An ABM Interview with Niraj Deo, Sr. Director, Product Management, Oracle Data Cloud—B2B Products
Kwanzoo: Niraj, can you start out by telling us why Oracle Data Cloud (Oracle BlueKai) is moving into Account-Based Marketing (ABM)?
Deo: We really see our ABM progression as an extension of the three core capability offerings we have in the Oracle Data Cloud:
- Audience Data Marketplace—A community of more than 40 third-party data providers that have access to a massive amount of data.
- ID Graph—Pulls together the many IDs across marketing channels and devices that comprise a given person and enables marketers to tie their interactions to an actionable customer profile.
- Data Management Platform (DMP)—Leverages integrations with modern activation channels (Google, Facebook, etc.) to build cross-channel campaigns and audience targeting.
Oracle Data Cloud has been providing data-driven capabilities to help our B2C customers solve their biggest marketing and customer experience challenges for some time now. We saw growing demand for similar data-driven solutions in B2B side and wanted to create a useful and differentiated solution for B2B marketers that leveraged all the learnings from our years of B2C experience. We predicted that B2B marketers would increasingly turn to programmatic marketing as a way to effectively grow their businesses and enhance customer experiences. Our success in B2C was largely due to our ability to tie all marketing and measurement to actual offline “households.” We knew early on that “accounts” would serve as the key entity for identity for B2B in our ID Graph. That’s what generated such significant interest in the account-based capabilities that we have today in the form of data, data management platforms, activations, etc.
Kwanzoo: We understand that Oracle Data Cloud now has access to more than one billion cookie and mobile ID based profiles. That is more than any other B2B data provider that we know of. How is Oracle Data Cloud able to scale to level of reach?
Deo: We’re able to scale to that level through the combination of our Marketplace business model and the ID Graph that we have created as part of our platform. It’s really a “sum is greater than the whole” type of approach. We leverage the best of our owned data assets from the BlueKai, Datalogix and AddThis acquisitions, but our platform and team are also designed to source data from leading 3rd party providers. Picture more than 40 different data partners feeding all types of data into our B2B products—providing taxonomy and scale B2B marketers than we’d be able to deliver on our own. That could be anything from company size, company age, company revenues, industries of those companies, job titles, C-level identification, job functions, etc. We were able to leverage the strength of our own data along with the Marketplace platform, to unify all the best possible sources of B2B data in a single location (on-line, offline, location-based data, etc.). Every data partner in the Marketplace has been selected for a specific purpose of skill, precision, and function where they add value. Bringing this many specialized partners together with the Oracle curated taxonomy that we have created for B2B; we’re able to generate scale relatively quickly. All this data enables our clients to be more sophisticated around targeting, retargeting, and customer experience activations.
Kwanzoo: Interesting. Based on what you just told us, where do you see the B2B product management of the Oracle Data Cloud focusing its time over the next year?
Deo: In phase one of our maturity, where we have been, we wanted to come up with a basic B2B targeting or data asset that B2B marketers could use across a variety of different verticals. In phase two, in addition to enhancing our basic B2B capabilities, we wanted to help users activate programmatic ABM at scale (which is typically a problem in the B2B world). We wanted our ABM solution to be capable of targeting both mid-sized and enterprise companies with the scale required to move the needle for our clients. So far we have linked 250 million profiles to accounts where we are seeing strong signals of employment. That population forms the base for our ABM solution. Imagine getting scale along with the quality, precision, and granularity that you need so that you are reaching the right people from the right accounts—and you are able to layer on all the other B2B insights we’re aggregated to be more purpose-driven in your ABM efforts. This is where we are on our product roadmap today.
Our next phase of maturity is to become more model-driven. More predictive. More intelligent on how to take everything forward. This is where key partners such as Kwanzoo come into play. There will be more granular insights available. Whether you are going after 100 accounts or 100,000 accounts, you can now have so many more attributes about these accounts. Who is visiting your website? Who is receiving the impression? Is he or she C-level, from Finance, or from HR? Kwanzoo is then able to put together a very customized ABM display campaign based on these precise insights. Eventually customers will be able to data mine the intent of the end user. That is what we are gearing towards—predictive recommendation-based targeting so that Marketing can align better with Sales.
Kwanzoo: How far do you think that Oracle Data Cloud can go with targeting in the ABM space?
Deo: We want to take targeting to the point where it is an important consideration in any B2B Go-to-Market strategy. ABM started out as “I want to go after these accounts in these industry verticals.” That was your basic level of Account Based Marketing. The next level of ABM was when customers said “I not only want to go after these accounts in these industry verticals but I want to filter these accounts to only target those that have purchased a certain product/service in the past —whether it is my product or my competitors’ that they have purchased.” This is much closer to how B2B businesses work—“I want to grow my install base and I want to target my competitors’ install base.”
But targeting goes beyond just Marketing. You want it to match what B2B businesses face as part of their business modeling. Product groups should be interested in targeting. C-level execs need it for whatever growth targets are set up. So ABM targeting has evolved from install base to past purchase to other corporate departments that are trying to help the business grow. ABM can be used to help define go-to-market strategies.
