by Jeffrey Ries
Chapter 3: The Lean Analytics Cycle
The Lean Analytics Cycle is vital in helping you get started on this part of the Lean support methodology with your business. There are four steps that will come with this process, and following each one can be crucial in ensuring that this works for you.
The best way to think about the Lean Analytics Cycle is like the scientific method. You need to do some thinking to determine what needs to be improved in your business, form a hypothesis to help lead your findings, and then perform experiments to see if that is the right process for you to keep following. If things don’t work out, you don’t just give up. You will continue to find new experiments, going with the same hypothesis if it works (otherwise you’ll need to form a new hypothesis) until you find the right solution.
The Lean Analytics Cycle will be incredibly helpful when you begin going through the entire process. Let’s take a look at the steps that you need to fulfill to use the Lean Analytics Cycle.
What do I need to improve?
Before you can do anything with the Lean Analytics Cycle, you must really understand your business. You need to know all the important aspects of your business, in addition to knowing what you want to change.
During this first step, you may need to talk to other businessmen to help you find what metric you should use, based on what is most relevant to your business right now. You may also want to take a look at your business model to find out what metric will work best for you.
After you have time to choose a metric, you should connect it to the KPI or the Key Performance Indicator. An example of this is the metric that is seen as a conversion rate if the KPI is the number of people who currently purchase the product.
To make this step easier, the first thing that you would want to do is write down three metrics that are important for your business. Afterward, write down the KPI that would be measured for each metric.
Never try to implement the Lean system without understanding the most important processes that need to be improved. Sure, you could probably make a long list of things that you may want to improve in your business. But you won’t really see the benefits of the Lean system if you don’t pick things that are important to the overall functioning of your business. Look closely at what your business needs to improve, and pick the one that is the most important before moving on.
Form a hypothesis
This is a stage where a level of creativity needs to come into play. The hypothesis is going to give you the answers that you need to move forward. You will need to look for inspiration, and you can find it in one of two ways. You can look for an answer for something like “If I perform ___, I believe ____ will happen, and ____ will be the outcome.”
The first place you can look into is any data that you have available. Often, this data will provide you with the answer that you need. If you do not have data at all, you may need to do some studying of your own to come up with an answer. You could use some of the strategies from your competitors, follow the practices that have worked well for others, do a survey, or study the market to see what the best option will be.
What you need to keep in mind here is that the hypothesis is there to help you to think like your audience. You want to keep asking questions until you understand what they are thinking, or learn to understand the behavior of your audience or customer.
Conduct an experiment
After you have taken the time to form a hypothesis, it is time to test it out with the help of an experiment. There are three questions that you need to consider to get started with an experiment:
Who is the target audience? You need to carefully consider who your customers are and whether or not they are the right customers, or if you should look somewhere else to get better results. Also, think about some of the ways that you reach them, and if there are better ways to do this.
What do you expect the target audience to do? This often includes purchasing the product, using the product, or something similar. You can then figure out if the audience understands what you want them to do; is it easy for them to do this action, and how many of the target audience are completing the task?
Why do you think they should accomplish the action? Are you providing them with the right motivation to accomplish the task? Do you think that the strategy is working? If they aren’t being motivated enough by you, are they doing these things for the competitors or otherwise?
Answering these questions is vital because they may help you understand your customer better than ever before. Creating your experiment during this stage does not have to be difficult. Try using the following sentence to help you get started:
“WHO will do WHAT because WHY to improve your KPI towards the defined goals or target.”
If you have gone through and come up with a good hypothesis in the previous step, then it shouldn’t be too hard to create a good experiment as well. Then, once you have the experiment, you can go through and set up the Lean Analytics so that you can measure your KPI and carry on in the experiment.
Measure your outcomes and make a decision
You can’t just get started with an experiment and then walk away from it. You need to measure how well it goes to determine if it is truly working; if some changes are needed; or if you need to work from scratch. You can then make a decision on the next steps you need to take. Some of the things to look for when measuring the outcomes during this stage include:
Was the experiment a success? If it is, then the metric is done. You can move on to finding the next metric to help your business.
