Subscribe: Customer Experience Matrix
http://customerexperiencematrix.blogspot.com/atom.xml
Added By: Feedage Forager Feedage Grade A rated
Language: English
Tags:
customer data  customer  data  it’s  marketers  marketing  martech  new  system  systems  technology  users  vendors  web 
Rate this Feed
Rate this feedRate this feedRate this feedRate this feedRate this feed
Rate this feed 1 starRate this feed 2 starRate this feed 3 starRate this feed 4 starRate this feed 5 star

Comments (0)

Feed Details and Statistics Feed Statistics
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: 2017-05-27T09:42:54.017-04:00

 



Coherent Path Auto-Optimizes Promotions for Long Term Value

2017-05-24T19:34:42.806-04:00

One of the grand challenges facing marketing technology today is having a computer find the best messages to send each customer over time, instead of making marketers schedule the messages in advance.  One roadblock has been that automated design requires predicting the long-term impact of each message: just selecting the message with the highest immediate value can reduce future income. This clearly requires optimizing against a metric like lifetime value. But that's really hard to predict.

Coherent Path offers what may be a solution. Using advanced math that I won’t pretend to understand*, they identify offers that lead customers towards higher long-term values. In concrete terms, this often means cross-selling into product categories the customer hasn’t yet purchased.  While this isn’t a new tactic, Coherent Path improves it by identifying intermediary products (on the "path" to the target) that the customer is most likely to buy now.  It can also optimize other variables such as the time between messages, price discounts, and the balance between long- and short-term results

Coherent Path clients usually start by optimizing their email programs, which offer a good mix of high volume and easy measurability. The approach is to define a promotion calendar, pick product themes for each promotion, and then select the best offers within each theme for each customer. “Themes” are important because they’re what Coherent Path calculates different customers might be interested in. The system relies on marketers to tell it what themes are associated with each product and message (that is, the system has no semantic analytics to do that automatically). But because Coherent Path can predict which customers might buy in which themes, it can suggest themes to include in future promotions.

Lest this seem like the blackest of magic, rest assured that Coherent Path bases its decisions on data.  It starts with about two years’ of interactions for most clients, so it can see good sample of customers who have already completed a journey to high value status. Clients need at least several hundred products and preferably thousands. These products need to be grouped into categories so the system can find common patterns among the customer paths. Coherent Path automatically runs tests within promotions to further refine its ability to predict customer behaviors. Most clients also set aside a control group to compare Coherent Path results against customers managed outside the system. Coherent Path reports results such as 22% increase in email revenue and 10:1 return on investment – although of course your mileage may vary.

The system can manage other channels than email. Coherent Path says most of its clients move on to display ads, which are also relatively easy to target and measure. Web site offers usually come next.

Coherent Path was founded in 2012 and has been offering its current product for more than two years. Clients are mostly mid-size and large retailers, including Neiman Marcus, L.L. Bean, and Staples. Pricing starts around $10,000 per month.

_________________________________________________________________________
* Download their marketing explanation here or read an academic discussion here. (image)



