it has doubled or even quadrupled selection rates vs. previous choices. All this explains why a small company whose product launched in 2011 has already landed so many large enterprises among its dozen or so clients.

Impressive as it is, I don’t see idio as a CDP because it is primarily limited to interest profiles and  content recommendations. What might yet change my mind is idio’s plan to go beyond recommending content based on likelihood of response, to recommending content based on its impact on reaching future goals such as making a purchase. The vendor promises such goal-driven recommendations in about six months.

Idio is also working on predicting future interests, based on behavior patterns of previous customers.  For example, someone buying a home might start by researching schools, then switch to real estate listings, then to mortgages, then moving companies, and so on. Those predictions could be useful in their own right and also feed predictions of future value, which could support conventional lead scoring applications. Once those features become available, idio may well be of interest to buyers well beyond its current customer base and would probably be flexible enough to serve as as Customer Data Platform.

Read More

Customer Data Platform Guide Reviews Tools to Build Marketing Databases

Raab Associates’ new Guide to Customer Data Platforms is now available (click here to buy).

You may not find that news to be fall-off-your-chair exciting. In fact, you’re more likely to wonder whether the world needs yet another report on anything at all. Fair enough. So before telling you what’s in the CDP Guide, I'll tell you why it exists.

Simply put, marketers need better databases. If you’re a working marketer, you almost surely know this from personal experience. But someone who only read industry news and vendor promotions might think all anyone had to do was to plug in the latest cool application and it would immediately be filled with fresh clean data like water from a tap. Dirty big data is our industry’s dirty little secret.


The problem isn’t new but it is getting worse. As customers interact across more channels, marketers need to not just meet them in every new location but recognize them and carry on a continuous conversation from one touchpoint to the next. Marketers can also become more effective by enriching that conversation with information from external sources such as Web pages, social media, and commercial databases. Both the carrot of better results and the stick of customer expectations are ever-more-urgently driving marketers towards building better databases.

The good news is that plenty of smart vendors have also recognized this need and are trying to help marketers on their journey. I call their systems Customer Data Platforms and define them as “a marketer-controlled system that supports external marketing execution based on persistent, cross-channel customer data.”

If there’s one absolutely critical point in that definition, it’s that CDPs put marketers in charge of building their own database. Taking control is the only way that marketers will ever get the databases they desperately need. It’s why CDPs are so important.

But too few marketers know who the CDP vendors are, what they do, and how they differ. The Guide to Customer Data Platforms is designed to provide this information. If the CDP vendors are tour guides on the path to better data, the CDP Guide is the reviews you read to decide which one you’ll hire. As far as we know, no other study serves this purpose.

Given its goal, the heart of the Guide is the vendor profiles: three to five pages on each vendor, describing capabilities for data management, predictive modeling, marketing campaigns, and message delivery, plus background on the vendor’s technology, clients, company history, and pricing. You’ll want to read those closely when you’re selecting a vendor. But first you’ll have to decide whether a Customer Data Platform is something to consider. Here is some information to help make that judgment.

- CDPs are something new. CDPs are systems that help marketers build and update customer databases, and make those databases available to support marketing programs. That may not sound very new, but most B2B marketing automation products today build very limited databases while most B2C marketing automation products rely entirely on an external data warehouse. The systems that do build databases are designed to be used by IT departments, not marketers. And many CDPs provide predictive modeling or best-treatment recommendations that go well beyond the storage functions of a basic data warehouse.

- You still can’t do this at home. CDPs may be tools for marketers, but that doesn’t mean that marketers build the databases themselves. Rather, CDP vendors provide services that build the database with varying degrees of marketer involvement. The difference is that the marketers work directly with the CDP vendors, instead of relying on IT staff that often has other priorities and an imperfect understanding of marketing needs. This makes it much easier and quicker for marketers to get the database they need.

- CDPs are an outgrowth of existing system types. Most CDP systems were created for a purpose that happened to require the same database-building capabilities as a CDP. These purposes fall into three groups which I discussed in last week’s post, so I won’t repeat them here. They’re work understanding because vendors in each group have a different set of skills, one of which will probably come closest to your needs.

- Convergence is coming. Even though the CDP vendors started with different applications, their shared abilities for identity matching, database management, analytics, and integration will allow them to support more of the same functions over time. As marketers understand the value of their databases more clearly, CDP vendors will be able to focus on selling their data platform features rather than applications the platform supports. Of course, once the platforms themselves are common, vendors will climb the value chain by offering better predictive analytics and cross-channel treatment optimization.

