5 Analytics to Help Map the Customer Journey

An emerging class of technology is helping companies use analytics to better understand customer journeys. Here are the capabilities at your fingertips.

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According to a report from Gartner, an emerging class of technology tools is helping businesses get more from data marketing analytics. In the report, the company evaluated a number of different providers and indicated which vendors delivered the most innovative experiences. Here’s a closer look at the criteria used in the report and how each of those features can help marketers better understand their business environment.

Customer and journey insights: The customer journey is rapidly changing, and marketers are increasingly talking to markets of one. With deeper personalization and customization, today’s marketing analytics tools need to deliver deep insights into who customers are, how the customer journey is changing, and where those issues intersect to influence a brand’s business goals.

Data integration: To what degree are your various analytics platforms talking to each other? And how easy is it to identify specific consumers within that pattern? For example, can you look at information on your social channels and website and map it to a specific customer segment? That level of data integration helps marketers achieve better results over time.

Data exploration by discovering useful patterns and presenting visualizations: Data are being used by a wider cross section of stakeholders to make decisions and monitor progress. As a result, it’s important to ask how easy it is to use the data that are presented. Data visualization tools are one way marketing analytics platforms help your team find patterns, visualize data, and really understand what’s happening with the business. Robust tools also make it easier to customize information and communicate performance to a range of audiences within the business.

Advanced models that can identify trends over time: A snapshot of data can be instructive, but it’s difficult to make important strategic decisions based on what happened in a single day. Advanced modeling can use data mining and predictive analytics to identify trends over time and project how they’re likely to play out in the future. For example, declining demand for specific products might suggest that it’s time to look at your product portfolio or shake up your messaging around a product line. These insights let you make moves before changes hit your bottom line.

Data integration to implementation tools: Ultimately, the information data analytics modules provide is only useful if it is put into action. Waiting for available talent can create unnecessary bottlenecks. Technology that connects with integration tools, or even automates the application of these insights, can help businesses take a leap forward in applying insights and reaping the results.

Marketing analytics tools are helping marketing departments be more effective than ever before. An important component of this is really understanding what capabilities these software types offer and how to maximize your return on investment.