Did the experiment fail? Then it is time to revise the hypothesis. You should stop and take some time to figure out why the experiment failed so that you have a better chance at a good hypothesis the next time.
The experiment moved but was not close to the defined goal. In this scenario, you will still need to define brand new experiment. You can stay with the hypothesis if it still seems viable, but you would need to change up the experiment.
Chapter 4: False Metrics vs. Meaningful Metrics
One of the prerequisites for working with Lean Analytics is understanding that most people are using their data wrong. When you don’t use your data correctly, you are not going to be able to come up with patterns, opportunities, or results that are achievable.
There are two points that come with this idea. These two points are:
There are many companies, as well as people, who will label themselves with descriptions like “data-driven.” Sure, they may use up a lot of their resources on compiling data. However, they then miss out on the “driven” part. Few are actually going to base a strategy on the information that they acquire from the data. They may have the right data, but they either don’t understand it or choose to react to it incorrectly.
Even if the actions of a company or person are driven by data, the problem of using wrong data still exists. Often, they will oversimplify these metrics and then use them according to the convention. Keep this in mind: just because other people do this or have done this doesn’t mean it is going to prove useful to the goals that you have. Consequently, the data is going to become garbage in, and then the analysis is garbage out. This is often known as false data.
As a business who is interested in working with Lean Analytics, it is important to learn the difference between false metrics and meaningful metrics. If you follow false metrics, you are going to be following a strategy that is not going to help you reach your goals, which will mean a lot of time, effort, and resources wasted.
The biggest false metrics to watch out for
As a business that is trying to cut out waste and ensure that you provide the best customer service and the best products possible, you must always ensure that you watch out for some of the false metrics that may come up. Many people who don’t understand how data works will be taken in by these false metrics that, in reality, will mean wasted time and resources. Some of the most common false metrics for you to watch out for include:
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p; The number of hits: Just because you have a website that is attractive and contains many points of interest, doesn’t necessarily mean that it will tell you what the users are really interested in. You should not focus on the number of hits your website gets. This may make you feel good about your website, and it can be neat to see how many people come and visit your website. But you need to focus more on what the customer is interested in or looking for.
Page views: This metric refers to how many pages are clicked on a site during a given time. This is a slightly better than hits, but you typically don't want to waste your time with this metric. In most cases, unless you are working with a business that does depend on page views, such as advertising, the better metric for you to use is to count people. You can do this with tools that will provide information on unique visitors per month.
Number of visitors: The biggest problem with this metric is that it is often too broad. Does this type of metric talk about one person who visited the same site a hundred times, or a hundred people who visited once? You most likely want to look at the second group of people because you’ve obtained more impressions. Otherwise, just looking at the number of visitors will not give you this information.
Number of unique visitors: This is a metric that is going to tell you how many people got to your website and saw the home page. This may sound good at first, but it is not going to give you any valuable information. You may also want to find out things like how many visitors left right away when they saw the page or how many stayed and looked around. Unique visitors can help you see that some new people are coming onto your website and checking things out but they don’t really tell you much about those visitors or what they are doing.
Number of likes, followers, or friends: This is a good example of a vanity metric that is going to show you some false popularity. A better metric that you can go with is the level of influence that you have. What this means is how many people who will do what you want them to do. While it is good to have followers and likes on your page to show that people are looking at your content, it is not as important as some of the other metrics that you can pick.
Email addresses: Having a big list of email addresses is not a bad thing by itself. But just because you have this large list does not mean that everyone on it is going to open, read, and act on the messages that you send out. You want to make sure that the email addresses that you do have are high quality and are from people who actually want to hear from you, even if that means your email list is a little bit smaller. If you are collecting emails, strive to get addresses from people who are actually interested in your product and services. Don’t just collect emails so you can boast of a large list.
The number of downloads: This is a common metric that is used for downloadable products. While it can help with your rankings in the marketplace when you are in the app store, the download number is not going to tell you anything in depth, and it won’t give you any real value. If you would like to get some precise answers here, you can pick some better metrics. The Launch Rate is a good place to start because it will show the percentage of those who downloaded, created an account, and then used the product. You can also use something like Percentage of Users Who Pay so you can see how many actually pay for anything.
