Terminus Offers Targeted Display Ads for B2B

Tuesday’s post on the Adobe Marketing Cloud illustrated the complexity of solutions that combine many marketing and advertising components. Despite my best efforts, and much cooperation from Adobe, I’m sure it still misses many nuances of how Adobe components do or don’t work together. Nor does it address the challenges that users face in making sense of it all. Coincidentally, yesterday's FierceCMO blog indirectly quotes Lenovo’s Michael Ballard on this very point: “Adobe is not a solution for everyone because it requires a lot of expertise and attention. To help the system run smoothly, Lenovo has both dedicated internal Adobe teams, as well as full-time support people on Adobe's payroll, which means the company spends equal amounts on both products and support services.” And Lenovo uses just only two of Adobe’s eight solutions.

For many smaller companies, a suite like Adobe, Oracle, IBM, Salesforce.com, SAS, or Teradata isn’t an option and would be overkill if it were. Those firms will generally do better with a simpler solution that was built as an integrated whole, such as a StrongView (recently reviewed here) or SiteCore (reviewed here not so recently).

But such integrated solutions are rarely comprehensive. This means that meeting a full range of needs requires connecting with external systems.  Some vendors do this better than others. A good foundational system also needs a robust customer database that can integrate data from all the channels, whether it supports them directly or via partners.

One area where partners are especially common is ad management. Although martech and adtech are on the path to merging into madtech, they’re still largely separate, especially outside the big enterprise suites. This means that integration has largely meant sharing audiences defined in a marketing automation system with demand side platforms (DSPs), so the DSPs can bid on audience members when they appear on ad networks. For B2B marketers, this has happened mostly through Demandbase and what was formerly Bizo (now part of LinkedIn). Vendemore (reviewed here) is another option.

Terminus, which officially launched in February, is new alternative. The system imports lists of target accounts from a company’s CRM or marketing automation system, or lets clients build their own lists from Terminus’ own B2B company database. Users can then specify the corporate roles they want to target. Terminus uses third party data partners to identify cookies belonging to people in the specified roles at the selected companies and connects to ad exchanges, Facebook, and mobile apps to bid on them. The system automatically monitors response and optimizes its bids to get the best results within a company’s campaign budget.

Here are a few features to consider when comparing Terminus with other B2B advertising solutions:

- self-service. The system provides users with a multi-step process to import their data, select target segments, assign creative materials, set daily and/or total campaign budgets, define campaign end dates, and start executing campaigns. During campaign set-up, the system shows how many companies and contacts match the segmentation criteria within the imported CRM or marketing automation data and how many Terminus can find in its cookie pool.

- email-based targeting. Terminus selects cookies by using LiveRamp and other data partners to match them against email addresses from the CRM or marketing automation system. The company says this is more accurate than targeting based on IP address. (The company can also use its own database to reach people not in the clients' systems.)  Integration is currently available for Salesforce.com CRM, Marketo, and HubSpot, with plans to add Salesforce.com Pardot and Oracle Eloqua shortly.

- ad inventory. Terminus currently integrates with well over fifty ad networks and exchanges. It is adding programmatic premium inventory and direct deals to the base programmatic impressions.

- sales stages. Terminus imports opportunity stage from the CRM system and can use this in segmentation. This lets it build separate campaigns to serve different creative to companies in different stages. But the system isn’t tracking messages to specific individuals, so it can’t avoid showing the same message multiple times to the same person.

- automated optimization. Terminus adjusts bids based on twenty variables such as ad size and time of day without asking marketers to make any decisions. Like most real time bidding systems, it typically aims to optimize click through rate or cost per click. The system will automatically serve alternative creatives and pick the best one; true a/b testing and reporting is planned for future release.

- reporting: the system shows spend, reach, impressions, and clicks for each account, giving marketers a precise view of how each account has been treated. Opportunity value can be imported to add return on investment analysis. Reports can be filtered by opportunity status, for example to show only closed deals. Reports can also summarize results by device, ad format, creative version, and other variables. Marketers can change creative from within the reporting screen if they spot something that isn’t performing well.

- pricing. Terminus charges a fixed platform fee and passes through the advertising expenses at cost. The company considers this among its most important innovations, compared with other firms that make their profit by marking up advertising expenses. The Terminus approach provides greater clarity and lets the company optimize its clients’ ad purchases without affecting its own profits. The platform fee is currently about $500 per month although Terminus expects to offer several tiers starting from $725 to $1,000 per month later this year.

