Monday, February 19, 2018

How Customer Data Platforms Help with Marketing Performance Measurement

John Wanamaker, patron saint of marketing measurement.
If you’ve been following my slow progress towards a set of screening questions for Customer Data Platforms, you may recall that “incremental attribution” was on the list. The original reason was that some of the systems I first identified as CDPs offered incremental attribution as their primary focus. Attribution also seemed like a specific enough feature that it could be meaningfully distinguished from marketing measurement in general, which nearly any CDP could support to some degree.

But as I gathered answers from the two dozen vendors who will be included the CDP Institute’s comparison report, I found that at best one or two provide the type of attribution I had in mind.  This wasn't enough to include in the screening list.  But there was an impressive variety of alternative answers to the question.  Those are worth a look.

- Marketing mix models.  This is the attribution approach I originally intended to cover. It gathers all the marketing touches that reach a customer, including email messages, Web site views, display ad impressions, search marketing headlines, and whatever else can be captured and tied to an individual. Statistical algorithms then look at customers who had a similar set of contacts except for one item and attribute any difference in performance to that.  In practice, this is much more complicated than it sounds because the system needs to deal with different levels of detail and intelligently combine cases that lack enough data to treat separately.  The result is an estimate of the average value generated by incremental spending in each channel. These results are sometimes combined with estimates created using different techniques to cover channels that can’t be tied to individuals, such as broadcast TV. The estimates are used to find the optimal budget allocation across all channels, a.k.a. the marketing mix.

- Next best action and bidding models.  These also estimate the impact of a specific marketing message on results, but work at the individual rather than channel levels. The system uses a history of marketing messages and results to predict the change in revenue (or other target behavior) that will result from sending a particular message to a particular individual. One typical use is deciding how much to bid for a display ad impression; another is to choose products or offers to make during an interaction. They differ from incremental attribution because they create separate predictions for each individual based on their history and the current context. Several CDP systems offer this type of analysis.  But it’s ultimately not different enough from other predictive analytics to treat it as a distinct specialty.

- First/last/fractional touch.  These methods use the individual-level data about marketing contacts and results, but apply fixed rules to allocate credit.  They are usually limited to online advertising channels.  The simplest rules are to attribute all results to either the first or last interaction with a buyer.  Fractional methods divide the credit among several touches but use predefined rules to do the allocation rather than weights derived from actual data.  These methods are widely regarded as inadequate but are by far the most commonly used because alternatives are so much more difficult.  Several CDPs offer these methods. 

- Campaign analysis. This looks at the impact of a particular marketing campaign on results. Again, the fundamental method is to compare performance of individuals who received a particular treatment with those who didn’t. But there’s usually more of an effort to ensure the treated and non-treated groups are comparable, either by setting up a/b test splits in advance or by analyzing results for different segments after the fact. The primary unit of analysis here is the campaign audience, not the specific individuals. The goal is usually to compare results for campaigns in the same channel, not to compare efforts across channels. This is a relatively simple type of analysis to deliver since it doesn’t required advanced statistics or predictive techniques. As a result, it’s fairly common or could be delivered by many systems even without the vendor creating special features to do it.

- Content performance analysis. This is very similar to campaign analysis except that audiences are defined as people who received a particular piece of content, which could be used across several campaigns. Again, there might be formal split tests or more casual comparison of results. Some implementation draw broader conclusions from the data by grouping content with similar characteristics such as product, message, or offer. But unless the groups are identified using artificial intelligence, even this doesn’t add much technical complexity.

- Journey analysis. Truth be told, no vendor in my survey described journey analysis as a type of incremental attribution. But it does come up in some discussions of marketing measurement and optimization. Like marketing mix and next best action methods, journey analysis examines individual-level interactions to find larger patterns and to identify optimal choices for reaching specified goals. But it looks much more closely at the sequence of events, which requires different technical approaches to deal with the higher resulting complexity.

Marketing measurement is one of the primary uses of Customer Data Platforms. Dropping attribution from the list of CDP screening questions shouldn't be interpreted to suggest it’s unimportant. It just means it’s that measurement  is too complicated to embed in a simple screening question. As with other important CDP features, buyers who want their CDP to support marketing measurement will need to define their specific needs in detail and then closely examine individual CDP vendors to see who can meet them.

