New Marketing Automation Options for Small Business in the VEST Report

I’m revving up for the next edition of our B2B Marketing Automation Vendor Selection Tool (VEST) report, which will include six first-time entries. I’ve already written about two of those, Inbox25 and AutopilotHQ (formerly Bislr). Here are thumbnails of the others.

GreenRope workflow

GreenRope is all-in-one software sold primarily to very small businesses such as lawyers, real estate agents, consultant, coaches and membership organizations. That puts it firmly in Infusionsoft territory, and perhaps even towards the lower end of that. The system has an impressively broad scope, adding full Web site creation to the usual all-in-one mix of email, lead scoring, landing pages, and CRM. The features within these functions are unusually sophisticated for a micro-business system: email includes dynamic content and a/b tests; Web pages also support a/b testing; Web forms allow progressive profiling; email and Web responses can automatically trigger a follow-up action.  CRM includes opportunity tracking, unlimited user defined fields, and automatic search of Facebook and LinkedIn when new contacts are added; algorithms can automatically estimate the right number of points for different events to build a predictive lead score.  The media library supports images, files, articles, and videos (through Vimeo integration); the calendar provides full event management; surveys can change questions based on previous answers and include automated follow-up actions.


The system also extends beyond sales and marketing functions to include customer forums, Wikis, support tickets, project schedules with tasks assigned to individuals, and coupons. Workflows can manage both marketing campaigns and internal projects. Additional functions are provided through integration with other systems, including Olark for chat, Twilio for voice and text messages, VoiceBase for transcription, Magento for ecommerce, Quickbooks for accounting, and Microsoft Outlook for email.

In other words, although GreenRope describes itself as “CRM and marketing automation,” it actually extends beyond those functions to manage activities throughout the business. This is very desirable for small organizations that want to automate their operations while running as few systems as possible.

GreenRope is also small-business-friendly, starting at $149 per month for up to 1,000 contacts and costing $199 per month for 5,000 contacts.  All plans include unlimited users and unlimited emails, which isn’t always the case with small business systems.

While GreenRope is new to the VEST report, the company itself was founded in 2008.  It currently has about 4,500 end users at a somewhat smaller number of companies.

Hatchbuck workflow

Hatchbuck is another all-in-one product for very small businesses. It has taken the approach of providing only core features and making them as easy to use as possible. This scope covers email, Web forms, multi-step campaign flows, and CRM. The system integrates via Zapier with ecommerce products. It recently added lead scoring and the ability to look up individuals on social networks. The company serves a mix of clients, with the largest segments including technology and manufacturing companies, travel, and professional services. Most clients have fewer than ten employees.

As the company’s strategy suggests, Hatchbuck provides basic capabilities for its core features but skips the more advanced options. It creates email templates with personalization and embedded links but no dynamic content. CRM captures activity history, tasks, deals, purchases and events but doesn’t integrate with a phone dialer. Forms can be associated with actions but there is no specialized survey builder. You get the idea. Instead of adding more features, Hatchbuck’s developers rigorously benchmark the number of clicks it takes to perform system functions in Hatchbuck and competitive products and track how customers use each feature to identify problems. The company also provides extensive training and support materials, including a required three hour Quickstart package to help new clients use the system effectively.

Hatchbuck was founded in 2011 and launched its product in 2013. It now has about 700 customers with over 2,000 end-users. Pricing starts at $99 per month for one user and 2,500 contacts and reaches $199 per month for three users and 10,000 contacts. All plans include unlimited emails. The Quickstart package costs $199 but the fee is waived for clients who sign a six month contract. The company also has special features for marketing agencies who use the system for their clients. Such agencies account for about one quarter of the Hatchbuck business.

Lead Liaison content creation

Lead Liaison calls itself “revenue generation software” to indicate that it provides more than a standard B2B marketing automation product. Additional features include lead distribution and buying signal alerts, but don’t extend to full CRM or the other operational functions. With a $500 per month starting price, it is targeted at small to mid-size businesses but not at the most tiny. Pricing is based on the number of contacts in the database, with unlimited users, emails, and page views.  The company doesn’t publicly state how many contacts that $500 gets you.

The system offers advanced versions of the usual marketing automation functions: email, landing pages, Web forms and surveys, lead scoring, multi-step nurture flows, media hosting, and CRM integration with Salesforce.com, Microsoft Dynamics CRM, and Sugar CRM. It also goes beyond these in several directions, including:

- company-level Web visitor identification based on IP address, which can be tied to Data.com or LinkedIn to pull back the names of individual contacts at the identified companies (although these are not necessarily the actual visitors).

- matching of contacts against social networks to add their social identifiers to the Lead Liaison record

- phone dialer with scripts, call notes, and a payment widget

- option to send emails from LeadLiaison’s own servers or through third party services including Mandrill, SendGrid, and SMTP Inc.

