Lean Analytics

What is Lean Analytics?

Lean Analytics is a part of Lean Startup methodology which consists of three elements – Building, Measuring and Learning. These three elements form up a lean analytics cycle of product development. This emphasizes that quickly builds an MVP (Minimum Viable Product). You can make smarter decisions with accurate measurements of Lean Analytics.

The main objective of lean analytics companies should maximize the learning in a short period of time. The result of using lean analytics will be a more effective and agile company.

Lean Analytics, which is a subcategory of Lean Startup methodology, covers only the measure and learn section of the cycle. This means without proper measurements, you cannot take any decision. Before applying this methodology, the companies should clearly know what should be tracked, for what reason it should be tracked and what are techniques to be used for tracking. The lean analytics cycle is shown in the picture below.

Recognizing a good metric

As we already know about the lean analytics cycle is the measurement of movement towards the defined goals. So once you have defined your business goals, then you need to know the measurements to make progress towards the goals. There are a few characteristics of good metrics. It is listed below.

1. Comparable – A good metric should be able to compare. You should be able to answer the following questions using your metric

• How was the metric the previous year or the previous month?
• Is your conversion rate increasing?

The conversion rates can be tracked best using Cohort Analysis.

2. Understandable – Metrics should not be more complicated or complex. It should be easily understandable by everyone so that they can know what the metric measures.

3. Ratio or a rate – Absolute numbers should not be used in Metrics. If anything is expressed in terms of percentage, it will be much better to compare and make decisions based on that.

4. Adaptability – Good metrics should change the way your business changes. If the metric is moving and you don’t know for what, then it’s not a good metric. It should always move along with you.

Types of Metrics

There are two types of metrics – Qualitative and Quantitative

The qualitative method means having direct contact with the customers, for example, customer interviews and feedbacks. It provides detailed knowledge into the metric

Quantitative is the number form of metrics. This method will help you to ask questions from the customers.

Qualitative things can be proved quantitatively.

Under Qualitative and Quantitative methods, there are Vanity and Actionable metrics.

• Vanity metrics do not change the behavior of your concern
• Actionable metrics will change the behavior of your concern
• Reporting Metrics will help you to find out how your business is performing in its everyday activities
• Exploratory metrics will help you to find out the undiscovered facts about your business
• Lagging metrics will give you the detailed history of the organization
• Leading metrics will help you to make future forecasts

Companies churn is an example of lagging metrics. Because it tells you the number of customers who have canceled their orders for a particular period of time. Customer complaints can be the leading metric as you can predict the customer’s reaction.

Correlation and Causation in Lead Analytics

For any business, it is very important to differentiate between the co-relational and causational relationship.

For example, ice cream consumption can be related to fever. The more people consume ice cream there are more chances of getting a fever. We cannot also come to the conclusion that only ice cream causes fever. This is because fever can also occur due to the seasons, which again correlates with ice cream consumption. Fever mostly occurs during the summer months which is the season where people eat more ice cream. This is a point in which correlation and casualty meet.

When we say that two things are correlated, then it means that two variables change equally, like ice cream and fever in the above example. A casual factor here is the summer months as they directly impact the other two dependent variables, ice cream and fever. This is because the summer months are more prone to ice cream consumption and fever.

Correlation helps to predict the future, and it will tell you what is going to happen.

Casualty is a superpower that will help you to change the future

The process goes like this

• Find the correlation in your data
• Test for casualty
• After finding the casualty factor, optimize it

Lean Analytics Framework

The Lean Analytics framework will help you to find out the business you are at and the stages of your business. Your business model should consider the customers and their buying process. You should ask yourself a few questions before framing a business model like

• How customers buy your product?
• Why they purchase from you?
• At which stage of your business they are in?
• What is the budget of your customers?

Do not copy others business models. Frame your own business model. Your business model should work best for your customers. Given below is the picture representing the business model.

Lean Analytics Stages

In the Lean Analytics stages, there are gates which the company needs to pass through to get to the next stage.

The five lean analytics stages and their gates are represented in the picture below.