Dynamic Yield Offers Flexible Omni-Channel Personalization

2017-05-20T16:15:44.531-04:00

There are dozens of Web personalization tools available. All do roughly the same thing: look at data about a visitor, pick messages based on that data, and deploy those messages. So how do you tell them apart?The differences fall along several dimensions. These include what data is available, how messages are chosen, which channels are supported, and how the system is implemented. Let’s look at how Dynamic Yield stacks up.Data: Dynamic Yield can install its own Javascript tag to identify visitors and gather their information, or it can accept an API call with a visitor ID. It can also build profiles by ingesting data from email, CRM, mobile apps, or third party sources. It will stitch data together when the same personal identifier is used in different source systems, but it doesn’t do fuzzy or probabilistic cross-device matching. Data is ingested in real time, allowing the system to react to customer behaviors as they happen.Message selection: this is probably where personalization systems vary the most. Dynamic Yield largely relies on users to define selection rules. Specifically, users create “experiences” that usually relate to a single position on a Web page or single message in another channel.  Each experience has a list of associated promotions and each promotion has its own target audience, content, and related settings. When a visitor engages with an experience, the system finds the first promotion audience the visitor matches and delivers the related content. This is a pretty basic approach and doesn’t necessarily deliver the best message to visitors who qualify for several audiences. But dynamic content rules, machine-learning, and automated recommendations can improve results by tailoring the final message to each individual. In addition, the system can test different messages within each promotion and optimize the results against a user-specified goal.  This lets it send different messages to different segments within the audience. Product recommendations are especially powerful.  Dynamic Yield supports multiple recommendation rules, including similarity, bought together, most popular, user affinity, and recently viewed.  One experience can return multiple products, with different products selected by different rules.  In other words, the system present a combination of recommendations including some that are similar to the current product, some that are often purchased with it, and some that are most popular over all.  Channels: this is a particular strength for Dynamic Yield, which can personalize Web pages, emails, landing pages, mobile apps, mobile push, display ads, and offline channels. Most personalization options are available in most channels, although there are some exceptions: you can’t do multi-product recommendations within a display ad and system-hosted landing pages can’t include dynamic content. Implementation: this also varies by channel. Web site personalization is especially flexible: the Javascript tag can read an existing Web page and either replace it entirely or create a version with a Dynamic Yield object inserted, without changing the page code itself. Users who do control the page code can insert a call the Dynamic Yield API.  Email personalization can also be done by inserting an API call, which lets Dynamic Yield reselect the message each time the email is rendered. The system has direct integration with major ad servers and networks, letting it send targeting rules with different ad versions for each target.Dynamic Yield’s multi-channel scope and easy deployment options will be appealing to many marketers. The company has more than 100 customers, primarily in ecommerce and media. Pricing is based on the number of unique user profiles managed and on system components. A small client might pay as little as $25,000 per year, although larger companies can pay much more.[...]



Will Privacy Regulations Favor Internet Giants?

2017-05-14T16:28:41.161-04:00

Last week’s MarTech Conference in San Francisco came and went in the usual blur of excellent presentations, interesting vendors, and private conversations. I’m sure each attendee had their own experience based on their particular interests. The two themes that appeared the most in my own world were:- data activation. This reflects recognition that customer data delivers most of its value when it is used to personalize customer treatments. In other words, it’s not enough to simply assemble a complete customer view and use it for analytics.  “Activation” means taking the next step of making the data available to use during customer interactions, ideally in real time and across all channels. It’s one of the advantages of a Customer Data Platform, which by definition makes unified customer data available to other systems. This is a big differentiator compared with conventional data warehouses, which are designed primarily to support analytical projects through batch updates and extracts.  Conventional data warehouse architectures load data into a separate structure called an “operational data store” when real-time access is needed. Many CDP systems use a similar technical approach but it’s part of the core design rather than an afterthought. This is part of the CDPs’ advantage of providing a packaged system rather than a set of components that users assemble for themselves. CDP vendors exhibiting at the show included Treasure Data, Tealium, and Lytics.- orchestration. This is creating a unified customer experience by coordinating contacts across all channels. It’s not a new goal but is standing out more clearly from approaches that manage just one channel. More precisely, orchestration requires a decision system that uses activated customer data to find best messages and then distributes them to customer-facing systems for delivery. Some Customer Data Platforms include orchestration features and others don’t; conversely, some orchestration systems are Customer Data Platforms and some are not. (Only orchestration systems that assemble a unified customer view and expose it to other systems qualify as CDPs.) Current frontiers for orchestration systems are journey orchestration, which is managing the entire customer experience as a single journey (rather than disconnected campaigns), and adaptive orchestration, which is using automated processes to find and deliver the optimal message content, timing, and channels for each customer. Orchestration vendors at the show included UserMind, Pointillist, Thunderhead, and Amplero.Of course, it wouldn’t be MarTech if the conference didn’t also provoke Deeper Thoughts. For me, the conference highlighted three long-term trends:- continued martech growth. The highlight of the opening keynote was unveiling of martech Uber-guru Scott Brinker’s latest industry landscape, which clocked in at 5,300 products compared with 3,500 the year before. You can read Brinker’s in-depth analysis here, so I’ll just say that industry growth shows no signs of slowing down.- primacy of data. Only a few presentations or vendors at the conference were devoted specifically to data, but nearly everything there depends on customer data in one way or another. And, as you know from my last blog post, the main story in customer data today is the increasing control exerted by Google and Facebook, and to a lesser degree Amazon, Apple, and Microsoft. If those firms succeed in monopolizing access to customer information, then many martech systems won’t have the inputs they need to work their magic. That could be the pin that bursts the martech bubble.- new privacy regulations. As Doc Searles (co-author of The Cluetrain Manifesto) pointed out in the second-day keynote , new privacy regulations also threaten to cut off the data supply of marketing and advertising systems, creating an “extinction level event”. Searles announced a “customer commons” that lets consumers share data on their own [...]



