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Preview: Customer Experience Matrix

Customer Experience Matrix



This is the blog of David M. Raab, marketing technology consultant and analyst. Mr. Raab is Principal at Raab Associates Inc. The blog is named for the Customer Experience Matrix, a tool to visualize marketing and operational interactions between a comp



Updated: 2017-03-23T06:38:40.858-04:00

 



Is MarTech Too Important To Leave To The Marketers?

2017-03-21T12:32:04.218-04:00

I’m still pondering the relationship between marketing and IT: what it is, will be, and should be. A few new ingredients have kept the pot boiling:- a chat with Abhi Yadav, founder of Zylotech, a MIT-bred, artificial intelligence-driven Customer Data Platform and message selection engine.  Those roots made it seem a likely candidate for IT-driven purchases, but Yadav told me his primary buyers are marketing operations staff.  In fact, he hasn’t even run into those marketing technology managers everyone (including me) keeps talking about. On reflection, it makes sense that marketers would be the buyers since Zylotech includes analytical and message selection features only used in marketing.  A system that only did data unification would appeal more to IT as a shared resource. Still, Yaday's comments are one point for the marketer-control team.- a survey from the Association of National Advertisers that found marketers who control their technology strategy, vendors, and enterprise standards are more likely to have a strong return on martech investment. (The study is only available to ANA members but they gave permission to publish the table below. You can see a public infographic here).  That’s two points for Team MarTech.- a study by IT staffing and services provider TEKsystems that found senior marketers with more advanced strategy were more likely to control their own technology.  The difference wasn’t terribly pronounced but it’s still the same pattern. MarTech is now ahead 3-0.  (I was actually more impressed that 65% of departments with no strategy were in charge. Yikes!)So far, the game’s a blow out. Marketing is usually in charge of its technology and does better when it is.  A doubter might question if marketers really make better choices or are just happier when they’re in control. I do suspect that IT people would be less confident that marketers are making optimal decisions. Still, there’s no real reason to doubt that marketers are the best judges of what they need.But the game’s not over. Let's call in a recent Ad Week article about global tech consultancies buying marketing agencies. The article cites Accenture, Deloitte, IBM, KPMG, McKinsey and PricewaterhouseCoopers and notes that each already has huge agency operations.  To the extent that these firms are working with marketing departments, it’s still more evidence of marketing being in charge. But the real story, at least as I read it, is these firms are getting involved because they see a need to integrate marketing technology with over-all corporate technology, just as marketing strategy needs to support corporate strategy. “The consultants’ bread and butter has traditionally been large IT and business-transformation projects,” says Julie Langley, a partner at fundraising, merger and acquisitions advisor Results International, in the article. “But, increasingly, these types of projects have ‘customer experience’ at their center.” To me, this is the key. As every aspect of customer experience becomes technology-driven, technology must be integrated across the corporation to deliver a satisfactory experience. Marketing may be the captain, but it’s still part of a larger team. If marketing can be a true team player, it gets to call the plays. But if marketing is selfish, then a coach needs to step in for the good of the whole. I’ll spare you the extended sports analogy. In concrete terms, if marketing picks systems that only meet marketing needs, then the integrated customer experience will suffer. Worse still, some new tech-driven offerings may be impossible. This could be fatal if other, nimbler competitors deliver them instead. Tech-based disruption is a real threat in many industries. Companies can’t just hope that each department working on its own will yield an optimal solution for the business as a whole.  In fact, they can be quite sure it won't.That’s why I’m not convinced by surveys showing marketers are happier or get better retur[...]



CrossEngage Orchestrates Customer Journeys Using Events

2017-03-15T15:56:53.186-04:00

It feels like forever since I first wrote about Journey Orchestration Engines (JOEs), although it is just one year. Orchestration was already a hot term when I started, so I take neither credit nor blame for its continued popularity. I will say that I’ve now seen enough orchestration systems to start making subtle distinctions among them.Subtle distinctions are needed because the systems are basically similar. They all ingest data from multiple sources; convert it into unified customer profiles; apply rules and analytics to find the best message for each customer in each situation; and, send those messages to external systems for delivery. Unified customer profiles make these products look like Customer Data Platforms. JOEs that expose their profiles for external access really are CDPs; JOEs that keep the profiles for their own use, are not. In theory, a JOE could connect to an external customer database rather than building its own, but I haven’t seen that configuration in practice.The main ways that JOEs differ include:Channel scope. Some systems are largely limited to online interactions, while others are built to combine online and offline channels. Some systems that look like JOEs work with only Web or email. But orchestration pretty much implies multiple channels so I’d probably exclude those from the JOE tribe.Decision methods. JOEs can work with conventional, rule-driven campaign structures or use automated techniques to customize the path followed by each customer. There’s also considerable variation in exactly what gets automated: some automate campaign assignments but use static content; some automatically run a/b tests and pick the winners; some automatically create customer segments that receive different content; some use machine learning to dynamically generate custom content. Journey framework. My original definition of JOE was quite rigorous: journey orchestration meant all campaigns were defined relative to a master model of the customer journey. This really means that stages in the journey are “states” that customers flow between, and campaigns are chosen in part based on each customer’s current state. I still think of JOEs that way and definitely see some systems organized along those lines. But when you start looking at some of the more automated decision methods, it’s harder to apply concepts of fixed states or journey flows. So I still check whether a system has a journey framework but don’t necessarily require a JOE to use it. I realize this means you could have a journey orchestration system without journeys. If that’s the silliest thing you’ve been asked to accept recently, you haven’t been watching the news.This is all a very long-winded introduction to CrossEngage, a Berlin-based firm that released its product about six months ago. CrossEngage works in online channels, using its own tags to capture Web interactions and API connections to ingest data from email providers, mobile apps, and other sources. It can also load CSV files if necessary. CrossEngage treats most data as either a customer attribute or event, using big data technologies that store inputs and to allow data access with minimal schema design. The system also stores some information that’s neither attribute nor event, such as products and locations. The vendor maps new sources into the system and can define logic to create custom events. (A self-service event builder is planned by July.) Customer data from different sources is stitched together using deterministic matching only (that is, CrossEngage will only connect different identifiers to the same person if an external source provides the relationship). A dashboard lets users see Web site events as they stream into the system. Users can apply filters to see only certain events. Campaigns also make heavy use of events, referencing them as entry and exclusion conditions, in combination with user-defined segments; as campaign goals (which may be one or several events); and, as campaign steps[...]



Forecast: Self-Assembling Application Bundles Will Manage Customer Experience

2017-03-13T11:43:16.688-04:00

I recently described a Deloitte paper on technology trends, focusing on their descriptions of IT management methods. The paper also covered broader trends including:Unstructured data, which they saw as a potentially bottomless source of insight. What’s interesting is they didn’t suggest many operational uses for it.  By contrast, traditional corporate data management is almost exclusively about business operations. Machine intelligence, which they described as broader than artificial intelligence. They saw deployment moving from offering insights, to interacting with people, to acting autonomously. They also described it as controlling internal business processes as well as customer interactions. That's not the way marketers tend to think but they're right: the bulk of company processes are not customer-facing.Mixed reality, which is a combination of virtual reality, augmented reality, and Internet of Things. They focused less on game-like immersive experiences than on new types of interfaces, such as gesture- and voice-based, and on remote experiences such as collaborative work. They also listed some requirements that aren't usually part of this discussion, including machines that can understand human expressions and emotions and security to ensure hackers don’t falsify identities or inject harmful elements into the remote experience (such as, telling you to make a repair incorrectly). Blockchain, which they presented as mostly in terms of easing security issues by verifying identities and allowing for selective sharing of information. Those are intriguing thoughts but don't present a specific vision of the future. A recent paper from Juniper Networks rushes in where Deloitte fears to tread.Juniper's term is "digital cohesion", which they desfine as "an era in which multiple applications self-assemble to provide autonomous and predictive services that continually adapt to personal behaviors.”  It somewhat resembles the ideas I offered in this post about RoseColoredGlasses.me and further elaborated here.  I guess that’s why I like it.Beyond having excellent taste to agree me, Juniper fills in quite a few details about how this will happen. Key points include:Disruptive competitors can use high speed networks, local sensor data, and centralized cloud processing to offer new services with compelling economics (e.g. Airbnb vs. Hilton). Smartphones provide pre-built mass distribution, removing a traditional barrier to entry by disruptive competitors.Consumers are increasingly open to trying new things, having been trained to do so and seen benefits from previous new things.Natural interfaces will eliminate learning curves as systems adopt to users rather than the other way around, removing another barrier to adoption.Autonomous services will self-initiate based on observing past behavior and current context.  Users won't need to purposely select them.  More barriers down.Services will be bundled into mega-services, simplifying user choice.Open APIs and interoperability will make it easy to add new services to the bundles. This is a key enabling technology. Better security and trust are essential for users to grant device access and share information with new services. Business relationships need to be worked out between the individual services and the mega-bundles. I’m sure you see the overlap between the Deloitte and Juniper pieces. Machine intelligence and insights from unstructured data will be critical in building services smart enough to make the right choices. Machine intelligence will also create an underlying infrastructure that’s elastic and powerful enough to deliver services reliably regardless of user location or aggregate demand. Mixed reality will be key for gathering information as well as delivering interactive user experiences. Loosely coupled systems and disaggregated services will make it easy to inject new services into a bundle. Blockchain could play a critical role in solving the security an[...]



