Group Product Manager, Adobe
Forrester recently named Adobe as a leader in their latest Forrester Wave: Customer Analytics Solutions Q2, 2018. Adobe scored the highest points in insights, action, usability, product road-map and vision, and partner ecosystem. Following this announcement, Ben Gaines, Group Product Manager, Adobe spoke to us about the state of Marketing Analytics and why Adobe Analytics is a more refined product than the rest of the competition.
Tell us about your role at Adobe and the team and technology that you handle.
I am a group product manager for Adobe Analytics Cloud. In my role, I work closely with Adobe clients to better understand their needs. I provide input on product strategy and roadmap, along with managing the planning and design of new features.
What is the state of Marketing Analytics in 2018? Which analytics have transformed or vanished from traditional marketing campaigns?
The story is mostly around transformation. Many of the channels that have been considered traditionally have not vanished. Instead, there is much more to deal with as we see interfaces like voice and AR take off. The state of marketing analytics is one in which there is increasingly greater complexity, paired with a realization that data is key to business success. This has created urgency for brands to be more sophisticated with their analytics practice. Not only do they have to account for new and emerging channels like voice and the connected car, disparate data sets must be lined up so that customers are engaged as individuals—not devices or channels. Expectations are at an all-time high as well, and consumers don’t care how difficult this is for brands. Fortunately, the growth and maturation of AI and machine learning technologies are providing a helping hand—one that is automating cumbersome processes and uncovering what is hidden to the human eye.
What makes Adobe Analytics a more refined product than the rest in the competition?
Adobe Analytics delivers the most value in helping brands go beyond simple vanity metrics—delivering deep consumer insights that move the needle on business goals. We do this by first having broad support for all the areas that consumers engage with brands nowadays—from desktop and mobile, to growing areas like video, audio, voice and the connected car. Through Adobe Sensei, our AI and machine learning framework, we then help do some of the heavy lifting—everything from automating analysis to flagging anomalies. Last but not least, we’ve created a canvas approach to data analysis as well, where our clients can use Analysis Workspace to interact with data across various dimensions and combine data sets to tell the right story. It is this robustness and flexibility and delivers meaningful insights.
Why is Predictive Analytics so sought-after in B2B and B2C industry?
Brands are heavily invested in their analytics technology, and every company is looking for ways to drive more value out of that investment. Predictive analytics is the next logical step, as it moves teams beyond basic canned reports and retroactive reporting. We are seeing an acceleration in predictive analytics because the technology itself has matured. AI and machine learning capabilities are robust enough where brands can begin to discover customer patterns that lead to conversion events. With AI doing the heavy lifting in analyzing massive volumes of data and automatically generating insights, data teams can more accurately predict which campaigns and marketing tactics produce the best results. For any brand, getting to this point means much greater ROI for their analytics investment.
How do you close the gap between expectations and delivery with your suite of analytics tools?
Expectations tend to differ across companies and individual users, so it generally depends on what type of organization is being considered. In many cases, senior leadership may expect their analytics investment to begin paying off right out of the box, without taking the necessary steps to build the right foundation. A true data-driven organization needs to create a culture that can rally around a common purpose. As a starting point, the proverbial walls need to be taken down across different teams, so that data is centralized and rationalized. Organizations that do this avoid targeting consumers with the same repetitive message multiple times across multiple devices and channels. On top of that, data analysis has to be made widely available across an organization. We can’t just rely on the data science team—everyone from product managers to the marketing team has to speak the same language and buy into the same vision and execution. If not, it becomes very difficult for data insights to drive real action.
Is it actually possible to gain 100% accuracy on attribution? How does Adobe Analytics level the playing fields for customers?
Nothing in the world of measurement is 100%, but when it comes to attribution, there is a lot of room for improvement. Most brands still rely on first-touch (e.g., the initial engagement with customers, such as a website visit or display ad) and last-touch (the desired conversion event, such as purchase). It is counter-intuitive to the way consumers engage with brands these days, and does not give fair credit to things like social media and owned brand content. Adobe Analytics recently introduced Attribution IQ, which unveiled a comprehensive set of 10 models that capture all the different ways in which consumers are influenced. This is the only solution in the market that lets brands truly dig in, not only seeing the impact of marketing investments across channels, but even within individual campaigns, products or internal promotions. For example, instead of just seeing how display ads compared against social ads, a brand can see how this is different amongst the Fall and Summer campaigns, and even across different demographics of customers.
Thanks for chatting with us, Ben.
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