Practically every business today owns a website. Whatever industry you work in, and whether you’re selling directly to consumers in B2C or providing a knowledge portal for B2B clients, your website is probably your most important digital channel.
In order to create and maintain a visitor-friendly website, you need to know how people are using your site and—more importantly—act on that knowledge.
Web analytics is the process of analyzing the behavior of visitors to your website. It starts with gathering data, but it’s much more than simply installing a script on your web server.
First, it lets you optimize your website to improve the visitor experience. Of course, many firms are doing that already. But it’s a competitive world out there, and it’s easy to throw big money at the problem without actually gaining much of an edge.
It’s essential to find out whether you’re putting your resources in the right place, attributing sales improvements to the right digital campaign and getting truly valuable results from your website optimization spend. The more sophisticated your analytics, the more effectively you can spend your marketing dollars.
However, analytics can do much more than that. You can use them to make data-driven marketing decisions, adopt integrated campaign approaches, measure campaign RoI or carry out qualitative and quantitative analysis of customer journeys.
Overall, they can tell you whether your business is achieving its objectives, and point the way forward to productive changes you could make in order to improve—in some cases, far beyond your website.
From diagnosis to prediction
You’re probably already carrying out diagnostic analyses such as looking at campaign performance. But by leveraging the mind+machine framework and the tool-agnostic approach, you can take your web analytics insights to the next level, by identifying opportunities for cross-selling and up-selling, or digging deeper into sales and user data with highly accurate attribution analysis.
The move from diagnosis to prediction isn’t right for everyone. Making it a success depends on your business and digital goals, both short-term and long-term, and where you currently are on the journey to digital maturity. For example, if you have yet to implement basic or even advanced reporting of website traffic data, predictive analytics probably won’t bring you any benefit.
Before embarking on the change, it’s worth asking subject-matter and domain experts to carry out a thorough audit of your as-is situation and digital maturity level. That means considering questions such as:
- Where are you now?
- Where do you want to be?
- How do plan to you get there?
- How could you measure success?
Based on your answers, your experts can design a custom web-analytics progression ladder, showing the steps you can take from here and the stages of development that lie ahead. There’s no one-size-fits-all solution; the ladder will be different for each company.
What could advanced analytics do for you?
The benefits you can achieve from next-level analytics range from improving the performance of online assets right through to designing better marketing campaigns and sharpening up internal process.
Here are a few use cases that are relevant to a wide range of companies and industries:
- Improved website traffic, sales conversion and forecasting
- Reducing customer churn
- Maximizing RoI on marketing campaigns
- Better forecasting of campaign design spend and content planning
- Improved customer / lead segmentation and targeting
- Personalized content and experience delivery
- Better engagement of customers, business partners, employees, etc.
- Structured internal knowledge management
- Increased operational efficiency.
Final thoughts
Web analytics is sometimes seen in quite a narrow way, as a digital or technical discipline that mainly relates to website optimization. In fact, because online interactions are so pivotal, it can deliver far-reaching benefits to the business that many site owners may not be aware of. However, unlocking these benefits depends on taking a step up from diagnostic to predictive analytics.