- Details count. CDP features may eventually converge, but for now the systems differ in many small ways that make a big difference. To take one example, nearly every CDP creates predictive models. But some can only predict response to specific promotions, based on who has responded before. Others can do the much more sophisticated analysis needed to predict which offer will best advance a long-term goal such as becoming a new customer. And even among those that model against long-term goals, some can actually estimate the incremental impact of a specific offer and others can just see most common correlations. We found similarly subtle differences in how data is collected (via the vendor’s own Web tags or by importing from other systems), the range of data sources (just marketing automation and CRM or those plus many others), natural language processing to extract useful information from text sources such as Web pages, how much history is kept and how it’s used, program execution, and end-user control. The CDP Guide clarifies these distinctions, but it’s still up to marketers to evaluate which differences will matter in their own business.

The CDP Guide itself contains quite a bit of other useful information, including a formal definition of CDPs, detailed explanations of what to look for in a CDP, and a history of marketing databases starting with the Sumerians (don’t worry, I skipped the boring parts). Again, the goal is to provide one package with everything you need to get started along the path of buying a CDP system.  From there, it's up to you.

Read More

New Study: Three Types of Customer Data Platform Address Cross-Channel Marketing Needs

My detailed study of Customer Data Platforms should be released next week. Now that the information is assembled, I can at last pull back and get a good overview of what I’ve found.

Perhaps the most interesting discovery has been that the CDP vendors cluster into three main groups.

• B2B data enhancement. These build a large reference database of companies and employees, which they match against records imported from their clients. They generally return corrected and enhanced data and lead scores based on models built from the client’s customer files. Their reference databases are built from multiple public, commercial, and proprietary sources, and are assembled using sophisticated matching engines. Most also perform their own scans of Web sites and social networks to extract sales-relevant information such as technology use and changes that suggest buying opportunities. These vendors vary considerably in the data they return, ranging from lead scores only to recommended marketing treatments to full customer profiles. Some also provide prospect lists of companies that are not already in the client’s own database. CDP vendors in this group include Infer, Lattice Engines, Mintigo, and ReachForce.

These systems compete with non-CDP products which also add or enhance prospect records but do not maintain a database with their clients’ customers. These include Web scanning systems such as InsideView, LeadSpace, and SalesLoft, and general data compilers including NetProspex, Demandbase, Data.com, ZoomInfo, and OneSource. The predictive modeling features also compete to some degree with end-user-oriented marketing analytics and modeling software such as Birst, GoodData, Cloud9 Analytics, AutoBox, and Predixion Software. Data cleansing competitors include services from firms such as D&B, as well as data management software for technical users such as Informatica, Experian QAS, and FullContact.

• Campaigns. These systems build a multi-source marketing database from the client’s own data and either recommend marketing treatments to execution systems or execute marketing campaigns directly. These are primarily used for consumer marketing although they also have B2B clients. Most have sophisticated matching capabilities. This group includes Silverpop with its Universal Behavior feature, NICE’s Causata, AgilOne, and RedPoint.

This group competes with conventional consumer marketing automation products, which provide similar campaign management abilities but lack the CDPs' database flexibility, database management, and customer matching features.

• Audience management. These systems build a database of customers and their responses to online display advertisements. They then build models that predict the customers’ probability of responding to future advertisements and provide recommendations for how much to bid and which content to display. These systems perform the same basic functions as standard online audience management systems (Data Management Platforms, or DMPs) and provide the same very quick responses needed for real time bidding (usually under 100 milliseconds). The major difference is that they also recommend messages in other channels, such as Web site personalization or email campaigns. Like DMPs, they work primarily at the Web cookie level, can link cookies known to relate to the same customer, and can be linked to actual customer names and addresses in external systems. This group includes IgnitionOne, [x+1], and Knotice.

This group overlaps with recommendation and ad targeting engines and DMP systems. Those products provide similar functions but do not track identified individuals and are often limited to single channel executions.

Given that each group addresses a different business need, you might wonder why I think they should all be lumped together under the CDP label. Quite simply, it’s because they are all addressing a portion of the same larger problem, which is how marketers can get a complete view of their customers and use that view to coordinate treatments across channels. What marketers truly need is a combination of the features from each group: data enhancement from external sources, for consumers as well as B2B; sophisticated customer matching and treatment selection; and integration of online advertising audiences with traditional customer databases. Each of these systems has the potential to grow into a complete solution, and the normal dynamics of software industry growth will push them towards pursuing that potential. So I expect the categories to overlap increasingly over the next few years and eventually merge into complete Customer Data Platforms as I envision them.

