suitecx Offers Industrial-Strength Customer Journey Maps and More

Customer journey mapping is now the buzziest of buzz words.  Every self-respecting marketing automation system offers something called a “customer journey map,” even if it’s exactly the same as last year’s campaign designer or does nothing more than connect functionless icons on a virtual whiteboard. Journey mapping is equally popular among agencies and consultants, although it also often is little more than a new label for the old sales funnel.

None of this affects me personally, but if I were a real customer experience expert I'd be annoyed at cartoon versions being presented as the real thing.  Sophisticated journey mapping has been around for more than a decade*.  It involves not just listing interactions or displaying them in a diagram, but also analyzing their contents, results, and supporting systems. Most customer experience teams have struggled to do this with spreadsheets, graphics programs, or generic flow charts. I’ve done it that way myself and, trust me, it’s painful.

suitecx is to static customer journey diagrams as Google Maps is to the Rand McNally Road Atlas: an interactive alternative with almost boundless functionality. Built by a team of customer experience veterans at the east bay group,** it’s clearly the system its designers always wanted but could never find elsewhere. The resulting sophistication makes it a bit scary at times, with more data crammed onto some screens than casual users can digest. But it also means the system is hugely flexible and will make serious users vastly more productive.

Customer journey mapping is just one feature within suitecx, which is sold as three modules: diagnosticcx to gather and organize customer experience information; visualizecx to display maps, findings, and recommendations; and precisioncx, which defines contact strategies and campaign flows.

The tool is designed around its own intended user journey. This would start in diagnosticcx, which collects information about a company’s business, customers, and processes using customer and employee surveys, interviews, and direct experience. The findings are organized into a list of customer interactions, which are classified by journey stage, channel, department, emotional outcome, segment, and other properties. The interactions are then linked to recommendations, which are themselves classified, prioritized on four dimensions (customer impact, company impact, cost, and feasibility), mapped onto a matrix, set on a timeline, and ultimately converted into detailed project plans.

visualcx supports the project by displaying the data in formats including story maps, process flows, interaction grids, a virtual wall with virtual sticky notes, and various summaries. The grids are automatically generated from the interaction list developed in diagosticcx or imported via spreadsheet.  Grids can be filtered on different interaction attributes and users can drill into individual interactions to see the underlying details. The story maps and process flows are built manually, alas, but still use the same drillable interactions.

precisioncx completes the project by letting users design new customer experiences.  These include over-all contact strategy and multi-step campaigns with segments, creative, triggers, metrics, and other attributes for each step. The system can’t execute the campaigns but an API is available that could export the campaign designs to execution systems.

This is all industrial-strength stuff, aimed at corporate customer experience departments, agencies, and consultants. Pricing is similarly industrial, starting at $15,000 per module for up to three users. suitecx also offers single-function “primer” products for a much more affordable $699 each (and a seven day free trial). Current modules include grid diagrams, story flows, and the virtual wall with sticky notes. Process maps and campaign flows are under development.

Early versions of suitecx have been used by the east bay group in their own work for years. The commercial product was formally released in December 2014 and currently has about 20 paid clients for the major modules.

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* the earliest reference I could find on Google was in a business school course syllabus from 2001.  Apparently the term had already been around long enough by then to be picked up in academia.

** which seems to have an issue with capital letters.

A Tale of Two Sittings: Best of Times with HubSpot and Teradata

Yes, that title is a pun on Dickens’ Tale of Two Cities. Just be glad I don’t review housewares, or this could be about rating knives and forks: A Tale of Two Settings: It Was The Best of Tines, It Was The Worst of Tines.

But I digress. Where was I? Ah yes, in Las Vegas, at ONE: Teradata Marketing Festival, which is Teradata's conference for users of its marketing applications. Despite the location and title, the program did not include jousting.

