Not the CDP Daily News

The World Health Organization has just declared that video addiction is a real disease but they've missed something even more insidious: the dangers of newsletter publishing. The CDP Institute Web site has been down for two days now (hopefully it will be back up by the time you read this and test that link), which means I haven't been able to publish the Institute's daily newsletter. (Yikes -- was my authorship a secret?)  This turns out to be very stressful for me, especially since I feel obligated to write the newsletter anyway so I'm ready whenever the site reappears. Gives a whole new meaning to the term "news junkie".

But, like the gun in a Chekov play, any copy that's created is begging to be used. So I'll post yesterday and today's items here for your enjoyment and my relief.  If you don't already subscribe and like what you see, visit the Institute site (once it's running) and join.

June 19, 2018

Google Invests $550 Million in Chinese E-Commerce Merchant JD.com
Source: GlobalNewswire
Just in case you had doubts that Google is serious about competing with Amazon in retail, consider this: Google just invested $550 million in Chinese e-commerce merchant JD.com. Google doesn’t do much business in China so this is about expanding in other markets and listing JD.com as a seller in Google Shopping. Google also announced several enhancements last week that help retailers display their inventory on-line and drive traffic to local stores. See this from The Street for more thoughts on the JD.com deal.

Adobe Expands Attribution Features
Source: Adobe
Adobe has expanded its attribution capabilities with Attribution IQ, an enhancement to Adobe Analytics that estimates the impact of campaigns in all channels on purchases. The offering includes ten different attribution models and lets users drill into results by customer segments, campaigns, and keywords.


IBM Computer Competes Effectively with Human Debaters
Source: CNET
I could tell you about Tru Optik’s Cross-Screen Audience Validation (CAV) service,
which draws on Tru Optik’s 75 million household database of smart TV viewers to give advertisers detailed information on audience demographics, reach and frequency by audience segment. But I doubt you care. So instead, ponder this: an IBM computer is now competing effectively with human debaters, showcasing skills like marshalling facts and choosing the most effective arguments. In other words: you’ll soon be able to argue with Alexa and lose.

June 20, 2018

RichRelevance Launches Next-Generation AI-Based Experience Personalization
Personalization vendor RichRelevance has launched its next generation of AI-based personalization tools. Key features include dynamic assembly of individual experiences, real-time performance tracking and continuous optimization. A helpful “Experience Browser” overlays the client’s Web site to display data, rules, and results for each decision in context. Marketers can set business rules to constrain the AI decisions and data scientists can draw on system data to define custom personalization strategies.


Automated Data Management: Immuta Raises $20 Million and Crate.io Raises $11 Million
Compared with AI-based personalization, automated data management gets relatively little attention, at least in martech circles. But its potential for solving the data unification problem is huge. Immuta, which marshals sensitive data for machine learning projects, just raised a $20 million Series B.
And Crate.io, an open source SQL database to manage feeds from machines and IoT devices, raised an $11 million Series A.  Now you know.

Mobile Phone Operators Take Baby Steps to Protect Location Data
I have a slew of other items about AI being used for cool things including seeing around corners, rendering 3D objects from photos, and delivering packages via two-legged robots (creepy!).  But let’s get back to reality with a report that several mobile operators were recently caught selling location data with little control over how it was used. The good news is that Verizon, AT&T and Sprint have shut off access to the two companies that were identified as misusing it. The bad news is, they’re still selling it to pretty much anyone else. Apple also recently changed App Store rules to limit apps publishers' access to people’s iPhone contact lists.  So maybe this is progress.

Customer Data Platforms Spread Their Wings

I escaped from my cave this week to present at two conferences: the first-ever “Customer Data Platform Summit” hosted by AgilOne in Los Angeles, preceding Shop.org, and the Technology for Marketing conference in London, where BlueVenn sponsored me. I listened as much as could along the way to find what’s new with the vendors and their clients. There were some interesting developments.

• Broader awareness of CDP. The AgilOne event was invitation-only while the London presentation was open to any conference attendee, although BlueVenn did personally invite companies it wanted to attend. Both sets of listeners were already aware of CDPs, which isn’t something I’d expect to have seen a year or two ago. Both also had a reasonable notion of what a CDP does. But they still seemed to need help distinguishing CDPs from other types of systems, so we still have plenty more work to do in educating the market.

• Use of CDPs beyond marketing. People in both cities described CDPs being bought and used throughout client organizations, sometimes after marketing was the original purchaser and sometimes as a corporate project from the start. That was always a potential but it’s delightful to hear about it actually happening. The widely a CDP is used in a company, the more value the buyer gets – and the more benefit to the company’s customers. So hooray for that.