Incidentally and tangentially related: I'll be giving a Webinar with ReachForce on October 2 on Data Quality for Hipsters, a name that started as a joke but does make the point that data quality is essential for cutting-edge marketing.  YOLO, so you might as well attend.  I'm already working on the mustache.

Read More

How Raab Associates Converted to ZohoCRM In One Weekend: a B2B CRM Success Story

Raab Associates is really two businesses: the technology consulting practice run by Yours Truly, and a marketing agency specializing in children’s books run by my beautiful and brilliant wife Susan. We keep them largely separate, but I am inevitably involved in her technology decisions. So when her ancient Goldmine CRM system finally crashed last week, we both scrambled to pick a replacement.

From my usual lofty perch in enterprise software world, Susan's requirements seem stick-figure simple: accounts, contacts, opportunities, lists, and mass emails. So our first thought was to find a system that offered those plus some cool new things like social media profiling. But a quick scan of the market showed that none of the neat new systems also offered the basic functions with with enough refinement and flexibility to meet Susan's needs.

This pushed us back to the more standard CRM options.  To my dismay, we found ourselves ruling out one after another for various. I even briefly suggested we reconsider Goldmine, an thought that was quickly rejected.  Eventually we took an unhopeful look at ZohoCRM, which I know as a popular small business system but had never considered particularly advanced. Happily, the system has a very thorough online user manual, so I was able to check it out in detail.

Even more happily, the answers all came back positive as I imagined working through Susan’s basic business processes in Zoho. Build contact lists, check. Mass emails, check. Opportunities linked to campaigns, check. Pull-down status list and callback date on opportunities, check. Custom filters across all field types, check. End-user report writer, check. Multi-field search, check. A bunch of other details that I no longer recall, check check check. Reasonable cost, double check: we would have grudgingly paid a couple hundred dollars a month for a solution, but Zoho’s mid-tier Professional edition costs all of $20 per month with no limits on database size (Susan has about 14,000 contact records – well above the minimum for many small business systems). We may even splurge for $35 per month enterprise edition, which provides some advanced automation features but is probably overkill for most small businesses.  Just call me Diamond Jim.

At this point, we were ready to sign up for the free trial account, which was a simple process and didn’t ask for a credit card. Let me point out that I purposely hadn’t signed up sooner because I didn’t want to waste time exploring a system that I wasn’t pretty confident would meet my needs. Diving in too soon is a classic mistake among software buyers – and, in this instance at least, I actually followed my own advice.  (While I'm patting myself on the back, I'll also point out that we evaluated the software against our actual business process, not an arbitrary feature checklist.  That's another best practice that too few buyers follow.)

We now pulled a small set of test records from Goldmine to test the import function. The online manual guided me through the exact steps necessary, complete with a handy checklist of preparatory tasks.  When I went to load the file itself, I got the first of many delightful surprises: Zoho took a guess at mapping the input fields, based on their names, and got about half right. That’s a pretty sophisticated function and a big time-saver. It’s the sort of refinement you don’t see in a new system because it’s not essential to get the product into market, but gets added after enough users request it and the developers have some breathing room. Zoho has actually been around since 1996 (although CRM came later), so they’ve had time to add a lot of those little helpers.

In any event, the test import worked perfectly the first time out, which was a great feeling of accomplishment. Susan and I played with the system a bit more now that we had some real data in it, and found all sorts of nice little options, like being able to rename objects (she calls an opportunity a “pending record”), rearrange the fields on each screen, change the order of sections, and move fields from one section to another.  Again, none of these is cutting edge, but they’re not always available and make a big difference in making the system more usable.  The interface itself was also highly intuitive – lots of nice dragging to move the fields around, for example. There were plenty of other unexpected goodies that I would have otherwise needed to configure or live without, like automatically listing the associated contacts when you view an account record, and listing the associated opportunities – I mean, pending records – when you look at a contact. And, oh yes, you can control which fields are displayed on those related records.

At this point we were feeling pretty good about actually pulling off the conversion, so I spent all day Sunday manually cleansing those 14,000 contact records to ensure the critical data was populated. Even Zoho couldn’t help with that one. I finished around midnight and had a moment of panic when I saw that Zoho would only import 5,000 records at a time.  But it turned out to accept all three batches without waiting for the first batch to finish, so I was able to submit them and get some sleep.