Adobe Marketing Cloud Marches Towards Martech and Adtech Integration

At pretty much the same moment I was publishing my post on the merger of martech and adtech into madtech, Adobe was announcing its latest marketing products, including a press release on uniting “Data-driven Marketing and Ad Tech” . Naturally, this caught my attention.

As you might expect, Adobe’s reality is considerably more complicated than the simplicity of the “madtech” vision. Like the other enterprise software vendors who offer broad martech and adtech solutions, Adobe has built its marketing cloud by buying specialist systems. And, again like its competitors, it has only integrated them to a limited degree.

In Adobe’s case, the various products remain as distinct “solutions” served by a common set of “core services”. The current set of eight solutions includes Analytics (Web, video and mobile analytics, née Omniture Site Catalyst), Social (social publishing, based on Context Optional), Target (Web optimization and personalization, derived from Omniture Test & Target/Offermatica), Experience Manager (Web content management , originally Day Software), Media Optimizer (based on Efficient Frontier and Demdex), Campaign (formerly Neolane); Primetime (addressable TV) and Audience Manager (data management platform, formerly Demdex). Of course, the products have all been modified to some degree since their acquisitions.  But each still has its own data store, business logic and execution components.

Rather than replacing these components with common systems, Adobe has enabled a certain amount of sharing through its core services. In the case of customer data, the “profiles and audiences” core service maintains a common ID that is mapped to identities in the different solutions. This means that even though most customer data stays in the solutions’ own databases, the core service can use that data to build audience segments. There's also an option to load some attributes into the core services profiles themselves.   Audiences, which are lists of IDs, can either be defined in solutions and sent to the core service or built within the core service itself.  Either way, they can then be shared with other solutions. Data from external systems can also be imported to the core service in batch processes and used in segmentation.

Adobe says that data stored in the solutions can be accessed in real time.  I'm skeptical about performance of such queries, but the ability to store key attributes within the core service profiles should give marketers direct access when necessary.  There’s certainly a case to be made that digital volumes are so huge and change so quickly that it would be impractical to copy data from the solutions to a central database. Where external data is concerned, marketers will increasingly have no choice but to rely on distributed data access.

But here’s the catch: Adobe's approach only works if all your systems are actually tied into the central system. Adobe recognizes this and is working on it, but so far has only integrated five of its solutions with the profiles and audiences core service. These are Analytics, Target, Campaign, Audience Manager, and Media Optimizer. The rest will be added over time.

The second big limit to Adobe’s current approach is sharing with external systems. Only Adobe solutions can access other solutions’ data through core services. This makes it difficult to substitute an external product if you already have one in place for a particular function or don’t like Adobe’s solution.

Adobe does connect with non-Adobe systems through Audience Manager, its data management platform, which can exchange data with a company’s own CRM or operational databases, business partners, and external data pools and ad networks. Audience Manager can hold vast amounts of detailed data, but does not store personally identifiable information such as names or email addresses. Audience Manager can also copy Web behavior information directly from Analytics, the one instance (so far as I know) where detailed data is shared between Adobe solutions.


So far, I’ve only been discussing data integration. The various Adobe components also have their own tools for segmentation, decision logic, content creation, and other functions. These are also slowly converging across products: for example, there is an “assets” core service that provides a central asset library whose components can be uploaded to at least some of the individual solutions. The segmentation interface is also being standardized product-by-product. There’s no point in trying to list exactly what is and isn't standard today, since this will only change over time.

The lesson here is that suites are not simple. Marketers considering Adobe or any other Marketing Cloud need to examine the details of the architectures, integration, and consistency among the components they plan on using. The differences can be subtle and the vendors often don’t explain them very clearly. But it pays to dig in: the answers have a big impact on whether the system you choose will deliver the results you expect.

Everstring Offers Fast, Flexible, Account-based Predictive Models for B2B Sales and Marketing

Remember how much simpler life was back in 2010? Among our quaint notions, we thought that B2B companies couldn’t build predictive models because they didn’t have enough data about their customers and prospects. The Internet has changed that, providing oceans of relevant detail from company Web sites, social media, job boards, and other sources. Today, at least a dozen vendors are offering predictive models for B2B lead scoring, sales intelligence, and customer success management.

Many of the original scoring vendors specialized in a single application.  But today, most are broadening their products to serve multiple purposes. This lets the vendor charge more to each client and blocks out potential competitors. Marketers also benefit since they only have to buy, learn, and integrate a single system.