• CDPs in vertical markets. The AgilOne audience were all retailers, not surprisingly given AgilOne’s focus and the relation of the event to Shop.org. But I heard in London about CDPs in financial services, publishing, telecommunications, and several other industries where CDP hasn’t previously been used much. More evidence of the broader awareness and the widespread need for the solution that CDP provides.

• CDP for attribution. While in London I also stopped by the office of Fospha, another CDP vendor which has just become a Sponsor of the CDP Institute. They are unusual in having a focus on multi-touch attribution, something we’ve seen in a couple other CDPs but definitely less common than campaign management or personalization. That caught my attention because I just finished an analysis of artificial intelligence in journey orchestration, in which one major conclusion was that multi-touch attribution will be a key enabling technology. That needs a blog post of its own to explain, but the basic reason is AI needs attribution (specifically, estimating the incremental value of each marketing action) as a goal to optimize against when it's comparing investments in different marketing tasks  (content, media, segmentation, product, etc.)

If there's a common thread here, it's that CDPs are spreading beyond their initial buyers and applications.  I’ll be presenting next week at yet another CDP-focused event, this one sponsored by BlueConic in advance of the Boston Martech Conference. Who knows what new things we'll see there?

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

It wasn’t until the end of a long demonstration that I finally understood what Zaius is. Which is pretty ironic, since they’re an almost perfect example of a Customer Data Platform – that is, a system that assembles customer data from multiple systems and makes it available for marketing and analytics. If anyone should recognize a CDP when they see one, it’s me. Come to think of it, if anyone is going to call something a CDP even when it isn't, that’s probably me, too.

So what fooled me about Zaius? It’s probably that most of their clients are mid-sized ecommerce companies, and the systems I’ve recently seen for ecommerce marketers have focused on personalized messaging and optimization. Zaius seemed to fall into those categories since much of our discussion focused on building marketing campaigns and doing attribution. I probably wasn’t helped by Zaius’ Web site, which calls it a “B2C CRM” and then lists single customer view, real-time marketing automation, and cross-channel attribution as its main features.  Single customer view is clearly CDP territory, but the marketing automation and attribution are not. In fact, CRM and marketing automation are feeder systems to CDPs, so you could argue it’s logically impossible for the same system to be both.

None of which really matters, I guess.  Let’s forget about labels and look at what Zaius does.

Turns out, the primary thing that Zaius does is to build that unified customer database. It has connectors to gather data from Shopify and Magento ecommerce systems; Salesforce ExactTarget, Oracle Responsys, IBM Silverpop, MailChimp, and SendGrid email services;  and the Segment, Tealium and Google tag managers.*  More prebuilt connectors are on the way. In the meantime, Zaius can capture data from Web sites through Javascript tags, from mobile apps through a System Development Kit, and from pretty much anything through APIs and batch uploads. The system loads data into a structured schema, which must be updated to accommodate new fields or objects.   Non-technical users can add custom fields on their own, but Zaius staff must add a new object. The system will reject records that have unexpected or invalid data and notify users of the problem. Zaius doesn’t automatically apply address standardization or other data transformations, although the vendor can create custom adapters to do some of that.

Once data is loaded, Zaius does deterministic identity resolution, which means it will chain together data using any identifier known to be associated with an individual. (For example, if a phone number and email address have been associated with the same person, any new record with either that phone number or email address will be linked to that person). It builds profiles of anonymous identifiers, such as cookies, and will link them to known individuals if they are later associated with a personal identifier. The system will merge identities if it discovers a connection, but it doesn’t do probabilistic matching across devices, fuzzy matching of similar postal addresses, or householding.

The data loading process also includes sessionization, which associates events that occurred around the same time. For example, multiple Web page views during a single visit would be a session. Zaius assigns events to sessions after they are linked to unified identities, so one session can include interactions across several channels. This might help users find customers who called on the phone after having trouble placing a Web order.

Zaius gives users tools to analyze the data it has captured, to create and export segments, and to run outbound marketing campaigns. Analytics include dashboards, attribution reports, and funnel analyses that track customers through a purchase process. Because Zaius is unifying data from multiple sources, its analyses can span events that happened in different systems. This means a funnel report could include an outbound email, a Web visit from a link in the email, and an ecommerce purchase during that visit. Neither the email or ecommerce system alone could track this entire path.

The system can report on customers at different stages in the life cycle, giving a useful overview of the user's business. It also lets users see tactical metrics, such as number of new customers acquired in the past month, and then drill down to see which campaigns produced those customers. Users who want to explore still further can look as deep as the specific events within an individual customer’s history. Security features can limit users to specified subsets of data, such as particular Web sites or product groups.