Should Customer Data Platforms Be "Marketer-Controlled"?

2017-03-14T08:46:47.584-04:00

Thomas Wieberneit argues in a thoughtful blog post that companies need one platform for consolidated customer data, but that Customer Data Platform isn’t it because the CDP is “marketer-controlled” by definition, and thus doesn’t support other departments. This hits a nerve. Many of the CDP vendors have told me their systems are used outside of marketing. Just last week, RedPoint unveiled a “Customer Engagement Hub”* that it defines as extending beyond marketing to all customer touchpoints. When I was recently writing a paper for B2B CDP CaliberMind, they listed sales and customer success teams along with marketing as likely buyers of their product.As these examples suggest, CDP technology can support all customer-facing departments. It’s true that different departmental applications will probably need the CDP data to be presented slightly differently. But applications within marketing also need different formats, and it’s already standard for CDPs to reformat their data for access by external systems. So there’s no technical reason to limit CDPs to marketing.Indeed, as I argued in my last blog post, the era of isolated marketing technology systems may be coming to an end as companies return to centralized systems to ensure a seamlessly integrated customer experience. In this world, the CDP is a shared enterprise asset, and, as such, clearly not something that marketers build and control for themselves.So what’s holding me back from changing the definition? Three things:History. The fact is that CDPs evolved from systems that were built specifically for marketing. This matters because it means marketing needs drove their design. CDPs built to support sales or customer success teams would probably look a bit different, even if they used the same underlying technology. To give a concrete example, systems built for customer success don’t need to advanced identity resolution because almost all the data they care about arrives with a customer ID attached. I know that history by itself isn’t a good reason to retain the marketer-centric definition of CDP since CDPs have outgrown their origins. But it doesn’t hurt to have something remind users outside of marketing that they should look closely at whether any particular CDP can fully support their needs. User control. The primary reason the CDP definition includes “marketer-controlled” is to distinguish CDPs from enterprise data warehouse (or data lake) projects run by corporate IT. That matters because such projects were historically multi-year undertakings that often failed altogether or required so many compromises among enterprise stakeholders that they didn’t meet all of marketing’s particular needs. Moreover, once built, such systems were slow and costly to update, so they didn’t adapt quickly to marketers’ fast-changing environment. This responsiveness is really the biggest change that CDPs introduced into the world of customer data management. As I noted earlier, department-controlled systems may soon be lost in a new round of centralization.  In theory, these new central systems will be vastly more responsive to user needs than the old central systems. But if I were a marketer, I’d be reluctant to give up my own systems until the new ones had proven their agility in practice.Departmental buyers. Whatever the long-term future of centralization, CDPs today are almost always bought by departments.  Even when corporate IT groups are managing the process, they are generally reacting to requests from departmental users rather than executing an enterprise master plan of their own. And when you start looking at which departments are making these purchases, marketing is by far the leader.  It’s true that other departments often find uses for a CDP once it’s deployed. Whether marketers really share control of their CDPs when this happens or simply treat other users as guests, I can’t say. From a seller’s stand[...]



Corporate IT Will Regain Control Over Marketing Technology (And That's Okay)

2017-03-14T08:47:29.781-04:00

One of my favorite factoids comes from a Computerworld survey in which IT managers placed marketing technologies at 20th of 28 items on their priority list. This either shows that martech managers are less important than they think or explains why martech managers are needed in the first place. Maybe both.But the disconnect between corporate IT and marketing technologists could be more than simply amusing. A recent Deloitte study described IT trends that seem incompatible with common martech strategies. If that’s really the case, martech may end up isolated from other company systems – or, more likely, huge investments in martech may ultimately be scrapped once it becomes essential to pull marketing back into the corporate IT fold.What are these worrisome trends? One was positioning of IT as an innovation driver; another was a loosely-coupled technical architecture of standards-based components; a third was replacing individual applications with enterprise-wide services. Each is problematic for a different reason:IT as an innovation driver suggests that IT should take a larger role in marketing technology than it plays when marketing runs its own systems. It probably also implies innovations that cross departmental boundaries, again reducing the independence of marketing technologists.a loosely-coupled architecture implies that marketing systems will conform to corporate standards to ensure interoperability. This also reduces marketing autonomy. More concretely, it conflicts with the internally-integrated marketing clouds and customer engagement platforms that some marketing departments have purchased to reduce complexity. enterprise-wide services suggest that marketing would be expected to use the corporate solutions rather than its own systems. This goes beyond setting standards for marketing systems to actually dictating at least some components of the marketing stack.These trends all pull towards reduced technical autonomy for marketing departments. Let's be clear that this doesn’t represent a power grab by IT departments seeking to regain lost turf. Indeed, the Deloitte paper mentions marketing just 12 times in 140 pages, confirming the Computerworld finding that marketing systems barely register as a concern for corporate IT.Rather, what’s happening here is a good-faith effort by IT managers to help their company and its customers. (The word “customer” occurs 139 times in the paper.)  Indeed, the paper proposes that technical innovations can lead to entirely new business models. Many of those models will surely integrate marketing functions as part of the whole. The paper also lists opportunities such as using machine intelligence to run systems more smoothly and more flexibly than human operators.  Those would benefit marketing systems as much as any others, putting the burden on marketing to justify keeping its system separate.There may be something oxymoronic about “tightly-integrated, loosely-coupled systems”. But that’s exactly what’s being proposed and it actually makes a great deal of sense. Perhaps the trend of marketing taking control of its own systems has run its course and the pendulum is now swinging back to centralization. If so, marketers, marketing technologists, and martech vendors need to ensure their systems meet the new requirements for loosely coupled integration. In fact, they should do this gladly, because the new integrations promise grand new benefits for companies and their customers.[...]



