Thunderhead ONE Provides Powerful Journey Orchestration

As I wrote a couple of posts back, I’ve recently noticed a new set of vendors offering “journey optimization engines”*. The key feature of these systems is they select customer treatments based on movement through a journey map. The treatments are usually executed through external systems such as email service providers, CRM, or Web content management. The systems also assemble the unified customer database needed to track customer journeys. This, of course, is a function they share with Customer Data Platforms. But CDPs don’t necessarily have journey mapping or treatment selection functions. On the other hand, journey optimization engines don’t always expose their data to external systems, which is a core requirement for CDPs. Journey optimization engines also provide at least some tools to analyze customer journeys and choose the best customer treatments. These may include predictive models, machine learning, and automatic creation of journey maps, but don’t have to.

Thunderhead ONE Engagement Hub is a charter member of this new little club. UK-headquartered Thunderhead itself was founded way back in 2001 and launched its original customer engagement product (highly personalized customer communications such as account statements) in 2004. ONE was developed by a U.S.-based engineering team.  It was released in Europe in 2015 and in the U.S. earlier this year.

Let’s look at how ONE handles the three core journey optimization functions:

- Data assembly. ONE provides its own Javascript tag to capture Web and email interactions and a SDK to connect with mobile apps. Other systems can feed data into ONE using a REST API or batch file imports. There are prebuilt API connectors for Salesforce.com CRM, Microsoft Dynamics CRM, and SAP Cloud for Customer. The system will automatically replicate the structure of imported data, maintaining relationships between different data elements. This allows ONE to store nearly any kind of data including not just customer attributes and identifiers, but also interaction and purchase details, touchpoint configurations, and product information.

Data is time-stamped to allow trending and give access to previous values of individual elements. Users can define calculations to create derived values such as engagement score, customer type, preferences, or interests. In addition to storing the imported information in a persistent database, ONE can lets users define in-memory profiles available for real-time access during interactions. These are updated immediately as new data is gathered, so the system is always working with the most current information.

ONE can link data using customer identifiers from different sources so long as there is a common element somewhere in the chain, such as an email address that is attached to a Web browser cookie through a form fill and to a mobile device through app registration. This allows the system to start tracking anonymous users when they first appear and later connect them to a personal profile when they identify themselves. But ONE does not standardize customer attributes, such as name or address, or use “fuzzy” matching to infer likely relationships.

All told, this is an exceptionally broad set of data management features. Many systems that build profiles – both CDPs and journey optimization engines – lack ONE's ability to store information about entities such as touchpoints or products. Nor do they always provide both a persistent data store and in-memory access. And while most can stitch together identities using shared identifiers, some rely on external systems to provide a common ID.

- Journey mapping. ONE lets users assign journey stages to activities and then classify interactions by activity type.  Interactions can also be tagged with other attributes such as channel, product, and marketing asset. The system uses this information to create many varieties of journey maps, including one that shows movement between stages broken out by channels, which is delightfully similar to the Customer Experience Matrix** I’ve been working with since 2006.*** Other versions filter the inputs to show maps for specific products, customer segments, or touchpoints within a channel (such as specific Web sites, retail stores, or phone agents). Maps can also compare attributes of different groups, such as customers who advanced towards purchase vs those who dropped out. Slicing the data in yet another way, maps can show the impact on engagement score of specific actions.  Hours of fun, eh?

- Execution. Users can create “conversations” that send messages to customers who match a specified combination of journey stage, customer attributes, and channels.  Eligibility and relevance rules can ensure the chosen messages are truly appropriate. One conversation can include several  messages in different channels.  Message contents can be drawn from a repository within ONE or from an external asset library.

The system uses machine learning to estimate how each customers will respond to each conversation and to calculate the value of the conversation. An arbitration function can then find the highest value conversation in each situation. The system can deploy conversations in real time, presenting CRM agents with recommended actions (along with a detailed customer profile and history) or Web pages with personalized contents (deployed in user-specified locations on the page). Personalized content and data can also be pushed to other execution systems such as email through API connections, either in batch or real time. External systems can access individual customer records through the ONE API.  Data can also be extracted from ONE to standard SQL databases, which external systems could then query.

Pricing for ONE starts at $30,000 per year and is based on the volume of interactions and personalization recommendations, with unit costs varying by channel.  The system has 38 clients in Europe and about a dozen in North America.

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*I would love to call these JOEs but don’t have the heart to inflict another obscure acronym on the industry. You’re welcome.

** Originally developed by my colleague Michael Hoffman. Click here for his take on it.

*** I’m not suggesting that Thunderhead based their map on the Customer Experience Matrix. Many people have come up with similar ideas. I do like to think that Hoffman and I were ahead of our time.

Magisto Uses AI To Create Emotion-Inducing Videos. How Do You Feel About That?

My on-going research into marketing applications of artificial intelligence led me today to Magisto, which promises not merely to automatically create videos, but to do in a way that elicits a user-specified emotion.  That was enough to pique my curiosity,especially combined with the claim that all you had to do was upload some video and photo clips and Magisto would do the rest.  I mean, this is something for people who are both cool and lazy – sign me up!

Which is exactly what I did.  I took a few video selfies (velfies?) around the office with some simple narration, uploaded them to Magisto, made the necessary choices, and waited a few minutes to see what came back.  To see the available range, I made two different versions, one with a storyteller theme and the other – why not? – for a fiesta. The music choices were for songs I didn't recognize, but that only confirms how long ago I stopped listening to current music. You can see the results here: storyteller and fiesta.

Obviously Magisto wasn't smart enough to recognize that one video was recorded sideways or that one clip was a retake of another clip.  But for something that took almost zero effort, it's not bad.  If I'd wanted to pay $9.99 per month for a business subscription, I could have made a longer video, reordered the clips (within some limits), and even added captions.  For more on creating a business video, see Magisto's own video on the subject.

What about that promise of making videos that elicit emotions?  Well, I'm not so sure.  The music sets a mood but that doesn't seem like enough to drive anyone to tears or cheers.  On the other hand, the results were much better than plenty of home-grown videos I've seen, and Magisto certainly knows a cute pet when it sees one.  So don't fire your agency quite yet.  But for your own amusement, Magisto is worth a try.

Salesforce Purchases Deep Learning Artificial Intelligence Vendor MetaMind: Yeah, That's a MarTech Trend

It’s worth a brief note to record that Salesforce.com purchased artificial intelligence vendor MetaMind on Monday. There aren’t many details available: in the announcement, MetaMind founder Richard Socher said Salesforce will use its technology to "automate and personalize customer support, marketing automation, and many other business processes." In a way, that vagueness is exactly what’s most interesting about the deal: it supports the notion that AI will be embedded in many system features rather than limited to a handful of specific tasks.

This vision of pervasive AI is how I personally expect the industry to develop. The cumulative impact will be to make all aspects of marketing more effective as treatments are tailored more precisely to individual customers and contexts. You can also see this as making marketers more productive in the sense of letting them generate more individualized customer treatments per work hour. Those benefits are two sides of the same coin.

Regarding MetaMind itself: I never explored the system in detail but the Web site shows it could do both visual and text analysis. That’s intriguing because those tasks were traditionally handled by highly specialized systems using very different techniques.  But general purpose "deep learning" systems that can be tuned for multiple uses are becoming more common, so MetaMind serves as an example of industry trends rather than a fabulous exception. This flexibility makes it a good choice for Salesforce to use as a foundation for all sorts of AI-based enhancements to its products. It’s safe to assume that other major platform vendors will follow a similar path.

One possible implication to consider is whether pervasive AI could serve as a catalyst for the long-expected martech industry consolidation. The argument would be that a general purpose AI engine allows enhancements across many different marketing functions, so there is scale economy for vendors who can use one AI tool. Presumably there would also be some marketing effectiveness/productivity benefit from having a single AI engine that could share its intelligence across different applications, rather than having each function develop insights independently. I’m by no means convinced this is truly what will happen but it’s something to think about.