What the conference did offer was a detailed look at Teradata’s current marketing applications, vision, and product roadmap. These were solid and comprehensive, although Teradata continues to make an unfashionable distinction between “omni-channel” marketing, which is conventional relationship marketing across all channels, and “digital” marketing, which is Web and email marketing. Teradata argues that many digital marketing departments still function independently of relationship marketing groups and therefore want their own tools. That’s probably true, especially at the big enterprises who are Teradata’s primary clients. But the trend is towards closer integration and you’d think Teradata would rather lead than follow. I do suspect that at least part of the reason for the distinction is internal: the omni-channel products are based on Teradata’s original marketing automation products, Aprimo and Teradata Relationship Manager, while the digital products are based on eCircle email the company purchased in 2012. To avoid misunderstanding, let me stress that Teradata does let users integrate the omni-channel and digital products if they want to and that digital includes text messages, mobile apps, social media monitoring and publishing, and Web landing pages as well as email.

Teradata’s marketing applications also extend beyond standard marketing automation to include marketing resource management and analytics. Indeed, there’s a case to be made that the company’s scope is superior to most competitive “marketing clouds”, which are usually pretty light on MRM and analytics and are often barely integrated. On the other hand, Tereadata seems to have something of a blind spot regarding advertising and anonymous customers: I got mixed messages from the various Teradata presentations about whether it considers support for paid media as part of its marketing applications.  The clearest statement I can extract from my notes is that they will store anonymous identifiers such as cookies in their database but not use them until they are linked with an identifiable individual. As readers of this blog know well, I feel all media (owned, earned, paid) and all users (anonymous and known) should be managed together.

Teradata itself sees its primary differentiator as analytics. It presents an appealing vision of “adaptive self-learning marketing automation” that combines historical data, predictive models, and prescriptive models. By “prescriptive”, it means recommending the types of marketing programs to create, as opposed to predicting which existing marketing campaigns are best for an individual customer. This all strikes me as correct, if not downright futuristic.

But down at the practical level, Teradata’s near-term roadmap was considerably less visionary. Maybe that’s the nature of roadmaps. Perhaps inspired by the venue, the Teradata folks did a lot of (metaphorical) kimono opening at the event, detailing their product plans in ways I rarely see in public. What they revealed were mostly incremental enhancements such as improved user interfaces to make marketing activities easier and more nimble. There were some more fundamental promises, including better integration across suite components, more open access by external systems, and a more unified view across campaigns of the customer journey. It’s solid but not flashy, which is a pretty good summary of the Teradata style.

I was barely home from Vegas before I headed up to Boston for HubSpot’s Open House, a small event for primarily for business partners. (HubSpot’s main user conference is the INBOUND show in September. No jousting there, either.) Although the Open House style was low key, there were a couple of substantial announcements: the company’s free CRM system is now generally available (and still free); expansion of its $50/month Sidekick sales productivity tool; and eleven new integration partners including some predictive technologies (BrightInfo and Infer) and paid media retargeting (PerfectAudience). These are interesting extensions beyond the current set of partners, who mostly support operational tasks such as content creation, events management, analytics, and CRM integration. There was also some modest boasting about HubSpot’s continued growth – which actually accelerated slightly to 58% year on year in the most recent quarter – and other achievements including 15,000+ customers, 2,300 partners, 900+ employees, and top satisfaction rating in industry surveys.

HubSpot was less forthcoming than Teradata about future directions, perhaps because they see little change from the current course. Their general intention is to continue serving their existing target market (companies from 10 to 2,000 employees) with marketing and sales tools. There is a bit of redefinition to being a “growth engine” that solves additional marketing and sales problems, but this is an incremental change at most. The company’s announced focus is the improve the existing product with a mantra of faster, lighter, and easier, not to lead major changes in how businesses interact with their customers. Or perhaps HubSpot feels that most companies have virtually no automation in how they market and sell, getting more companies to adopt the existing HubSpot tools and best practices would itself be a major change. Fair enough.

On the other hand, co-founder and CTO Dharmesh Shah did tell me HubSpot is about a year away from supporting custom objects in its data model, which would open up some major new opportunities for the software. So perhaps there’s a bit more vision than they’re talking about publicly. People in Boston aren’t quite so free about opening their kimonos.

