Tuesday, April 26, 2016

Teradata Sells Its Marketing Applications Business for $90 Million

Teradata announced on Friday that it had signed a deal to sell its marketing applications business for $90 million to private investment firm Marlin Equity Partners. The company had announced plans to sell the business last November. The sale involves the former Aprimo, eCircle, FLXone data management platform, real-time interaction manager, and other cloud-based marketing products. Teradata will retain its on-premise Customer Interaction Manager (CIM) and an on-premise version of Real Time Interaction Manager (RTIM).

The price is much lower than usual for cloud-based marketing systems. Teradata reported just under $200 million revenue for marketing applications last year. This includes about $40 million for the pieces that Teradata is keeping. So the $90 million price is about 0.56x revenue ($90/$160). This compares with Marketo stock selling at roughly 5x revenue ($1 billion market cap on $200 million revenue). It’s true that Teradata lost $45 million on the marketing applications business last year, but that’s still less on a percentage basis than Marketo’s loss of $71 million. The differential suggests that buyers saw little potential for growth in the businesses that Teradata is selling. The low price may also reflect a departure of many human assets from the Teradata business in recent years.  Teradata itself paid $540 million for Aprimo back in 2011, again roughly 5x revenue. 

It’s not surprising that the buyer is a private equity firm. That was what had been rumored. Marlin hasn’t had much previous involvement in marketing applications but it did buy email provider Blue Hornet in December. Presumably it will combine the two businesses, reduce the losses, and try to sell the result either to other businesses or on the stock market. I don’t understand why Marlin thinks the combined firms would be much more attractive than the separate businesses but presumably they feel there is greater growth potential for a better-managed business. Or, Marlin may plan to split up its acquisitions and sell individual components such as marketing resource management and data management platform separately.

In a move that borders on surreal, Teradata's Marketing Application division itself today announced the latest release of its integrated marketing cloud.  I suppose this signals the hopes within the marketing applications team to remain intact.  Whether that's more than wishful thinking, only time will tell.

I’d like to say I was clever in predicting that Teradata will hold onto CIM and RTIM, but this was something the company announced just after it said it was selling the marketing applications group. CIM and RTIM both started as separate products from Aprimo and, for the most part, remained technically distinct.  My understanding is they held onto the on-premise pieces because they were important to major Teradata clients, whereas the businesses being sold were used by smaller companies who were not buying much else from Teradata.

The very low price certainly isn’t good news for other SaaS marketing vendors, but I think it’s more about the unique situation of the Teradata products than the industry in general. So I’d expect valuations of other cloud-based marketing firms to be largely unaffected.

Thursday, April 21, 2016

SAS by the Sip: SAS Viya Offers Open APIs to Individual Services in the Cloud

SAS held its annual Global Forum conference this week, which marked the company’s 40th anniversary. One key to its long-lived success was an early decision to sell software by annual subscription, rather than the one-time perpetual license standard in the industry when SAS started. This provided a steady income stream and focused attention on customer satisfaction to ensure renewals.

In recent years, much of the software industry has adopted a subscription model under the label of “Software as a Service” (SaaS).  But the triumph of SAS’s pricing approach has been accompanied by new challenges to SAS’s business. Subscription pricing notwithstanding, SAS has largely sold its software for on-premise operation by its clients and required them to purchase a large stack of core technologies. This demanded a high initial investment but made expansion relatively easy – an approach that made sense when SAS's core analytical applications were pretty much essential to many clients. By contrast, the new SaaS vendors run software on their own servers and allow clients to access it remotely. This greatly reduces implementation effort and allows volume-based pricing, both of which lower entry costs to the client. The new SaaS software has also been relatively easy to integrate with other systems through open APIs and standard scripting languages such as Python. This also makes it easier to sell SaaS applications for narrow tasks rather than as part of a massive suite.

SAS’s growth and financial performance have been just fine despite the new competition, thanks to technical leadership in its core analytical products and pry-it-from-my-cold-dead-hands loyalty of its core customers. But the benefits of the new SaaS systems have made new sales harder, especially in peripheral markets such as marketing applications.

I’ve subjected you to this long-winded exposition because it provides context for SAS’s major announcement at its conference: a true SaaS version called SAS Viya.* This is a cloud-native system** that will reproduce existing SAS functionality and be compatible with the existing SAS 9 products. More exciting than the cloud deployment (which SAS had previously offered for SAS 9), Viya will be accessible through open APIs and scripting languages including Python, Java, and Lua, and – gasp – some components will be offered as on-demand services. In the SAS universe, this is truly revolutionary. It should open the door to new clients who were not likely to invest in a conventional SAS implementation.  Initial Viya apps will be available in third quarter 2016.

For marketers in particular, SAS also announced Customer Intelligence 360, a SaaS version of its primary marketing suite. Like Viya, this is a separate product from the existing Customer Intelligence 6 suite, which will continue to be offered. The initial release is not a function-for-function duplicate of CI 6 but a “digital marketing hub” that delivers real-time messages in digital channels (email, Web, and mobile apps). Key features include customer-level data collection via on-page scripts, and applications for marketing tasks such as sending an email, delivering in-app messages, or building Web a/b tests. These applications combine previously separate SAS functions such as model building, visual analytics, segmentation, and content creation. They include some nifty advanced features such as recommending when to run tests and automatically discovering which customer segments are most responsive to each test version. The initial CI 360 release includes two modules, Discover (mobile and Web reporting) and Engage (digital interactions including testings). They will eventually be followed by marketing resource management. CI 360 works on a very flexible customer data hub, although that’s a separate product owned by SAS’s Master Data Management group.

CI 360 uses much of the same technology as Viya, including REST APIs and HTML5 interface. It will officially run on Viya once Viya is released. Like Viya, it does not require clients to purchase the full SAS stack and will be priced on volume rather than a simple subscription. In the case of CI 360, fees will be based on the number of “customer equivalent records” and marketing messages. A minimum installation might start around $10,000 per month, considerably less than the current CI 6 product and competitive with other mid-market digital marketing solutions. The initial CI 360 modules are available now.

* The name is a little odd but it could have been SASaaS, so I guess we can be thankful for small mercies.

** Viya can run on the Amazon Web Services public cloud, SAS’s own cloud, or a company’s own private cloud.

Wednesday, April 13, 2016

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.


*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.

Monday, April 11, 2016

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.

Wednesday, April 06, 2016

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.

Tuesday, March 29, 2016

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 $100,000 per year.  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.

* 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.

Thursday, March 24, 2016

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!