Hive9 Marketing Performance Management Includes Customer Journey Optimization

As I mentioned in last week's post on the MarTech Conference, there appears to be an emerging class of vendors doing what might be called “journey management” – although I think I’ll rename that “journey orchestration” since (a) orchestration is a trendier term right now and (b) orchestration more accurately reflects the key notion of a system that coordinates other systems.*  This coordination includes both gathering data from multiple sources and sending messages through other systems. Sending messages distinguishes journey orchestration engines from “pure” Customer Data Platforms, which assemble data but don’t make decisions about customer treatments. Some not-so-pure CDPs do combine the data assembly and decisioning, but they don’t use a system-assembled customer journey as the framework for message selection.

Whoa.  What the heck does that last sentence mean?  Let me unpack it a bit:

- By “system-assembled customer journey” I mean the systems automatically derive a customer journey from the customer data they’ve assembled. That’s quite different from pre-defining an ideal customer journey and trying to force customers to follow it. It’s even more different from taking conventional multi-step campaigns and calling them "journeys”.  A true "system-assembled journey" would be built by examining the sequence of events for each customer and finding the most common paths to purchase. This still isn't a purely objective process because some human or machine judgement is still needed to exclude irrelevant details, assign interactions to journey stages, and select the most important sequences.  But it's much more data-driven than starting with an marketer-created design.

- By “journey as the framework for message selection” I mean that marketing messages or campaigns are triggered when customers reach a particular journey step. Again, this is different from defining selection rules separately for each campaign, which is how conventional marketing automation and real-time interaction systems work. It’s also different from systems that draw journey maps but don’t connect them with campaigns for execution. Attaching all campaigns to a single journey map simplifies creation of selection rules and provides greater visibility into relationships among campaigns. In other words, journey orchestration makes it easier to coordinate customer treatments across multiple campaigns, which is one of the key problems with conventional marketing automation and interaction management approaches.**

Ok, let’s assume you’re now convinced that “journey orchestration engine” has a specific meaning that describes something useful. Your next question, presumably, is where can I buy one? (Oh, you’re not that easy to sell? Listen closely: It’s new. It’s bright. It’s shiny. New. Bright. Shiny. Newbrightshiny. Now are you ready to buy? I thought so.) My blog post listed three vendors from the MarTech show: Pointillist, Usermind, and Thunderhead. I promise I’ll review those soon. But I had already spoken with another relevant vendor before the show, Hive9. So let’s start with them.

If you look at Hive9’s Web site, you may wonder whether I’ve sent you to the right place.  They position themselves as “marketing performance management” with no mention of anything resembling journey orchestration. That’s because Hive9 actually has three connected modules: one for marketing planning, one for marketing measurement, and one for optimization (which is what I’m calling journey orchestration). These were all developed within B2B marketing agency Bulldog Solutions, which spun off Hive9 about a year ago.

The planning module was the original product. It lets marketers set up a hierarchy with plans at the top, going down to programs, campaigns, and tactics. Tactics have owners, budgets, start and end dates, revenue targets, types (usually a channel or asset) and other attributes such as journey stage, audience, business unit, geography, and language. These can be tailored to each client. Tactics can be tied to Workfront for project management, filtered on pretty much any attribute, and displayed on a Gantt chart-style calendar. Integration with Oracle Eloqua and Salesforce.com lets a new tactic automatically create a corresponding campaign in either system.  Each plan can have a marketing funnel with its own set of stages and targets for conversion rates, velocity, and deal size.

The measurement module reads information from plans and imports revenue, accounts, opportunities, and contacts from CRM, marketing automation, and other systems. Standard integrations are available for Salesforce.com, Oracle Eloqua, Marketo, Google Analytics, Adobe Marketing Analytics, and other systems. Other sources can be integrated through API connections or flat file imports. The system relies primarily on customer identifiers provided by source systems although it can stitch together identities when different systems share some IDs.

Once the data is loaded, the measurement module provides dashboards and other reports to show marketing results including revenue impact; counts, conversion rates and velocity by funnel stage; and whatever other data the client has integrated, such as social sentiment or customer satisfaction. Revenue impact can be measured with first-touch, last-touch, evenly-weighted, position-based, and several other algorithms.  The vendor plans to add statistically inferred weights in April. Results can be filtered by plan, audience, tactic type, assets, or other attributes; compared across time periods; and examined for trends. Dashboards are customized by the vendor for each client, although Hive9 plans to add self-service capabilities in the future.

The optimization module is where journey orchestration happens. Journey stages are defined within the optimization module.  Tactics can be tagged directly with stages, or stages can be assigned to assets which are themselves assigned to tactics.  Events or assets managed in other systems can also be tagged with a journey stage and channel.  However the connection is made, campaign responses are tagged with channel and journey stage and then assembled into a journey map. The map shows the number of interactions by channel and stage and highlights the most common path taken by buyers. This isn’t fully automated journey mapping because the stages are preassigned by the marketer. But the system does discover the most popular paths and most effective marketing assets on its own. So that’s pretty close.

Even more important, the optimization module can contain rules that trigger external marketing campaigns when a customer enters a given journey stage. This is what really qualifies Hive9 as a journey optimization engine. Here's how it works: users can set up a rule tied to journey stage, product type, or customer attribute such as industry or persona. Customers who qualify for a rule can be sent to specified marketing automation campaign. Rules can avoid repeating messages to the same person and can select a “next best message” for the external system to deliver. This definitely qualifies as journey orchestration.

Hive9 pricing starts around $25,000 per year for mid-market clients.  Modules are priced separately. Fees are based on the size of the company marketing budget for the planning module, on the number of records, dashboards, and data sources for the the measurement module, and on the number of touchpoints for the optimization module.

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* Also, this lets me call systems that do this “journey optimization engines”, giving a three letter acronym of JOE, which is so darn cute.

** You may notice that “journey optimization” sounds a lot like what I’ve previously called “state-based marketing”. Both do select marketing treatments based on a customer’s location within a state/stage framework. If I had to draw distinction, I’d say that journeys suggest forward progression from one stage to the next, while movement among states is not necessarily linear. Similarly, journey orchestration engines send marketing messages through other systems, while state-based systems could use internal or external delivery functions. In other words, journey orchestration is a special type of state-based system.

Open Letter to Scott Brinker: Suggestions for Next MarTech Conference

Dear Scott:

Congratulations to you and Third Door Media on another great MarTech conference. (And, as an aside, I’m astounded that you found time to write a blog post on the Stackies within a day of the conference ending. Do you NEVER sleep?)

I think I’m still technically on the advisory board for this conference, so I thought I’d share some thoughts. To encourage input from the larger community, I’m posting it publicly on this blog. You’re welcome.

Over-all, the conference went tremendously well. Attendees I spoke with were uniformly pleased with the quality of the presentations. The closest thing I heard to a criticism was that some sessions were more theoretical than action-oriented, but that is really a matter of taste. One person told me he liked the case studies best; no surprise there. Someone else said they were surprised at how many had a B2C focus, although that was more an observation than a complaint. The only frustration I heard consistently was having to choose between two interesting sessions when they were on at the same time.

From my own perspective, the worst thing was that no one laughed at the cave man joke.  Unga bunga bink! I did feel the exhibit hall was more crowded than optimal, although some vendors seemed to like being able to grab attendees as they walked by.  I’ll also complain that many vendors lacked signage that explained what they did – not your fault, of course, and maybe a conscious strategy to force people to engage?   Perhaps we could have one of the automated content generation vendors read all the vendor materials and write optimal signs for them!

On to suggestions for next year. I expect you’ll go to even more tracks, which will make it still harder to choose which to attend. Video recordings would let people catch up on sessions they missed; if that’s too expensive, voice recordings to accompany the slides would be a big help.