• In the first lean analytics stages, you need to find out a problem for which the people are searching for a solution. This stage is more crucial for B2B business. If you have found such a problem, then you can move on to the next stage.
• In the second lean analytics stages, stickiness, you should create an MVP product for the early adopter customers. In this stage, you should aim for user engagement and retention. You can find out this when people start using your product. You can also know about user engagement and retention from the time they stay on your site. If the users stay for a long time, then it proves that you have provided what they needed. After this, you can pass it on to the next stage, Virality.
• In this third lean analytics stage, you need to get more customers in the most cost-efficient way. Once you get the customers, you can move on to the next stage, Revenue.
• In this fourth lean analytics stage, you can focus on the calculation of your revenue and also do the economics work. You can concentrate on optimizing the revenue and can calculate the LTV:CAC ratio. LTV means the revenue expected from a customer, and CAC means the cost involved to acquire the customer. The ratio can be found out by dividing the LTV by CAC. You can assume your margins are good if the LTV is three times your CAC. Now, after calculating your revenue, you can move to the last stage, Scale.
• In this lean analytics stage, you can take the necessary actions to grow your business. You can make plans where to concentrate more in order to increase the growth of the business and expand it.

The One Metric That Matters (OMTM)

Getting knowledge about the metrics is not enough. You should know which metric to be used at which stage of business. The focus is an important resource in lean analytics. If you distribute your focus on different metrics, then you will be directed ahead of learning. Every stage requires one particular metric that you need to work on. Select that one metric to work with and fix your target after doing research. You can decide on a target by carrying out research about your competitor’s strategies and benchmarks.

For example, a company had its metric as Churn. Their target is to keep the churn rate below 4%. So if the churn rate is less than or equal to 4%, it means that their target is achieved. If the churn rate is more than 4%, then it means that the company is under a problem, and they need to take measures to decrease the churn rate.

Hence to establish a competitive business you and your entire company should focus on the One Metric That Matters (OMTM).

Lean Analytics Cycle

The above picture represents the Lean analytics cycle. There are four main steps involved in this process which is discussed in detail below

1. Find out what to improve

You should understand about your business first because lean analytics cycle will not help you in that. You should know the important aspect of your business and should know what to be changed in it.

In this first step, lean analytics cycle needs to help from other businessmen to find out your metric. Lean analytics cycle will help to find a metric that is relevant to your business. You can also find out a metric based on your business model.

After choosing a metric to connect it to a KPI (Key Performance Indicator). For example, the metric is conversion rate if your KPI is the number of people buying your product.

So the first thing is to write down three important business metrics and the KPI you need to measure for each metric.

2. Form a hypothesis

This is the stage where you need to be more creative. The hypothesis gives you the answer for “If I perform _____, I believe _____ will happen and ____ will be the outcome”

This is the stage where you need inspiration. You can find out that in two ways

One thing is when you have the data and the other when you don’t have the data

If you have data then you can find out easily what is the thing that is causing the difference.

If you have no data then you can try different things. For example, you can study the market, do a survey, use the competitor’s strategies, follow best practices and others.

The bottom line is hypothesis will help you to get a place in the minds of the audience by either asking questions or understanding their behavior.

3. Conduct an experiment

After framing a hypothesis now you need to convert it into an experiment. For that, you need to ask yourself three questions

• Who is your target audience? – Who you are expecting to do things? Are they the right type of audience? How can you reach them?
• What do you expect them to do? – Is the audience understanding what you expect them to do? Is it easy for them to do? How many are performing what you expected?
• Why do you think they should do that? – Are you motivating the audience to do things? Which of your strategy is working best for you? Are they doing things for your competitors?

These three questions will help you to have a deeper understanding of your customers. Lean analytics stages are called customer development.

Creating an experiment can be defined in a single sentence like

“WHO will do WHAT because WHY to improve your KPI towards the defined goals or target”

If you have a great hypothesis, then you can create a good experiment.

So once you have an experiment set up your lean analytics to measure the KPI. Then carry on with your experiment.

4. Measure your outcomes and decide what to do

In this lean analytics stages, you can know the outcome of the experiment.

• If the experiment is a success then your metric is done. Find out the next metric to work with.
• If the experiment failed then you need to revise your hypothesis. Also, try to identify the reasons for the failure.
• If the experiment has moved away and not near to the defined goals, then you need to define a new experiment. You can have the same hypothesis just make changes to your experiment.

So the process is smart, fast, and iterative. It can be defined is simple words – Identify, Hypothesize, Test and finally React.

Conclusion

Hope this article would have helped you to learn the basics of lean analytics which will help you to grow and expand your business. Start with it today

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