Martech Vendors Can't Avoid Ad Audience Battles

2017-05-06T15:13:52.304-04:00

It’s been said that sports are soap operas for men. You can see business news the same way: a drama with heroes, villains, intertwining story lines, and endless plot twists. One of the most interesting stories playing out right now is online advertising, where the walled gardens of Google, Facebook, and other audience aggregators are under assault by insurgent advertisers who, like most rebels, aspire as much to replace their overlords as destroy their power. What they’re really fighting over is control of the serfs – oops, I meant consumers – who create the empires' wealth.Recent complaints about ad measurement. audience transparency, and even placement near objectionable Web content are all tactics in the assault, aimed both at winning concessions and weakening their opponents. More strategically, support for letting broadband suppliers resell consumer data is an attempt create alternative suppliers who will strengthen the insurgents’ bargaining position. Yet another front opened up last week with an announcement from a consortium of adtech vendors, including AppNexus, LiveRamp, MediaMath, Index Exchange, LiveIntent, OpenX, and Rocket Fuel, that they had created a standard identity framework to support personal targeting of programmatic ads. The goal was to strengthen programmatic’s position as an alternative to the aggregators by making programmatic audiences larger, more targetable, and more unified across devices.The consortium was quite explicit on this goal. To quote the press release:"Today, 48 percent of all digital advertising dollars accrue to just two companies – Facebook and Google," said Brian O'Kelley, CEO of AppNexus. "That dynamic has placed considerable strain on the open internet companies that generate great journalism, film, music, social networking, and information. This consortium enables precision advertising comparable to that of Google and Facebook, and does so in a privacy-conscious manner. That means better outcomes for marketers, greater monetization for publishers, and more engaging content for consumers."But behind the rallying cry, the alliance between advertisers and programmatic ad suppliers is uneasy at best. After all, programmatic threatens the core ad buying business of the agencies and faces its own problems of measurement and objectionable ad placement. How the two groups cooperate against a common enemy will be a story worth watching.Martech vendors have so far remained pretty much neutral in the ad wars, feeding audiences to both sides with the pragmatic indifference of merchants throughout history. But the new ad tech consortium brings the battle closer, since it involves the personal identities that have been the martech vendors’ stock in trade. In particular, LiveRamp (which links anonymous cookies to known identities) belonging to the consortium creates a connection that will likely pull in other martech players. Of course, the convergence between adtech and martech has long been predicted – it's more than two years since I oh-so-cutely christened it “madtech”  and the big marketing clouds started to  purchase data management platforms and other adtech components even earlier.  The merger is probably inevitable as programmatic advertising looks more like personalized marketing every day.  Martech vendors have growing reason to side with the programmatic alliance as it becomes clear that audience aggregators could threaten their own kingdoms by cutting off access to personal data and taking control of contact opportunities. In short, what seems like a remote, and remotely entertaining, conflict in adland is more closely connected to the central martech story than you may think. So it’s worth watching closely and deciding what role your business will play when they call your cue.[...]