Everstring is relative newcomer to the B2B predictive modeling arena, founded in 2012 but only seriously entering the market after it received $12 million in Series A funding last August. The late start has let it adopt a broader scope from the beginning, offering both lead scoring and new prospect identification. The company plans to extend its offerings later this year to include real-time treatment recommendations.

But what really sets Everstring apart are two other factors: it works at the account rather than individual level, and it builds models really, really fast – as in, six minutes for a new model once data connections are in place. The two factors are related: Everstring can work quickly because it only imports a client’s account list and sales activities, saving complicated data mapping and analysis, and because it has preclassified its master database of six million U.S. businesses into clusters based on similarities in products, technologies used, hiring patterns, news events, social data, and other factors. This means that building a new model only requires using activity history to identify the client’s responsive accounts and finding which segments have the highest concentrations of those accounts.

That’s pretty light work compared with loading individual level data and identifying which attributes are most predictive for each client’s business. Matching against six million companies rather than 100 million individuals speeds things up too. The approach also lets clients score anonymous leads if IP address or similar information can identify their company. Models for different products can be based built by selecting only accounts that purchased that product.

Once a model is built, Everstring can score any new leads by just by identifying the segment their company belongs to and applying that segment’s score. Lists of new prospects require simply taking names from the highest-scoring segments.

Sounds pretty simple, eh? That’s because I’ve over-simplified. The data gathering and actual math are actually quite complicated.  Beyond that, Everstring does more than provide segment scores, which  measure the fit between a new account and the client’s previously responsive prospects. Specifically, it also measures purchase intent by based on more than 1 billion clicks per day on third party Web sites and emails. And it measures engagement by analyzing visitor behaviors on the client’s own Web site, gathered through a tracking pixel, plus other data imported from marketing automation. The combination of fit, intent, and engagement will guide the real-time treatment recommendations and can support additional scoring applications.  Fit scores alone are much more limited..

So, how do you deploy all this? Everstring has standard integrations with Salesforce.com, Marketo and Oracle Eloqua, which can send data for the initial model building and score new accounts as they are added to those systems. A real-time API can integrate with other CRM and marketing automation systems.

Pricing for Everstring is based on the types of models and volume. Lead scoring usually runs from $60,000 to $100,000 per year. New prospect names is additional. Pricing for real-time message selection isn’t yet set. The system currently has about 25 clients, nearly all added since last August.

Marketing Technology of the Future: Beyond the Customer Data Platform

The last three minutes of my MarTech Conference presentation are driving me crazy.

The preceding portions cover the current state of Customer Data Platforms. I have no trouble talking about that. But it somehow got into my head that the last section should look at how CDPs will fit into the long-term future of marketing technology. I have some fuzzy notions that this future martech will be radically different from today.  But to cover it succinctly I must first think it through in detail. That has been considerably harder than I expected. Here’s what I have so far.

Current Trends

These are developments happening now that will provide the context for industry changes. Some will be the topic of other MarTech presentations.

• Convergence of Adtech with Martech. These have until recently been largely separate: Adtech deals with messages to audiences whose members may share common characteristics but are not individually identified. Martech deals with known individuals. As anonymity becomes increasingly unavailable, marketers will know exactly who is receiving their advertising messages. Martech targeting techniques will therefore be used in adtech as well. Conversely, some adtech features will become standard martech practice.  More about that later.

• Contextual data. Social networks, mobile devices including phones and wearables, and the Internet of Things will provide ever-more details about the precise situation of each customer during each interaction.  Location is an obvious data point, but marketers will also know your local weather, your mood and physical condition, what you’re wearing, when and what you last ate, and whether your car needs gas. This may sound seriously creepy but the good news is you’ll get better-targeted messages.  I’d love to call this “contextual marketing” but that term is taken.

• Marketing to machines. I’m not talking about marketing with machine-generated data (e.g., your running shoes telling Nike how many miles you’ve logged) or marketing through messages on your machine (your washer suggesting you buy Tide-brand detergent).  I’m talking marketing to machines that are making purchase decisions on their own.  I discussed this last year in Do Self-Driving Cars Pick Their Own Gas Stations? and More on Marketing To Things.  Frankly, I've been surprised to see little else written on the topic. Trust me, this will be big: imagine convincing Siri to recommend your restaurant every time someone asks where they should go to lunch.

Implications for Martech

Given the trends I’ve just listed, I see martech changing significantly.