Segmentation in Zaius can draw on any data in the system. The system provides a form-based segment builder that can create complex expressions. These can be saved and used within other segment definitions. Users can export segments to other systems, including a two-way audience synchronization with Facebook and Google. In addition, a real-time API lets external systems query Zaius directly to find individual customer profiles. Segment exports and API access are what qualify Zaius as a CPD.

Segments can in turn be used in marketing campaigns.  These are built with templates that let users specify the channel (email, push, or SMS) and delivery type (once, recurring, continuous, or event triggered). Users can create email messages using a drag-and-drop interface that supports advanced personalization, such as selecting the top products in a customer’s most commonly purchased category. Personalization variables can be built with a scripting language or by inserting pre-built objects. Testing features let users define a test duration, evaluation criterion, and content versions. Users can set aside a portion of the audience to automatically receive the winning version when the test is complete. Zaius lacks more advanced optimization such as multi-variate tests that automatically create different combinations of features, finding segments within the audience that respond best to different versions, or predictive modeling.  Zaius sends email and SMS lists to external vendors for delivery. It uses Amazon SNS to send push messages. The vendor plans to add direct mail and browser push channels in the future.

Zaius was launched in 2014, with an original focus on providing a unified customer view and analytics. Its initial clients were large enterprises but most sales are now to mid-market firms with at least 100,000 contacts. Pricing is based on volume and starts at $1,000 per month.


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*Segment and Tealium are CDPs themselves, but let’s not confuse things even more.

Datorama Applies Machine Intelligence to Speed Marketing Analytics

As I mentioned a couple of posts back, I’ve been surveying the borders of Customer Data Platform-land recently, trying to figure out which vendors fit within the category and which do not. Naturally, there are cases where the answer isn’t clear. Datorama is one of them.

At first glance, you’d think Datorama is definitely not a CDP: it positions itself as a “marketing analytics platform” and makes clear that its primary clients are agencies, publishers, and corporate marketers who want to measure advertising performance. But the company also calls itself a “marketing integration engine” that works with “all of your data”, which certainly goes beyond just advertising. Dig a bit deeper and the confusion just grows: the company works mostly with aggregated performance data, but also works with some individual-level data.  It doesn’t currently do identity resolution to build unified customer profiles, but is moving in that direction. And it integrates with advertising and Web analytics data on one hand and social listening, marketing automation, and CRM on the other. So while Datorama wasn’t built to be a CDP – because unified customer profiles are the core CDP feature – it may be evolving towards one.

This isn't to say that Datorama lacks focus. The system was introduced in 2012 and now has over 2,000 clients, including brands, agencies, and publishers. It grew by solving a very specific problem: the challenges that advertisers and publishers face in combining information about ad placements and results. Its solution was to automate every step of the marketing measurement process as much as it could, using machine intelligence to identify information within new data sources, map those to a standard data model, present the results in dashboards, and uncover opportunities for improvement. In other words, Datorama gives marketers one system for everything from data ingestion to consolidation to delivery to analytics.  This lets them manage a process that would otherwise require many different products and lots of technical support. That approach – putting marketers in control by giving them a system pre-tailored to their needs – is very much the CDP strategy.

Paradoxically, the main result of Datorama’s specialization is flexibility. The system’s developers set of goal of handling any data source, which led to a system that can ingest nearly any database type, API feed or file format, including JSON and XML; automatically identify the contents of each field; and map the fields to the standard data model. Datorama keeps track of what it learns about common source systems, like Facebook, Adobe Analytics, or AppNexus, making it better at mapping those sources for future implementations. It can also clean, transform, classify, and reformat the inputs to make them more usable, applying advanced features like rules, formulas, and sentiment analysis. At the other end of the process, machine learning builds predictive models to do things like estimate lifetime value and forecast campaign results. The results can be displayed in Datorama’s own interface, read by business intelligence products like Tableau, or exported to other systems like marketing automation.

Datorama’s extensive use of machine learning lets it speed up the marketing analytics process while reducing the cost. But this is still not a push-button solution. The vendor says a typical proof of concept usually takes about one month, and it takes another one to two months more to convert the proof of concept into a production deployment. That’s faster than your father’s data warehouse but not like adding an app to your iPhone. Pricing is also non-trivial: a small company will pay in the five figures for a year’s service and a large company's bill could reach into seven figures. Fees are based on data volume and number of users. Datorama can also provide services to help users get set up or to run the system for them if they prefer.