CaliberMind Offers B2B Orchestration with a Twist

2017-03-04T11:46:18.072-05:00

I spent quite a bit of time debating with myself how to classify CaliberMind. But instead of presenting my conclusion and defending it, I’ll just tell you what CaliberMind does. We’ll circle back to classification at the end.Unify B2B data. CaliberMind ingests data from Salesforce Sales cloud and Marketo, Oracle Eloqua, Salesforce Pardot, and HubSpot marketing automation systems. It reports on missing data and fills in the blanks using data from external vendors. It also uses those vendors to find identifiers belonging to the same person (such as multiple email addresses or alternative company names) and to link contact and lead records to accounts. The system can accept feeds from major advertising systems (GoogleAdwords, Bing, Facebook Ads), from Web analytics (Google Analytics, Mixpanel), and various data stores (MySQL, Amazon Redshift and S3, MongoDB, Apache Hive, etc.). CaliberMind has embedded a third-party data load and transformation tool to manage such inputs. The system stores structured data in Redshift, semi-structured data in MongoDB, and unstructured data in S3. Report on journeys. CaliberMind system builds an account-level journey visualization that shows different types of events (outbound contacts, inbound contacts, account created, opportunity created, deal won, etc.) on parallel time lines. It imports opportunity stages or account statuses from the source systems rather than creating its own journey stages. Attribution reports show the timing of different types of contacts relative to the date of the final sale, aggregated across multiple accounts. The system doesn’t explicitly report the impact of different contacts but it does consider their effects when recommending which messages to send next.Create personas. Users can define a list of personas and then assign them profile attributes such as job titles or company sizes. More interesting, they can also submit texts related to each persona. These might be job descriptions, advertising copy, blog posts, video transcripts, email messages, or anything else written in English. (Other languages will be added in the future.) The system uses natural language processing to analyze these and build a profile of what they have in common. This can later be used to determine how closely other texts match each persona. The system can also classify new contacts by persona, based on their profile attributes and associated texts such as content consumed or emails written. The assignments can be adjusted over time as new information becomes available.Match content to individuals. CaliberMind also uses the texts associated with each contact to build a personal profile. Because the language processor can understand things like level of interest, buyer role, and stage in the purchasing process, it can identify new and generate alerts about important events. The system can also pick up references to other individuals and infer their own roles and interests. Push results to other systems. CaliberMind draws on its individual-level profiles to push personality insights, engagement tips, and content recommendations to sales people. These can be loaded into the CRM database or displayed in a window on the CRM desktop. CRM users can also see the account-level journey reports and revenue summaries including forecasts. Marketing automation systems could get the same details but usually take more general information, such as persona codes used in segmentation. User-created rules can pick records meeting specified criteria and send them to different marketing automation campaigns. A Salesforce app is pending approval on the App Exchange and Salesforce single sign-on is scheduled for later this year.Expose detailed data. CaliberMind’s own interface lets users examine the data loaded into the system. Individual-level reports can display details down to the level of single emails or Web visits. These re[...]



Why MarTech Fails: A Data-Driven Answer

2017-02-26T12:30:17.526-05:00

Do you suffer from Martech Fatigue Syndrome (MFS)?  Symptoms include obsessive concern with the number of martech vendors, anxiety at the prospect of evaluating new systems, and fear of missing out on an important new capability.  Severe cases have reported hallucinations of vendor logos covering vast surfaces and nightmares of being buried under a collapsed martech stack.  MFS is rarely life-threatening but can disrupt the quality of your every day marketing.  If you or someone you know shows symptoms of MFS, please call Scott Brinker immediately.So far as I know, Martech Fatigue Syndrome is not yet a real thing.  But I've definitely sensed a certain weariness in recent discussions of marketing technology.  The initial excitement about new opportunities has become exhaustion as marketers realize they need to keep making investments even though they're not using their existing systems to the fullest.  See, for example, this Kitewheel study, which found 72% of agencies use less than 40% of their tools every week.So what’s the problem? Have marketers simply purchased the wrong technologies – after all, they’re new at the system buying business and martech is filled with bright and shiny distractions. Or are they buying the right systems only to find that other roadblocks get in the way of success?Many people have asked similar questions. So many, in fact, that I've found a half-dozen surveys in the past two months touched on the topic.  You can see the questions and their answers at the bottom of this post.But each survey asks different questions and gets slightly different answers.  To look for over-all patterns, I’ve combined the answers on the following table, putting similar items on the same row and keeping related ones nearby.  I’ve grouped the answers into general topic areas: organization, management support, marketing strategy, data management, delivery systems, and external factors. High-rated issues within each survey are shaded orange and low-rated issues are shaded green.  In other words, orange cells are the biggest problems, green cells are the smallest, and white cells are in between.The reason for all that careful arrangement is to see any clusters in the answers. Sure enough, some do emerge: the biggest problems are concentrated in organizational issues (lots of orange).  The one exception that people think their own skills are perfectly adequate. Of course.The management support area is mostly neutral except for a slash of orange for Return on Investment.  That make sense: measuring ROI is always a challenge for marketers.  To be clear, the answers are referring to the ROI of marketing programs in general, not martech investments in particular.  If anything, the surprise is that related items like management support and budget are less of a roadblock.Marketing strategy isn’t a major problem in most cases, with just one survey as an exception. As with skills, this basically means that marketers are confident they know how to do their jobs.The next two items, data management and delivery systems, are where technology comes in. There’s more green here than orange, confirming our hunch that access to adequate technology isn’t marketers’ main problem.The final group, external factors, is no problem at all.As a final bit of analysis, I've normalized all the answers to create a combined ranking for each category, splitting out ROI and internal skills since they are so different from everything else.  Apologies in advance to any real statistician who is horrified at the procedural flaws in this approach.   The rankings do seem to come out about right, and putting it all into one graph meets the goldfish attention span test.Bottom line: measuring ROI and organizational roadblocks are the biggest reasons marketers fall to get value from technolo[...]



Zaius Offers Mid-Market Customer Data Platform Plus Analytics and Campaigns

2017-02-17T12:26:58.782-05:00

It wasn’t until the end of a long demonstration that I finally understood what Zaius is. Which is pretty ironic, since they’re an almost perfect example of a Customer Data Platform – that is, a system that assembles customer data from multiple systems and makes it available for marketing and analytics. If anyone should recognize a CDP when they see one, it’s me. Come to think of it, if anyone is going to call something a CDP even when it isn't, that’s probably me, too.So what fooled me about Zaius? It’s probably that most of their clients are mid-sized ecommerce companies, and the systems I’ve recently seen for ecommerce marketers have focused on personalized messaging and optimization. Zaius seemed to fall into those categories since much of our discussion focused on building marketing campaigns and doing attribution. I probably wasn’t helped by Zaius’ Web site, which calls it a “B2C CRM” and then lists single customer view, real-time marketing automation, and cross-channel attribution as its main features.  Single customer view is clearly CDP territory, but the marketing automation and attribution are not. In fact, CRM and marketing automation are feeder systems to CDPs, so you could argue it’s logically impossible for the same system to be both.None of which really matters, I guess.  Let’s forget about labels and look at what Zaius does.Turns out, the primary thing that Zaius does is to build that unified customer database. It has connectors to gather data from Shopify and Magento ecommerce systems; Salesforce ExactTarget, Oracle Responsys, IBM Silverpop, MailChimp, and SendGrid email services;  and the Segment, Tealium and Google tag managers.*  More prebuilt connectors are on the way. In the meantime, Zaius can capture data from Web sites through Javascript tags, from mobile apps through a System Development Kit, and from pretty much anything through APIs and batch uploads. The system loads data into a structured schema, which must be updated to accommodate new fields or objects.   Non-technical users can add custom fields on their own, but Zaius staff must add a new object. The system will reject records that have unexpected or invalid data and notify users of the problem. Zaius doesn’t automatically apply address standardization or other data transformations, although the vendor can create custom adapters to do some of that. Once data is loaded, Zaius does deterministic identity resolution, which means it will chain together data using any identifier known to be associated with an individual. (For example, if a phone number and email address have been associated with the same person, any new record with either that phone number or email address will be linked to that person). It builds profiles of anonymous identifiers, such as cookies, and will link them to known individuals if they are later associated with a personal identifier. The system will merge identities if it discovers a connection, but it doesn’t do probabilistic matching across devices, fuzzy matching of similar postal addresses, or householding. The data loading process also includes sessionization, which associates events that occurred around the same time. For example, multiple Web page views during a single visit would be a session. Zaius assigns events to sessions after they are linked to unified identities, so one session can include interactions across several channels. This might help users find customers who called on the phone after having trouble placing a Web order.Zaius gives users tools to analyze the data it has captured, to create and export segments, and to run outbound marketing campaigns. Analytics include dashboards, attribution reports, and funnel analyses that track customers through a purchase process. Because Zaius is unifying data from multiple sources, [...]