- social media posting to Facebook, LinkedIn, and Twitter, including an option to store posts in a queue that will release them on a regular schedule

- a nifty Web page scanner that can copy an existing Web page or form from any source into a version that the marketing automation user can edit by, say, inserting a Lead Liasison form or link

- agency-friendly features including single log-in to multiple accounts.

Perhaps the most interesting feature of LeadLiaison is a content creation wizard that connects to a network of prequalified writers for blog posts, white papers, press releases, newsletters, Web pages, social media posts, and other materials. This is directly integrated with the system: users fill out a form specifying their requirements, which Lead Liaison submits to the network.  Once a writer (whose identity is hidden from the user) accepts the project, the system tracks the material through production states and eventually loads it into the Lead Liaison asset library.  Assets are automatically coded so users can track consumption.  The system can also limit distribution based on date range, number of downloads, and whether visitors are asked or required to provide an email address to receive it. Pricing is modest: a blog post costs $50 with five day turnaround. Although the writers are anonymous, LeadLiaison plans to let users favor authors of specific pieces for future assignments.

LeadLiasison was launched in 2013. It has under 200 clients and serves a mix of B2B and B2C marketers.

dbSignals workflow

dbSignals is brand new: the system was formally launched just last week. (Full disclosure: I’ve consulted for them.) The system straddles B2B and B2C marketing automation, using a flexible data structure typical of B2C products but also providing Salesforce.com integration, the B2B hallmark. It also includes its own lightweight CRM.

The marketing automation functions themselves are quite sophisticated: dynamic content, multi-step branching campaign flows, multivariate testing, fine-grained user rights management, option to use internal or external email services, and integration with external HTML templates. Supported channels include email, SMS, direct mail, surveys, landing pages, and social media. There are also options to support marketing agency users, including an ability to rebrand the system with the agency or client’s own identity. 

And, yes, the system also can look up the social profiles of individual contacts and add them to its database.  That feature has quickly become a new standard.

But what really distinguishes dbSignals are two features beyond the normal scope of marketing automation. The first is prospect data: the company has negotiated deals to let its clients access detailed files with 235 million consumer names and 60 million B2B names. These are selectable within the normal system interface, along with whatever names a client loads on its own.

The second feature is machine learning.  This is initially being deployed to identify the most responsive list segments within the prospect data. The process is wholly automated: the only choice users make is whether to turn it on.  Once they do, the system analyzes the client's customer list or past campaigns, builds a predictive model, runs test campaigns to validate and refine the model, and then runs a roll-out campaign once the model is stable. Models are further adjusted after later campaigns. dbSignals will soon add other uses for machine learning including churn prediction, lifetime value prediction, and attribution of the incremental impact of marketing programs.

Prospect data and machine learning are closely integrated.  Indeed, one of the reasons machine learning can be so fully automated is that the system can rely on the prospect data elements to be available -- including up to 2,000 variables on a consumer profile. Beyond that, dbSignals uses the machine learning results to “reserve” the best prospect names for each client in advance of campaign selection.  This is needed because dbSignals limits the number of promotions sent to any name within a specified time period.

Both the prospect data and machine learning are in turn made possible by dbSignals' underlying technology, which uses the Cassandra data store instead of a standard relational database.  Few marketers will care, but, trust me, it really matters for speed, scale, and flexibility.

dbSignals also offers an unusual pricing model, basing charges on the number of users and/or message volume rather than database size. This makes it easier for clients to take full use of the prospect data. Fees start as low as $500 per month.

The initial version of dbSignals was introduced in 2014. The company currently has about two dozen clients including a mix of B2B and B2C organizations.

Customer Data Platforms Revisited: The Future of Marketing Data

It’s nearly two years since I introduced the concept of a Customer Data Platform, defined as a marketer-controlled system that builds a multi-source customer database and exposes it to external execution systems.  You may recall that I listed several sets of products as CDPs: B2B predictive lead scoring and customer success management; campaign management with an integrated customer database; and data management platforms to support online advertising. Systems were included only if their data (or derived data such as model scores) was available to other systems for campaigns and messaging.

All those categories have done well since my original posts on the topic. Established vendors have grown quickly and attracted funding; new vendors have joined the mix, also often with substantial funding. So I suppose I could pat myself on the back for spotting an important trend and let it go at that.

But things aren’t quite so simple. A look at the entire CDP ecosystem uncovers important patterns that are hidden when you look at individual vendors or vendor categories. Here's a summary of what I've seen.

