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.
Showing posts with label real time bidding. Show all posts
Showing posts with label real time bidding. Show all posts
Saturday, September 16, 2017
Thursday, October 15, 2015
EverString Takes Another $65 Million and (More Important) Launches Predictive Ad Targeting Solution
EverString announced a $65 million funding round and new ad targeting product on Tuesday. (It also released a new survey on predictive marketing which is probably interesting, but I just can't face after last weekend’s data binge.)
The new funding is certainly impressive, although the record for a B2B predictive marketing vendor is apparently InsideSales’ $100 million Series C in April 2014. It confirms that EverString has become a leader in the field despite its relatively late entry.
But the new product is what’s really intriguing. Integration between marketing and advertising technologies has now gone from astute prediction to overused cliché, so nobody gets credit for creating another example. But the new EverString product isn’t the usual sharing of a prospect list with an ad platform, as in display retargeting, Facebook Custom Audiences, or LinkedIn Lead Accelerator. Rather, it finds prospects who are not yet on the marketer’s own list by scanning ad exchanges for promising individuals. More precisely, it puts a tag on the client's Web site to capture visitor behavior, combines this with the client's CRM data and EverString's own data, and then builds a predictive model to find prospects who are similar to the most engaged current customers. This is a form of lookalike modeling -- something that was separately mentioned to me twice this week (both times by big marketing cloud vendors), earning it the coveted Use Case of the Week Award.
Once the prospects are ranked, EverString lets users define the number of new prospects they want and set up real time bidding campaigns with the usual bells and whistles including total and daily budgets and frequency caps per individual. EverString doesn’t identify the prospects by name, but it does figure out their employer and track their behaviors over time. If this all rings a bell, you’re on the right track: yes, EverString has created its very own combined Data Management Platform / Demand Side Platform and is using it build and target audience profiles.
In some ways, this isn’t such a huge leap: EverString and several other predictive marketing vendors have long assembled large databases of company and/or individual profiles. These were typically sourced from public information such as Web sites, job postings, and social media. Some vendors also added intent data based on visits to a network of publisher Web sites, but those networks capture a small share of total Web activity. Building a true DMP/DSP with access to the full range of ad exchange traffic is a major step beyond previous efforts. It puts EverString in competition with new sets of players, including the big marketing clouds, several of which have their own DMPs; the big data compilers; and ad targeting giants such LinkedIn, Google, and Facebook. Of course, the most direct competitors would be account based marketing vendors including Demandbase, Terminus, Azalead, Engagio, and Vendemore. While we’re at it, we could throw in the mix other DMP/DSPs such as RocketFuel, Turn, and IgnitionOne.
At this point, your inner business strategist may be wondering if EverString has bitten off more than it can chew or committed the cardinal sin of losing focus. That may turn out to be the case, but the company does have an internal logic guiding its decisions. Specifically, it sees itself as leveraging its core competency in B2B prospect modeling, by using the same models for multiple tasks including lead scoring, new prospect identification, and, now, ad targeting. Moreover, it sees these applications reinforcing each other by sharing the data they create: for example, the ad targeting becomes more effective when it can use information that lead scoring has gathered about who ultimately becomes a customer.
From a more mundane perspective, limiting its focus to B2B prospect management lets EverString concentrate its own marketing and sales efforts on a specific set of buyers, even as it slowly expands the range of problems it can help those buyers to solve. So there is considerably more going on here than a hammer looking for something new to nail.
Speaking of unrelated topics*, the EverString funding follows quickly on the heels of another large investment – $58 million – in automated testing and personalization vendor Optimizely, which itself followed Oracle’s acquisition of Optimizely competitor Maxymiser. I’ve never thought of predictive modeling and testing as having much to do with each other, although both do use advanced analytics. But now that they’re both in the news at the same time, I’m wondering if there might be some deeper connection. After all, both are concerned with predicting behavior and, ultimately, with choosing the right treatment for each individual. This suggests that cross-pollination could result in a useful hybrid – perhaps testing techniques could help evolve campaign structures that use predictive modeling to select messages at each step. It’s a half-baked notion but does address automated campaign design, which I see as the next grand challenge for the combined martech/adtech (=madtech) industry. On a less exalted level, I suspect that automated testing and predictive modeling can be combined to give better results in their current applications than either by itself. So I’ll be keeping an eye out for that type of integration. Let me know if you spot any.