LeadGenius Adds a Dash of Artificial Intelligence to Account Based Marketing

2017-02-13T14:17:34.465-05:00

You may have noticed that I’m writing a little less about artificial intelligence than I had been. It may be that Skynet has imprisoned the real David Raab to block him from issuing dire warnings about its imminent threat to humanity and replaced him with a less alarmist simulation. You can’t actually prove that isn’t happening. But the David Raab, or Raab-bot, writing this will tell you it’s because he’s concluded that AI is destined to become so pervasive that it doesn’t make sense to treat it as a distinct topic. It will simply be embedded in everything and so should be evaluated as part of whatever it belongs to.LeadGenius is a good example. The company is in the business of assembling B2B marketing lists – an industry dating back centuries to city directories and beyond. But LeadGenius was founded in 2011 to commercialize university research into combining AI with human inputs. It has since expanded from list gathering to all stages in the Account Based Marketing process, sprinkling in dashes of artificial intelligence at every step along the way.Let’s look at those stages, using the four step structure of the Raab Guide to ABM Vendors. 1. Identify target accounts. This includes assembling data on potential accounts and selecting the right targets. Like many data gatherers, LeadGenius uses a combination of Web and other sources to build company and contact lists. Nearly every vendor who does this applies some form of natural language processing to extract information from unstructured sources. LeadGenius does this too. But it goes further by using artificial intelligence to identify records with questionably accurate information. It then sends these to humans for direct verification by telephone. The company guarantees 99% accuracy in its data, which is significantly better than most competitors can offer. AI's contribution here is to let LeadGenius call only the companies that need human contact, reducing over-all effort substantially.  To find the right targets, LeadGenius loads a client's current CRM lists. It analyzes these for accuracy and completeness, providing users with reports that highlight problem areas. There’s probably some AI at work in that analysis. LeadGenius then identifies major file segments within the customer base and finds similar companies in the broader universe, estimating potential buyers and revenue by segment. Somewhat surprisingly , LeadGenius doesn’t create predictive lead scores, having found its more useful to prioritize prospects based on company attributes like size and industry. LeadGenius does use artificial intelligence, or at least its country cousin “fuzzy logic”, to map business titles into buyer roles, taking into account how different terms are used at different size companies to describe the same role.2. Plan interactions. LeadGenius has a basic email campaign capability, including segment definition, email templates with personalization variables, and email sequences. There don’t seem to be any particular AI features here, although we’ll see in a moment that email does play a key role in LeadGenius’ AI utilization.3. Execute interactions. LeadGenius sends emails through corporate or individual salespeople’s email accounts. It captures replies and uses AI-based natural language processing to classify them, distinguishing answers that indicate interest from out-of-office messages and clear rejections. Hot leads are pushed back to salespeople’s inboxes.  All response classifications are added to the database where they can be used in future selections. So AI does indirectly drive interaction flows. Response data can also be posted to Salesforce.com or Marketos, with additional integrations planned for the near future. Messages through other channels would have to be executed through mar[...]



Quaero AdVantage CDP Bridges Identified and Anonymous Data

2017-02-02T19:57:29.555-05:00

It’s a common pattern: several vendors proudly roll out new products they developed in secret, only to find they’re all very similar. The amazing coincidence isn’t really so amazing: everyone sees the same problems and has the same technologies available to solve them. So they come up with similar solutions.Simultaneous rollout.  I've had this picture in my head for years.  Apologies to Dr. Seuss.We’ve seen some of that in the Customer Data Platform industry, but there’s a twist. Many CDPs evolved from older systems and inherited some of their ancestors’ characteristics. One of those lineages goes back to marketing databases from simpler days, when postal mail and email were the main channels. The big challenges for those systems were loading complex data structures (addresses, transactions, message history, etc.), cleaning that data, and identifying records that belonged to the same individual. In that world, there was no such thing as an anonymous customer and most data was neatly structured. As I say, a simpler time.Quaero’s AdVantage is a good example of a system with deep roots in the old methods – but updated to handle modern challenges. Quaero itself was founded back in 1999 as a marketing services provider (meaning they built custom marketing databases and attached tools like the Unica campaign manager). It was purchased in 2008 by CSG International, a telecom customer communications specialist, and repurchased by the original management in 2014. By then, the managers had already started work on a next-generation platform designed to handle both traditional and online data, using relational databases for one and a NoSQL system (in this case, Hadoop) for the other. The company has recently introduced this to the market as AdVantage.The split architecture of AdVantage is actually pretty common among CDPs, since anonymous and identified customer data are often kept separate for privacy reasons. It’s also common to hold all the raw data in a NoSQL data lake and extract it to a relational database where it's refined and restructured for analysis. AdVantage does that too. It’s a bit less common for vendors to be so open about these details; Quaero management's transparency is probably another result of their maturity.What’s truly unusual is the sophistication of AdVantage’s data processing itself. After nearly two decades of wrestling with customer identities, Quaero has mastered tricks that many newer vendors have yet to see.* More concretely, the system provides over 1,000 prebuilt “workflows” that perform tasks within data staging, loading, cleaning, transformation, aggregation, scoring, and measurement. These can be configured to specific situations, giving users a great deal of power without writing actual queries or scripts. Workflows can also be strung together to create larger flows, which AdVantage visualizes nicely.  This lets users trace exactly how the system got to its results. Configuring the workflows is still definitely technical work, which is either done by IT staff or the Quaero services team. But AdVantage makes it more efficient than hand coding and vastly more accessible to anyone other than the original coder. Another important feature is that AdVantage flows work with metadata, meaning they are not mapped directly to the underlying data stores. This means an implementation can move to different platforms without losing most of the work. That makes it easier to adopt new technologies and to convert to more powerful platforms if a system outgrows its original installation. AdVantage’s features for working with identified customers are especially mature, handling different kinds of “fuzzy” name and address matching as well as creating a “golden record” of best values from all sour[...]



#FlipMyFunnel Launches Account Based Marketing University

2017-01-23T18:18:11.741-05:00

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Not the ABMU mascot
It sometimes seems that Account Based Marketing is really Marketing Automation 2.0, in that many people leading the charge to ABM were also involved in launching B2B marketing automation ten years or so ago. How none of us has aged a day is a mystery we shan't discuss.

One of the lessons learned during the growth of marketing automation was how important it is to get marketers trained in new techniques. The ABM community addressed this early with thought leadership from the ABM Consortium and now more extensively with the ABM University, a project launched earlier this month by the high-energy folks at #FlipMyFunnel.

ABMU offers 250 online lessons taught by more than 40 thought leaders, including Yours Truly (although in fact I haven’t done anything yet). There will be tests and a certificate of completion. Introductory price for the course is $500, planned to go up to $1,000. (Don’t be confused by the Sign Up for Free button on the Web page. They’re trying to get rid of it. !#$@#$ martech.)

Professors of #ABMU include:
Craig Rosenberg, Co-founder and Chief Analyst at Topo, Inc.
Christopher Long, Director of Marketing Operations at WP Engine
Maria Pergolino, SVP of Marketing, Global Marketing at Apttus
Matt Senatore, Service Director, Account-Based Marketing at SiriusDecisions
Koka Sexton, Founder at Social Selling Labs
Julia Stead, Director of Demand Generation at Invoca
Justin Gray, CEO at LeadMD, Inc.
Matt Heinz, President at Heinz Marketing
David Raab, Owner at Raab Associates
Tyler Lessard, CMO at Vidyard
Jill Rowley, Queen of #SocialSelling
Lincoln Murphy, Growth Architect at Sixteen Ventures

So far there is no ABMU fight song or mascot, although I have pointed out to them that Aardvark costumes are widely available at reasonable prices.

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Customer Data Platform Industry Profile: A Look Inside the Numbers

2017-01-19T09:02:42.787-05:00

My snarky twin at the Customer Data Platform Institute just published a new report on the CDP industry. Since few industry vendors release financial or business details, the report relies on public sources including Owler for revenue estimates, Crunchbase for funding history, and LinkedIn for employee counts. Most vendors did provide client counts, and several privately shared other information where the public data was clearly wrong. You can download the report here. I'll wait while you do that.  (Sound of fingers tapping.)Okay, you've downloaded it, right?  Good.As you see, the report only presents figures for the industry as a whole. We feel those are reasonably accurate but that data for individual vendors are too unreliable to show separately. That may sound illogical but bear in mind that figures for the larger vendors are more reliable, so many errors that are significant for individual small vendors don’t materially change the total. Also remember that some vendors provided information in confidence and we made estimates of our own for some others. I do feel I can safely publish statistics for three groups within the industry.  This gives some additional insight without exposing any proprietary or misleading vendor data.  The groups are based on each vendor's original business.  They are:Tag managers. This may seem an unlikely starting point, but it actually makes sense.  Tag management was originally about collecting data once (when a Web page loaded) and then sharing it with other systems that would otherwise have their own tags. This gave the Web site owner more control over what went where and reduced the number of tags on each page.  The data sharing was similar to what happens in integration platforms/data hubs  like Jitterbit and Zapier. So tag managers were always about data distribution.  To become true CDPs, the tag vendors had to ingest data from additional sources and send the data to a persistent database. Ingesting new sources can be challenging but vendors could grow incrementally by choosing which sources to accept.  Feeding a persistent data is basically just adding a new destination for data sharing.  So the transition to CDP offered a reasonable path to escape being a commodity tag manager. Campaign managers. I’m using this term loosely to include companies that offered any sort of marketing message selection. It includes systems that do email, Web site messages, mobile app messages, and omnichannel campaigns. These vendors all started out as CDPs in the sense that they always built unified customer databases. Among other things, this meant that most included reasonably robust cross-channel identity resolution. These vendors didn’t necessarily start by sharing their database with other systems.  But they do it now or I wouldn't consider them a CDP. Data assembly systems. This is a bit of a catch-all category but almost every system in this group was designed primarily to create a customer database that would be accessible to other systems.  Intended uses included analytics, marketing execution, or both. (I say "almost" because the group includes two systems that built databases primarily to support their own attribution services.) There’s more variety within this group than the other two.  But many vendors provide advanced identity resolution and all are strong at providing external access. Here are key statistics for each group. original purpose: vendors funding 2016 revenue customer count revenue / customer employee count revenue / employee Tag Management [...]