Customer Management Functions

CDPs exist because marketers need to coordinate customer (and prospect) interactions across channels. That coordination involves three basic tasks: gathering and unifying customer data from all sources; using that data to select the best treatment for each interaction; and delivering those treatments through the appropriate channel systems. Each of those three tasks has several subtasks. These layers are illustrated by the following diagram, which includes a unified data layer – the classic CDP.

Vendor Categories

So far so good, but it’s really just theory. Things get interesting when you look for specific systems that perform the subtasks. It turns out that there are several categories of specialist systems within each subtask, each doing similar or complementary things in slightly different ways. Connecting the logical flow to actual systems is important because looking at real products tells you what the market is saying: that is, what buyers are willing to pay for and where change is concentrated.

The following table shows what I found when I did this analysis. The list of vendors in each section isn't necessarily comprehensive, especially in crowded segments like B2B marketing automation. I should also stress that I’ve only included Decision-layer vendors who also build their own database. This makes them potential CDPs and means they have many Data-layer functions. In a sublimely liberating act of inconsistency, I have NOT limited the Delivery layer to vendors who build their own database. In fact, most do not.

Investment

The right-most column on the previous table shows the level and types of investment being made in each vendor class. I haven’t collected precise details but the general patterns are pretty strong. The major observation is that current investment is heavily concentrated on the Decision layer, with interest in predictive modeling and message selection (which could also be labeled as personalization). There’s some investment on the Data layer in data gathering vendors, especially along the lines of acquisitions by big companies (Oracle/Datalogix, D&B/NetProspex, etc.). This is a general sign of maturity. Similarly, most recent investment on the Delivery layer has been acquisitions (IBM/Silverpop, Oracle/Responsys, Teradata/Appoxee, etc.), which is a sharp contrast from the heavy venture capital funding a couple of years back. Again, this shows the relative maturity of the space.

(Caveats: although it doesn’t show up in this analysis, I do still see some interesting investment in marketing automation niches such as app marketing, distributed marketing, and agency systems. I’m also increasingly intrigued at the “tag management” vendors on the Data layer (Tealium, Signal, Ensighten, etc.), which are reinventing themselves as data integration hubs. I didn’t see that one coming.)

Implications

It’s tempting to interpret these results are showing that data assembly is a solved problem, allowing marketers to invest Decision systems on the next layer down. But any marketer can tell you, and every survey I’ve seen confirms, that most companies are nowhere near having fully integrated their customer data.

What I think is really going on is that people are investing in Decision systems that build their own multi-source databases, providing both Data and Decision functions in one package. Remember that my original CDP categories included B2B predictive vendors and campaign management vendors who did exactly that. So it seems the proper way to look at things is more along the lines of the following diagram, which shows there are several different ways to solve the customer data integration challenge: you can buy a stand-alone CDP that has only data-level functions; buy a Decision system that also builds an integrated database; or buy a Delivery system that does data, decisions, and execution. As the diagram indicates, most of the Decision vendors do incorporate the CDP functions, while only a few of the Delivery vendors do.

The diagram labels the Data + Decision combination as a “Marketing Platform”.  I think this is reasonably consistent with how most people use the term, since the key feature of a “platform” is its ability to integrate with external systems for delivery and other purposes. I’ve labeled the Data + Decision + Delivery combination as an “Integrated Suite” and used question marks to show that not all suites provide a complete Data solution. This is because many suites aren’t very good at bringing in external data or letting external systems access the data they’ve assembled.

As I noted in the previous section, most of the industry funding and excitement is centered on the Decision layer, which is where the Marketing Platforms live. The practical advantage of those systems over Data-only solutions is obvious: Decision systems deliver a revenue generating application while Data-only systems do not.

But think about that for a moment.  Each Decision system builds its own multi-source database and each integrates separately with the Delivery systems.  Having multiple Decision systems is a nightmare of redundancy:

It seems pretty clear that the better solution is to have a single Decision system controlling everything, which is arguably what most people (and vendors) have in mind when they describe a Marketing Platform. Indeed, this is exactly the direction that most Decision-layer CDPs are headed, by expanding the scope of their products from an initial point solution, such as B2B lead scoring, to encompass other applications. It’s safe to say that the people who built these systems always planned, or at least hoped, to grow in this direction.

Does the growth of Decision-layer CDPs mean that Data-only CDPs will fail? I’ll admit that only a few such systems have appeared in the past two years. But I’m not quite ready to give up on the concept.

Why?  Well, as Tolstoy never said, all good customer databases look alike, but every decision system is different. This means it’s hard to support all types of decisions within a single product. So it does seem that multiple decision systems will appeal to marketers who have the skills to use them and the scale to justify the added expense. Those marketers would benefit from a Data-layer CDP, which would make it easier to deploy best-of-breed decision tools even when those tools lack data unification functions.