_____________________________________________________________
*lamest transition ever
The new funding is certainly impressive, although the record for a B2B predictive marketing vendor is apparently InsideSales’ $100 million Series C in April 2014. It confirms that EverString has become a leader in the field despite its relatively late entry.
But the new product is what’s really intriguing. Integration between marketing and advertising technologies has now gone from astute prediction to overused cliché, so nobody gets credit for creating another example. But the new EverString product isn’t the usual sharing of a prospect list with an ad platform, as in display retargeting, Facebook Custom Audiences, or LinkedIn Lead Accelerator. Rather, it finds prospects who are not yet on the marketer’s own list by scanning ad exchanges for promising individuals. More precisely, it puts a tag on the client's Web site to capture visitor behavior, combines this with the client's CRM data and EverString's own data, and then builds a predictive model to find prospects who are similar to the most engaged current customers. This is a form of lookalike modeling -- something that was separately mentioned to me twice this week (both times by big marketing cloud vendors), earning it the coveted Use Case of the Week Award.
Once the prospects are ranked, EverString lets users define the number of new prospects they want and set up real time bidding campaigns with the usual bells and whistles including total and daily budgets and frequency caps per individual. EverString doesn’t identify the prospects by name, but it does figure out their employer and track their behaviors over time. If this all rings a bell, you’re on the right track: yes, EverString has created its very own combined Data Management Platform / Demand Side Platform and is using it build and target audience profiles.
In some ways, this isn’t such a huge leap: EverString and several other predictive marketing vendors have long assembled large databases of company and/or individual profiles. These were typically sourced from public information such as Web sites, job postings, and social media. Some vendors also added intent data based on visits to a network of publisher Web sites, but those networks capture a small share of total Web activity. Building a true DMP/DSP with access to the full range of ad exchange traffic is a major step beyond previous efforts. It puts EverString in competition with new sets of players, including the big marketing clouds, several of which have their own DMPs; the big data compilers; and ad targeting giants such LinkedIn, Google, and Facebook. Of course, the most direct competitors would be account based marketing vendors including Demandbase, Terminus, Azalead, Engagio, and Vendemore. While we’re at it, we could throw in the mix other DMP/DSPs such as RocketFuel, Turn, and IgnitionOne.
At this point, your inner business strategist may be wondering if EverString has bitten off more than it can chew or committed the cardinal sin of losing focus. That may turn out to be the case, but the company does have an internal logic guiding its decisions. Specifically, it sees itself as leveraging its core competency in B2B prospect modeling, by using the same models for multiple tasks including lead scoring, new prospect identification, and, now, ad targeting. Moreover, it sees these applications reinforcing each other by sharing the data they create: for example, the ad targeting becomes more effective when it can use information that lead scoring has gathered about who ultimately becomes a customer.
From a more mundane perspective, limiting its focus to B2B prospect management lets EverString concentrate its own marketing and sales efforts on a specific set of buyers, even as it slowly expands the range of problems it can help those buyers to solve. So there is considerably more going on here than a hammer looking for something new to nail.
Speaking of unrelated topics*, the EverString funding follows quickly on the heels of another large investment – $58 million – in automated testing and personalization vendor Optimizely, which itself followed Oracle’s acquisition of Optimizely competitor Maxymiser. I’ve never thought of predictive modeling and testing as having much to do with each other, although both do use advanced analytics. But now that they’re both in the news at the same time, I’m wondering if there might be some deeper connection. After all, both are concerned with predicting behavior and, ultimately, with choosing the right treatment for each individual. This suggests that cross-pollination could result in a useful hybrid – perhaps testing techniques could help evolve campaign structures that use predictive modeling to select messages at each step. It’s a half-baked notion but does address automated campaign design, which I see as the next grand challenge for the combined martech/adtech (=madtech) industry. On a less exalted level, I suspect that automated testing and predictive modeling can be combined to give better results in their current applications than either by itself. So I’ll be keeping an eye out for that type of integration. Let me know if you spot any.
_____________________________________________________________
*lamest transition ever
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