MDC DOT Provides Marketing Automation for Direct Salespeople

I briefly mentioned MDC Dot in an earlier blog post about giving sales people access to marketing automation capabilities. This may not have done them justice since they are more specialized than that general description implied. What MDC Dot really does is serve organizations that wrangle a herd of independent sales people, like financial advisors or direct sales representatives. Those firms have very specific requirements for balancing central control over content and brand image with the agents’ desire for flexibility and personal client relationships. It’s actually a crowded space, with companies like Balihoo and MindMatrix competing aggressively for sales. What sets MDC Dot apart is that it targets organizations where the sales people have modest skills, modest needs, and even more modest budgets.  Pricing starts at $15 per month per salesperson for a database of up to 500 active contacts and reaching a still-modest $90 per month for 10,000 active contacts.

Beyond low price, MDC Dot offers two key capabilities to suit its target audience. The first is a structure that links customers to their original salesperson, even if they later contact the organization through a Web search or visit to the corporate Web site. This is especially important for businesses that pay commissions to the customer’s original salesperson. MDC Dot does this by tagging each customer with the original salesperson’s ID and then ensuring that customer is sent back to the salesperson’s own microsite when they return. The initial tagging and subsequent redirection both require inserting some MDC Dot code onto whatever content the salesperson uses for the initial customer interaction. The redirection works best when the corporate Web site and the salespeople’s microsites are all on a subdomain hosted by MDC Dot.

The second key feature makes it easy for sales people to use content created by the central marketing team by automatically inserting tags such as the salesperson’s name and contact information. This customized content can include entire microsites, landing pages, emails, campaign flows, and social media posts. Salespeople can also set up their own contents.

Beyond these special features, MDC Dot also provides basic contact management including notes, tasks, tags, attachments, and activity tracking. Users can send emails, which come from the user’s own domain. Reports show email and Web activities, campaign results, and customers at each stage of the sales funnel. Screens are designed for simplicity and mobile devices. The interface is designed with the target of no more than three mouse clicks to accomplish any one task.

Corporate users can see the list of salespeople they are managing, along with performance statistics for each user. They can’t see the actual customers in the salesperson’s database. Corporate users also have tools to build email and social content and to set up contact sequences. These sequences are what puts the "dot" in MDC Dot: they’re built by connecting “Qualification Dots” to assign sequence members (all contacts, by contact type, via campaign manager, via sequence group), “Activity Dots” to react to behaviors within the sequence (opened an email, clicks an email, or visited a Web page, or not), and “Action Dots” to either send an email or transfer customers to another sequence group.

Sequences are built by connecting the dots (get it?). As you may have noticed, the set of available dots is pretty limited, although they are adequate to create basic email campaigns. At the moment each sequence can contain just one “Activity Dot” split, but multiple splits should be available later this year. Several sequences can be assigned to the same campaign and execute in priority order. This allows more complex treatments despite the simple design of the individual sequences.

The system also can do basic lead scoring and assign prospects to sales funnel stages. It lacks segmentation tools, although users can build an on-screen list of customers based on tags and then add the listed customers to campaigns. Landing pages must currently be built by the vendor. Tools to let users build their own filters and landing pages are under development.

MDC Dot was introduced in December 2014 and currently has more than 2,000 paying end users.

Will Machines Replace Marketers? Artificial Intelligence Isn't Ready Yet But Watch Your Back

Anyone who has chatted with me in recent months knows that I’ve added the impending domination of humans by intelligent machines to my usual list of obsessions. This most definitely applies to marketing, where I found many artificial intelligence-based solutions once I began looking for them. After accumulating a list, I’ve decided it’s time to pull together an overview of the topic.

My thesis was that AI-based systems already exist for most tasks that marketers perform, but are not yet connected into a single robo-marketer that (or is it who?) could do the job from start to finish. To test this, I listed the tasks that go into building a marketing program and matched these against my list of AI-based products.

Quite to my surprise, the machines haven’t risen so far after all. Of the three broad tasks I defined – planning, content creation, and execution – only content creation is served by what I consider to be strong AI* solutions. Some AI options are available for execution, but most are conventional predictive modeling products that I don’t count as strong AI because they still require humans to deploy their results. Marketing planning, which includes the all-important task of campaign design, is almost wholly untouched by AI.

Let’s look at each task in turn.

