Saturday, September 16, 2017

Vizury Combines Web Page Personalization with a Customer Data Platform

One of the fascinating things about tracking Customer Data Platforms is the great variety among the vendors.

It’s true that variety causes confusion for buyers. The CDP Institute is working to ease that pain, most recently with a blog discussion you’re welcome to join here.  But for me personally, it’s been endlessly intriguing to trace the paths that vendors have followed to become CDPs and learn where they plan to go next.

Take Vizury, a Bangalore-based company that started eight years ago as an retargeting ad bidding platform. That grew into a successful business with more than 200 employees, 400 clients in 40 countries, and $30 million in funding. As it developed, the company expanded its product and, in 2015, released its current flagship, Vizury Engage, an omnichannel personalization system sold primarily to banks and insurance companies. Engage now has more than a dozen enterprise clients in Asia, expects to double that roster in the next six months, and is testing the waters in the U.S.

As often happens, Vizury’s configuration reflects its origins. In their case, the most obvious impact is on the scope of the system, which includes sophisticated Web page personalization – something very rare in the CDP world at large. In a typical implementation, Vizury builds the client’s Web site home page.  That gives it complete control of how each visitor is handled. The system doesn't take over the rest of the client's Web site, although it can inject personalized messages on those pages through embedded tags.

In both situations, Vizury is identifying known visitors by reading a hashed (i.e., disguised) customer ID it has placed on the visitor’s browser cookie. When a visitor enters the site, a Vizury tag sends the hased ID to the Vizury server, which looks up the customer, retrieves a personalized message, and sends it back to the browser.  The messages are built by templates which can include variables such as first name and calculated values such as a credit limit.  Customer-specific versions may be pregenerated to speed response; these are updated as new data is received about each customer. It takes ten to fifteen seconds for new information to make its way through the system and be reflected in output seen by the visitor.

Message templates are embedded in what Vizury calls an engagement, which is associated with a segment definition and can include versions of the same message for different channels. One intriguing strength of Vizury is machine-learning-based propensity models that determine each customer’s preferred channel. This lets Vizury send outbound messages through the customer’s preferred channel when there’s a choice. Outbound options include email, SMS, Facebook ads, and programmatic display ads. These can be sent on a fixed schedule or be triggered when the customer enters or leaves a segment. Bids for Facebook and display ads can be managed by Vizury’s own bidding engine, another vestige of its origins. Inbound options include on-site and browser push messages.

If a Web visitor is eligible for multiple messages, Vizury currently just picks one at random. The vendor is working an automated optimization system that will pick the best message for each customer instead. There’s no way to embed a sequence of different messages within a given engagement, although segment definitions could push customers from one engagement to the next. Users do have the ability to specify how often a customer will be sent the same message, block messages the customer has already responded to, and limit how many total messages a customer receives during a time period.

What makes Vizury a CDP is that it builds and exposes a unified, persistent customer database. This collects data through Vizury's own page tags, API, and mobile SDK; external tag managers; and batch file loads.  Data is unified with deterministic methods including stitching of multiple identifiers provided by customers and of multiple applications on the same device. The system can do probabilistic cross-device matching but that's not reliable enough for most financial service applications.  Vizury doesn’t do fuzzy matching based on customer names and addresses, which is not a common technique in Asia.

The system includes standard machine learning algorithms that predict product purchase, app uninstalls, and message fatigue in addition to channel preference and ad bidding. Results can be applied to tasks other than personalization, such as lead scoring.  Algorithms are adapted for each industry and trained on the client’s own data. Users can't currently apply machine learning to other tasks.

Vizury uses a typical big data stack including Hadoop, Hive, Pig, Hbase, Flume, and Kafka. Clients can access the data directly through Hadoop or Hbase.  Standard reports show results by experience, segment, and channel, and users can create custom reports as well.


Pricing for Vizury is based on the number of impressions served, another echo of its original business. Enterprise clients pay upwards of $20,000 per month, although U.S. pricing could be different.





Friday, September 08, 2017

B2B Marketers Are Buying Customer Data Platforms. Here's Why.

I’m currently drafting a paper on use of Customer Data Platforms by B2B SaaS marketers.  The topic is more intriguing than it sounds because it raises the dual questions of  why CDPs haven’t previously been used much by B2B SaaS companies and what's changed.  To build some suspense, let’s first review who else has been buying CDPs.

We can skip over the first 3.8 billion years of life on earth, when the answer is no one. When true CDPs first emerged from the primordial ooze, their buyers were concentrated among B2C retailers. That’s not surprising, since retailers have always been among the data-driven marketers. They’re the R in BRAT (Banks, Retailers, Airlines, Telcos), the mnemonic I’ve long used to describe the core data-driven industries*.

