Mike Dadlani of Ibotta Talks Virtual CRM and Data Strategy

Marketing Innovation

Keep up to date on the latest announcements, events, and happenings from the connectivity community.

Mike Dadlani of Ibotta Talks Virtual CRM and Data Strategy

Marketing Innovation

We all know that first-party data is king. But what if you don’t have access to first-party interactions with your company or media? Enter the virtual CRM, which consists of online and offline third-party datasets from which you can compile an in-depth profile of consumers. Layer on identity resolution and you have a powerful source of truth from which you can deliver better experiences to your audiences and gain happier customers over time.

To learn more about virtual CRM, Randy Antin, our Product Marketing Head of Audiences, interviewed Mike Dadlani, Vice President, Media & Data Partnerships, at Ibotta. Listen to the full interview, or read an excerpt of their conversation below.

Randy: I am a big fan of Ibotta and am a customer and partner, but I’d love for you to tell our audience a little bit more about what Ibotta is.

Mike: Sure. Ibotta is a seven-year-old consumer-facing mobile app that is currently only available in the United States. We have 26 million registered users. The core functionality of the app is a platform that gives consumers the ability to earn cash back on things they buy in both the offline and the online world.

When we built the business seven years ago, we had a specific focus on offline grocery, working with consumer-packaged goods manufacturers, and we’ve since expanded into mobile commerce and became a data business. We took the data we collected from our partners and users and built a variety of data products, ranging from consumer insights and analytics to consumer research, and most recently for the adtech base, audience targeting and offline sales measurement.

Randy: That’s really interesting. I think that’s where Ibotta data has become pretty enticing for a lot of different verticals, and especially since you have this broad reach across multiple retailers and different types of verticals,  it can show a lot of promise for people in different industries.
You’ve been focused on the audience seed and how you can build it out through scale using a lookalike model to expand reach, but what about on the virtual CRM side? How do you define that and why would someone want one?

Mike: When I think about virtual CRM, I think about taking a CPG brand’s first-party data and combining it with other deterministic data sets that are valuable to them. The reason that’s valuable for a CPG manufacturer, and really all marketers, is because the ecosystem is shifting to create more accountability from data providers and the overall ad tech industry.

As a brand looks to have more transparency into who they’re reaching with an ad, how they’re reaching them, and what the impact that has on their bottom line, having access to a virtual system that gives deterministic data on buyers of their products or buyers of competitive or complementary products allows them to create more ownership and have more control over the audiences they’re building or using for targeting.

Given our strategic partnership, there are a lot of CPG brands that are making attempts to bring their programmatic buying in-house. The only way they can do that is by building a data asset that they have unrestricted access to and the ability to not only create seeds, but also to model to create scale, and having a virtual CRM system that should, in turn, make their ads more effective and remove the black box aura of what they’ve historically been used to using.

Randy: That’s an interesting point. It’s all about having control and ownership over data in the best, and most cost-efficient way, with the end goal of creating a customer relationship that, in the past, the CPGs haven’t had an ability to do because they don’t have the data.

Mike: That’s correct. I think one thing I would add is that partnering with the right data providers will give them access to insights and results in a more real-time fashion that will allow them to optimize their media spend over time. Historically, the sales list that CPG brands rely on to measure the effectiveness of a marketing campaign are often coming six, eight, twelve weeks after a campaign ends. If your data provider is pushing data into your virtual CRM or pushing that data weekly, you should in turn have the technology to run an exposure-to-conversion analysis mid-flight that would allow you to optimize your spend prior to the campaign ending, making your overall dollars work harder for you over time.

Randy: Yes, and that feels like sort of a holy grail for CPGs to get to the level where e-commerce and retailers are already at: being able to tie in that transaction data, maybe not in real time, but at least a lot faster, to better influence those campaigns.

Mike: Absolutely.

Randy: Regarding the CPG clients you’ve seen who have taken things in-house or built out their own DMPs or virtual CRMs, what types of data sets do you think they should acquire?

Mike: It’s about creating a balance. Finding access to SKU-level purchase data that is mapped directly to a consumer is the holy grail, or at least we’d like to think so, but having that in a scalable fashion will not be fully inclusive of everything they need.

We’re a strong believer in location data. We have a partnership with a variety of location companies that allows us to layer our transaction data on top of location data to inform consumer behavior in a more expanded way. I would say, finding access to as much purchase data as you can, layering in location data, and then building out your own first-party data set that can be context driven will help you understand a consumer’s shopping behavior on your website or on partner websites, where they might be vindicating recipes or other unique content.

Bringing those three things together will create the strongest signals, and then of course, with any model you’ll want to have an additional layer of demographic data on a variety of consumers, typically large scale, that allows you to take the purchase and location data you collect, create a very strong seed signal, and then use demo and other data points to model out.

Randy: What stage do you think the industry is at right now?

Mike: We’re still in the early stages—particularly in the context of CPG—regarding what these brands are going to do. It’s one thing to say, ‘I want to bring programmatic buying in-house.’ It’s another thing to find data science, data engineering, and data analytics resources that have an interest in joining a legacy consumer packaged goods company, especially given that a lot of those resources today are being consumed by companies like ours, LiveRamp, Oracle, IRI, so I think it’ll be really interesting. I think the CPG brands have a lot of work in front of them, if that’s something they truly want to accomplish and do effectively, but I definitely think it can be done.

Randy: Are there any CPG clients or brands that you work with or have been reading about that you think are paving the way?

Mike: Yes, absolutely. I think P&G is definitely one of the most innovative and forward-thinking when it comes to how they plan their future media buys.

Another one is Heineken—a really sophisticated client that we’re starting to do some fun work with. They have a passion to bring programmatic buying in-house. There’s a handful of CPGs that have talked about it, and I know there’s a roster of companies that are actively seeking ways of doing this, whether that’s jumping in head first or identifying one brand within their larger portfolio that they want to test it with. It’s definitely happening as we speak.

Randy: Mike, this has been great. In closing, I was wondering if you could speak to how clients are potentially using point-of-sale transaction data when it comes to measurement or optimization.

Mike: It’s a fantastic question and something I talk about a lot internally. Point-of-sale data by default is incredibly useful for brands that want to get a post-campaign look at performance to understand sales lift or a return on ad spend.

What point-of-sale data struggles with is that it’s very difficult to use for in-flight optimization. That’s a game that we’re excited to start playing in, given our one-to-one relationship with customers. We believe giving brands the ability to get insight into conversion data on a nightly basis while the campaign is in flight will be a great opportunity to give brands a hand up in making sure their immediate dollars are working as hard for them as they possibly can.

I would never be one to say brands shouldn’t also invest in post-campaign measurements to understand in aggregate how things perform, but it should be an ‘and’ strategy not an ‘or’ strategy, as it relates to combining both in-flight optimization and post-campaign measurement using real-time signals and point-of-sale data across the board.

Randy: Great. This has been really thought-provoking. Thank you so much for your time.

Mike: You got it. Thanks for having me, Randy.

For more information about LiveRamp’s partnership with Ibotta, read this blog.