I woke up bright and early (well, actually, late and cranky), feeling pleased that Susan could start using the system without missing a business day.  Alas, we found that somehow there were twice as many account records as expected. A quick call to Zoho support pointed us to a rollback function that should have cleaned up the problem in a few seconds. Sadly, it rolled back one set of records but not the other (remember, there had only been one import).  I spoke again with Zoho support, who promised to look into it but hadn’t accomplished anything several hours later.  At that point, I realized – duh – that it would take about two minutes to delete the records manually (you can only delete 100 at a time, but it’s three keystrokes for each batch, so you can probably do about 50 batches per minute). Once I figured that out, I cleaned out the old records and reimported everything, and we had a clean set of data.

Susan has been working with the system for the past two days, and I’ve been peeking over her shoulder and poking around a bit myself.  ZohoCRM is certainly not perfect – there are bunch of little things she would like to do, such as preview a template-based email with the variables populated. There are also some oddities like two unrelated sets of email templates, a vestige of Zoho's earlier separate systems for CRM and mass mailings. Those quirks take a bit of getting used to but are far from show-stoppers. There are some other tasks that cumbersome at the moment, but I suspect we’ll be able to automate once we have time to explore those functions. And, yes, there are some things it doesn’t do that Susan would like, such as associating multiple email addresses with the same contact. I wouldn’t exactly say they’re trivial – certainly not to Susan – but she can live with them.

We're generally satisfied with customer support: phone calls aren’t always answered immediately, but after about a minute on hold, a very nice lady picks up the line and offers to take a message. I appreciate the human touch, and, more important, the opportunity to get immediate help if something is truly urgent. We do get callbacks in an hour or two and the agents have been pleasant and helpful, which is about all I can ask. There’s a “how’d we do?” email after each interaction, which is a good sign that Zoho is trying to do a good job.

Bottom line: We’re still in the honeymoon period, so I may find Zoho isn’t really as great as I think.  On the other hand, I proposed to Susan almost immediately after meeting her and that's worked out just fine.  So I'd say ZohoCRM is worth a close look for small business CRM, even for people who think it may be too simple for their needs.

Read More

LeadSpace Offers A No-Memory Approach to B2B Lead Scoring

My discussion last week of Infer, Mintigo, and Lattice Engines raised the question of what other B2B data vendors might be considered Customer Data Platforms. It’s easy to exclude companies that provide basic B2B lists (D&B, Data.com, Netprospex, ZoomInfo, etc.) since they’re clearly in a different business. But there’s another set of vendors that look very much like Mintigo, Infer, and Lattice Engines building detailed profiles by extracting data from Web sites, social networks, and other sources. This group includes InsideView, OneSource, SalesLoft and LeadSpace. So far as I know, none of them maintains a permanent copy of a client’s own customer file, which is the essence of being a Customer Data Platform. But if you’re a marketer needing to identify and score B2B prospects, you’d still want to give them a look.

I bring this up because a colleague suggested reconsider classifying LeadSpace as a CDP, which prompted me to learn more about them. Here’s what I found.

- LeadSpace, like the other vendors, scans Web sites, blogs, Twitter feeds, LinkedIn profiles, job hunting sites, and other sources to build a picture of a company’s business, managers, technologies, and similar attributes. Of course, every vendor argues it does this better than anyone else.  I  suspect there are indeed significant differences.  But I haven’t done any testing or seen anyone else’s test results – so all I can say is that wise buyers will test for themselves before making a choice.

- LeadSpace does build lead scores, something its Web site doesn’t reflect. This is one of the major points of differentiation among vendors in this space, so it’s worth understanding exactly what kind of scores each company provides. In LeadSpace’s case, the company builds “ideal buyer profiles” that measure how similar a lead is to a sample of existing customers provided by a client. Most clients have multiple profiles for different products or customer segments. Other companies in this group build different types of scores: say, for response to a specific campaign, or becoming a sales accepted lead, or having a high lifetime value. Some also estimate the incremental financial value of taking an action. It’s easy for buyers to gloss over these differences, but that would be a big mistake: they largely what kinds f applications a system can support. So be sure to explore them in detail (or read our explanations once we release the CDP Report itself.)