Infusionsoft Announces Freemium Marketing Automation to Expand Its User Base

2017-04-27T22:27:58.362-04:00

Infusionsoft has always presented combined methodical management of its own business with evangelical cheerleading for its small business clients. The contrast was even greater than usual at the company’s annual ICON conference in Phoenix this week. For Infusionsoft managers, the big news was a new product called Propel, which delivers prepackaged programs for business owners who don’t want to get involved in the details of marketing. For attendees, who were a largely partners and power users, the most exciting announcements were improvements to the current product such as a vastly better Web form builder. Propel will let Infusionsoft serve business owners who find the current product too complicated or expensive. Pretty much by definition, those people weren’t at ICON. So while Infusionsoft managers and some far-sighted partners were almost giddy about the growth that Propel could create for their businesses, the larger audience was more interested in ICON’s usual training sessions and inspirational hoopla. Propel addresses a fundamental problem that has limited the growth of all small business sales and marketing systems: the vast majority of small business owners don’t have the time, money, skills, or interest to use them well.  Vendors have addressed this either by reducing the required effort through easier-to-use interfaces, content templates, and prebuilt campaigns, or by providing services that do the work on business owners’ behalf.  Infusionsoft has done both and also used a relatively expensive mandatory start-up package ($999 or higher) to screen out buyers who aren't serious about using the system. This has worked well for Infusionsoft – the company has grown steadily, although it no longer releases client counts as it positions itself for an as-yet-unscheduled Initial Public Offering. But it also limits the market to the most aggressive small business owners. Infusionsoft sees Propel as a third way to serve less-ambitious businesses: not just by making the product simpler to use, but by removing some tasks altogether. For example, prebuilt campaign templates typically require users to create or customize the actual content, and often require them to set up campaign flows following cookbook-style directions. Propel will include default content tailored to a particular industry or product. It will automatically scrape a client’s Web site to find a logo and brand colors and apply them. When customization is unavoidable, Propel will let campaign designers build wizards that ask users key questions. The system will then automatically adjust the campaign by inserting relevant information or changing the campaign flow. The goal is campaigns that can be set up in a few minutes with no training and deliver immediately visible benefits. Infusionsoft hopes these will entice business owners who don’t want to commit from the start to a long-term marketing plan.The success of this approach is far from certain.  Business owners must still take some initial steps that could be daunting. Infusionsoft managers are acutely aware of the issues and doing everything they can to remove start-up barriers. This includes making it easy to import existing email addresses from phone contact lists, personal email accounts, spreadsheets, accounting systems, or elsewhere. More radically for Infusionsoft, there will be a free version of the system and no start-up fee. This will clearly attract a new set of less-committed users. Delivering enough value for these to stick with the system will be difficult. So will making the system so easy to use that customer support costs are close to zero. It could be hard for Infusionsoft’s entrepreneur-loving staff to limit the help they give to new clients. Infusionsoft also announced two other major changes during the conference. The simpler one was “partner first”, which translates to relying more on partners to train new cli[...]