What's more surprising is that the B's, A's, and T's weren't also early CDP users.  I think the reason is that banks, airlines, and telcos all capture their customers’ names as part of their normal operations. This means they’ve always had customer data available and thus been able to build extensive customer databases without a CDP.

By contrast, offline retailers must work hard to get customer names and tie them to transactions, using indirect tools such as credit cards and loyalty programs. This means their customer data management has been less mature and more fragmented. (Online retailers do capture customer names and transactions operationally.  And, while I don’t have firm data, my impression is that online-only retailers have been slower to buy CDPs than their multi-channel cousins. If so, they're the exception that proves the rule.)

Over the past year or two, as CDPs have moved beyond the early adopter stage, more BATs have in fact started to buy CDPs.  As a further sign of industry maturity, we’re now starting to see CDPs that specialize in those industries. Emergence of such vertical systems is normal: it happens when demand grows in new segments because the basic concepts of a category are widely understand.  Specialization gives new entrants as a way to sell successfully against established leaders.  Sure enough, we're also seeing new CDPs with other types of specialties, such as products from regional markets (France, India, and Australia have each produced several) and for small and mid-size organizations (not happening much so far, but there are hints).

And, of course, the CDP industry has always been characterized by an unusually broad range of product configurations, from systems that only build the central database to systems that provide a database, analytics, and message selection; that's another type of specialization.  I recently proposed a way to classify CDPs by function on the CDP Institute blog.** 

B2B is another vertical. B2B marketers have definitely been slow to pick up on CDPs, which may seem surprising given their frenzied adoption of other martech. I’d again explain this in part by the state of the existing customer data: the more advanced B2B marketers (who are the most likely CDP buyers) nearly all have a marketing automation system in place. The marketers' initial assumption would be that marketing automation can assemble a unified customer database, making them uninterested in exploring a separate CDP.  Eventually they'd discover that nearly all B2B marketing automation systems are very limited in their data management capabilities.  That’s happening now in many cases – and, sure enough, we’re now seeing more interest among B2B marketers in CDPs.

But there's another reason B2B marketers have been uncharacteristically slow adopters when it comes to CDPs.  B2B marketers have traditionally focused on acquiring new leads, leaving the rest of the customer life cycle to sales, account, and customer success teams.  So B2B marketers didn't need the rich customer profiles that a CDP creates.  Meanwhile, the sales, account and customer success teams generally worked with individual and account records stored in a CRM system, so they weren't especially interested in CDPs either.  (That said, it’s worth noting that customer success systems like Gainsight and Totango were on my original list of CDP vendors.)

The situation in B2B has now changed.  Marketers are taking more responsibility for the entire customer life cycle and work more closely with sales, account management, and customer success teams. This pushes them to look for a complete customer view that includes data from marketing automation, CRM, and additional systems like Web sites, social media, and content marketing. That quest leads directly to CDP.

Can you guess who's leading that search?  Well, which B2B marketers have been the most active martech adopters? That’s right: B2B tech marketers in general and B2B SaaS product marketers in particular. They’re the B2B marketers who have the greatest need (because they have the most martech) and the greatest inclination to try new solutions (which is why they ended up with the most martech). So it’s no surprise they’re the earliest B2B adopters of CDP too.

And do those B2B SaaS marketers have special needs in a CDP?  You bet.  Do we know those needs are?  Yes, but you’ll have to read my paper to find out.

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*It might more properly be FRAT, since Banking really stands for all Financial services including insurance, brokers, investment funds, and so on.  Similarly, Airlines represents all of travel and hospitality, while Telco includes telephone, cable, and power utilities and other subscription networks.  We should arguably add healthcare and education as late arrivals to the list.  That would give us BREATH.  Or, better still, replace Banks with Financial Services and you get dear old FATHER.

**It may be worth noting that part of the variety is due to the differing origins of CDP systems, which often started as products for other purposes such as tag management, big data analytics, and campaign management.   That they've all ended up serving roughly the same needs is a result of convergent evolution (species independently developing similar features to serve a similar need or ecological niche) rather than common origin (related species become different over time as they adapt to different situations).  You could look at new market segments as new ecological niches, which are sometimes filled by specialized variants of generic products and are other times filled by tangentially related products adapting to a new opportunity.

My point here is there are two separate dynamics at play: the first is market readiness and the second is vendor development.  Market readiness is driven by reasons internal to the niche, such as the types of customer data available in an industry.  Vendor development is driven by vendor capabilities and resources.  One implication of this is that vendors from different origins could end up dominating different niches; that is, there's no reason to assume a single vendor or standard configuration will dominate the market as a whole.  Although perhaps market segments served by different configurations are really separate markets.