- LeadSpace doesn’t maintain its own permanent master database of all companies on the Internet. Rather, it conducts a fresh scan as each client requests research into its target audience.  This is another big difference from its competitors, who do run continuous scans and keep the results. LeadSpace argues that its approach avoids outdated information, saves the cost of storing and updating a persistent database, and lets the system collect precisely the right attributes for each situation – which can’t be known in advance. The company also points out that even a new scan will capture some history: the public Twitter feed goes back one year, as do job site listings. I have doubts about these arguments – I think older data can show important trends, am sure there’s plenty of outdated information on current Web pages, and suspect there’s the important attributes are pretty similar from one project to another.  Perhaps LeadSpace is really making the subtler argument that the incremental value of older information doesn’t justify the incremental cost of scanning and storing it, which is perfectly possible.  The company does store some old information, such as common job titles, to help analyze and classify inputs.

- LeadSpace doesn’t load a copy of its clients’ customer names, either. That’s essential for a CDP, which by definition has the potential of evolving into a primary marketing database. But it's not essential for LeadSpace's primary business of lead scoring, where even can be built on just a sample of a few hundred records. The arguments for and against the permanent master database also apply here, so I won’t repeat them. In addition, LeadSpace says its clients care more finding prospects with the right attributes, such as industry, company size, and technology fit, than trends in their behaviors or new job titles. Again, I’m not sure I agree, but should point out that LeadSpace mentioned combining their own scores with behavior data captured in marketing automation: so LeadSpace itself is at least implicitly acknowledging that behaviors are important.. LeadSpace's approach also means it can’t monitor a set of names and issue alerts when they do something interesting.  This is definitely something salespeople like to do. LeadSpace is closing that particular gap by developing a service, soon to enter beta testing, that will do a monthly scan of a client’s customer records.  It will feed the results back to the client's CRM or marketing automation, which themselves will highlight any changes.

- LeadSpace provides both prospect lists (i.e., new names) as well as data enhancement (i.e., information on names provided by the client). Most of its competitors also do both, but some do only enhancement. Again like its competitors, LeadSpace provides an interface for sales people to view the details associated with an existing customer. This is where its on demand approach comes in handy, since the interface can present information in categories tailored to each client’s needs. The system also lets sales people rate each lead with a thumbs up or thumbs down, providing feedback to fine tune the scoring model. I haven’t seen that particular feature in competitive systems but it’s not something I’ve specifically researched.

LeadSpace was founded in 2007 as a prospecting tool that let salespeople enter a company name and receive a list of individuals and their associated information and social conversations. The evolutionary path from there to the current system , launched in 2012, is fairly obvious. The company currently has more than 50 clients, mostly large B2B technology vendors. Pricing is based on the number of records either enhanced or provided in prospect lists, and starts around $25,000 per year.

Read More

Infer Keeps It Simple: B2B Lead Scores and Nothing Else

I’ve nearly finished gathering information from vendors for my new study on Customer Data Platform systems and have started to look for patterns in the results. One thing that has become clear is that the CDP vendors fall into several groups of systems that are similar to each other but quite different from the rest. This makes sense: most of the existing CDP systems were built to solve specific problems , not as general-purpose data platforms. Features will probably converge as vendors extend their products to attract more clients. But right now the groups are quite distinct.

One of these categories is systems for B2B lead scoring. I found three CDPs in this group: Lattice Engines (which I reviewed in April), Mintigo (reviewed in June), and Infer, which I'm reviewing right now.

Like the others, Infer builds a proprietary database of pretty much every company on the Internet by scanning Web sites, blogs, social media, government records, and other sources for company information and relevant events.  It then imports CRM and marketing automation data from its clients' systems, enhances the imported records with information from its big proprietary database, and builds predictive models that score companies and individuals on their likely win rate, conversion rate, deal size, and lifetime revenue.

The models are applied to new records as they enter a client’s system, creating scores that are returned to marketing automation and CRM to use as those systems see fit. The most typical application is deciding which leads should go to sales, be further nurtured by marketing automation,  or discarded entirely. But Infer customers also use the scores to prioritize leads for salespeople within CRM, to measure the quality of leads produced by a marketing program, assess salesperson performance based on the quality of leads they received, and even adjust paid search campaigns based on the quality of leads generated by each source and keyword.

Infer differs from its competitors in many subtle ways: the scope of its data sources, its matching processes to assemble company and individual data, the exact types of scores it produces, its modeling techniques, and reporting.  It also differs in one very obvious way: it returns only scores, while competitors return both scores and enhanced profiles on individual prospects.  Infer gathers the individual detail needed for such profiles, but has decided so far not to make them available. Its reasoning is that scores provide the major value from its system and profiles would detract from them – perhaps because sales people might ignore them scores in favor of profile data. Focusing on scores alone also makes Infer simpler to set up, operate, and understand.