Here's Why Airlines Treat Customers Poorly

2017-04-22T01:16:38.257-04:00

Last week’s passenger-dragging incident at United Airlines left many marketers (and other humans) aghast that any company could purposely assault its own customer. As it happens, airline technology vendor Sabre published a survey of airline executives just before the event. It confirms what you probably suspected: airline managers think differently from other business people.  And not in a good way.The chief finding of the study is that the executives rated technology as by far their largest obstacle to improving customer experience. This is very unusual: as I wrote in a recent post, most surveys place organizational and measurement issues at the top of the list, with technology much less of an issue. By contrast, the airline executives in the survey– who were about 1/3 from operations, 1/3 from marketing, sales, and service, and 1/3 from other areas including IT and finance – placed human resources in the middle and organizational structure, consensus, and lack of vision at the bottom.  The chart below compares the two sets of answers, matching categories as best I can.It would be a cheap shot to point out that the low weight given to “lack of vision” actually illustrates airline managers’ lack of vision. Then again, like everyone else who flies, I’ve been on the receiving end of many cheap shots from the airlines. So I’ll say it anyway. But I’ll also argue that the answers reflect a more objective reality: airlines are immensely complicated machines whose managers are inevitably dominated by operational challenges. This is not an excuse for treating customers poorly but it does explain how easily airline leaders can focus on other concerns. Indeed, when the survey explicitly asked about priorities, 51% rated improving operations as the top priority, compared with just 39% for aligning operations, marketing and IT, and only 35% for building customer loyalty.There’s a brutal utilitarian logic in this: after all, planes that don’t run on time inconvenience everyone. The study quotes Muhammad Ali Albakri, a former executive vice president at Saudi Arabian Airlines, as saying, “Two aspects generally take precedence when we recover irregular operations [such as bad weather]: namely crew schedules and legality and aircraft serviceability. Passengers’ conveniences and connecting passengers are also taken into consideration, depending on the situation.” In context , it’s clear that by “situation” he means whether the affected passengers are high-revenue customers.But as you may remember from that college philosophy course, most people reject pure utilitarianism because it ignores the worth of humans as individuals. Even if you believe businesses have no ethical obligations beyond seeking maximum profit, it’s bad practice to be perceived as heartless beasts because customers won’t want to do business with you. So airlines do need to make customer dignity a priority, even at the occasional cost of operational efficiency. Otherwise, as the United incident so clearly illustrates, the brand (and stock price) will suffer. If you’re a truly world-class cynic, you might argue that airlines are an oligopoly, so customers will fly them regardless of treatment. But it’s interesting to note that the Sabre paper makes several references to government regulations that penalize airlines for late arrivals and long tarmac waits. These factors clearly influence airline behavior. There's even a (pitifully slim) chance that Congress will respond to United's behavior. So the balance between operational efficiency and customer experience isn’t fixed. Airlines will react to political pressures, social media, and even passenger behaviors. The fierce loyalty of customers to airlines that have prioritized customer experience, such as Southwest and Virgin America, should be lesson to the others about what’s possible. That those airlines h[...]



Monetate Adds Machine-Learning Based Real Time Ecommerce Personalization

2017-04-13T21:45:13.926-04:00

Monetate is one of the oldest and largest Web testing and personalization vendors, founded in 2008 and now serving more than 350 brands. Its core clients have been mid-to-large ecommerce companies, originally in the U.S. and now also in Europe. I’ve been meaning to write about them for some time but when we finally connected late last year they had a major launch coming this April, so it made sense to hold off a little longer. That day has come. Monetate last week announced its latest enhancement, a machine-learning-powered “intelligent personalization engine” that supplements its older, rules-based approach. Machine learning by itself isn’t very exciting today: pretty much everybody seems to have it in some form. What makes the launch so important for Monetate is they had to rebuild their system to support the kind of machine learning they’re doing, which is real-time learning that reacts to each visitor’s behaviors as they happen,Montetate now holds its data in a “key-value store” (meaning, instead of placing data into predefined tables and fields, it stores each piece of information with one or more identifiers that specify its nature). This is a “big data” approach that lets the system add new types of information without creating a new table or field. In practical terms, it means Monetate can give each client a unique data structure, can rapidly add new data types and individual pieces of data, and can maintain a complete, up-to-the-moment profile for each customer. These are all essential for real-time machine learning. (Of course, the system still has some standard events shared by all clients, such as orders and customer service calls. These are needed to allow standard system functions.)Important as these changes are, the basic operation of Monetate is still the same. First, it builds a database of customer information. Then, it draws on that database to help test and personalize customer experiences.The database is built using Monetate’s own Javascript tags to capture behavior on the client’s ecommerce site. Users can also add other first- and third-party data through file uploads, by monitoring real-time data streams, or by querying external sources on demand. Monetate stitches together customer identities across sources and devices to create a complete profile. It can also build a product catalog either by scraping product information directly from the Web site or by importing batch files. Customer browsing and purchase behavior are matched against this catalog.Testing and personalization rely on Monetate’s ability to modify each visitor’s Web experience without changing the underlying Web site. It achieves this magic through the previously-mentioned Javascript tag, which can superimpose Monetate-created components such as hero images, product blocks, and sign-up forms. Users manage this process by creating campaigns, each of which contains a user-specified target audience, actions to take, schedule, and metrics. Users can designate one metric as the campaign goal; this is what the system will target in testing and optimization. They can track additional metrics for reporting purposes.The campaign audience can be based on Monetate’s 150 standard segments or draw on Web site behaviors, visitor demographics, local weather, imported lists, customer value, or other information derived from the database. Actions can virtually insert new objects on a Web page, or hide or edit existing objects. Users can build content with Monetate’s own tools or import content created in other systems. The content itself is dynamic so it can be personalized for each visitor. Actions can be reused across campaigns and campaigns can contain rules to select different actions in different situations. The new intelligent personalization engine automatically picks the best available content for each customer, drawing o[...]



