Avoid information overload by reporting actionable analytics data.
- How do we know what information is the most important in analytics? Unfortunately, analytics has suffered from information overload. Too many times have lengthy spreadsheets, charts, and graphs been used to communicate information resulting in drowsy eyes and confusion. I'll show you how to avoid reports with meaningless data and how to get to the actionable issues. One of an analyst's most important skills is finding relevant information.
But even more important is communicating it. Typically, most people rely on dashboards for their initial information. With a few graphs and charts, people can get a visual view of the right now status of meeting campaign goals. The problem is that many analytics programs have default dashboards, which is a one size fits all approach. The dashboard may not contain the most important information for your business and your business goals.
Most analytics products allow customers to develop dashboards. These are particularly helpful since they can be customized for the use of different people in the organization. IT can have their performance dashboard. Marketing, a detailed campaign dashboard. And the executive level, the high level summary dashboard. Customizing data presentation to particular groups is vital to reducing unnecessary information overload.
When creating a dashboard, the idea is to present meaningful information, that is, information that can be acted upon, such as a trend that could impact a business or a campaign. For example, if I see that I have a lot of mobile visitors, but they are leaving the website quickly and without engagement, it is indicating a problem with my website on a smartphone. Action needs to be taken. Now, in order to get approval for that action, information must be presented quickly and clearly.
One thing I've learned over the years is that decision makers don't respond to spreadsheets. They respond to stories and reports that show how to improve sales. So if I take that same information about my mobile users and I repackage that into a report that shows how much revenue is being lost by day, by month, and by quarter, now I have their attention. Then I can propose a solution that shows the financial benefits of improving the mobile experience.
One warning, however. To better understand reporting in your analytics program, you need to understand that attribution, that is, how the final conversion is attributed. Let's say a visitor first finds my website from a Google search. They visit a few pages and then leave. But a few days later, they come back. But this time, they remembered the website, typed it in, and came directly. From that visit, they found the product they wanted and bought it.
Now most analytics programs employ what is called last-click attribution, that is, in this scenario, the direct visit gets the credit as being the source of the sale. Of course, that's not correct. They first found it through search. There are multiple attribution models that can be used for different types of businesses, first click, last click, linear, which gives credit to each source. The key is finding which model works best for your needs and provides an accurate representation of your visitors.
- Performing keyword research for SEO
- Building links
- Managing and measuring SEO campaigns
- SEO for local business
- Targeting, matching, and bidding on keywords for paid search advertising
- Writing compelling paid search ads
- Developing content that target specific audiences
- Using influencers for content marketing
- Investing in an email marketing lists
- Designing an email with a strong call to action
- Developing a social media marketing strategy
- Advertising on social media
- Engaging mobile users
- Designing for conversion
- Using landing pages to sell
- Copywriting tips
- Collecting analytics data
- Automating marketing
- Developing customer loyalty