MixPanel Overview

1 line pitch: Powerful, self-serve product analytics to help you convert, engage, and retain more users.
Mission: “We help the world learn from its data”Stage: Series CProduct audience: Digitally enabled businesses
Website: https://mixpanel.com/home/

Mixpanel is primarily a product analytics platform, designed to give marketers and product teams insights into how to best acquire, convert, and retain customers, with real-time data across devices and channels.

Problem Statement:

In early last decade a lot of decisions were taken without data, then the emergence of tools like GA tool over market to know more and take more data informed decisions. These decisions were generalised based on overall cohort and data was anonymised.

While it was easier to understand what is happening on a whole. It became difficult to understand segments and users. This led to generalised solution that are not optimised for cohorts or detailed user journeys.

Value Proposition:

Build Better ProductsPowerful, self-serve product analytics to help you convert, engage, and retain more users.
While platforms like Google Analytics or Kiss metrics show you aggregate information about your users, Mixpanel gives you much more data about website visitors and customers.

Mixpanel offers advanced analytics functionality. Unlike Google Analytics, which is based on page views, Mixpanel is based on event tracking. It's used by 30% of Fortune 100 SaaS companies, who use it to boost product engagement and customer retention. Mixpanel gives you clear insights into the health of each account. Thus, it is not only about knowing high level journey, but provides teams with a superpower to know and understand their customers.
There are 4 major section that help to analyse data:

1. UNDERSTAND: Powerful reports to answer any question
2. INSIGHTS REPORT: How is my product used?
3. FUNNELS REPORT: Where and why do users drop off?
4. RETENTION REPORT: Which users retain best?

These 4 areas of data builds the core value around the product and provides user with sufficient insight to make a major number of decisions based on data.

Business Model & Monetization

Product type: B2B SaaS

MixPanel is a B2B SaaS business that help businesses improve products by taking better decisions based on data. Today MixPanel takes a simple approach to monetization where it charges it users based on Monthly active users.
Current business model monetizes on users who have large user base i.e. more than 10K MAUs. These users are charged proportional to MAUs. The perceived value is aligned in a way that more users mean more insight or more filtered insight for an organisation.
The Pricing page is designed in such a way that each ICP is able to quickly determine what they should opt for and should see quick value in features

Customer Profile

There are majorly 3 segments of business which defines how the product is to be used. The requirement though are same on high level to analyse user data, fundamentally it changes based on decisions to be taken or Jobs to be Done.

1. Startups
2. • Very small companies, onboarding the initial clients. Someone like Voosh
3. SMEs
4. • Medium size corporates who have stable customer base and looking to expand, someone like MixPanel
5. Enterprise
6. • Large corporate with multiple products lines and different geographies. Someone like Uber

Litmus Test - Monetization Readiness

Natural frequency

On high level natural frequency of a B2B SaaS tool mostly relates to how much value does organisation see in the tool and how easy to use they find the tool. Most of the users start with using at once or twice a day or invest time initially to understand the tool.
However as the tool learning is done, the usability shifts based on use case and users how much they use data in day to day decision making. The numbers mostly remain consistent with the size of organisation.

Retention

There are multiple reasons that cause a user to churn in the product life cycle even after experiencing the CVP.

• Learning curve: This is where a lot of casual users drop off as the initial effort to value ROI seem low to these casual users and they drop off without putting effort to see enough insights on data
• Secondary app: As this is a highly competitive market, users might choose alternative either due to more or specific features or better offer
• Pricing: A lot of users hit a pricing block as soon as they start paying for the product and numbers keep going up.
• Customer support: A consistent churn of 2-3% is seen in B2B SaaS companies is mostly due to bad support or a perception of bad support.

Retention Curve: Flattening

• In the first and second month there is a major drop of free users and people who do not use the product regularly start dropping off.
• Post that there is smaller and consistent churn of around 3-4% as few might find the product costly and keep dropping as the number increases. Or the people who started on free, cross the threshold and decide to drop off at the place of paying
• As the users who stay for 8-10 months, they get dependent on the tool and have a higher switching cost and hence the churn flattens

The above curve shows that core and power users once used to platform stay back and are willing to engage regularly on the platform.

Deeper engagement

Depth of engagement are based on 3 major aspects when it comes to B2B analytics SaaS:

1. How many users does the org cater to and are tracked: This mostly aligns to the size of organisation if the organisation is bigger in size the depth will be higher and more users are tracked
2. How many data points are tracked regularly: This depends upon the organisation culture and the complexity of the product. If an organisation has lot more emphasis on data, they will track the smallest point whereas the other one’s just over the top
3. How many data points are being analysed by a person: This comes to the personality and role of the user, as user who need to or likes to play with data will be analysing more data points compared to other users.

As the product in itself is a high usage high result product. A significant amount of users who do not see value churn out in first two months. The B2B SaaS analytics products are designed in such a way that core and power users see value regularly and come back to analyse more data points. Where as casual users leave.

• Casual users: ~70% of them churn in starting 2 months as low usage does not have sufficient value for the users. This is mostly due to lack of learning and commitment.
• Core users and Power users has higher probability to stay back as the perceived value to cost is high. And also they have given good amount of time to do learning for the product to get deeply engaged to product

Hence, for activated users, it is highly likely that more than 50% of activated users are core or power users. This number would normally shoot-up to 70% also. Because of the usability of the tool.

