6 min readThere’s no such thing as a one-size-fits-all solution when it comes to analytics. [...]
In the 20th century, the key business intelligence challenge we faced was a shortage of useful empirical data. In the 21st, the issue is having so much it’s hard to know where to start looking. Companies with adequate resources and institutional willpower can throw considerable sums towards establishing advanced-analytics programs. The issue becomes what to do with all of the information being gathered.
In recent years, McKinsey Analytics has conducted extensive research on companies making big investments in advanced-analytics tools, and found that as little as 10% of the potential value these approaches unlock has been realized by the lion’s share of investors. One recurring issue is a consistent failure to tie these technologies to clear, actionable business goals.
In this guide, we’ll explore what kinds of data are most relevant to common business problems, and look at key features offered by various analytics tools that can help you solve them.
In order to find the right analytics tool, you need to identify a clear business objective. A bank, for example, might seek better modelling for credit risk assessments, or insights into customer usage patterns. A manufacturer, on the other hand, would prefer BI analytics that help with inventory optimization.
Here are some key considerations:
Web analytics capture the behavior of users browsing the internet. Thanks to the prevalence of Google’s free Universal Analytics platform, it’s difficult to find a business – from the Fortune 500 down to a rural “mom and pop” feed store – that doesn’t take a great interest in how its web presence is performing.
Unfortunately, web analytics is also probably the single biggest source of wasted time and money in the industry, thanks to confusion about how its metrics translate into actionable insights. Are you failing to convert leads because of unintuitive navigation, or because your online marketing isn’t bringing in the right audience?
Google Analytics remains an extremely comprehensive and flexible tool for those with clearly defined business goals and the skill to make use of its reports. It can be augmented with enterprise options (Analytics 360) that provide dedicated support, deeper support for AdWords and search optimization, and training.
The real x-factor in web analytics is integration, which is where third-party solutions distinguish themselves. By linking web metrics to other data streams (including business and marketing intelligence), you can see web analytics within the greater context of your business as a whole.
IBM’s Watson Customer Experience Analytics applies artificial intelligence to automatically identify points of customer “struggle.” Using sophisticated customer behavior modelling, the AI isolates problem areas, allowing you to craft specific fixes. Its insights evolve in real time, helping you to see immediate results.
Because the language and functionality of web analytics have become so familiar, there’s a temptation to assume what works for web will apply to other use cases. Beware!
Not long ago, we wrote a piece on the differences between web and IoT analytics, and as a deep-dive we think it’s well worth your time. For now though, we want to summarize a few key points:
The initial IoT analytics offerings by big guns like IBM and AWS betrayed a “jack-of-all-trades” mentality. It only had generic dashboards with a handful of preset metrics and reports. If you wanted something customized, you had to look elsewhere.
Those heavy-hitters eventually began to recognize they were losing marketshare to more agile firms like Mnubo, C3, or PTC which provided purpose-built IoT analytics solutions. Over the past year or so, they’ve begun to shift approach. But for companies that have made a serious top-to-bottom commitment to analytics, there are significant time and cost benefits to using platforms designed for IoT analytics rather than imperfect in-house setups.
In the end, there’s nothing more important in analytics than knowing what you need to know. Generic solutions provide generic answers — because they only allow you to ask generic questions.
The right analytics tool will help guide your discovery process by adapting to the information you want it to measure. And that’s how you arrive at questions worth answering.