Maybe a better delineation among the tracks themselves would help too. I’d love to see a track of sessions analyzing product groups within the big landscape: i.e., one for marketing automation, one for content marketing, one for marketing analytics, etc. That would also be a good way to segregate analysts like me from people who want to avoid them. You might also have separate technology and organization tracks and maybe even have an ad tech track (a very underrepresented topic this year). If things get really big, you could also split B2B vs B2C or enterprise vs. SMB. The idea would be to find categories that are more or less mutually exclusive in terms of their audience, although of course some cross-over would be expected and a Good Thing.

Another way to help people with narrow interests would be to have special interest groups, such as ‘birds of feather’ tables at lunch or open-mike roundtables during sessions. I could see tables for CMOs, CMTOs, CTOs, marketing ops, newbies, and other peer groups with their own sets of challenges.  Or maybe separate bar set-ups during the receptions.

Speaking of social interaction, I still think the conference could do more to help people have fun. I’ve suggested this before but would still like to see 30 second videos submitted by attendees on topics like “tall tales I've heard from vendors”, “organizational horror stories”, “if people said what they really thought” (video of a typical meeting with thought bubbles showing people's real thoughts), or pitches for an imaginary, absurd martech product. People could do these in advance or have an opportunity to record them during the show. You’d show the best ones in between speeches or on a loop in the exhibit hall. I suspect there are some really hilarious MarTech folks out there. (Heck, I know there are – I’ve seen some of their project plans.)

I could also see some more conventional competitions in the exhibit hall. These would be team sports, so you could have marketing vs. IT, big companies vs. little companies, vendors vs buyers, etc. Or maybe mixed teams to practice their alignment skills. Picking a sport is tough: foosball is obvious but might give an unfair advantage to the IT folks. We could balance that with something that marketers are especially good at – maybe darts, which I believe are still the standard tool for setting media budgets. Just a thought.

There’s always Powerpoint karoke, although that’s best for after hours.

Or let’s get really high tech. We could have a speech recognition vendor listen to the presentations and score them for buzzword bingo. Or, at least, do a word cloud of what people are saying, either in speeches or in Twitter comments. We could have a competition among speakers for the most Tweets (although I guess that already happens).

Back to team sports. How about teams of attendees competing to create the most successful marketing campaign during the conference? We’d have vendors create preintegrated stacks with tools for research, content creation, campaign execution, optimization, and analytics. The vendors and team members would design, execute and optimize their campaigns, which shouldn’t take much time if the systems are really efficient. Two days is enough time to get some initial results. The campaigns could be for imaginary products or, better still, for worthy charities. Attendees could form their own teams or we could let something like CrystalKnows profile them and then assign attendees to teams with a good balance of skills and personalities.

If that’s too complicated, we could do a standard bake-off competition where vendors and novice users are given a simple task (e.g., create an survey or dynamic email) and do it while everybody else watches and then votes on the results.

Looking forward to next year!

Best,

David

MarTech Madness: Marketing Technology Managers Come Out to Play in San Francisco

MarTech Madness hit this week in San Francisco as 1,200 delighted marketing technologists escaped from their cubicles to cavort under the genial gaze of @chiefmartec Scott Brinker at the latest MarTech Conference. My own experience was a bit skewed because I was pulled into many side meetings, but the attendees seemed pleased and the sessions I did attend were excellent as always. MarTech is unique in its focus on the process of marketing technology management, with sessions covering organizational, staffing, and training issues even more than the technology itself. It’s also unusual in attracting people from both B2B with B2C companies, two groups that rarely mix.

As usual, I spent much of my conference time prowling the exhibition booths. I was struck by the number of content-related exhibitors – nearly half, based on my analysis of the conference program. Many of those were doing standard content management tasks such as workflow, approvals, and repository management.  But several offered some kind of interactive content, which I’d define as content that captures information about the user either through recording behaviors or explicitly asking for input (HapYak, ion interactive, LookBook HQ, SnapApp, and arguably Uberflipand Vidyard). This is a category that also caught my eye at the Content2Conversion conference last month.  Two mentions officially makes it a trend.

There were also several vendors doing what I’ll tentatively call “journey management” (Pointillist, Usermind, Thunderhead [represented at the conference by its partner Arke], and arguably IBM Journey Designer). These resemble conventional campaign managers but execute campaigns based on movement through a comprehensive journey map. These products differ from the earlier generation of journey managers in being tied to live customer data and execution systems, rather than simply drawing a map. There’s considerable variety among these vendors so I need to analyze them more closely before I can confidently call them a meaningful category. But seeing several products emerge simultaneously with similar features is usually a sign that something interesting is afoot.

As these observations suggest, many of my conversations during the conference – especially after the bars were open – related to the ever-popular game of What’s The Next Big Thing? The spotlight has clearly moved from predictive analytics.  ABM is still the current focus but it’s starting to feel dangerously familiar. Interactive content and journey management are definitely candidates, but the consensus at the deepest (and best lubricated) discussion seemed to be what you might call user-tuned content* – that is, content that adjusts to the viewer’s behavior, presumably using artificial intelligence to choose what to do next. That’s different from capturing behaviors with interactive content, as described above, although the same system could do both: in fact, I'd put LookBook HQ in both categories.

"User-tuned content" appeals to me in part because my own MarTech presentation was on machine intelligence, and one strand of development I foresee is combining systems that recognize an individual customer and select the best product or offer (e.g. Evergage or BlueConic); identify the customer's persona (e.g. CrystalKnows, Mariana, CaliberUX); and automatically write data-driven content tuned to that persona (Automated Insights, Narrative Science, Data2Content, and Arria). Concretely, imagine an ecommerce Web site that gives each customer different product descriptions based on her personality. That’s a step beyond current personalization or recommendation engines, which select the best message or product but don’t change how it's presented or use manually-created variations.  It’s also a step closer to what a good human salesperson does, tailoring their presentation to whatever they feel will best resonate with the person they’re talking to.

The pieces needed to deliver user-tuned content are all available, although I haven’t seen any one vendor combine them into a single product. Maybe that won’t happen soon enough to the very next Big Thing, but I’m pretty sure it will be a Big Thing in the not too distant future.

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*I really wanted to call this "smart content", but that's used by HubSpot for something else.  "Intelligent content", "responsive content", "contextual content", "dynamic content", "personalized content", "tailored content" and many others are also taken.  "User-tuned content" has been used in pretty much the sense I have in mind, although specifically relating to content tuned to the needs of autistic children.

Mariana Uses Artificial Intelligence to Build Personas and Find Target Audiences

Can computers understand an individual human’s personality (and then, presumably, use that understanding to better target marketing messages)? It turns out that’s no longer even a question: if you haven’t yet played with CrystalKnows, be prepared for some weirdly accurate insights into yourself and those you know well, based on public Internet information. And, yes, Crystal advises you how to interact with others based on those insights, going so far as to suggest changes to your emails to better fit the style of the recipient. If there’s a gap between this and letting a computer just manage the whole relationship without any human involvement, it’s almost too small to worry about.

So the real challenge isn’t having the computer analyze your data; it’s having enough data for the computer to analyze. People like me, who blog and Tweet incessantly, are easy enough to understand (or, at least, it’s easy to describe our carefully curated public personas). But many of your marketing targets are less visible. Finding enough of them to be useful is a major limitation for systems that rely on machine intelligence to help target marketing messages.

This is where Mariana comes in. The whiz-bang part of its pitch is using artificial intelligence (“deep learning” as in the Mariana Trench – get it?) to build personas by analyzing a sample of your existing customers. Users see attributes for each persona such as interests, titles, functions, tenure, and average deal size. Pretty cool, I must say.