The stumbling block for this approach is still the cost of integrating multiple systems: as the diagram shows, there are still plenty of connections in this model. But there’s at least some hope (although I remain skeptical) that newer technologies will make the integration easier. The other bright spot for the Data-only CDPs is that they should be attractive as partners or acquisitions for Decision and Delivery systems that haven’t built their own CDP functions.


And what about the suites? I’ve said for years that the first law of software market development is “suites win”, precisely because most companies will sacrifice best of breed functionality to avoid the costs of integration. Indeed, the big marketing clouds from Oracle, Salesforce.com, IBM, Adobe, and others all include extensive Delivery layer functions. I think it’s fair to say that while their commitment to being “open platforms” is genuine, they see that as a way of letting clients supplement the core functions the suites provide internally.  This is quite different from the idea of a shared Data and Decision platform that specifically avoids offering Delivery services. Still, there’s a  very good chance that a suite which can easily integrate supplementary functions will give marketers enough freedom to overcome the problems of lock-in, while still delivering the convenience of pre-integrated core functions. So I’m not quite ready to abandon “suites win” as a rule, although I’m a bit less certain than previously.

Looking Ahead

It’s fun to handicap the horse race among vendors and categories, but what really matters is the contest itself.   All these smart people and money are finally giving marketers the unified customer databases they so desperately need.  This removes a fundamental obstacle to the cross-channel integrated marketing that everyone recognizes is increasingly important. So let’s look at the view once we've climbed that mountain.

I’d like to tell you I see a new and perfect world, but what's actually there is more mountains.  Once unified databases become available, marketers will face a new set of challenges including:

- more need for predictive models and external data. I only lump those together because they’re already getting a lot of attention. Having a powerful database just makes them even more important.

- new focus on automated content creation and campaign design. Lack of skilled users and adequate content are already huge barriers to effective multi-channel marketing.  Removing the database barrier will only make them stand out even more. So we can expect smart people to address them through technology. Indeed, there is already plenty of activity in these areas but I think it’s fair to say that so far none of vendors have had a major impact. This is arguably the next exciting frontier for marketing technology.

- more developments in cross-channel customer tracking. Again, the need for this has been obvious and some major investments have already been made. Cookies are becoming increasingly inadequate as cookie-hostile channels like mobile become more important. Marketers will soon reach a tipping point (or maybe they already have) where they realize they must abandon cookies and move on to other approaches such as device identification or external identity databases. A new standard will eventually emerge, although I can’t even guess what it might be.

- tighter integration between advertising and marketing technology. These two realms are now largely separate with a few exceptions such as retargeting. But as personalized ad messages become increasingly possible, marketers will have ever-greater incentive to target and, ultimately, coordinate messages across channels using shared data. This is highly dependent on the improved customer tracking, so it might have to wait a bit.

- better marketing attribution. If there’s a last stop on the road to marketing Nirvana, attribution might be it. Once marketers have assembled all that data and associated everything with the right customer, they’ll finally be able to deploy advanced analytical methods to really understand the long- and short-term incremental impact of their marketing efforts. Then, and this itself would be heavenly, we’ll never again hear anyone quote John Wanamaker about not knowing which half of his advertising is wasted.

Recommendations for Marketers

Nirvana is still far distant.  Marketers face immediate choices in how to spend their time and budgets. The trends I’ve just described do have some immediate practical implications. Here are my suggestions:

- Experiment like crazy. The various Decision-layer vendors currently offer different specialties, such as lead scoring vs. product recommendations vs. churn predictions. Vendors in each area are expanding their scope so there’s a good chance you’ll eventually pick one to do almost everything. To have the best odds of making a good selection, you’ll want to learn about as many vendors as possible in advance. So run tests to build an understanding of the applications, technologies, and corporate culture. The good news is that each approach can probably pay for itself in improved performance, so these tests should be more or less self-financing.

- Keep an eye out for new data. Many of the Decision-layer vendors bring their own data to the party, and evaluating that data is one part of understanding what they offer. But there are also other data sources that are not tied to a Decision system. You’ll want to explore these to understand what value they provide value and whether to make them part of your long-term data foundation.

- Plan for integration. You may not have shared customer data or decisions today, but it’s increasingly likely they’re in your future. So every new marketing system should be evaluated in part on its ability to integrate with other systems. This involves sending data to the central database and reading data from it, as well as integrating with Decision-layer systems for predictive models, rules-based selections, optimization, recommendations, personalization, and more. Even if you’re going to use an integrated suite, you’ll want to assess how easily you can supplement its functions by tying into external products, and what kinds of products are already available for integration.

Summary

The stand-alone Customer Data Platform is one solution to the challenge of providing a multi-source, shared marketing database, but it isn't the only option.  Whichever solution marketers ultimately find most appealing, they will benefit from gaining control of their data and moving on to new opportunities that database makes possible..