• Data synergy. I just made that term up but the idea is old: related bits of data are worth more when they’re combined. So knowing you just booked a trip to Alaska and knowing you just walked into a department store are each marginally useful by themselves, but together they let you offer me a great deal on a warm coat. It helps even more if I know you don’t already have one. The implication of this is that there’s a lot of value gained from combining data from different sources into shared repositories. It also implies there’s a lot of value in “identity association” technologies that link related data to the same person.  If you've been wondering why companies like Oracle, Acxiom, and Nielsen have been buying big data aggregators, you can stop.

• Everything is biddable. Another implication of data synergy is that each opportunity to communicate with an individual will be much more valuable to some people than others. Let’s stick with that Alaska trip: selling you a coat might be worth less than selling you a hotel room. So, when you walk into the store, the hotel chain might be willing to pay more for the chance to send you a message than the store itself. Having all the messaging devices connected to a central database and bidding system makes this possible – in fact, it already happens with real time bidding on Web ads. Now Martech platforms get to do the same. And oh, to make this work well, Martech has to hugely improve its ability to measure the actual impact of each message – so advanced, predictive attribution also plays a leading role in the martech world of the tomorrow.

• Campaigns are dead but the customer journey lives on. If each interaction is bid separately, then the notion of campaigns that lead the customer through a sequence of contacts on a flow chart is irrelevant. Honestly, I’m glad to see it go: the very first campaign management system I saw, more than twenty years ago, had exactly that sort of interface and it’s well past time for a change. But this doesn’t mean we can stop thinking about customer journeys. Any model that supports bidding on individual messages must understand where each customer is in her journey and how the message will influence the result.  Of course, different marketers will be tracking different journeys.

• Automation takes over. It’s obvious that mass data consolidation, real time bidding, optimized messaging, and omnichannel execution require near-total automation of the entire process. This has to be really intelligent automation that finds patterns, notices when they change, and optimizes treatments with minimal human guidance. And where will all that highly tailored content come from? You guessed it: automated content creation systems, which are more common and further advanced than you may realize. See Paul Roetzer’s recent post on artificial intelligence in marketing automation for an introduction to the topic  and watch Humans Need Not Apply if you want to get really scared.

• Machines buy martech. If the over-all trend is machines selling to other machines, why should  martech be left out? In fact, testing the huge number of new martech options (or even generating Scott Brinker's martech landscape supergraphic) is something machines could do really well. Once suitably open architectures are in place, it should be easy to plug in new components like a better predictive modeling system, new type of video promotion, or the latest social media app. Even if those components are less than fully automated, they could be identified, screened, integrated, and assessed with minimal human intervention.  Machines would almost surely assess results better than humans, since they’d be more objective and better able to look for subtle effects on customer behavior than human analysts.

• Humans keep things running. It’s possible that machines will eventually control every aspect of our marketing.  But I think humans will have a role to play at least for a while. This won’t necessarily be the traditional “creative” work such as copywriting and design, which machines already do better than we care to admit. But they’ll still need people to come up with non-incremental products, non-obvious insights, and deals with other organizations. Even things that machines could do better than people won’t be wholly machine-run for a while, just due to the normal lags in technology and organizational development. This may sound like a dark view of humanity’s future, but I’m more optimistic than it seems. Technology never works quite as well as promised, so I figure humans will still be needed to keep things running.

Implications for Marketers

If Martech moves in the directions I’m proposing, marketers need to do certain things to prepare.

• Expect imperfection. Sure we’ll have vastly more data than ever, but don’t assume it will be perfectly complete, accurate, or integrated. In fact, you can guarantee it won't.  Predictive models make mistakes as well. Look for systems that are designed to accommodate incomplete information, check for differences between actual and expected performance, and adapt gracefully to failures. Above all, demand transparency so you can see what the automated systems are doing and have some idea of why. This will probably require help from other systems, but make sure the monitors are as independent as possible so they’re not fooled by shared mistakes. If you’re really feeling clever, examine imperfections for opportunities – you may find bargains in bidding on messages to customers that other systems have rejected because their data is unavailable or contradictory.

• Plan for change. As anyone who has tried to modify a complex campaign workflow already knows, sophisticated systems can be brittle. High performance automated systems are likely to optimize themselves for specific conditions, which is great until those conditions change. Be sure you can easily introduce new data sources, components, objectives, and execution channels. And be sure you can always revert to a simpler, more manual mode of operation if things really go bad.