Do CMOs Really Spend More on MarTech Than CIOs? A New Study Says No.

2017-03-29T22:11:12.410-04:00

Like many people in the marketing technology industry, I was tickled in 2011 when Gartner predicted that CMOs would soon have bigger tech budgets than CIOs, and even more tickled when Gartner said in 2016 that it had happened.  But my recent pondering of the relationship of marketing and IT departments had me rethinking the question. On an anecdotal level, I’ve never seen or heard of a company where the marketing technology group was anywhere near the size of the IT department. And from a revenue perspective, there’s no way that marketing technology companies make up half the total revenue of the software industry. But just as I was working myself up for some back-of-the-envelope calculations, the good people at International Data Corporation (IDC) announced a report with authoritative figures on the topic. Actually, the study estimates spending on 20 technologies and 12 corporate functional areas across 16 enterprise industries in eight regions and 53 countries, comparing the amounts funded by IT departments and by business departments. They haven’t published the figures for marketing in particular but did graciously provide them to me with permission to reprint them here. Without further ado, they are:As you see, marketing technology expenses for 2016 are estimated at $82.3 billion, which is just 6.7% of the $1,235.3 billion for all categories. Slightly more than half of the marketing spend is business-funded, which presumably means it’s spent by CMOs. But that wasn't what Gartner had in mind: they were definitely comparing corporate IT budgets against marketing IT budgets.  I understand Gartner's logic but I find the IDC figures more plausible. One reference point is the known revenues of martech vendors. Adobe, which may well be the largest, just reported $1.6 billion in 2016 revenue for its marketing cloud (apparently including analytics and advertising products).  Even if there are ten other vendors as large as Adobe, the top ten would have just a 20% share of the $82 billion. It’s hard to imagine the market is really that fragmented, even allowing for expenses that are unrelated to software. Another reason I prefer the IDC figures is that surveys consistently show that marketing technology is far down the priority list of IT managers.  That wouldn’t be the case if martech spend were equal to all other tech spending combined. Indeed, one of the main reasons that marketers have been eager to take control of their technology has been the neglect, benign or otherwise, shown by corporate IT. So let's assume the IDC figures are much closer to correct.  Does it matter?  I'd answer it does because understanding the real relationship between martech and other systems is important.  Marketers need to recognize that their systems are a small part of a big picture and can’t work independently of the rest of the company. Yes, marketers should control their internal systems. But the IDC figures show that sales and customer service spend more on tech than marketing. So, when it comes to customer-facing systems, marketers shouldn’t expect other departments to simply adopt marketing systems as a new core.More likely, all departments will need to coordinate their existing systems with a shared, enterprise-level resource. This suggests that the common core / edge model of marketing systems needs to modified to distinguish an enterprise-wide core from a marketing department core.  In some ways, this isn't a huge change because marketers have always co-existed with enterprise-core systems such as human resources and accounting.  But marketers are much more likely to want control of customer-related systems like the Web site and Customer Data Platform.  In this model, those are also part of the enterprise core.Of course, having enterprise core systems leads ri[...]



