Customer Journey Analytics: Omnichannel Analysis Really Can Happen in Real Time
Have you ever received a completely irrelevant communication from a business that you have some — or a lot — of history with? Maybe an email offering you a discount on something you just paid full price for? Maybe coupons for something you’d never purchase?
In today’s digital economy, businesses using omnichannel analytics are more likely to outsmart their competitors and please their customers. Your job is to make sense of the loads of information. Which data sets are crucial? Which ones provide little to no value? Where is the actionable signal amidst all the noise? How do we find and use insights before they expire? Make no mistake, insight definitely has a shelf life, and long queries and queues to answer questions and act on data is a surefire way to lose business.
Last month, we announced customer journey analytics in Adobe Analytics, empowering organizations to be more inventive in the way they centralize, normalize, and interact with different layers of data. With this new capability, organizations can easily centralize and standardize omnichannel data. This means you can have a single store of data based on a common schema that will save you countless hours of data preparation.
But more importantly, normalizing your CX data will create a seamless process for describing your customers’ interactions consistently across various touchpoints, letting you more easily deliver experiences that meet expectations regardless of channel. For example, a retailer that can combine point-of-sale data into a customer profile will know to send you an offer for 25% off an accessory and not the item you just paid full price for.
We’re also making it easier for you to curate and explore your customer data in ways that provide some interesting benefits. Candidly, visualization needs more customer intelligence, like journey-based analysis tools and attribution functionality. And critically, it needs to provide interactivity — meaning the ability to immediately access and interrogate data.
Interactivity matters in reporting and visualization because business users typically don’t know the questions ahead of time, and they’re typically not writing their own SQL. They also have follow-up questions that they want answered now. Customer journey analytics brings the interactive, self-serve experience of Analysis Workspace to omnichannel data analysis.
A Gartner survey found that nearly half of IT and business leaders who invest in customer analytics see customer journey analysis as their top priority. And for good reason, according to Harvard Business Review, as delivering seamless and personalized experiences across the customer journey has the potential not only to increase customer satisfaction by 20%, but also lift revenue by up to 15% while lowering costs by as much as 20%. This is a win-win for customers and brands.
From the start, the team developed the customer journey analytics interface with cues taken directly from our creative side of the business, particularly Photoshop’s layers. With Photoshop, users can source, edit, and layer images and graphics on top of one another to create a new visual, giving you different lenses to view your image. In customer journey analytics, we figured we could apply different lenses or layers to data instead.
The resulting UI lets you curate metrics like orders, conversion, and visits across channels with Adobe Experience Platform, the first purpose-built customer experience management platform with real-time customer profiles and continuous intelligence. And you can drag and drop layers of data together to uncover new insights about how customers engage with your brand. It provides many different lenses into the overall customer journey.
With customer journey analytics, teams could bring in new data sets like those from point-of-sale systems and call centers to produce insights that are better aligned with how consumers interact. It helps to close a creativity gap seen in data analysis as well, empowering individuals to be more inventive in the way they combine, explore, and experiment with different layers of data. Brands can better support decision-making with more comprehensive insights and self-serve access.
In summary, with customer journey analytics, you can achieve the following:
- Answer complex questions. Being able to layer and curate omnichannel data means that brands can see the customer journey in full context across a variety of channels, from acquisition to closed sale to post sale. Compare customer segments, analyze fallout behavior, uncover high-performing journeys, and more, all across online and offline channels. Unlike traditional dashboards with limited interactivity, you can dig into layered data sets and present collections of insights for different audiences in real time.
- Empower anyone to work with data. Customer journey analytics is robust for data scientists but is accessible to a broad set of business users, like marketers or product managers. It’s meant to not only democratize data, but to also democratize analysis. A new reporting engine helps foster a more data-driven culture, giving anyone a visual and creative way to query data specific to their role.
- Gain a competitive advantage with AI and machine learning. With
- Data Science Workspace
- , you can use prebuilt artificial intelligence (AI) and machine-learning models in
- Adobe Experience Platform
- to influence various points of the customer journey. So, you can unearth hidden insights, make better predictions across the customer journey, suggest recommended best next steps, or automate cumbersome processes.
Let’s talk about what cross journey analytics in Adobe Analytics can do for your business. Talk to one of our sales representatives today at 877-722-7088.