Infer might be right, but it’s hard to imagine they'll will stick with this position once they start selling directly against competitors that offer scores plus profiles.  They will surely lose many deals for that reason alone.  On the other hand, Infer’s initial clients have been companies where free trials versions generate huge lead volumes, including Box, Tableau, NitroPDF, Zendesk, Jive and Yammer. Scores that accurately filter non-productive leads are more important to those companies than individual lead profiles.  Perhaps there are enough such firms for Infer to succeed by selling only to them.

Whether or not Infer expands its outputs, it faces another challenge: convincing buyers that its scores and data are better than its competitors. This might well be true: based on the information I’ve gathered, Infer seems to have a richer set of data sources and more sophisticated identity matching than at least some competitors. But my impressions may be wrong, and most buyers will won’t dig deeply enough to form an opinion.  Instead, their eyes will glaze over when the vendors start to get into the details, and they’ll simply assume that everybody’s data, matching, and modeling are roughly equivalent.

The only real way to measure relative quality is through competitive testing of which scores work better.  Each buyer needs to run her own tests since results may vary from business to business. How many buyers will take the time to do this, and which vendors will agree to cooperate, is a very open question.

That said, I did speak with some current Infer users, who were quite delighted with how easy it had been to deploy the system and with results to date. This is hardly a random sample – these were pioneer users (the system was only launched about a year ago) and hand-picked by the vendor. But their experience does confirm that performance is solid.

Infer pricing is based on the number of records processed and connected systems.  The vendor doesn’t reveal the actual rates but did say it is looking at options to make the system more affordable for smaller clients.

Read More

NitroMojo and Marketing Advocate Specialize in Marketing Automation for Channel Partners

As I noted in a post last year, there is a universe of specialized marketing automation systems for companies that sell through channel partners. These products address several interrelated challenges: distributing leads to partners without losing track of performance; distributing partner-customized versions of company-created content; and helping partners run their own marketing campaigns. Here are two more vendors with related offerings:

NitroMojo focuses primarily on lead distribution and tracking. Its particular strength comes from sending follow-up email surveys directly to leads to find out what happened: were they contacted by the channel partner? did they eventually buy? is there someone else at their company to talk to? is there something else they might purchase? This addresses one of the central dilemmas of selling through partners, which is losing contact with the leads and, as a result, not being able to measure effectiveness of corporate lead generation programs. NitroMojo says about 60% of leads reply to the surveys, giving enough information for meaningful analysis of program, partner, and salesperson performance.

The system also provides sales reps and sales managers with basic sales automation, including abilities to enter and rate new leads, review and prioritize existing leads, track call results, send materials from a central library, and schedule future calls. Corporate marketers can build campaigns with multiple events, create landing pages, capture revenues and costs, distribute leads with complex routing rules, score leads on behaviors and salesperson ratings, and measure performance.  Pricing starts around $3,000 per year plus $100 per user per month, which is usually less than the cost of marketing automation and sales automation systems that NitroMojo would replace. The current version of NitroMojo system was introduced about a year ago and had three global clients with more than 150 users when I spoke with the company in April.

Marketing Advocate is designed to help technology resellers who lack in-house marketing skills. It provides a resellers with a vendor-sponsored microsite that gives them access to marketing content, prospect lists, acquisition email campaigns, and automated nurture emails.  Resellers define their target prospects when they set up the system and then purchase suitable lists from suppliers including NetProspex, Jigsaw, and Harte-Hanks. These prospects, and other names uploaded by the reseller, receive standard campaign emails at three week intervals until they respond by visiting a landing page. The system then sends them personalized emails offering contents related to their behaviors. The leads are also scored and, when ready, can be passed to a telephone lead qualification service or directly to the vendor’s sales automation system. The sponsoring vendor doesn’t see the lead names until the reseller enters them into the system.

The point of all this is to minimize the effort that the resellers themselves must put into marketing. Marketing Advocate typically builds 25 to 30 prospecting campaigns tailored to different customer segments, and lets the resellers select the campaign and segments they want to pursue. The company also assembles and selects content to offer in the emails, has negotiated arrangements with the list providers, gives reports that analyze program response quantity and quality, and offers a concierge service to review results with resellers and discuss improvements. The system can also integrate with event management software and Google AdWords. Partner agencies are available for telephone lead qualification, search engine optimization, and paid search.

Marketing Advocate typically costs $500 to $700 per month per reseller, with some portion of the expense usually subsidized by the sponsoring vendor. Marketers pay $1 per name for prospects. The system is used by divisions at several major technology vendors including IBM, Microsoft, and HP.

Read More