Planning

The lack of AI-driven planning solutions is especially surprising since so many planning tasks lend themselves to an AI approach. These tasks include market analysis (identifying potential buyers for a product, defining the needs and interests of potential buyers, identifying competitors, calculating potential market size and adoption rates), selecting marketing strategy, and selecting tactics (which can be defined as campaigns, experiences, or – if you’re cool enough – stages in the customer journey).

It seems well within the capabilities of current technology to find people who indicate a specific need, based on their Web searches or social comments, and then to understand who those people are in terms of demographics, behaviors, and other attributes. But the closest I could find were a couple of products that build profiles of groups the marketer defines in advance, such as brandAnalyzer by brandAnalyzer from Global Science Research and Empirical Insights from SG360. The one system that does look like strong AI is Bottlenose.  It performs the relatively common function of identifying trends but uses enough advanced technology, including natural language processing, topic discovery, and sentiment analysis, to impress me.

Similarly, competitor analysis should be well within the capabilities of companies like Radius, Everstring and Growth Intelligence, which already ingest the contents of corporate Web sites to understand each company’s business. But those vendors focus on finding prospects for B2B sellers. If any of them offers a competitor identification service, I’m not aware of it.

Business and marketing strategies are often compared to chess, a game that AI systems can famously play better than humans. I think the analogy is sound: like chess, business and marketing strategy involves a relatively limited number of moves with easily predicted short term consequences and large databases of past competitions which computers can study to predict longer term results. Strategic planning can also be supported by a rich treasury of simulation and optimization techniques that are very familiar to AI developers. Yet the closest I can find to strategic planning is optimization of media plans by media mix model vendors like MMA , Analytic Partners , Nielsen  and IRI.  But that is so tactical I classify it as part of execution. Otherwise, I haven’t seen anyone use AI to recommend a marketing strategy.

Nor has anyone really promised to design marketing campaigns using an AI system. This is another area that seems a natural application: computers can certainly use past results to predict the short- and long-term results of individual messages and, with a bit less certainty, of streams of messages. But while there are plenty of systems that predict the “next best message” or recommend what content the user is most likely to select, no one seems to have taken the obvious next step of using AI to design the best message sequence and refine it over time through automated testing. The closest I’ve seen are Amplero and Insightpool.  But they both start with individual data, so I'll discuss them in the section on execution.

Content Creation

Ironically, the most common reaction when I bring up machine-based marketing seems to be “well, they'll never write copy”. In fact, writing is one task where machines have already demonstrated huge success. General purpose writing programs including Wordsmith from Automated Insights , Quill from Narrative Insights and Arria NLG already write over one billion newspaper articles and reports each year, specializing in data-rich topics like sports and financial reports. Wordsmith also writes other sorts of reports, including summaries of marketing campaign performance.

Still closer to home for marketers, InboundWriter and Acrolinx score marketing content for effectiveness before it is released.

Most impressive of all from an AI perspective, Persado and Captora actually create content on their own. Persado does this by selecting and then testing content derived from a huge database of marketing language, classified by emotional, descriptive, and formatting categories. It works across email, landing pages, text messages, social posts, Facebook ads, app notifications, and other media. Captora automatically analyzes the topics and performance of content from the client and its competitors, finds opportunities for new campaigns, and creates appropriate landing pages to attract search traffic.

I consider Persado and Captora to be true AI-based marketing because they can actually replace work done by humans. But, as both vendors would probably rush to point out, what they really do is expand the volume of work that gets done, enabling marketers to execute hundreds of campaigns with the same human effort as it previously took to do dozens. So they are less about reducing the number of marketers than expanding marketer productivity.

Execution

While planning and content are arguably the most strategically important tasks that marketers do, there’s no question that they spend most of their time on execution. This is especially true since my definition of execution includes measurement and optimization because all three are so closely intertwined.

I broadly divide execution into audience development, message selection, and attribution. Audiences include paid media (purchased ads and lists), earned media (public relations and social influencers), and owned media (company Web sites and email lists, in-store promotion, call centers, etc.). Message selection includes content recommendations and personalization. Attribution includes everything that measures the results of marketing efforts – although, in practice, much message selection also relies on simple attribution to improve selection results.