Time spent by customers on a page or website: The only time that this is going to be useful is for businesses that are tied directly to the behavior of the engaged time. For example, a customer could spend a lot of time on your web page, but what if they are spending that time on the help pages or on the complaints pages? This is not necessarily a good thing for your business, so this metric is not the best one for you to go with.
If you want to pick out a metric that will actually help your business get ahead, then you must make sure that you avoid some of these false metrics. They may look good on the surface, but in reality, they are just giving you information that could be pretty useless, and you will end up wasting a lot of time and money to follow them.
Chapter 5: Recognizing and Choosing a Good Metric
Part of the Lean Analytics methodology is finding a good metric to help you out. The Lean Analytics Cycle is a measurement of movement towards a goal that you already defined. So, once you have taken the time to define your business goals, then you must also think about the measurements you can make to progress towards the goals.
This can be hard to do. How are you supposed to find a good metric that can make sure you go towards the goals that you set out? Some of the characteristics that you can look for when searching for a good metric include:
Comparable: You know that you have a metric that is good if it is comparable. You want to be able to compare how things have changed in the last year, or even from one month to another. This gives you a good idea if there have been any changes, positive or negative, with your business process, customer satisfaction, and more. You can ask yourself these questions about the metric to help test for this: How was the metric last year, or even last month?
Is the rate of conversion increasing? You can use the Cohort Analysis to help with conversion rate tracking.
Understandable: The metric that you use should never be complex or complicated. Everyone should be able to understand what it is. This ensures that they know what the metric is measuring.
Ratio: You should never work with absolute numbers when you are working with metrics. If you find that you have those, you should try to convert them to make comparisons easier, which in turn makes it easier to make decisions.
Adaptability: If you have chosen a good metric, it should change the way that the business changes. If you notice that the metric is moving, but you have no idea why it is moving, then it is never a good metric. The metric should move with you, not randomly on its own, or it won’t be a secure one to use.
Types of Metrics
There are two metric types that you are able to use when doing Lean Analytics. These include qualitative and quantitative metrics. To start, qualitative means that the metric has a direct contact with your customers. This would be things such as feedback and interviews. It is going to provide you with some detailed knowledge of the metric.
You can also work with quantitative metrics. These are more of a number form of metrics. You can use these to ask the right types of questions from the customer.
Of course, both of these methods have other things under them that make them easier to use. You will find that both of these methods have actionable and vanity metrics.
Vanity metrics will not end up changing the behavior of the thing you are concerned about. These are a big waste of your time, and you should avoid them as much as possible. They seem to provide you with some good advice and something that you can act upon, but often they don’t lead you anywhere and can make things more difficult. If you are working with a company to help you determine your metrics, be very wary if they start touting the benefits of following any of the vanity metrics.
Actionable metrics are going to end up changing the behavior of the thing you are concerned about. These are the types of metrics that you want to work with on your project. They are metrics that can lead you to the plan that you should follow and can make it easier to come up with a strategy to make your business more efficient.
Reporting metrics is a good way to find out how well the business is performing when it does even everyday activities.
Exploratory metrics are going to be useful for helping you to find out any facts that you do not know about the business.
Lagging metrics are good to work with when you want more of a history of the organization and you want as many details as possible to help with a decision. The churn of a company can be a good example of the lagging metrics. This is because it is going to show you how many customers have canceled their orders for a specific amount of time.
Leading metrics are good because they can help provide you with the information that you need to make future forecasts for the business. Customer complaints can be a good example of leading metric
s because it can help you to predict how a customer will react.
You will need to determine which kind of metric you want to use based on the problem or project that you are working on. Working with one metric is usually best. Doing so will help keep you on track, so you know what to look for. Don’t waste your time trying to work on more than one metric. You will only get confused and end up with no clear idea about the strategy to follow.
Chapter 6: Simple Analytical Tests to Use
Another thing that you should concentrate on to do well with Lean Analytics is to have some familiarity with the tests that are used. These tests are helpful because they are going to be used to help you examine any assumptions that you are trying to use here. These tools can also be used to help you identify customer feedback so you can respond to them properly. Let us take a look at some of the best analytical tests that you can use when working with Lean Analytics.