Artificial Intelligence, Virtual Reality, and Government Control: Perfect World or Perfect Storm?

2017-01-09T21:04:23.218-05:00

If it weren’t the print edition, I would have sworn today’s New York Times business section had been personalized for me: there were articles on self-driving cars, virtual reality, and how “Data Could Be the Next Tech Hot Button”. That precisely matches my current set of obsessions. It’s especially apt because the article on data makes a point that’s been much on my mind: government regulation may be the only factor that prevents AI-powered virtual reality from taking over the world, and governments may feel impelled to create such regulation in self-defense of their authority. The Times didn’t make that connection among its three articles.  But the fact that all three were top of mind for its editors and, presumably, readers was enough to illustrate their importance.I’m doubly glad that these articles appeared together because they reinforced my intent to revisit these issues in a more concise fashion than my rambling post on RoseColoredGlasses.Me. I suspect thread of that post got lost in self-indulgent exposition. Succinctly, the key points were:- Virtual reality and augmented reality will increasing create divergent “personal realities” that distance people from each other and the real world.- The artificial intelligence needed to manage personal reality be beyond human control. - Governments may recognize the dangers and step in to prevent them.  Maybe these points sound simplistic when stated so plainly. I’m taking that risk because I want to be clear.  But depth may add credibility. So let me expand on each point just a bit.- Personal reality. I covered this pretty well in the original post and current concerns about “fake news” and “fact bubbles” make it pretty familiar anyway.  One point that I think does need more discussion is how companies like Facebook, Google, Apple, and Amazon have a natural tendency to take over more and more of each consumer’s experience.  It's a sort of “individual network effect” where the more data one entity has about an individual, the better job they can do giving that person the consistent experience they want.  This in turn makes it easier to convince individuals to give those companies control over still more experiences and data. I’ll stress again that no coercion is involved; the companies will just be giving people what they want. It’s pitifully easy to imagine a world where people live Apple or Facebook branded lives that are totally controlled by those organizations. The cheesy science fictions stories pretty much write themselves (or the computers can write them for us).  Unrelated observation: it's weird the discussions which Descartes and others had about the nature of reality – which sound so silly to modern ears – are suddenly very practical concerns.- Artificial intelligence. Many people are skeptical that AI can really take control of our lives. For example, they’ll argue that machines will always need people to design, build, and repair them. But self-programming computers are here or very close (it depends on definitions), and essential machines will be designed to be self-repairing and self-improving.  Note that machines taking control doesn't require malevolent artificial intelligence, or artificial consciousness of any sort. Machines will take control simply because people let them make choices they can’t predict or understand. The problem is that unintended consequences are inevitable and for the first – and quite possibly the last – time in history, there will be no natural constraints to limit the impact of those consequences. Random example: maybe the machines will gently deter humans from breeding, something that could maxim[...]



Optimove Optibot Automates Campaign Optimization

2017-01-05T22:20:48.025-05:00

I finally caught up with Optimove for a briefing on the Optibot technology they introduced last September. For a bit of background, Optimove is a Journey Orchestration Engine that focuses on customer retention. It assigns customers to states (which it calls microsegments) and sends different marketing campaigns to people in each state. See my original Optimove review from three years ago (!) for a more detailed explanation.

What’s new about Optibot is that defining microsegments and picking the best campaign actions per segment have now been automated. Optimove previously analyzed performance of microsegments to find clusters within the microsegment with above or below average results. When it found one, it gave users a recommendation to treat these clusters as separate microsegments and potentially stop promoting to the poorly performing group. Optibit takes the human out of this loop, automatically splitting the microsegments into smaller microsegments when it can.

Optibot also automatically tests different actions against people within each microsegment. If it finds that different actions work better for different clusters, it will assign the best action to each group. (In practice, it slowly shifts the mix in favor of the better actions, to be more certain it is making a sound choice while minimizing the opportunity cost of poorly-performing actions.) The system gives reports that compare actual performance with what performance would have been without the additional segmentation and optimization. That’s a helpful reassurance to the user that Optibot is making good choices, and of course a nice little demonstration of Optimove’s value.

Finally, Optibot provides users with recommendations for things they can do, such as create new actions for microsegments that are not responding well to existing actions. I’ll assume that Optibot does this because it really can’t create new actions by itself, and not just so marketers have something to do other than watch cat videos all day.

I’m probably making Optibot sound simpler than it really is. There’s a lot of clever (and fully automated) analysis needed to find the right clusters, given that there are so many different ways the clusters could be defined. Optibot also needs a goal to pursue so it knows which actions and clusters are giving more desirable results. Defining those goals is also still a job for human marketers.  Fortunately, it only has to be done when a program is being set up, so it won’t cut too deeply into precious cat video viewing time.

Sarcasm aside, the real value of Optibot isn’t that it automates what marketers could otherwise do manually. It’s that it manages many more segments than humanly possible, allowing companies to fine-tune treatments for each group and to uncover pockets of opportunity that would otherwise be overlooked. Marketers will indeed need to create more content, and will no doubt find other productive uses for their time. And, frankly, if Optibot meant fewer 60 hour work weeks, that would be okay too.(image)



Boxever Puts Airline Data in Context for Better Passenger Experience

2017-01-06T11:31:57.120-05:00

Everyone loves a good origin story* and Boxever has a classic: the company started as system to recommend add-on purchases on airline booking sites but found that prospects lacked access to customer data, so it pivoted to build customer databases. Similar stories are common in the Customer Data Platform universe but it’s the details that make each one interesting. So let’s give Boxever a closer look.Boxever’s foundation is the customer database. The system can ingest data from any source and has prebuilt connectors for standard operational systems used by its clients (mostly airlines and travel agencies). Data can be loaded in real time during Web or call center interactions, by querying external sources through API connections, or through batch uploads. All inputs are treated as events, allowing the system to capture them without precise advance data modeling. But the system does organize inputs into a base structure of guests (i.e., customers), sessions, and orders. Clients can extend this model with additional objects such as order items. The system can usually classify new inputs automatically and flags the remainder for human review. The system is exceptionally good at capturing the frequently-changing elements peculiar to the travel industry, including location (current and destination), weather (at the current and destination location), prices, available products (e.g. vacant seats and upgrades), loyalty status, and even current flight information. Most of this data is read from external systems at the start of an interaction, used during the interaction to provide context, and stored with the interaction records for future analysis. Again reflecting the specialized needs of travel marketers, Boxever sessions can include things like airport visits, flights, or stays in a location, in addition to the conventional Web site visits or telephone calls. Boxever also provides extensive customer identification capabilities, both to support real-time interactions and to merge profiles behind the scenes. It can match on specific identifiers, such as a loyalty account number, on combinations of attributes such as last name and birthdate, and on similarities such as different forms of an address. It can assemble profiles on travel companions, who are often not as well known to an airline as the person who booked the ticket. It also calculates personal propensities to buy airline services and from specific partners such as hotels and retailers. These propensities are used to make recommendations.All this data is assembled by the system into personal profile that includes attributes and an event timeline. The event timeline captures both customer actions and system actions, such as running processes or changing data. The timeline can be displayed to customer service agents or used as inputs for automated decisions. Users can also define segments and contexts using any data in the personal profile.The decisioning features of Boxever are organized around offers. Users first set up templates for each offer type, in which they define the parameters required to construct an offer. Parameters vary depending on the channel that will deliver the offer and can include text, images, Web links, products and prices (which can be validated against external systems), and other components of the message to be delivered.  Other offer parameters include the context in which it's available and actions to take in other systems if the offer is accepted. Actual offers are created by filling in the appropriate parameters. Offers are embedded in decision engines. These contain rules that specify when particular offers are available. The d[...]