Each execution category is served by advanced technology. Paid audiences are built with predictive modeling for list selection, programmatic media buys for advertising, and automated content analysis to understand intent. Earned audiences are built through influencer identification and predictions of who will cover which stories. Owned audiences are refined through more predictive models and behavior analysis. Message selection also relies on advanced analytics to recommend the right content for each individual and to find the best-performing messages for groups. Likewise, attribution systems apply sophisticated methods to isolate the incremental impact of individual marketing actions on long-term results. This long-term perspective is what distinguishes attribution from the measurement built into message selection systems, which instead chase immediate results such as email click-through or Web page conversion.

These technologies are certainly impressive, but few of them actually remove marketers from the process – which you’ll recall is my definition of marketing AI.  Predictive model scores, for example, are usually plugged into marketer-created rules that decide who receives which treatments. Even the recommendation engines rarely do more than predict which messages an individual is most likely to select.  Human-built rules still determine which messages are available and when messages will be presented.

There’s a lot of gray in this picture. Model scores and recommendation engines often replace complex segmentation rules even though some other rules remain. They may not replace marketers altogether but they do enable marketers to run larger numbers of more refined programs.  And they're a supporting technology for true AI marketing systems even if they are not AI themselves.

On the other hand, I think programmatic media buying does rise to the level of true AI. Again, the critical distinction is whether they replace human marketers – and I think that media buyers are pretty much not needed to execute programmatic programs. Obviously a human still has to set up the programs and provide the creative, but the programmatic systems then make complex judgements on their own.  Are these “judgements” significantly more advanced than the “judgements” that go into a lead scoring predictive model or personalized content recommendation? I’m not really sure. Maybe I’m misled by the fact that “media buyer” is an established profession while “lead scorer” or “content personalizer” are not job titles that people had before computers were available.

There are a few execution products that approach the border of true AI and may actually cross it. These include:

- Amplero, a newly released system that uses tree analysis to find very small market segments and then identifies the best content for each segment. What separates it from other personalization tools is that it optimizes against long-term value, such as revenue over the 14 days following each message, and its decisions take into account messages previously presented. I’d definitely consider Amplero true AI if it could plan sequences of messages, which would be pretty much the same as building multi-step campaigns. The system doesn’t do this yet but the vendor tells me they’re working on it.

- Insightpool, which identifies social media influencers, predicts how likely they are to take a user-specified action, and then recommends multi-step campaigns to encourage that result. Influencer identification and activity prediction are impressive but not unique; what makes me classify Insightpool as AI is its ability to select campaigns. This is something that you’d ordinarily expect a human marketer to do, even if the marketer was working with lists that the other functions had prepared.

- OneSpot converts existing pieces of content into multiple ad formats to permit reuse, classifies them (automatically, so near as I can tell) by purpose, and then delivers them to precisely targeted or retargeted individuals through programmatic ad exchanges in the optimal sequence to meet long-term goals. The reformatting, classification, and sequencing all strike me as things that humans would otherwise do manually, and of course I’ve already argued that programmatic media buying itself qualifies as marketing AI.

Final Thoughts

I know this post is too long to be effective but I wanted all to get this information down in one place. Artificial intelligence is an important topic in our general society and seems to attracting increased attention, even though Google Trends suggests otherwise. Marketers in particular are thinking about it as they adjust to rapidly changing technologies that increasingly rely on predictive analytics and other automation for effective management.

Given the hype that accompanies pretty much every new technical development, it’s helpful to see that AI-based marketing isn’t as far along as one might expect. But don't take that as a reason to relax: while it’s not time to panic, it’s definitely time to prepare. AI marketing systems already present some significant opportunities and their scope can only grow – perhaps exponentially as key techniques become more widely distributed. Now is the time to start building a realistic understanding of how these systems work, what they can and can’t do, and how they’ll fit into your future.


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* “strong AI” is used by AI experts to mean systems match or exceed human intelligence. I’m using it in that roughly sense, although with the more specific meaning of “systems that perform tasks that otherwise require human marketers”.