But the really special part is what comes next.  Mariana finds other people who match the personas, using its deep understanding of social data to accurately identify and find contact information on larger numbers of relevant people than other vendors.

How many more? Mariana told me that while other vendors might find social profiles on less than 30% of the records on a B2B email list, it regularly finds data on 50% to 80%. The difference is that Mariana uses its artificial intelligence to analyze connections between people – including unique access to the Twitter social graph, as well as where people work or have worked, groups they belong to, and the types of work they do. This helps it pick the right person when several people share the same name and lets it build detailed, accurate profiles ncluding Twitter and Facebook handles, employer, job function, and interests.  Of course, it can also assign each person to the correct persona for each client.

What does Mariana do with this information?  It currently offers two products: Prospect IQ to find new prospects and Conversion IQ to market to names already in the client's database.  Both products start by uploading a training set of target accounts or closed and won opportunities and contacts from CRM. The system then does its magic to look up the companies and individuals in that set, find data about them, and built personas based on common clusters of attributes.  Clients can build one set of personas for their entire customer base or create separate personas within segments based on company size or industry.  You can call this Account Based Marketing if you like; Mariana does.

For Prospect IQ campaigns, users can define a target audience based industry, company size, location, and persona.  The system then returns contact counts for Facebook, Twitter, and email. Users create the marketing messages, helped by Mariana’s suggestions for which topics would interest each persona. They then choose which personas get which messages, set a budget and timeframe for each channel, and let Mariana execute the campaign.  The system will send message to the same person in multiple channels when possible and can run automated a/b creative tests to optimize results. Responses go to a user-specified landing page; Mariana does not reveal prospects’ identities unless they respond.

Conversion IQ follows a similar process except that Mariana works from a set of known contacts uploaded by the client. These are cleaned, enhanced with company and individual data, and assigned personas. Users then define campaigns and have Mariana execute them. Mariana has prebuilt integrations with Twitter, Facebook, Salesforce.com CRM, Pardot, Eloqua, and Marketo.

Pricing for Mariana is based on the number of campaign responses. It starts at $3,000 per month for a three month pilot, which includes the cost of advertising media at specified CPMs. The company was started in 2013 and officially released its product on March 1 of this year. It reports that pilot projects during the test stage yielded ten times the conversion rate of conventionally targeted ads.

ICON16: How Infusionsoft Plans To Dominate Small Business Marketing (and Make Life Better For Small Businesses Everywhere)

I spent part of last week at ICON16, Infusionsoft’s annual combination of customer conference, revival meeting, and group therapy session for small business owners. The company made a few announcements, most notably a vastly improved email builder and tighter integration with online accounting software from Quickbooks Online and Xero. These changes were evolutionary at best, but company leaders made clear this was on purpose: their current strategy is to make small improvements to ease of use, not add major new or deeper features. This is based on Infusionsoft’s learning over the past ten years that the main barriers to success with its software are small businesses not knowing what to do with it and not seeing immediate value from the efforts. The real focus of the conference was introducing the latest version of Infusionsoft’s Small Business Success Method, an approach to helping small businesses plan their marketing programs. Infusionsoft’s goal is to bake as much as possible of the methodology into the software, its partner ecosystem, and prebuilt assets such as campaign plans.

This all makes perfect sense and is wholly consistent with Infusionsoft’s historical focus on helping small business grow. Although the company’s growth slowed a bit in 2015 (customers up 30% to 35,000; revenue up 25% to $100 million) CEO Clate Mask said the management has recently committed to a goal of five million customers by 2030. This is certainly audacious – it implies growing 40% per year for 15 years in a business that is highly prone to disruption – but big long-term goals are part of Infusionsoft’s culture. Whether it’s realistic is another question.  Some companies serving small business have indeed reached multi-million client counts (see table), but their products cost much less and are essential for basic operations.** Infusionsoft’s challenge is to convince a large fraction* small business owners that their product is also essential.

company	services	customers (2015)	revenue (2015)	revenue / customer
GoDaddy	domain registration, Web site hosting	13.8 million	$1.6 billion	$116
Endurance International (without Constant Contact)	domain registration, Web site hosting	4.7 million	$747 million	$159
Constant Contact	small business email	650,000	$367 million	$564
Intuit Quickbooks	small business accounting	4-5 million	$2.1 billion	$420 - $520
Infusionsoft	small business integrated sales and marketing software	35,000	$100 million	$2,857
HubSpot	integrated marketing and CRM software	18,100	$181 million	$10,000

Infusionsoft managers recognize that expanding the customer base requires making marketing easier. This is a major reason for developing the Small Business Success Method and baking it into the software. Product managers described a plausible glide path from having the system identify opportunities to having it suggest marketing programs to having it execute those suggestions autonomously. Machine intelligence would play a key role at every step of this evolution. Although such work is in early stages at Infusionsoft, the product is being engineered to allow insertion of automated marketing features as these become available. The company seems to be giving lower priority to a more flexible database, which will also be essential to handling future needs such as unstructured data. It is already behind small business oriented competitors that permit custom data tables, including HubSpot and Ontraport.  (Infusionsoft does allow custom data fields in its standard tables.)

I still think Infusionsoft could be displaced by an aggressive, well-funded competitor, especially as marketing technologies and methods continue to change.. Infusionsoft argues that their partner network is a competitive barrier that would be hard for another software vendor to overcome. This is true to some extent but Infusionsoft doesn’t dominate among marketing agencies in the way that Intuit Quickbooks dominates among accountants. Nor are many of the tiniest businesses ever going to use a marketing agency.  Similarly, Infusionsoft's existing customer base provides invaluable data to help machine intelligence systems make recommendations, but other firms could use data from fewer customers in similar ways.  Infusionsoft also argues, I think correctly, that very small businesses are harder to serve than even slightly larger ones, because the smallest business owners have so many other priorities and often so little interest in marketing. This makes channel partners, methodologies, and client support even more important – making Infusionsoft’s head start in experience, methodology, and partner network harder to overcome.

Of course, the only people who really need to worry about Infusionsoft’s business prospects are its investors and employees, although channel partners and clients do have some stake in the results. So let’s move on to the fun stuff, which is technology.

As usual at these conferences, I spent a good chunk of my time cruising the exhibition floor for interesting new vendors. One booth was staffed by attractive young ladies in dark glasses and tight-fitting police uniforms, which literally gave me nightmares. Adding insult to injury, their company provided small business financing, which isn’t even of interest to me.

Among the more relevant firms, I noted a high concentration of reporting tools, including freshlime, hipdash, cloudlink, graphly, and Wicked Reports. These address a recognized gap in Infusionsoft’s own reporting, particularly regarding visualization, custom reports, and integration of external data. Of these, I found WickedReports the most interesting: they use customer tracking to do multi-channel lead attribution and customer value analysis. According to the person I spoke with – who was fortunately not wearing a police uniform – they actually track all contacts with each customer, meaning they have the data to assign fractional credit to each touch using advanced statistical methods.  But they don’t expose that data, having judged that Infusionsoft customers aren’t ready for anything so sophisticated.  I’m pretty sure they’re right.

Two other vendors also caught my eye. ThinkingChat promised "artificial intelligence lead capture agents"  engage site visitors and capture contact information. I was a bit disappointed to find that what they really do is scan for keywords in the chat inquiries, and then provide fixed responses. That's clever but a very low grade of AI at best.  Keywords alone have major limits – “price is too high” “price is a bargain” or “what’s your price” would all trigger the same reply if the system simply looks for the word “price”. ThinkingChat adds a bit more flexibility by letting users use a keyword to direct the dialog to different collections of keywords and answers, for example to give prices for different products if a product name is mentioned first.It will also recognize when a visitor is having a problem and admit it can’t answer a question, although the user must manually review the failed dialogs to make refinements. On the other hand, ThinkingChat reports that it more than doubles the rate of capturing contact information on their clients’ Web sites, which is a pretty good deal for as little as $149 per month. So it's worth a look.