Sunday, February 18, 2018

Will GDPR Hurt Customer Data Platforms and the Marketers Who Use Them?

Like an imminent hanging, the looming execution of the European Union’s General Data Protection Regulation (GDPR) has concentrated business leaders’ minds on their customer data. This has been a boon for Customer Data Platform vendors, who have been able to offer their systems as solutions to many GDPR requirements. But it raises some issues as well.

First the good news: CDPs are genuinely well suited to help with GDPR. They’re built to solve two of GDPR’s toughest technical challenges: connecting all internal sources of customer data and linking all data related to the same person. In particular, CDPs focus on first party (i.e., company-owned) personally identifiable information and use deterministic matching to ensure accurate linkages. Those are exactly what GDPR needs. Some CDP vendors have added GDPR-specific features such as consent gathering, usage tracking, and data review portals. But those are relatively easy once you’ve assembled and linked the underlying data.

GDPR is also good for CDPs in broader ways. Most obviously, it raises companies’ awareness of customer data management, which is the core CDP use case. It will also raise consumers' awareness of their data and their rights, which should lead to better quality customer information as consumers feel more confident that data they provide will be handled properly. (See this Accenture report that 75% of consumers are willing to share personal data if they can control how it’s used, or this PegaSystems survey in which 45% of EU consumers said they would erase their data from a company that sold or shared it with outsiders.)  Conversely, GDPR-induced constraints on acquiring external data should make a company’s own data that much more valuable.

Collection requirements for GDPR should also make it easier for companies to tailor the degree of personalization to individual preferences.  This Adobe study found that 28% of consumers are not comfortable sharing any information with brands and 26% say that too-creepy personalization is their biggest annoyance with brand content. These results suggest there’s a segment of privacy-focused consumers who would value a privacy-centric marketing approach. (That this approach would itself require sophisticated personalization technology is an irony we marketers can quietly keep to ourselves.)

So, what's not to like?  The downside to GDPR is that greater corporate interest in customer data means that marketers will not be left to manage it on their own.  Marketing departments have been the primary buyers of Customer Data Platforms because corporate IT often lacks the interest and skills needed to meet marketing needs.  GDPR and digital transformation don't give IT new resources but they do mean it will be more involved.  Indeed, this report from data governance vendor Erwin  found that responsibility for meeting data regulations is held by IT alone at 36% of companies and is shared between IT and all business units (not just marketing) at another 55%.  I’ve personally heard many recent stories about corporate IT buying CDPs.

Selling to IT departments isn’t a problem for CDP vendors. Their existing technology should work with little change.  At most, they'll need to retool their sales and marketing. But marketers may suffer more. Corporate IT will have its own priorities and marketing won’t be at the top of the list. For example, this report from master data management vendor Semarchy found that customer experience, service and loyalty applications take priority over sales and marketing applications. More broadly, studies like this one from ComputerWorld consistently show that IT departments prioritize productivity, security and compliance over customer experience and analytics. Putting IT and legal departments in charge of customer data is likely to mean a more conservative approach to how it's used than marketers would apply on their own.  This may prevent some problems but it's also likely to make marketers' jobs harder.

A greater IT role may also reverse the current trend of adding analytical and marketing applications to CDP data management functions. Marketers generally like those applications because it saves them the trouble of buying and integrating separate analytical and marketing systems. IT departments won’t use those features themselves and will probably be more interested in making sure CDP data can be shared by external applications from all departments. Similarly, IT buyers may favor CDP designs that are less tuned specifically to marketing needs and more open to multiple uses. This will favor some technical approaches over others.

The final result is likely to be clearer division of the CDP market into systems that focus on enterprise-wide customer data management and that give marketers integrated data, analytics, and customer engagement. If both types of vendors find enough buyers to survive, the expanded choice means that everyone wins. But the combined data, analytics and execution CDPs could be squeezed between data-only CDPs and the integrated applications of big marketing clouds. If there's not enough room left for them, marketers choices will be reduced.  Should that happen, GDPR will have done CDP vendors and marketers more harm than good.

