5 Tips to Optimize Your Analytics Investment
Enterprise retailers are spending millions on analytics because, in theory, the right solution can provide incredibly valuable insights to guide marketing, product development, sales initiatives and more, thus generating significant return on investment. But do the analytics platforms that companies currently use live up to the potential and generate returns that justify the investment? No, according to top marketers in the 2015 CMO Survey, who rated analytics performance subpar at best.
Survey respondents, including top marketing executives for Fortune 1000 and Forbes Top 200 companies, rated analytics performance 3.2 on a seven-point scale, indicating deep dissatisfaction with analytics ROI. So how can companies do better? It starts with defining goals before choosing an analytics solution and deploying an analytics strategy that identifies challenges, defines solutions, and supports company objectives. Here are five tips that can help marketers make the right choices:
1. Know the major classes of data analytics: solutions and tools. Tools include affordable and easily deployed instruments like Optimizely, which have the advantage of being free or low cost and installed simply via a single line of code on a website. These tools specialize in one to two functions (e.g., tracking clicks). The second type is typified by Omniture, part of the Adobe Marketing Cloud — a class of analytics that's larger in scale as well as higher in price. The range of features for the solutions class is much more impressive, but it shares a key vulnerability with inexpensive options like Optimizely: both are frequently deployed without chief marketing officers having a clear view of what problems the technology addresses or goals it’s designed to accomplish.
2. Resist the urge to chase the latest shiny object. To generate ROI, analytics must deliver substance, not just style. But as the big data tsunami came ashore, inundating marketers with so much substance (i.e., data) that even tech-savvy marketers couldn’t make sense of it all, a new phenomenon emerged that often emphasizes the latest technology as the panacea to your big data problem. Many new technologies are aesthetically pleasing, enabling users to visualize data in new ways. But the vital question is this: Do pretty visualizations provide you access to new, important data and insights, or are they just giving the same big data that’s paralyzing you a new look?
3. Define the problem and identify specific solutions. This seems like common sense, but too many companies adopt analytics technologies on the vague premise that it will help them “optimize” marketing outreach rather than clearly define challenges and chart a course toward specific outcomes. It’s crucial to be specific. Instead of seeking “optimization,” make sure analytics assessments are targeted by defining objectives such as an average order value increases, customer base expansion and higher visitor conversions. At the same time, be realistic about what analytics can measure, acknowledging, for example, that they can’t chart the entire omnichannel marketing journey.
4. Choose your analytics tools carefully. Deploying too many analytics platforms can create a vicious circle, but many companies fall into that trap. CMOs understand there's value in data, see that there are many low-cost or free tools on the market that all promise to help them make sense of it all, and deploy a range of solutions. However, they then find that they’re generating too much data to process in a meaningful way. The fastest way to find yourself in the trap of analysis paralysis is to Frankenstein your analytics suite. Each tool or solution needs to map back to the specific areas of the customer journey you’re trying to understand. By visually mapping each tool to the customer journey, marketers provide themselves with an opportunity to gain crystal-clear clarity over what each tool is meant to do and identify areas of redundancy in either the business process or technology.
5. Use qualitative research as well as quantitative data. The most powerful form of data that marketers aren't leveraging is qualitative. Quantitative data can shed new light on consumer actions, but it doesn’t answer the “why” behind consumer behavior. In the past, getting to the “why” took a major investment in market research, but new platforms are now available that streamline and automate qualitative research. A well-designed qualitative solution can replace 10 or more quantitative tools. Qualitative research makes a great complement to quantitative solutions, providing a clearer picture overall. Big data should answer questions. If you need a trained statistician to interpret the data for you, you may want to ask yourself if you’re looking at the entire picture or trying to infer conclusions from an incomplete data set.
As the 2015 CMO Survey showed, most top marketing executives don’t feel they’re getting the most out of their analytics investment. With a strategy that emphasizes real-world challenges and specific solutions and combines qualitative and quantitative data, marketers can maximize analytics ROI — and gain a competitive edge.
Collin Sebastian is the chief product officer of YouEye, a research platform that automates in-person interviews.
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