• Focus on the analytics layer.  One implication of data synergy is that the richest databases will live outside of your company’s own data center: they’ll be too big, too complicated, and updated too frequently to maintain a copy in-house. Similarly, if companies are bidding to deliver messages everywhere a customer appears, they won’t own the touchpoints. So the only piece the company can expect to own is the analytical layer – the bidding and content engines. Those engines should be freely swappable as well. What’s left to hold things together is a core of profile data shared by the analytical and content engines. This is connected to the external data store on one end and the touchpoint systems on the other. All told, it’s a rather wispy little framework, but it should be enough to provide the glue needed to link all the other components.

So, where does that leave us?  Gigantic external data pools linked to personal identities, real time bidding, messages delivered through paid channels: it's "adtech without the privacy" if you want to put it in a nutshell.  That isn't where I expected to end up, but that's exactly why I needed to write this. I can’t guarantee I won’t change my mind after further reflection, but for now I think this gives a reasonable picture of what martech might look like five or ten years from now.  In the shorter term, I still expect the central role will be played by Customer Data Platforms or (more likely) by Marketing Platforms that combine the data parts of a CDP with a multi-purpose analytical and decision layer.

Now all I have to do is figure out how to cram this into three minutes. 

...hmm...

On further reflection, it comes down to a hybrid of martech plus adtech, which is inevitably named madtech:

Reborn AutoPilot Aims to Simplify Multi-Channel Marketing

Autopilot*, formerly AutopilotHQ and Bislr before that, relaunched itself today. I wrote about Bislr in November 2013.  Back then, they positioned themselves as a “marketing operating system” that provided core functions but would ultimately let users connect with third party apps. The latest incarnation describes itself as “software for multi-channel marketing” but still provides core functions and connects with third party apps.  So what has changed?

The difference is in the details. AutoPilot has spent much of the past 18 months making the system easier to use and now has handful of actual integrations available. These include Salesforce.com for CRM, InsideView and FullContact for data enhancement, Twilio for text messaging, Lob for postcards, Segment for event tracking, and GoodData for reporting. Email and landing pages are still native to the product but the vendor has added tools to import, edit, and reuse HTML from external Web sites and email systems. This allows marketers to adopt Autopilot without discarding their current tools, easing the transition.

If there’s a substantive difference between the earlier Autopilot vision and the latest edition, it’s that the vendor spoke in 2013 of making it easy to build custom apps for Autopilot, while today they speak of integrating with existing best-of-breed systems. The current approach makes Autopilot easier to adopt although it also reduces the difference between Autopilot and other “marketing platforms” that have their own app stores.

But from Autopilot’s own perspective, its real differentiator is simplicity. It sees itself as filling the gap between simple email systems and enterprise marketing automation products. That space is plenty crowded, although Autopilot may be a bit easier to use than most of its competitors. The drag-and-drop campaign builder is attractive; more important, actions in external apps appear as icons, making them as accessible the vendor's own features.  Autopilot also provides a library of prebuilt campaign “guidebooks” that give new users an easy way to get started. The library is expected to grow as Autopilot users contribute their own guidebooks to the list.

The company’s other differentiator is a seriously aggressive pricing model.  This starts at $4 per month (you read that right) for up to 500 names in the database. A more realistic 10,000 contacts is still just $160 per month including unlimited email.  In comparison, mid-market stalwart Act-On charges $1,150 per month for 10,000 names. The vendor expects to survive at such low prices by minimizing sales and support costs, allowing almost total self-service in both areas. (There’s no phone support although users can submit questions by email during West Coast business or look in the community forums and knowledgebase.)  Whether this can satisfy new small business users remains to be seen.

I know you're wondering by now whether I’ll classify AutoPilot as a Customer Data Platform. (Rumor has it, there’s a drinking game that involves reading my blog posts aloud and taking a shot every time the phrase comes up. Get a life, people.) In fact, I do not: AutoPilot doesn’t do the complex data management a CDP requires. But the system does integrate with Segment, an expanded tag management system that qualifies as a CDP quite nicely. So you might consider AutoPilot as part of a complete CDP package.

AutoPilot was founded in 2011 and introduced the first version of its system in 2013. The company has accrued more than 100 clients, Most are small businesses but some are bigger.


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* The Web site is www.autopilothq.com. The domain autopilot.com belongs to a firm selling salt chlorine generators. So far as I know, there’s no connection.