The next level of ABM is to take predictive and intelligence-based targeting and tie it to CRM data (e.g. win-loss ratios, which account were most profitable, which sales teams were most successful, which geographies were not productive, etc.). Users will be listening and learning from that data. They will be modeling from it. They will soon be using mathematics, science, and machine-driven techniques to come up with a different set of target accounts that will be much more effective than earlier selected accounts based on attributes such as past purchase or event/tradeshow participation. This level of ABM will leverage machines that will be able to go through so many more data points than a human can do. We will eventually see a more mature and automated way at arriving at Account Based Marketing and Targeting. Users will be able to react much faster to changing market conditions.
Kwanzoo: Right now Oracle Data Cloud allows multiple filtering around job functions and job titles. Do you see any further innovation happening in that area?
Deo: Yes, in addition to the traditional attributes such as company size, industry, or job title, we have begun compiling precise clusters around “IT decision makers,” “HR decision makers,” and “Finance decision makers.” Users can overlay them on top of the target audiences they have created and filter the segments even further. For example, a customer wants to create an audience of 10,000 accounts and we come up with five million users. They can certainly target all of these people or they can filter further by company size, job functions, or now they can filter by one of these ready-made clusters. These clusters could be industry functions, decision makers by industry verticals, or a group of certified people in a certain skill set. This way users can reach out to the right set of people that matter to them.
Kwanzoo: What opportunities do you see arising for marketers based on your ability to collect more mobile device IDs?
Deo: Mobile is a very important activation channel in the B2B space. Most customers being with using a cookie based approach for targeting desktop browsers online. Given the rapid evolution of how people connect to the web and consume content means that clients are also very interested in extending the same automated, programmatic approaches to the mobile video or mobile social and video channels. We have to be prepared to help engage with both audiences. That is where our ID Graph comes into play. We can segment not only by users’ characteristics but also by the channels where we want to engage those users. Let’s say you are targeting five million profiles. We can show which ones are cookies and which ones are mobile device IDs. From the mobile device IDs, which ones are ad IDs versus non-ad mobile IDs. This empowers B2B marketers to launch very specific campaigns by channel.
Today, in a given ABM campaign it is probably 70% cookies and 30% mobile device IDs. In the next two to three years, I can easily see that flip to where ABM campaigns are 30% cookies and 70% mobile device IDs.
Kwanzoo: Where could marketers and your users be leveraging data better in your opinion?
Deo: Obviously with a variety of stakeholders out there (customers, agencies, DSPs, etc.) we see a need for education across the board. While there are a few customers who are always pushing us to go out a bit further, most customers are not taking full advantage of data capabilities we offer today. Most customers are comfortable doing campaigns the way they have been because they are simply unaware that certain capabilities exist. Unfortunately that often leads to a lot of leakage of effort, resources, and ROI. Many campaigns are not seen as effective.
We find that the educated and aware customer is typically the more effective business and will leverage components such as scalability. They will find a way to involve all the data assets into their ABM campaign or the customer experience that they are defining on their digital properties. We need to keep pushing out use cases for our customers. At the same time we need customers to start thinking about the new and modern ways to leveraging data for activation.
Kwanzoo: Niraj, where do you see cookie- and IP-based data heading in the future?
Deo: If you look at the evolution of identity, 20 years ago we used postal addresses. 15 years ago we started using cookies. Everything has a sunrise and a sunset aspect to it. Look at business cards. When business cards are used, there is website, social handle, and phone number information. Yet some people don’t even use business cards anymore.
I think we will see the same thing happen with identity. In 15 years the cookies will fade away as the primary identity marker just as postal addresses did. Maybe mobile device IDs will become the primary identity component. Social IDs will probably take on a stronger role in B2B as they have in B2C and cookies and IP data will disappear. As it goes more toward a reliance on social IDs you will also see more of known and less of an anonymous environment when engaging with your customers. You will be able to opt-in and opt-out in a social world just like you can with email today. I think it will be interesting to see if cookies survive and how mobile device ID will transition in the next 10 years.
Kwanzoo: Finally, where do you see improvements happening around reporting and measurement with the access to all of this data?
Deo: At the end of the day, users have to know that they are going after the right audiences for install base, events, brand awareness, and so forth. Users need to know when and how audiences are engaging with them. Is it through the website? Is it through a two-minute video? There has to be a feedback loop. This is critical. Users need to know how to measure the effectiveness and frequency of the investments that they’ve made. We start with those 250 million users and the links to the most likely employers of those users.
From this start we can help track account coverage, awareness, reach, and engagement. Of course, in the B2B world, it is also important to know when Sales got involved and how long it took to close the deal. We are continually working to tie all of this data together. We want to unify workflows to show User X’s exposure and engagement with marketing, how that translated to Opportunity X, and what happened with Opportunity X in the last mile leading to conversion. We want to provide outcomes to customers which could be reporting, or analytics, or modeling. We want customers to take the observations, insights, and results and feed them back into the process for a more effective and efficient campaign the next time. That is where we are heading…closing the loop.