Wondering How Customer Data Platforms Relate to Other Marketing Systems? Here's a Picture

2017-03-27T16:06:37.117-04:00

I was asked the other day about the distinction between Customer Data Platforms and Journey Orchestration Engines. My immediate answer was “Some CDPs are JOEs and some JOEs are CDPs. A CDP is a JOE if has journey orchestration. A JOE is a CDP if its data is accessible to other systems. Think Venn diagram with two intersecting circles.”  It's not clear the answer helped, but it did get me thinking about clarifying with a Venn diagram.  The diagram I originally had in mind was this one, showing that CDPs unify customer data and make it available, while JOEs unify customer data and select messages. Systems that do all three are both a CDP and a JOE.On reflection, that’s not the right way to draw a Venn diagram. Each circle should represent one set of traits. So the picture should really look like this:That's fine, but it seems odd that “unify customer data” has no system associated with it. Is there a type of system that just unifies customer data without making it accessible or selecting messages? Come to think of it, there is.  Systems that just do customer matching used to be called Customer Data Integration but I don’t hear that much any more.  Sometimes people talk about Identity Resolution but mostly it seems that Customer Data Integration has been absorbed by the larger category of Master Data Management (MDM) systems, which integrate all kinds of data. So let’s add MDM as the label for that.     But why stop there?  Let's see how other systems would fit into the diagram. First to come to mind was marketing automation platforms (MAPs), which also select messages (like a JOE) but don’t build a unified customer database or offer open data access. The diagram with MAPs included looks like this:The next is a Data Lake. It provides open data access like a CDP, but doesn’t build a unified view of the data.  Adding that to the diagram gives us:Hmm, what about CRM? In many ways its out there with MAP: another system that selects messages but doesn’t build a unified database. So we need to introduce a new split, of marketer-controlled vs. sales controlled. I'll give control a different color for clarity.  Apologies to CRM people that your circle is so tiny; I'm not suggesting anything about the importance of your systems.Still thinking about control, Data Management Platforms (DMPs) look a lot like Marketing Automation Platforms: they’re marketer-controlled systems that select messages (sort of) but don’t unify data from all sources or provide open access. So unless we want to further subdivide the marketer controlled space, they share the same location as MAPs.Since control has its own color, Data Lake and MDM jump out as not having an owner. In fact, they’re both typically owned by corporate IT, so we can easily add that circle.This raises one more question: is there an IT-controlled equivalent of a CDP?  That would be a system that unifies customer data and provides open access but is owned by IT not marketing.  You betcha.  It might be an Enterprise Data Warehouse (EDW) if that has all the access features of a CDP (high speed, flexibility, etc.). But most EDWs don’t meet that standard. So let’s just call it an Enterprise-controlled CDP, or ECDP, if you’re wild and crazy enough to accept a four letter acronym. You’ll remember there’s some debate about whether marketing or IT should really own the CDP.  This doesn't provide an answer but it does give a clearer picture of the question.I've summarized this information in a table below.  Still confused?  We just posted a answers to Frequently Asked CDP Questions on the CDP Institute blog.  Maybe that will help.  [...]



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[...]



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 filte[...]



Forecast: Self-Assembling Application Bundles Will Manage Customer Experience

2017-05-14T14:10:20.524-04:00

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 g[...]



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 le[...]



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[...]



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. &n[...]



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 [...]



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 selecti[...]



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 i[...]



#FlipMyFunnel Launches Account Based Marketing University

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

(image)
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.

(image)



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. [...]



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[...]



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 ac[...]



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 [...]