My favorite product by far was DilogR, which provides interactive content including assessments, quizzes, and surveys; dynamic videos that let users choose which sections to view; and interactive images.  This puts them in competition with firms like SnapApp, ion interactive, Survey Monkey, and Brightcove but at a fraction of the cost: DilogR plans start as low as $97 per month.  I think interactive content is an extremely important tool for engaging prospects, but have been frustrated at the time and cost it has taken my own clients to deploy it. DialogR could open this up to many more marketers, providing value for their businesses and customers alike.  I'm glad I found it.
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* There are just over 5 million small businesses in the U.S. with employees plus about 15 million companies with no employees and at least $10,000 revenue.  The total market is larger because Infusionsoft can also sell to companies outside the U.S..

** To put this in perspective: five million clients at $3,000 per client would give Infusionsoft $15 billion revenue, ranking it just behind SAP as the worlds fifth-largest software company and nearly three times Salesforce.com's 2016 revenue of $6.67 billion.***  One possible inference is that Infusionsoft would need a much lower-priced offering to reach that many customers.  Company managers did seem to be giving this some thought but it would conflict with their strong position that only business owners willing to invest serious time and effort in marketing would succeed with their product.

***Note also that Salesforce.com is just two years older than Infusionsoft; by the time it had reached Infusionsoft's current age, its revenue was already $4 billion.  Even if you count from Infusionsoft's first venture capital funding in 2007, Salesforce.com had $500 million revenue at the same age.  In other words, Infusionsoft is growing much more slowly than Salesforce.com.  That's not surprising: small businesses are slow adopters and Infusionsoft faces more competition than Salesforce ever did.  But it's hard to imagine Infusionsoft ever matching Salesforce.com's growth rate.

Azalead Account Based Marketing Tracks Web Site Visitors, Orchestrates Outbound Messages, and Reports on Results

Account Based Marketing. Perhaps you’ve heard of it?

Okay, just kidding: ABM gets just slightly less attention than Donald Trump and arguably generates a similar amount of confusion. Many of the industry vendors are addressing that problem (the confusion, not Trump) by working together in the Account Based Marketing Consortium which, among other things, has published an excellent survey and nifty six-level functional framework. You can download them both here.

The purpose of the framework is to help marketers understand the differences between ABM vendors. Let’s apply it to ABM Consortium member Azalead, a Paris-based firm that is planning to enter the U.S. market.

We’ll start with an overview of what Azalead does. It lets marketers create lists of target accounts by identifying Web site visitors (at the company level) based on IP address, by reading data through a CRM system integration, or by uploading lists any source. Users can export account lists for retargeting, connect via API with the company Web site to personalize messages shown to visitors from target firms, or send lists to CRM. Azalead tags can be embedded in Web pages or emails to track response. Opportunities and revenues can also be imported from CRM. The system reports on impressions (i.e., messages sent), responses, opportunities and pipeline value for targeted accounts and gives salespeople lists of all identified Web site visitors or of visits from target accounts.  It can also rank accounts with a system-generated engagement score.

Now that you have a more or less coherent view of Azalead functions, we can map these to the Consortium framework.

Account Selection: users can select accounts from the list of identified Web site visitors, from the list of accounts imported from CRM, or from any other list uploaded to the system. There is no predictive scoring although users can build lists by filtering on whatever attributes have been attached to the records, such as industry, country, or company size. Azalead has created its own technology to identify site visitors whose IP address cannot be tied directly to a specific company. Results vary, but in Europe this can increase the percentage of identified visitors from the usual 30% to as high as 50%..

Insights: once a company has been identified, Azalead can present users – typically sales people – with a screen that shows the company name, basic information (industry, revenues, etc.) from an external database, engagement history as captured by Azalead’s Web and email tags, and contact names from the CRM system. 

Content: Azalead doesn’t create content.

Orchestration: Azalead can create lists based on engagement level, opportunity stage (imported from CRM), and other account attributes.  Different lists can be selected for different marketing treatments.

Delivery: Azalead tags on a company Web site can both identify visitors and return basic parameters including company name, industry and size. These parameters can drive personalized Web site messages. Messages in other channels are generated by sending lists to marketing automation, CRM, retargeting, or other external systems.

Measurement: Azalead offers a summary dashboard and more detailed information on retargeting, display ads, and Web site visitors. A pipeline impact report plots sales opportunity probability against marketing engagement for individual deals, helping marketers to see the impact of their efforts.

So, what information is missing here? The framework doesn’t explicitly cover integration, arguably because that’s a supporting technology rather than a functional capability. Azalead has API-level integration with Salesforce.com and Microsoft Dynamics CRM. A general purpose API allows custom integration with Web sites and other systems. The company is working on API integration with major marketing automation platforms, but currently uses its own tags to track response to emails sent by those systems.

The framework also doesn’t including pricing or vendor background, which are also not functional capabilities.  Azalead pricing is published on its Web site. A limited system starts at $1,000 per month for a 3,000 monthly Web visits and 400,000 ad impressions. A system with all features starts at $3,200 per month for 20,000 Web visits and 1.4 million impressions. The company was founded in 2013 and currently has about 80 customers, mostly tech companies in Europe. It plans to open a New York office later this year.

Why Designing Your Marketing Technology Stack is a Waste of Time

My post last week about machine intelligence sparked a Twitter comment from @Jetlore, “The term 'machine learning' is like the term 'mobile' 7-10 years ago. It's simply something that all good software will do.” On reflection, this is absolutely correct – it is why there are already so many different uses of machine learning across the marketing landscape. This got me thinking about whether we could learn something from other technologies that were once bleeding edge but later became commonplace.

The most obvious of those is electricity. Nicholas Carr has explored the analogy between electric power utilities and information technology utilities in his books, but I haven’t (yet) read them. Instead I did a bit of my own research into the early twentieth century transition from steam to electric power in factories, eventually finding the key information in a paper From Shafts to Wires: Historical Perspectives on Electrification by Warren D. Devine, Jr., (The Journal of Economic History, June 1983).


The story turned out to be quite interesting, at least to me. In 1898 electric motors provided less than 5% of factory power.  By 1929 they provided nearly 80%. That 30 year span means a generation of managers spent their entire careers dealing with the shift. More precisely, they dealt with many shifts: the first electric motors simply drove the same overhead shafts that had previously been powered by steam engines or water wheels (leather belts transferred power from the shafts to individual machines). Then the motors drove numerous small shafts instead of one big shaft; then separate motors were attached to individual machines; finally, the machines themselves were redesigned to take advantage of having a motor of their own. Once the machines had been optimized for electric motors and factories had been redesigned to make the best use of this new configuration, the pace of change slowed down.

The analogy with machine learning and with marketing technology in general is clear. Initial applications fit the new technology into the old process: that’s why I love this picture of a robot secretary, which was someone’s initial (presumably joking) idea of how computers could replace human secretaries.* Applications then evolve into something completely different as people uncover the best ways to use the new technology. Those changes create other changes in related systems: getting rid of the overhead power shafts let factories become bigger and more efficient because machines could be placed anywhere and the ceiling was now free for better lights. ventilation, and overhead cranes. One article quoted Henry Ford as saying that his moving assembly line would have been impossible without electrification.

The obvious lesson is that marketers should also expect continued flux as new technologies are invented and refined. But while the need to plan for such change is a commonplace among industry gurus, myself included, I haven’t seen much attention paid to the less-obvious conflict between planning for change and standard approach of defining requirements, designing an architecture to meet those requirements, and then buying components to flesh out that architecture. Just as the physical architecture of factories changed as electric motors were deployed in different and more effective ways, the architecture of marketing systems can be expected to change as the technologies mature.

