One of the toughest aspects about analytics is not getting the right data or even getting the right data right. It’s not about making sense of the numbers or extracting meaningful insights. All of these things can be tough. But the toughest challenge? Getting the data or insight actually used in organizations and getting at the heart of the decision-making processes. There are two issues here: accountability and relevance. As soon as you want to start measuring things, you start to make people accountable, and inevitably there can be resistance to that. However, another challenge is having data and metrics that are relevant to what the business or a person is trying to do. If metrics are not relevant, then they won’t get used.
The main challenge to getting relevant metrics is aligning them as closely as possible to the objectives. If metrics aren’t aligned to what people and organizations are actually trying to do, then people aren’t going to take much notice of them. All too often metrics are “retro-fitted” into an organization because they’re available rather than because they’re useful. However, aligning metrics with objectives is often easier said than done; frequently the problem lies with the nature of the objectives themselves.
Objectives are often like marshmallows: they look good on the outside but they are soft and squidgy on the inside. Marshmallow objectives don’t really describe what the end result looks like and do not give an idea of whether the result is being achieved. An example of a marshmallow objective? For the development of a new checkout process, such an objective would be “to improve the user experience and build loyalty.” However, objectives should be as SMART as possible – i.e., specific, measurable, achievable, realistic, and time bound. The key to getting objectives as measurable as possible and having relevant metrics is to make them as specific as possible. You’ve got to ask yourself “what does good look like?”
Asking yourself what good looks like is a useful way of creating harder and more specific objectives. It’s a particularly useful technique when there may be no immediate or clear financial outcome. It’s easier in my checkout example, above, to make the objective “smarter” by setting specific goals in terms of the increase in orders as a result of the improved experience. But if you’re developing a new section of content or a new site, how do you get smart objectives?
The approach is to keep asking the question “what does good look like?” and trying to describe what will be happening on the site, off the site, or whereever that objective is being achieved. For example, you may be looking to refresh the content in the help and support section but what will good look like afterwards? Hopefully more people will be able to find the help and support they need more easily, meaning that they don’t need to call the call center or send in an email. It should also mean that people are more satisfied with the experience than they were before and would be more likely to use the help and support section.
By drilling into what good looks like, we can begin to define metrics that can then measure whether these desired outcomes are being achieved or not, either on the site or in the contact center. Hopefully the number of emails and calls for certain types of queries will be going down and customer satisfaction levels will be going up. Going through this exercise may show that not all the data that is needed is currently available. Actions may need to be taken to improve the configuration of the web analytics systems, to introduce a voice-of-the-customer program, or to extend the scope of an existing one. In some cases it may mean getting access to data that sits in another part of the organization such as the contact center.
This simple approach to creating objectives that are as specific as possible can be used at all levels and in many ways, from determining the success of the whole digital channel through to understanding the effectiveness of a particular piece of product development or a specific campaign. So each time you come to measure something, ask yourselves: Why are we doing this? What will good look like? How can we measure success?
Marketers create personas to better understand their target audience and what it looks like. If marketers can understand potential buyer behaviors, and where they spend their time online, then content can be targeted more effectively.
What’s behind a successful data-driven marketing strategy?
One of the major challenges in the martech industry is getting the attention of prospects in a world where they are bombarded by content and emails on all sides.
Facebook is addressing one of the biggest missing pieces of its chatbot offering: analytics.