Friday, February 02, 2018

Celebrus CDP Offers In-Memory Profiles

It’s almost ten years to the day since I first wrote about Celebrus, which then called itself speed-trap (a term that presumably has fewer negative connotations in the U.K. than the U.S.). Back then, they were an easy-to-deploy Web site script that captured detailed visitor behaviors. Today, they gather data from all sources, map it to a client-tailored version of a 100+ table data model, and expose the results to analytics and customer engagement systems as in-memory profiles.

Does that make them a Customer Data Platform? Well, Celebrus calls itself one – in fact, they were an early and enthusiastic adopter of the label. More important, they do what CDPs do: gather, unify, and share customer data. But Celebrus does differ in several ways from most CDP products:

- in-memory data. When Celebrus described their product to me, it sounded like they don’t keep a persistent copy of the detailed data they ingest. But after further discussion, I found they really meant they don’t keep it within those in-memory profiles. They can actually store as much detail as the client chooses and query it to extract information that hasn't been kept in memory.  The queries can run in real time if needed. That’s no different from most other CDPs, which nearly always need to extract and reformat the detailed data to make it available. I’m not sure why Celebrus presents themselves this way; it might be that they have traditionally partnered with companies like Teradata and SAS that themselves provided the data store, or that they partnered with firms like Pega, Salesforce, and Adobe that positioned themselves as the primary repository, or simply to avoid ruffling feathers in IT departments that didn't want another data warehouse or data lake.  In any case, don’t let this confuse you: Celebrus can indeed store all your detailed customer data and will expose whatever parts you need.

- standard data model. Many CDPs load source data without mapping it to a specific schema. This helps to reduce the time and cost of implementation. But mapping is needed later to extract the data in a usable form. In particular, any CDP needs to identify core bits of customer information such as name, address, and identifiers  that connect records related to the same person. Some CDPs do have elaborate data models, especially if they’re loading data from specific source systems or are tailored to a specific industry.  Celebrus does let users add custom fields and tables, so its standard data model doesn’t ultimately restrict what the system can store.

- real-time access.  The in-memory profiles allow external systems to call Celebrus for real-time tasks such as Web site personalization or bidding on impressions..  Celebrus also loads, transforms, and exposes its inputs in real time.  It isn't the only CDP to do this, but it's one of just a few..

Celebrus is also a bit outside the CDP mainstream in other ways. Their clients have been largely concentrated in financial services, while most CDPs have sold primarily to online and offline retailers. While most CDPs run as a cloud-based service, Celebrus supports cloud and on-premise deployments, which are preferred by many financial services companies.  Most CDPs are bought by marketing departments, but Celebrus is often purchased by customer experience, IT, analytics, and digital transformation teams and used for non-marketing applications such as fraud detection and system performance monitoring.

Other Celebrus features are found in some but not most CDPs, so they’re worth noting if they happen to be on your wish list. These include ability to scan for events and issue alerts; handling of offline as well as online identity data; and specialized functions to comply with the European Union’s GDPR privacy rules.

And Celebrus is fairly typical in limiting its focus to data assembly functions, without adding extensive analytics or customer engagement capabilities.  That's particularly common in CDPs that sell to large enterprises, which is  Celebrus' main market.  Similarly, Celebrus is typical in providing only deterministic matching functions to assemble customer data. 

So, yes, Celebrus is a Customer Data Platform.  But, like all CDPs, it has its own particular combination of capabilities that should be understood by buyers who hope to find a system that fits their needs.

As I already mentioned, Celebrus is sold mostly to large enterprises with complex needs.  Pricing reflects this, tending to be "in the six or seven figures" according the company and being based on input volume, types of connected systems, and license model (term or perpetual, SaaS, on-premise, or hybrid).  The company hasn’t released the number of clients but says it gathers data from "tens of thousands" of Web sites, apps, and other digital sources.  Celebrus has been owned since 2011 by D4T4 Solutions  (which looks like the word “data” if you use the right type face), a firm that provides data management services and analytics.