This means managers need tools designed to deal with continuous change.  These include systematic ways to decide when to adopt a new technology and when to wait for further improvements, and ways to ensure that a technology you adopt doesn’t prevent you from taking advantage of future technology that is more important. Early twentieth century managers invented industrial engineering, standardized fittings, and return on investment analysis for precisely those reasons.  Todays’ marketing technology managers need similar tools but I don’t hear much discussion about how to create them.

The second less-obvious point, although I guess we can credit it to Carr, is that the reward for successfully managing these continuous changes is nothing more than survival. Like today’s marketing technology, electric motors were purchased from outside suppliers who made the same equipment available to everyone. Sound choices were essential and making the wrong choice could be fatal (literally, where electricity was involved). But being a smart, fast follower was good enough; being a pioneer or master user of the new technology didn't ultimately matter because your surviving competitors ended up with similar tools. The final success of firms depended on the quality of their products, distribution, and, yes, marketing, not in whether they used electric motors. This meant that electricians, who at one point were considered super-elite if not magical, ultimately became nothing more than valued but prosaic craftsmen. I suppose that will be the fate of marketing technologists as well: today we are river pilots navigating a wild rapids, an exhilarating task with life-or-death responsibility. But at some point we’ll reach calmer waters, and then we’ll seem, and be, less important.

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* On the other hand, conspiracy theorists: why does the robot in this picture have a reflection while the lady does not?  Perhaps she is the true alien, sent to infiltrate our planet with her diabolically irresistible technology.

BlueConic Launches Marketing Technology Self-Assessment Tool

It's a little more than a year since I collaborated with BlueConic on a marketing technology maturity model.  They've been busy improving their product, in particular by adding a set of templates for prebuilt marketing programs, which they call "blueprints".  Users first select a goal, such as "decrease bounce rates".  They are then led through a sequence of tasks to collect the necessary customer data; assemble the data into profiles; segment customers using the profiles; and deliver the messages to external systems.  The goal is to make it easier for marketers to create useful programs with the system.

But BlueConic was also working on a little side project: an online self-assessment tool based on the maturity model.  This asks users about a dozen questions about their marketing methods, business processes, and organizational resources.  It then provides an assessment of how they compare with other companies and makes recommendations for how to make improvements.  I provided much of the content, so obviously I'm biased, but I do think the results are pretty interesting and useful.  And it's free...did I mention free?  You can read more about the tool in this BlueConic blog post and access it here.  Enjoy!

Study: Half of Marketing Jobs Will Be Replaced by Machine Intelligence

One of the highlights of last week’s Content2Conversion conference was a keynote by the always-stimulating Tim Riesterer of Corporate Visions, who argued that an effective sales presentation should (1) start with an unfamiliar factoid that shows why change is essential (a concept similar to the CEB "challenger sale") (2) show that you have a solution and (3) contrast your solution with other approaches to clarify how it's different and better.  Riesterer’s own talk followed exactly that template, a bit of consistency I always admire. This in turn got me thinking about my presentation on machine learning systems at the MarTech conference in March. I pretty much finished drafting it last week, but it was still an interesting exercise to imagine recasting it along the lines Riesterer proposed.

Linear thinker that I am, this meant first looking for an appropriate factoid about why the growth of machine intelligence poses a threat that can’t be ignored. This led to several hours of research into what’s being written about machine intelligence, which I'll somewhat sheepishly admit is my idea of a good time.  A reasonable starting question was how many marketing jobs are threatened with replacement by intelligent systems.  Some quick Googling led to an article that quoted economist W. Brian Arthur as estimating that machines could replace 100 million U.S. jobs by 2025.*  That’s pretty scary but a second, even more intriguing article quoted a study by Oxford economists Carl Benedikt Frey and Michael A. Osborne that estimated the probability of 702 specific job categories being replaced by “computerisation” (British spelling). This offers the possibility of showing how much employment risk is faced by marketers in particular.

Frey and Osborne list two categories with “marketing” in their title:

• “Marketing Managers” with a 1.4% probability of computerization, and
• “Market Research Analysts and Marketing Specialists” with a 61% probability.

That’s a pretty sharp divergence but it eerily matches my observations last week about the landscape of machine intelligence systems for marketing: that virtually no systems attempt to manage marketing strategy and planning, but many systems automate supporting tasks such as trend analysis and data extraction. A similar pattern emerges in the Frey and Osborne data if you look at a broader set of job titles: managers will remain employed but many analysts, researchers, and other types of assisting jobs will go away. Looking at other business functions relevant to marketing, the study sees most types of creative jobs as relatively safe and most types of sales jobs at risk. The forecast is mixed for technology jobs, again with the more senior jobs being relatively secure but run-of-the-mill computer programmers and support specialists likely to be replaced.  Since there are nearly three times as many marketing analysts and specialists (468,000) as marketing managers (184,000), it's safe to conclude that anything from half to two-thirds of marketing jobs are at risk.

Frey and Osborne did their calculations based on the assumption that it will be hardest to computerize jobs that require manual dexterity, creativity, and social skills. These assumptions may already be obsolete – the paper was written in 2013 and this 2014 video by machine learning expert Jeremey Howard suggests that new developments in “deep learning” are making machines more powerful than anticipated, especially in areas relating to creativity and social interaction.  Frey and Osborne also conclude that management jobs are relatively safe in part because managers need social skills to motivate their staff – a need that will diminish if the staff is largely replaced by machines. So I'd say there's a good chance that all but the most senior jobs are less secure than Frey and Osborne suggest.**

That's all interesting, but what does it mean for my presentation?  Let’s go back to Riesterer’s three-part template.

• The first step was proving that change is necessary. I think showing marketers that half to two-thirds of their jobs will vanish in the next ten years should do the trick. 

• The second step was offering a solution.  I’m proposing that learning to manage intelligent machines will be the key to future success. My MarTech presentation will offer some specific suggestions on how to do that.  

• The third step was contrasting the proposed solution with other approaches. That’s easy if the alternative is to continue with traditional marketing methods. It’s a bit harder if the alternative is making other types of changes, if only because you’d have to list what those alternatives might be. I can think of a few approaches I can easily out-argue, such as random experimentation or buying new technology without addressing organizational and process issues. A more challenging competitor is to focus on optimizing the customer experience rather than use of machine intelligence. Customer experience is inherently a more appealing focus because it sounds strategic and customer-centered while machine intelligence sounds narrowly mechanical. Still, the ultimate question is which approach will give better results, and I suspect machine intelligence – by making marketers more productive and thus freeing them to do more new things – will eventually win out.  (Or not.  You could argue that machine intelligence is like electricity: vastly better than its predecessors and destined to be ubiquitous, but something that vendors will make equally available to everyone, and thus not in itself a long-term competitive advantage.)

In any event, my My MarTech presentation won't follow this template because it's already written.  But it's an interesting approach to the topic – and one I'm sure I'll have a chance to use in the future, since this is a subject that won't go away.

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*What Arthur actually said is machines in the U.S. could produce output equal to the 1995 U.S. economy, which employed 100 million people.  Close enough.  As a point of reference, the current U.S. total of jobs is around 150 million.

**To be clear, Frey and Osborne are explicit that they are NOT making predictions about whether, how quickly, or how many jobs will actually be lost.  They do somewhat casually suggest it's reasonable to expect about half of U.S. jobs will be potentially computerizable in "a decade or two" but cite many factors that could prevent computers from actually taking those jobs.

Future of Marketing Content: Reflections on the Content2Conversion Conference

I spent the early part of this week at Demand Gen Report's Content2Conversion conference. The event was superbly run, as usual, but I didn't sense any over-arching pattern until I was literally on my out the door and stopped for one last chat with some colleagues.  Then I knitted together – at least to my own satisfaction – what had seemed to be disconnected observations.