The World May Be Ending But, If Not: 3 Tips To Be a Better Marketer in 2017

2017-01-06T16:11:52.147-05:00

About eighteen months ago I started presenting a scenario of a woman named Jane riding in a self-driving car, unaware that her smart devices were debating whether to stop for gas and let her buy a donut. The point of the scenario was that future marketing would be focused on convincing consumers to trust the marketer’s system to make day-to-day purchasing decisions. This is a huge change from marketing today, which aims mainly to sell individual products. In the future, those product decisions will be handled by algorithms that consumers cannot understand in detail. So consumers’ only real choices will be which systems to trust. We can expect the world to divide itself into tribes of consumers who rely on companies like Amazon, Apple, Google, or Facebook and who ultimately end up making similar purchases to everyone else in their tribe.The presentation has been quite popular – especially the part about the donut. So far the world is tracking my predictions quite closely. To take one example, the script says that wireless connections to automobiles were banned after "the Minneapolis Incident of 2018". Details aren’t specified but presumably the Incident was a cyberattack that took over cars remotely. Subsequent reports of remote Jeep hacking hacking fit the scenario almost exactly and the recent take-down of the DYN DNS server by a botnet of nanny cams and smart printers was an even more prominent illustration of the danger. The resulting, and long overdue, concern about security on Internet of Things devices is just what I predicted from Minneapolis Incident.Fond as I am of that scenario, enough has happened to justify a new one. Two particular milestones were last summer’s mass adoption of augmented reality in the form of Pokémon Go and this autumn’s sudden awareness of reality bubbles created by social media and fake news.The new scenario describes another woman, Sue, walking down Michigan Avenue in Chicago. She’s wearing augmented reality equipment – let’s say from RoseColoredGlasses.Me, a real Web site* – that presents shows her preferred reality: one with trash removed from the street and weather changed from cloudy to sunshine. She’s also receiving her preferred stream of news (the stock market is up and the Cubs won third straight World Series). Now she gets a message that her husband just sent flowers to her office. She checks her hair in the virtual mirror – she looks marvelous, as always – and walks into a store to find her favorite brand of shoes are on sale. Et cetera.There’s a lot going on here. We have visual alterations (invisible trash and shining sun), facts that may or may not be true (stock market and baseball scores), events with uncertain causes (did her husband send those flowers or did his computer agent?), possible self-delusion (her hair might not look so great), and commercial machinations (is that really a sale price for those shoes?). It's complicated but the net result is that Sue lives in a much nicer world than the real one. Many people would gladly pay for a similar experience. It’s the voluntary nature of this purchase that makes RoseColoredGlasses.Me nearly inevitable: there will definitely be a market. Let’s call it “personal reality”.We have to work out some safeguards so Sue doesn’t trip over a pile of invisible trash or get run over by a truck she has chosen not to see. Those are easy to imagine. Maybe she gets BubbleBurstTM reality alerts that issue warnings when necessary.  Or, less jarringly, the system might substitute things like flower beds for trash piles. Maybe the street traffic is replaced by herds of[...]



BlueVenn Bundles Omnichannel Journey Management, Personalization, and Single Customer View

2016-12-14T19:52:10.609-05:00

BlueVenn has only been active in the U.S. market only since March 2016, although many U.S. marketers will recall its previous incarnation as SmartFocus.* The company offers what it calls an omnichannel marketing platform that builds a unified customer database, manages marketing campaigns, and generates personalized Web and email messages. The Venn in BlueVennThe unified database process, a.k.a. single customer view, has rich functionality to load data from multiple sources and do standardization, validation, enhancement, hygiene, matching, deduplication, governance and auditing. These were standard functions for traditional marketing databases, which needed them to match direct mail names and addresses, but are not always found in modern customer data platforms. BlueVenn also supports current identity linking techniques such as storing associations among cookies, email addresses, form submits, and devices. This sort of identity resolution is a batch process that runs overnight.  The system can also look up information about a specific customer in real time if an ID is provided. This lets BlueVenn support real time interactions in Web and call center channels.Users can enhance imported data by defining derived elements with functions similar to Excel formulas. These let non-technical users put data into formats they need without the help of technical staff. Derived fields can be used in queries and reports, embedded in other derived fields, and shared among users. To avoid nasty accidents, BlueVenn blocks changes in a field definition if the field is used elsewhere. Data can be read by Tableau and other third-party tools for analysis and reporting.BlueVenn offers several options for defining customer segments, including cross tabs, geographic map overlays, and flow charts that merge and split different groups.  But BlueVenn's signature selection tool has always the Venn diagram (intersecting circles).  This is made possible by a columnar database engine that is extremely fast at finding records with shared data elements. Clients could also use other databases including SQL Server, Amazon Redshift (also columnar), or MongoDB, although BlueVenn says nearly all its clients use the BlueVenn engine for its combination of high speed and low cost. Customer journeys - formerly known as campaigns - are set up by connecting icons on a flow chart. The flow can be split based on yes/no critiera, field values, query results, or random groups. Records in each branch can be sent a communication, assigned to seed lists or control groups, deduplicated, tagged, held for a wait period or until they respond, merged with other branches, or exit the flow. The “merge” feature is especially important because it allows journeys to cycle indefinitely rather than ending after a sequence of steps. Merge also simplifies journey design since paths can be reunified after a split. Even today, most campaign flow charts don’t do merges.BlueVenn Journey FlowTagging is also important because it lets marketers flag customers based on a combination of behaviors and data attributes. Tags can be used to control subsequent flow steps. Because tags are attached to the customer record, they can be used to coordinate journeys: one application cited by BlueVenn is to tag customers for future messages in multiple journeys and then periodically compare the tags to decide which message should actually be delivered. Communications are handled by something called BlueRelevance. This puts a line of code on client Web sites to gather click stream data, manage first party cookies, and deliver per[...]



Can Customer Data Platforms Make Decisions? Discuss.

2016-12-09T16:43:03.390-05:00

I’ve had at four conversations in the past twenty four hours with vendors who build a unified customer database and use it to guide customer treatments. The immediate topic has been whether they should be considered Customer Data Platforms but the underlying question is whether Customer Data Platforms should include customer management features.That may seem pretty abstract but bear with me because this isn’t really about definitions. It’s about what systems do and how they’re built.  To clear the ground a bit, the definition of CDP, per the CDP Institute, is “a marketer-managed system that creates a persistent, unified customer database that is accessible to other systems". Other people have other definitions but they are pretty similar. You’ll note there’s nothing in that definition about doing anything with data beyond making it available.  So, no, a CDP doesn’t need to have customer management features. But there’s nothing in the definition to prohibit those features, either. So a CDP could certainly be part of a larger system, in the same way that a motor is part of a farm tractor. But most farmers would call what they’re buying a tractor, not a motor. For the same reasons, I generally don’t to refer to systems as CDPs if their primary purpose is to deliver an application, even though they may build a unified customer database to support that application.The boundary gets a little fuzzier when the system makes that unified database available to external systems – which, you’ll recall, is part of the CDP definition. Those systems could be used as CDPs, in exactly the same way that farm tractors have “power take off” devices that use their motor to run other machinery.  But unless you’re buying that tractor primarily as a power source, you’re still going to think of it as a tractor. The motor and power take off will simply be among the features you consider when making a choice.*So much for definitions. The vastly more important question is SHOULD people buy "pure" CDPs or systems that contain a CDP plus applications. At the risk of overworking our poor little tractor, the answer is the same as the farmer’s: it depends it on how you’ll use it. If a particular system offers the only application you need, you can buy it without worrying about access by other applications. At the other extreme, if you have many external applications to connect, then it almost doesn’t matter whether the CDP has applications of its own. In between – which is where most people live – the integrated application is likely add value but you also want to with connect other systems. So, as a practical matter, we find that many buyers pick CDPs based on both integrated applications and external access.  From the CDP vendor’s viewpoint, this connectivity is helpful because it makes their system more important to their clients. The tractor analogy also helps show why data-only CDPs have been sold almost exclusively to large enterprises. Those companies have many existing systems that can all benefit from a better database.  In tractor terms, they need the best motor possible for power applications and have other machines for tasks like pulling a plow. A smaller farm needs one tractor that can do many different tasks. I may have driven the tractor metaphor into a ditch.  Regardless, the important point is that a system optimized for a single task – whether it’s sharing customer data or powering farm equipment – is designed differently from a system that’s designed to do several things. I[...]



