TechBytes with Chris Wareham, Senior Director, Product Management, Adobe Analytics
Senior Director, Product Management, Adobe Analytics
Data, data, and more data… modern marketers in 2017 are surrounded by more data types than previously known to the business group. Smart moves for data-driven marketing requires a clear distinction between audience data, customer data and B2B data… and of course, the impact they have on building intelligent automation. Rather than guessing with on-off data, top CMOs recommend marketers to rely on data management and analytics platform that provide a 360-degree view of customers for a better experience. A top-line customer intelligence platform, Adobe’s Analytics Data Workbench is a modern marketer’s greatest ammunition to develop customer propensity modeling, audience clustering and identify trends and relationships between large data sets. To understand how Adobe distinguishes between disparate data sets, we had the pleasure of talking to Chris Wareham, Senior Director of Product Management, Adobe Analytics.
MTS: Tell us about your role at Adobe Analytics and the team you handle?
Chris Wareham: I am the senior director of product management for Adobe Analytics. In that role, my team and I are focused on driving product strategy to meet the business needs of our customers and develop new analytics capabilities that are on the cutting-edge of what’s available in the marketplace.
MTS: How do you segregate disparate data and segment them into – Customer data, Audience data, B2B data?
Chris: When you have online and offline data coming in from a variety of sources, it’s key to build segment data in a way that helps create strong audience profiles to provide insights into your target demographics. The first step in this process is feeding both online and offline data into a centralized data-management platform (DMP). You can do this by matching up your offline information to self-identifying users online (syncing data when users log-in or identify themselves online in another way) or by working with third-party match providers, who can help you identify and match users within your offline data. Either way, fully leveraging your brand’s own customer data is essential to establishing a foundational data repository for audience targeting.
After you’ve gathered your own customer data (what we call “first-party data”), you can then start filling in information gaps with B2B and audience data (what we call second and third-party data) by identifying key use cases that you want to better understand and developing segments against those use cases. Suppressing your existing customers from prospecting and customer-acquisition campaigns alone will provide a massive lift in terms of return on investment (ROI) and demonstrate the value of the platform. While you may eventually have a DMP that contains hundreds or thousands of segments to define discrete portions of your customer base, it’s critical to establish broad pillars of audience definition so that more granular targeting is rooted in strong data.
MTS: How do you define these data sets at Adobe?
Chris: At Adobe, we think of these data sets as:
- First-Party Data: We consider first-party data to be the data collected from a company’s CRM, website, analytics and any other touchpoints a customer has with a brand. This data is completely focused on the company’s unique customer journey when engaging the brand in channels the brand controls. This data is highly reliable because it consists of data from people who have voluntarily shown some level of interest in your products and services.
- Second-Party Data: Second-party data is another brand’s first-party data, which is shared and accessible to a brand in a similar market or with similar audiences; for instance, a hotel chain might gain insights from data collected by a car rental agency. The customers of these types of companies share a similar consumer journey, so their behavior on each company’s site or app is useful to the other company’s marketing efforts. Second-party data sources have the potential to increase scale and accuracy.
- Third-Party Data: Third-party data is data that marketers acquire from an entity that doesn’t have a direct relationship with consumers. Third-party data providers typically collect information from various interactions and use it to create audience profiles and provide insights as to preferences and behaviors of certain groups. Marketers can purchase third-party data to help supplement first and second-party data to build a more robust audience profile.
MTS: Tell us more about Analytics Data Workbench and how it paints the entire online + offline customer journey?
Chris: Analytics Data Workbench gives brands a platform to combine all their online and offline customer data. However, the real value comes with its predictive capabilities and real-time analysis, which allows brands to spot new opportunities quickly and easily. Data segmentation and audience clustering help brands sift and filter through vast amounts of data to discover unique insights, while churn analysis, propensity modeling and engagement scoring all help marketers understand customer behavior and the biggest influencers when it comes to purchasing decisions. With these insights in hand, brands can tailor and curate customer experiences that will resonate with its key audiences.
MTS: Despite cutting-edge customer intelligence available to them, why do marketers still find it hard to make accurate marketing attribution?
Chris: The challenge is two-fold: 1) having clear line of sight into customer interactions across channels and 2) being able to connect the business outcomes to the understanding of those channels. There are many tools available to marketers and they’re mostly characterized by archaic notions of what is important in driving business outcomes; The data exists to track users through their customer journey, so that our clients can build a cross-channel attribution strategy that is focused on driving business outcomes, not just trying to figure out which channel gets more marketing dollars.
MTS: How does Adobe Analytics make marketing attribution faster, easier and more accurate?
Chris: Adobe Analytics gives brands the power to visualize all data in one place, and allows them to accurately assign credit to every conversion. By going beyond static attribution and taking advantage of machine learning technology to shed light on the importance of every touchpoint on the way to a conversion, Adobe Analytics provides marketers with valuable insights on key moments throughout the customer journey.
MTS: What’s the next frontier for mobile-focused customer intelligence analytic platforms?
Chris: As the world becomes increasingly mobile-driven, we’ll see many new opportunities for consumer intelligence analytics platforms – perhaps most importantly, marketers will be able to leverage novel customer data through new interaction points, including voice assistants/search and IoT devices. With the increasing popularity of voice assistants (data shows that online sales of voice-enabled devices grew 39 percent year-over-year from 2016), it is increasingly important for brands to begin building their own voice-enabled customer experiences. And because voice-enabled devices and digital assistants allow consumers to engage through one of the most natural forms of communication, the data from these interactions allows for deepened insights into a customer’s relationship with a brand.
Mobile IoT devices like smartwatches, fitness trackers and more are another major frontier for customer analytics, as the data sets from these devices will provide the insights required for intensive personalization, increased use of beacon technology, further connection between online and offline data, and more.
MTS: How would marketing strategies change with the availability of real-time digital experience data?
Chris: That depends on the situation, but overall more data and more insight into what drives customers leads to greater ROI. Going beyond static attribution is key here as it helps marketers understand how different touchpoints add value, which then can inform strategies. That is the key – not just understanding what drove conversion, but seeing the full value of what led up to conversion, and adjusting spend and tactics accordingly.
MTS: Thanks for chatting with us, Chris.
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