The first strand was the number of systems that offer detailed information about content consumption. Vendors including Highspot, SnapApp, Ceros, Uberflip, and ion interactive all let marketers track customer behaviors within a piece of content – such as how much time is spent on each page or even regions within a page. On reflection, it struck me as amazing that we have this level of detail available, given that just a few years ago marketers couldn’t even tell whether a given piece of content had been looked at. The uses for this information are obvious, including helping marketers to understand which topics are most appealing and giving salespeople insight into the interests of individual prospects. But I wonder how many marketers or content creators are ready to take advantage of this information. Of course, it’s clear that they should. But I suspect most are already overwhelmed by the less precise information available through less advanced technologies. This leaves them with little appetite for still greater detail.

Naturally, my own preferred solution to this technology-created flood of data is still more technology. Some of this involves advanced analytics to extract the significant needles of information from the hayfields of detail, although I don’t recall seeing vendors who do that type of analysis at the show or hearing speakers discuss them. But the more interesting response is to automate content creation and selection directly, using the detailed information to create new content and to send the most appropriate content to each individual. Again, there weren’t many solutions at the show that promised to do this, apart from Captora – which extracts keywords from a company’s Web site and its competitors’ sites, constructs draft landing pages for the most important topics, and deploys them (after some manual polishing) with links to CRM or marketing automation data capture forms. Captora is focused on paid and organic search marketing, so it can’t pick which ads to display to which prospects. But I also chatted with people from Adaptive Campaigns (which did not exhibit), whose system uses rules to generate highly customized programmatic display ads. And, on the way to the airport, I caught up with Idio, another system that automatically analyzes content and picks the best match for each individual – although Idio doesn’t do any content creation or dynamic customization.

As you know from the Machine Intelligence in Marketing Landscape in my last post, I’ve also identified a several other systems that use automated methods to generate and select content. I’ll even predict that machine generated content will be a major trend in the near future – precisely because it’s the only practical way for marketers to take full advantage of the detailed information now available on content consumption.

This connects to another theme that I did actually hear articulated at the conference: the need to move beyond “quality” content to appropriate content. That’s an interesting evolution, since recent discussions have often focused on the challenge marketers face in just getting the volume of content they need for increasingly segmented programs. That requirement hasn’t ended, but I heard more discussion of how to create the right content mix and how to create content that is compelling enough to attract attention. To some extent, this argues against the notion of machine-generated content, which will probably never be better than mediocre and formulaic. But I can easily imagine a world where humans create a few great pieces of tentpole content and use a lot of simple, machine-created messages to feed people to it.  The machine-based messages won't be brilliant but they'll be effective because they're highly tailored to their targets. This tailoring will be enabled by behavioral and intent data, which were also popular topics at the conference.

I also have one other observation, which was totally unexpected (the best type!).  It might be just my imagination, but I think I sensed a bit of overconfidence among marketers about their ability to buy new technology. This is certainly surprising, given that marketers until recently have been more frightened of technology than anything else. I’ll speculate that a new generation of marketers are more comfortable with technology in general and are now reaching positions where they have control over purchasing decisions. Mostly that's great: the industry can’t advance if marketers are afraid to try new things. But some of these buyers may not realize that they are unfamiliar with the full scope of products available or that deploying complex technology is much harder than signing up for a new software-as-a-service application. Let me be clear that this concern is is based on one conversation I had and one comment that a friend overheard.  So I might be overreacting. Still, it’s something to guard against; overconfidence can lead to cavalier decisions that are just as harmful as indecision based on fear.

Landscape of Machine Intelligence Systems for Marketing

I’ll be speaking next month at the MarTech conference on How Machine Intelligence Will Really Change Marketing. This required assembling a list of marketing systems using machine intelligence, which pretty much inevitably led to the logoscape below.

I wasn’t initially enthusiastic about the idea – could there by anything less original?—but have found the result surprisingly useful. In particular, it illustrates several points that would otherwise have been hidden or much harder to convey. These include:

• Lots of systems. You may think that machine intelligence is still a pretty rare thing. Not so. I found 23 categories with 140 systems, and know there are dozens of other products I could have included.

• Some categories are already crowded. Boxes with a lot of logos have a lot of competitors. This doesn’t make them mature in the sense of having a widely accepted standard approach. But it does mean that many people have recognized they are a successful use for machine intelligence. Conversely, categories with few competitors are more speculative – although a few strike me as pretty sure to succeed in the end.

• Few systems for marketing strategy. Some research I’ll cite at MarTech suggests that marketers split their time roughly equally between strategy and planning, program design and content creation, and data management and analytics. I’ve classified vendors into those categories. I then make a further distinction between systems that help marketers with decisions and systems that make decisions without marketer involvement. This distinction is very loose, but that’s a topic for another day.  What’s immediately obvious is there are very few systems to do strategy and planning, and none of those are actually deciders. My take on this is that CMOs aren’t ready to delegate strategic decisions to machines, although another explanation is that CEOs aren’t ready to delegate marketing strategy to the CMOs.

• Decider systems for design. The design category is crowded with systems for the established applications of personalization and programmatic ad bidding. Perhaps more surprising, there is also a rapidly growing number of products to create contents such as copy, email dialogs, and even Web pages. Nearly all of these are deciders – perhaps because they work with volumes of choices so huge that only computers can handle them. Helper systems aren’t much use in those situations.

• All kinds of systems for data. This is the most populated area, with roughly half the categories and half the total vendors. It's also the group with the most vendors I didn’t include – for example, there are probably 100 social media monitoring systems alone, most of which use at least some basic machine intelligence for language processing. This group is about evenly split between helpers and deciders, reflecting the variety and complexity of data-related tasks.  One reason this group is so large is that many of the applications, such as data extraction and predictive model building, are also used for purposes outside of marketing.

I’ll draw some other lessons from this chart in my MarTech talk. You can still join us by registering here. In the meantime, I hope this chart helps you realize the scope of machine intelligence applications in marketing today and inspires you to explore more deeply how they can help in your own work.

Marketing attribution systems: a quick look at the options

I’ve seen a lot of attribution vendors recently. If you're a regular reader here, you saw my reviews of Claritix (last week) and BrightFunnel (in December).  Last week caught up with Jeff Winsper of Black Ink, which I'll hopefully review before too long.  Bizible also popped up recently although I don’t recall the occasion; possibly something related to their interesting survey on “pipeline marketing” and attribution methods.

My rational brain knows that there’s probably no reason for this flurry of sightings beyond pure coincidence. But it’s human to see patterns where they don’t exist, so I did find myself wondering if attribution is becoming a hot topic. I can easily come up with a good story to explain it: marketing technology has reached a new maturity stage where the data needed for good attribution is now readily available, the cost of processing that data has fallen far enough to make it practical, and the need has reached a tipping point as the complexity of marketing has grown. So, clearly, 2016 will be The Year of Attribution (as Anna Bager and Joe Laszlo of the Internet Advertising Bureau have already suggested).

Or not. Sometimes random is just random. But now that this is on my mind, I've taken a look at the larger attribution landscape.  Quick searches for "attribution" on G2 Crowd and TrustRadius turned up lists of 29 and 17 vendors, respectively – neither including Brightfunnel or Claritix, incidentally.  A closer look found that 13 appeared on both sites, that each site listed several relevant vendors that the other missed, and that both sites listed multiple vendors that were not really relevant. For what it's worth, eight vendors of the 13 vendors listed on both sites were all bona fide attribution systems -- which I loosely define to mean they assign fractions of revenue to different marketing campaigns.  I wouldn't draw any grand conclusions from the differences in coverage on G2 Crowd and TrustRadius, except to offer the obvious advice to check both (and probably some of the other review sites or vendor landscapes) to assemble a reasonably complete set of options.

