Everyone is racing to connect all their online data to a single user, to provide a view into that consumer’s behavior and engagement with a product/brand across a plethora of touch points and channels. There are many marketing benefits in having all the information about your consumer at your fingertips; however, is it cost-effective to embark upon multi-source consumer-level data integration? Are there alternatives?
When we talk about consumer-centric data, we are really talking about three categories of data types: behavioral, attitudinal, and demographic. Behavioral data will tell you what your consumer is doing online, typically starting with ad-serving tracking. Attitudinal data will tell you why your consumers are behaving a certain way, by uncovering their perceptions/opinions of your product/brand. Finally, demographic data will tell you who these consumers are. As you can imagine, having this granular data can be extremely telling and powerful -- especially if the data is not limited only to online media, but also includes mobile and social. This is powerful because it will enable multi-screen, multi-touch, and cross-channel measurement (including websites) at the consumer level. Add demographics and even attitudinal feedback, and you have the full picture.
Several players have claimed success in this space, specifically the largest DMPs (data management platforms). Combining the three types of data usually involves taking verified offline data, such as mailing lists, with demographics, and connecting it using emails to online consumer-level behaviors. Some data aggregators leverage online panels or surveys to refine and include attitudinal response, and some simply extrapolate the three types of data by applying statistical models to online patterns. Having data aggregated for you is extraordinarily helpful, though I urge you to dive deeper into the services these providers offer. You may find in some cases their data is extremely thin, incomplete, subject to aggressive assumptions and in some cases flat-out inaccurate -- not too surprising, since this is true for many marketing data sources and you need to be smart in the way you select and use third-party data. You may also look into doing it yourself or look for alternatives.
Using Big Data to provide a single view of a consumer has become Big Business! There is a mad rush to be more granular and detailed in the data we collect, though in some cases I would argue it can be over-kill and overwhelming. Acquiring consumer-level data has its set of challenges:
1. Privacy -- there is increasing pressure for regulation, with questions about the safety, security and ethics of data usage and control in interactive marketing.
2. Complexity – integration across data sources at a consumer level is often very complex.
3. Lack of data standards – data is often fragmented without any unique or common identifiers.
4. Expensive – although there is some universal integration happening in the
industry, for the most part we still lack a uniform framework for data integration. Custom work is often expensive, and time- and resource-intense.
5. Panel/survey bias – there are two challenges with panel/survey data:
a. A recent study conducted by an online panel taskforce concluded that a vast majority of online panels do not conform to using probability-based recruitment; therefore the findings and insights off that panel may be biased and not representative. We all know survey error does exist, but if it is larger than expected, it will certainly skew the overall results.
b. Panels and survey data may not be scalable for smaller or niche campaigns due to sample size limitations.
Don’t get me wrong - I do believe that consumer-level data is very powerful and offers rich insights. If you can overcome the challenges, it will work for you. Let me also suggest two alternatives that just may get you the answers you need
1. Marketing econometric modeling (MEM) – a method of aligning data based on a time-series versus at the consumer level. Traditionally this approach has been used to develop offline media mix models; however, this will also work well in evaluating daily online activity across multiple channels, devices, media, etc. Although data is still not always easy to come by, MEM integration is much simpler. MEM may not address multi-screen measure but can be used for multi-touch and is already being used for cross-channel measurement and analysis.
2. Agent-based modeling (ABM) – agents are autonomous decision-making entities (consumers), and agent-based models are made up of agents and a framework for their interactions. It is a virtual representation of your consumer base matched up with your segmentation. Each agent is assigned behaviors and representative patterns. These models provide sufficient levels of detail about the behaviors of your consumer. The approach can be used to address very detailed questions and allow for multi-screen, multi-touch and cross-channel measurement and analysis.
Whether you rely on consumer-level data, time-series or simulated agents, one thing is clear: marketers today need the ability to track a consumer across the complex web of marketing communications and touch points to better understand marketing ROI.