3 Insights to Help Build Your Unified Customer Database

2016-12-01T15:26:55.759-05:00

The Customer Data Platform Institute (which is run by Raab Associates) on Monday published results of a survey we conducted in cooperation with MarTech Advisor. The goal was to assess the current state of customer data unification and, more important, to start exploring management practices that help companies create the rare-but-coveted single customer view. You can download the full survey report here (registration required) and I’ve already written some analysis on the Institute blog . But it’s a rich set of data so this post will highlight some other helpful insights. 1. All central customer databases are not equal.We asked several different questions whose answers depended in part on whether the respondent had a unified customer database. The percentage who said they did ranged from 14% to 72%:I should stress that these answers all came from the same people and we only analyzed responses with answers to all questions.  And, although we didn’t test their mental states, I doubt a significant fraction had multiple personality disorders. One lesson is that the exact question really matters, which makes comparing answers across different surveys quite unreliable. But the more interesting insight is there are real differences in the degree of integration involved with sharing customer data. You’ll notice the question with the fewest positive answers – “many systems connected through a shared customer database” describes a high level of integration.  It’s not just that data is loaded into a central database, but that systems are actually connected to a shared central database. Since context clearly matters, here is the actual question and other available answers: The other questions set a lower bar, referring to a “unified customer database” (33%), “central database (42%) and "central customer database” (57%). Those answers could include systems where data is copied into a central database but then used only for analysis. That is, they don’t imply connections or sharing with operational customer-facing systems. They also could describe situations where one primary system has all the data and thus functions as a central or unified database. The 72% question covered an even broader set of possibilities because it only described how customer data is combined, not where those combinations take place. That is, the combinations could be happening in operational systems that share data directly: no central database is required or even implied.  Here are the exact options:The same range of possibilities is reflected in answers about how people would use a single customer view. The most common answers are personalization and customer insights.  Those require little or no integration between operational systems and the central database, since personalization can easily be supported by periodically synchronizing a few data elements. It’s telling that consistent treatments ranks almost dead last – even though consistent experiences are often cited as the reason a central database is urgently required. This array of options to describe the central customer database suggests a maturity model or deployment sequence.  It would start with limited unification by sharing data directly between systems (the most common approach, based on the stack question shown above), progress to a central database that assembles the data but doesn’t share it with the operational systems, and ultimately achieve the perfect bliss of unity, which, in martech terms, means all operational systems are us[...]



Pega Customer Decision Hub Offers High-End Customer Journey Orchestration

2016-11-28T09:57:11.249-05:00

My previous posts about Journey Orchestration Engines (JOEs) have all pointed to new products. But some older systems qualify as well. In some ways they are even more interesting because they illustrate a mature version of the concept. The Customer Decision Hub from Pega (formerly PegaSystems) is certainly mature: the product can trace its roots back well over a decade, to a pioneering company called KiQ Limited, which was purchased in 2004 by Chordiant, which Pega purchased in 2010. Obviously the system has been updated many times since then but its core approach to optimizing real-time decisions across all channels has stayed remarkably constant. Indeed, some features the product had a decade ago are still cutting edge today – my favorite is simulation of proposed decision rules to assess their impact before deployment.Pega positions Customer Decision Hub as part of its core platform, which supports applications for marketing, sales automation, customer service, and operations. It competes with the usual enterprise suspects: Adobe, Oracle, Salesforce.com, IBM, and SAS. Even more than those vendors, Pega focuses on selling to large companies, describing its market as primarily the Fortune 3000. So if you’re not working at one of those firms, consider the rest of this article a template for what you might look for elsewhere.The current incarnation of Customer Decision Hub ihas six components: Predictive Analytics Director to build offline predictive models, Adaptive Decision Manager to build self-learning real-time models, Decision Strategy Manager to set rules for making decisions, Event Strategy Manager to monitor for significant events, Next Best Action Advisor to deliver decisions to customer-facing systems, and Visual Business Director for planning, simulation, visualization, and over-all management. From a journey orchestration perspective, the most interesting of these are Decision Strategy Manager and Event Strategy Manager, because they’re the pieces that select customer treatments. The other components provide inputs (Predictive Analytics Director and Adaptive Decision Manager), support execution (Next Best Action Advisor), or give management control (Visual Business Director).Decision Strategy Manager is where the serious decision management takes place. It brings together audiences, offers, and actions. Audiences can be built using segmentation rules or selected by predictive models. Offers can include multi-step flows with interactions over time and across channels. Actions can be anything, not just marketing messages, and may include doing nothing. They are selected using arbitration rules that specify the relevance of each action to an audience, rank the action based on eligibility and prioritization, and define where the action can be delivered. The concept of “relevance” is what qualifies Decision Hub as a JOE. It measures the value of each action against the customer’s current needs and context,. This is the functional equivalent of defining journey stages or customer states, even though Pega doesn’t map how customers move from one state to another. The interface to set up the arbitration rules is where Decision Hub’s maturity is most obvious. For example, users can build predictive model scores into decision rules and can set up a/b tests within the arbitration to compare different approaches. Event Strategy Manager lets users define events based on data patterns, such as three dropped phone calls within a week. These events can trigger specific actions or fa[...]



HubSpot Announces LinkedIn, Facebook Partnerships and Free Marketing Automation Edition at INBOUND Conference

2016-11-14T14:59:08.902-05:00

HubSpot held its annual INBOUND conference in Boston last week. Maybe it's me, but the show seemed to lack some of its usual self-congratulatory excitement: for example, CEO Brian Halligan didn’t present the familiar company scorecard touting growth in customers and revenues. (A quick check of financial reports shows those are just fine: the company is expecting about 45% revenue increase for 2016.) Even the insights that Halligan and co-founder Dharmesh Shah presented in their keynotes seemed familiar: I'm guessing you've already heard that video, social, messaging, free trials, and chatbots will be big.My own attention was more focused on the product announcements. The big news was a free version of HubSpot’s core marketing platform, joining free versions already available of its CRM and Sales systems. (In Hubspeak, CRM is the underlying database that tracks and manages customer interactions, while Sales is tools for salesperson productivity in email and elsewhere.)  Using free versions to grow marketing automation has consistently failed in the past, probably because people attracted by a free system aren't willing to do the substantial work needed for marketing automation success.  But HubSpot managers are aware of this history and seem confident they have a way to cost-effectively nurture a useful fraction of freemium users towards paid status. We'll see.The company also announced enhancements to existing products. Many were features that already exist in other mid-tier systems, including branching visual workflows, sessions within Web analytics reports, parent/child relationships among business records, and detailed control over user permissions. As HubSpot explained it, the modest scope of these changes reflects a focus on simplifying the system rather than making it super-powerful. One good example of this attitude was a new on-site chat feature, which seems basic enough but has some serious hidden cleverness in automatically routing chat requests to the right sales person, pulling up the right CRM record for the agent, and adding the chat conversation to the customer history. One feature that did strike me as innovative was closer to HubSpot’s roots in search marketing: a new “content strategy” tool reflecting the shift from keywords to topics as the basis of search results. HubSpot’s tool helps marketers find the best topics to try to dominate with their content.  This will be very valuable for marketers unfamiliar with the new search optimization methods. Still, what you really want is a system that helps you create that content.  HubSpot does seem to be working on that.With relatively modest product news, the most interesting announcements at the conference were probably about HubSpot’s alliances.  A new Facebook integration lets users create Facebook lead generation campaigns within HubSpot and posts leads from those campaigns directly to the HubSpot database. A new LinkedIn integration shows profiles from LinkedIn Sales Navigator within HubSpot CRM screens for users who have a Sales Navigator subscription. Both integrations were presented as first steps towards deeper relationships. These relationships reflect the growing prominence of HubSpot among CRM/marketing automation vendors, which gives companies like Microsoft and LinkedIn a reason to pick HubSpot as a partner. This, in turn, lets HubSpot offer features that less well-connected competitors cannot duplicate. That sets up a positive cycle of growth and exp[...]