I've presented the vendors listed in the two review sites below, grouping them based on which site included them and whether I qualified them as relevant to a quest for an attribution vendor.  I've also added a few notes based on the closer look I took at each system in order to classify it.  The main questions I asked were:

• Does the system capture individual-level data, not just results by channel or campaign?  You need the individual data to know who saw which messages and who ended up making a purchase.  Those are the raw inputs needed for any attempt at estimating the impact of individual messages on the final result.  

• Does the system capture offline as well as online messages?  You need both to understand all influences on results.  This question disqualified a few vendors that look only at online interactions.  In practice, most vendors can incorporate whatever data you provide them, so if you have offline data, they can use it.  TV is a special case because marketers don't usually know whether a specific individual saw a particular TV message, so TV is incorporated into attribution models using more general correlations.

• How does the vendor do the attribution calculations?  Nearly all the vendors use what I've labeled an "algorithmic" approach, meaning they perform some sort of statistical analysis to estimate the attributed values.  The main alternative is a "fractional" method that applies user-assigned weights, typically based on position in the buying sequence and/or the channel that delivered the message.  The algorithmic approach is certainly preferred by most marketers, since it is based in actual data rather than marketers' (often inaccurate) assumptions.  But algorithmic methods need a lot of data, so B2B marketers often use fractional methods as a more practical alternative.  It's no accident that the only B2B specialist listed here, Bizible, is the only company that uses a fractional method, as do B2B specialists BrightFunnel and Claritix.  It's also important to note that the technical details of the algorithmic methods differ greatly from vendor to vendor, and of course each vendor is convinced that their method is by far the best approach.

• Does the vendor provide marketing mix models?  These resemble attribution except they work at the channel level and are not based on individual data.  Classic marketing mix models instead look at promotion expense by channel by market (usually a geographic region, sometimes a demographic or other segment) and find correlations over time between spending levels and sales.  Although mix models and algorithmic attribution use different techniques and data, several vendors do both and have connected them in some fashion.

• Does the vendor create optimal media plans? I'm defining these broadly to include any type of recommendation that uses the attribution model to suggest how users should reallocate their marketing spend at the channel or campaign level.  Systems may do this at different levels of detail, with different levels of sophistication in the optimization, and with different degrees of integration to media buying systems. 

Of course, there are plenty of other points that differentiate these systems.  But this list should be a useful starting point if you're considering a new attribution system -- as well as a reminder of the need to define your requirements and drill into the details before you make a final selection.

Attribution Systems

G2 Crowd and TrustRadius

• Abakus: individual data; online and offline; algorithmic; optimal media plans
• Bizible: individual data; online and offline; fractional; merges marketing automation plus CRM data; B2B
• C3 Metrics: individual data; online and TV; algorithmic; optimal media plans 
• Conversion Logic: individual data; online and TV; algorithmic;optimal media plans
• Convertro: individual data; online and offline; algorithmic; mix model; optimal media plans; owned by AOL
• MarketShare DecisionCloud: individual data; online and offline; algorithmic; mix models; optimal media plans; owned by Neustar
• Rakuten Attribution: individual data; online only; algorithmic; optimal media plans; formerly DC Storm, acquired by Rakuten marketing services agency in 2014
• Visual IQ: individual data; online and offline; algorithmic; optimal media plans

G2 Crowd only

• BlackInk: individual data; online and offline; algorithmic; provides customer, marketing & sales analytics 
• Kvantum Inc.: individual data; online and offline; algorithmic; mix models; optimal media plans
• Marketing Evolution:  individual data; online and offline; algorithmic; mix model; optimal media plans
• OptimaHub MediaAttribution  individual data; online and offline; attribution method not clear; data analytics agency with tag management, data collection, and analytics solutions

TrustRadius only

• Adometry: individual data; online and offline; algorithmic; mix models; optimal media plans; owned by Google
• ThinkVine: individual data; online and offline; algorithmic; mix models; optimal media plans; uses agent-based and other models
• Optimine:  individual data; online and offline; algorithmic; optimal media plans

Other Systems

G2 Crowd and TrustRadius

• AdGear Advertiser: full stack advertising platform inc. ad serving, bidding, exchange technology
• DialogTech: tracks inbound phone calls
• Google Analytics Premium: ad data analytics including algorithmic attribution
• Invoca  tracks inbound phone calls
• Telmetrics: tracks inbound phone calls

G2 Crowd only

• Adinton: Adwords bid optimization and attribution; uses Google Analytics for fractional attribution
• Blueshift Labs: real-time segmentation and content recommendations; individual data but apparently no attribution
• IBM Digital Analytics Impression Attribution: individual data; online only; shows influence (not clear has fractional or algorithmic attribution); based on Coremetrics
• LIVE: for clients of WPP group; does algorithmic attribution and optimization
• Marchex: tracks inbound phone calls
• Pathmatics: digital ad intelligence; apparently no attribution
• Sizmek: online ad management; provides attribution through alliance with Abakus
• Sparkfly: retail specialist; individual data; focus on connecting digital and POS data; campaign-level attribution but apparently not fractional or algorithmic
• Sylvan: financial services software; no marketing attribution 
• TagCommander: tag managemenet system; real-time marketing hub with individual profiles and cross-channel data; custom fractional attribution formulas
• TradeTracker: affiliate marketing network
• Zeta Interative ZX: digital marketing agency offering DMP, database, engagement and related attribution; mix of tech and services

Claritix Assembles Marketing Data for Analysis: Maybe That's Enough

Most of the work in any marketing analytics project is integrating data from multiple systems. Claritix carries this insight to one logical conclusion by offering a system that does data assembly, basic reporting, and little else.  No fancy attribution methodologies or custom journey maps here (although they’re on the way). I’m not fully convinced this is enough to justify using Claritix but am open to the possibility. Here’s a deeper look.
 

As I just said, Claritix’s chief function is assembling customer data from multiple sources. The system has prebuilt connectors to import data from popular vendors including Salesforce.com, Marketo, Hubspot, SAP, SugarCRM, and Facebook. It can connect with others through standard APIs. The imported data is loaded into MongoDB, a NoSQL database that offers great flexibility and ease of deployment. Claritix applies sophisticated algorithms to cleans the data and match contacts based on similarity.  It also uses matches created elsewhere such as lead IDs used to synchronize CRM and marketing automation data or cookie IDs imported from Google Analytics. The matching happens at both the contact and account level. Imported data includes contacts, funnel stages, campaigns, channels, revenue, and content.

Users can access this data through dashboards, charts, and views. There are different dashboards for the main data types (campaigns, funnel stages, channels, etc.). These provide basic information such as impressions, engagements, visits, deals and revenue by campaign, or sources, stages, conversion rates, and average duration by funnel stage. The specific measures depend on the data type. Users can drill into details down to the contact level. Views can show results for user-defined segments.

Claritix also lets users assemble information into binders, which are contain pages that are snapshots of dashboards, charts, and notes. These can be exported to PDF or slides or viewed directly within Claritix. Binders can update themselves at regular intervals. Collaboration features let users attach virtual “sticky notes” to screen images and share these via Slack or Claritix’s own communication channels.

So far as I know, that’s pretty much all that the system does. There is no capability, for example, to write the assembled data back to source systems for their own use.  Claritix tells me this has been quite sufficient for their initial clients, who have liked the fact that set-up is virtually all automated or handled by the vendor.  This has let them assemble data across multiple systems in ways that would otherwise have been impossible or hugely expensive. Certainly price is an advantage: Claritix starts at $1,000 per month for up to 10,000 contacts in the database, with the cost per contact decreasing for higher volumes. A system with more advanced reporting, such as Brightfunnel (which I reviewed in December and has been a consulting client) starts at $3,000 per month or higher. Still, you have to decide whether you’ll need the features that Claritix is missing; if so, you’ll end up missing many of the beneifts that good marketing measurement provides.   As Captain Planet used to say, the power is yours.