ActionIQ Merges Customer Data Without Reformatting

2016-11-09T13:48:27.326-05:00

One of the fascinating things about the Customer Data Platform Institute is how developers from different backgrounds have converged on similar solutions. The leaders of ActionIQ, for example, are big data experts: Tasso Argyros founded Aster Data, which was later purchased by Teradata, and Nitay Joffe was a core contributor to HBase and the data infrastructure at Facebook.  In their previous lives, both saw marketers struggling to assemble and activate useful customer data. Not surprisingly, they took a database-centric approach to solving the problem.What particularly sets ActionIQ apart is the ability to work with data from any source in its original structure. The system simply takes a copy of source files as they are, lets users define derived variables based on those files, and uses proprietary techniques to query and segment against those variables almost instantly. It’s the scalability that’s really important here: at one client, ActionIQ scans two billion events in a few seconds. Or, more precisely, it’s the scalability plus flexibility: because all queries work by re-reading the raw data, users can redefine their variables at any time and apply them to all existing data. Or, really, it's scalability, flexibility, and speed, because new data is available within the system in minutes.So, amongst ActionIQ’s many advantages are scalability, flexibility, and speed. These contrast with systems that require users to summarize data in advance and then either discard the original detail or take much longer to resummarize the data if a definition changes.ActionIQ presents its approach as offering self-service data access for marketers and other non-technical users. That’s true insofar as marketers work with previously defined variables and audience segments. But defining those variables and segments in the first place takes the same data wrangling skills that analysts have always needed when faced with raw source data. ActionIQ reduces work for those analysts by making it easier to save and reuse their definitions. Its execution speed also reduces the cost of revising those definitions or creating alternate definitions for different purposes. Still, this is definitely a tool for big companies with skilled data analysts on staff.The system does have some specialized features to support marketing data. These include identity resolution tools including fuzzy matching of similar records (such as different versions of a mailing address) and chaining of related identifiers (such as a device ID linked to an email linked to an account ID). It doesn’t offer “probabilistic” linking of devices that are frequently used in the same location although it can integrate with vendors who do. ActionIQ also creates correlation reports and graphs showing the relationship between pairs of user-specified variables, such as a customer attribute and promotion response. But it doesn’t offer multi-variable predictive models or machine learning. ActionIQ gives users an interface to segment its data directly. It can also provide a virtual database view that is readable by external SQL queries or PMML-based scoring models. Users can also export audience lists to load into other tools such as campaign managers, Web ad audiences, or Web personalization systems. None of this approaches the power of the multi-step, branching campaign flows of high-end marketing automation systems, but ActionIQ says most of its clients are happy with simple list crea[...]



Walker Sands / Chief Martech Study: Martech Maturity Has Skyrocketed

2016-11-04T08:24:08.435-04:00

Tech marketing agency Walker Sands and industry guru Scott Brinker of Chief Martech yesterday published a fascinating survey on the State of Marketing Technology 2017, which you can download here.  The 27 page report provides an insightful analysis of the data, which there’s no point to me duplicating in depth. But I will highlight a couple of findings that are most relevant to my own concerns.Martech maturity has skyrocketed in the past year. This theme shows up throughout the report. The percentage of responders classifying their companies as innovators or early adopters grew from 20% in 2016 to 48% in 2017; marketers whose companies invest the right amount in marketing technology grew from 50% to 71%; all obstacles to adoption were less common (with the telling exception of not needing anything new).Truth be told, I find it hard to believe that things can have shifted this much in a single year and that nearly half of all companies (and 60% of individual marketers) are innovators or early adopters. A more likely explanation is the new survey attracted more advanced respondents than before.  We might also be seeing a bit of “Lake Wobegon Effect,” named after Garrison Keillor’s mythical town where all the children are above average.  Evidence for the latter might be that 69% felt their marketing technology is up to date and sufficient (up from 58%), making this possibly the most complacent group of innovators ever.Multi-product architectures are most common. I have no problem accepting this one: 21% of respondents said they use a single-vendor suite, while 69% had some sort of multi-vendor approach (27% integrated best-of-breed, 21% fragmented best-of-breed, 21% limited piecemeal solutions). The remainder had no stack (7%) or proprietary technology (4%). But don’t assume that “single-vendor suite” necessarily means one of the enterprise marketing clouds.  Small companies reported using suites just as often as large ones. They were probably referring to all-in-one products like HubSpot and Infusionsoft."Best of breed marketers get the most out of their martech tools." That’s a direct quote from the report, but it may overstate the case: 83% of integrated best-of-breed users felt their company was good or excellent at leveraging the stack, compared with 76% of the single-vendor-suite. That not such a huge difference, especially given the total sample of 335. Moreover, companies with fragmented best-of-breed stacks reported less ability (67%) than the single-vendor suite users. If you combine the two best-of-breed groups then the suite users actually come out ahead. A safer interpretation might be that single-vendor suites are no easier to use than best-of-breed combinations.  That would still be important news to companies that think pay a premium or compromise on features because they think suites make are easier to deploy.   Integration isn’t that much of a problem. Just 20% of companies cited better stack integration as a key to fully leveraging their tools, which ranked well behind better strategy (39%), better analytics (36%) and more training (33%) and roughly on par with more employees (23%), better defined KPIs (23%), and more data (20%). This supports the previous point about best-of-breed working fairly well, whether or not the stack was well integrated. I would have expected integration to be a bigger issue, so this is a bracing reality check. O[...]



Singing the Customer Data Platform Blues: Who's to Blame for Disjointed Customer Data?

2016-10-28T23:09:07.945-04:00

I’m in the midst of collating data from 150 published surveys about marketing technology, a project that is fascinating and stupefying at the same time. A theme related to marketing data seems to be emerging that I didn’t expect and many marketers won’t necessarily be happy to hear.Most surveys present a familiar tune: many marketers want unified customer data but few have it. This excerpt from an especially fine study by Econsultancy makes the case clearly although plenty of other studies show something similar.So far so good. The gap is music to my ears, since helping marketers fill it keeps consultants like me in the business. But it inevitably raises the question of why the gap exists.The conventional answer is it’s a technology problem. Indeed, this Experian survey makes exactly that point: the top barriers are all technology related.And, comfortingly, marketers can sing their same old song of blaming IT for failing to deliver what they need.  For example, even though 61% of companies in this Forbes Insights survey had a central database of some sort, only 14% had fully unified, accessible data. But something sounds a little funny. After all, doesn’t marketing now control its own fate? In this Ascend2 report, 61% of the marketing departments said they were primarily responsible for marketing data and nearly all the other marketers said they shared responsibility.Now we hear that quavering note of uncertainty: maybe it’s marketing’s own fault? That’s something I didn’t expect. And the data seems to support it. For example, a study from Black Ink ROI found that the top barrier to success was better analytics (which implicitly requires better data) and explicitly listed data access as the third-ranked barrier.But – and here’s the grand finale – the same study found that data integration software ranked sixth on the marketers’ shopping lists. In other words, even though marketers knew they needed better data, they weren’t planning to spend money to make it happen. That’s a sour chord indeed.But the song isn't over.  If we listen closely, we can barely make out one final chorus: marketers won’t invest in data management technology because they don’t have the skills to use it. Or that’s what this survey from Falcon.io seems to suggest. In its own way, that’s an upbeat ending. Expertise can be acquired through training or hiring outside experts (or possibly even mending some fences with IT). Better tools, like Customer Data Platforms, help by reducing the expertise needed. So while marketers aren't strutting towards a complete customer view with a triumphal Sousa march, there’s no need for a funeral dirge quite yet.[...]



Survey on Customer Data Management - Please Help!

2016-10-26T22:48:27.774-04:00

I'm working with MarTech Advisor on a survey to understand the state of customer data management.  If you have five minutes or so, could you please fill it out?  Link is here.  And if possible, pass on to people in other companies who could also help.  You'll get a copy of the final report and my gratitude.  Just for reading this far, here's a kitten:


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