Willingness To Pay

Though willingness to pay looks easy to determine, to check if the perceived ROI is enough for users to go from being a free user to paying user. However, what is more difficult to grasp is how much user is willing to pay or if he willing to go higher or lower.

We start with our understanding of casual. core and power users and understand if the willingness to pay change and how much? We will see if there are other data points or proof around the product that double down on these insights.
Below are some aggregated insight from 12 customer interviews across roles and orgs.
From the above, we can see that there is a willingness to pay in core and power users where the amount may be less compared to actual price. There seems to be reluctance and bias on pricing where users are not able to clearly see value due to complicated nature of B2B SaaS in Product analytics.

Substitute Pricing

As CVP lies around finding the right data to take better decision. The core problem MixPanel solves is that it provides the custom data of the users coming to website, help them in different ways to find pattern.

Now as the problem is generic and there are lot of tools out there helping to provide data, MixPanel stands out by providing very relevant data, lot of customization and ease of use. Below chart provides more detail what problem is MixPanel solves that other tools are not able to.

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What are users buying into

1. Ease of Use - One of the USP for MixPanel is that of its simplicity, where users can take complicated action with utter simplicity of using filters and queries that are not tough to write. Event he look and feel of dashboards are designed, so the simplicity pays into every aspect of the tool.
2. Behavioural data - MixPanel provides access to customer data and more than that it connects to DB to understand the behavioural data of customers. This becomes increasingly interesting as you are able to track customer behaviour end to end and see what and what not excites them
3. Ecosystem - Another major advantage that comes with MixPanel is its integration with multiple tools with simplicity, thus allowing user to quickly use the insights and data gathered. MixPanel connects to multiple tools across categories like marketing, analytics, productivity.

Customer reviews:

Substitutes Comparison

Major factors that constitute to know what competitors are is that tools which provide similar kind of data analysis for users. On top of that there are other things that define how well a product is in terms of overall output to its customers these include:

• Rating
• Ease of Use
• Flexibility
• Support
• No of features
• Pricing

Now here we are not able to compare pricing as most pricings are either tiered are not disclosed. But will do a basic analysis how industry performs on pricing.

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A simple analysis of substitutes tell us that this is a competitive market and a big market. While there are different tool, I see tools having issues to make a balance between features, ease of use, pricing, support and integrations.

Design Pricing

Who to charge

Understanding segment is the initial part of understanding pricing. One should take into consideration multiple factors including but not limited to:

• Willingness to pay
• Cost
• Competitive pricing
• Perceived value

We look below into different user segment and understand the perceived value:
If we look into the RFM analysis, the sure one’s to charge are the loyalists and the campions. However, as we look deeper into segments. We see only Champions and loyalists might not be profitable for business as the data storage cost and processing cost for even other segments can be significant. As the cost is fixed + maintenance cost, if we do not charge mid level client , the cost can sky rocket. So a minimum usage can be free but not post that.
Now, we should target the customers which are mid-high on recency and frequency. So mostly, we can target Potential loyalist, loyalist and champion. Given they have more probability of being hooked to product and will retain for a longer time. All three segment shall be paying mostly for ease of use, behavioural analysis and integrations available. 

When to charge

When it comes to charging for the product there are major 2 steps: Realise constant perceived value for the user and then reach the inflection point to charge.Perceived value:The value of the product is realised as users starts to see insights they could not gather before or the amount of effort and time needed was way higher to collect the data regularly.
The perceived value mostly helps users become more efficient, and this indirectly save money and time also.

User Journey

1. Unhappy from past experience: Journey starts with user feeling pain of not able to analyse data properly either due to limited access to data or complexity of the current tool.
2. Search: This leads user to start picking up some keyword and start finding the list of relevant tool

Perceived Value timeline:

1. Found MixPanel post search and after comparison with alternatives, chooses to take up MixPanel after alignment with requirment.
2. Added events as per the requirment and mapped the sdk with the code. So the data starts to flow.
3. See data post data flow, start seeing different events, pages, retention data and other data points.
4. Analyse data point to insight: Data points are just data, perceived value comes when you realise these data points into insight that help user take decisions.
5. Create dashboards: As users realise value, they keep these data points and pin them to dashboard to come back to them regularly

Inflection point

1. People drop off : From inflection point, either a person do not see enough value and choose a secondary app
2. People go on creating more personalised dashboard and gets retained for the long term

As access to data is most important value from the tool. Post the inflection point we charge user for access or post person has crossed the inflection point. 

What to charge for?

The perceived value comes from the data points a person analyse and reanalyse. This data analysis help user to see more insight and let them use those insights regularly. While the more data points mean more and better insight and then better results.

Result ← Decision ← Insights ← data points
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The engagement framework talks about two major areas of platform currency: Access and no of date points. While Access currency is a clear analysis of given access to features or not, it is more complicated with data. If we try to see what is the overall data for a client is made up of:
Client data = N(meta data) * Event * users + Fixed data
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While Meta data and fixed data can be considered as constants. Major variation on cost for MixPanel and also for perceived value to user comes from no of events and no of users.
Based on above analysis Platform currency shall be no. of users as it is easy to understand then events. How much to charge
Understanding perceived value vs perceived time can be a mixture of analysing what product provides as a value to the users. There are different ways to look at it.Given above the Major perceived value is generated in time saved for analysts and perceived value of opportunity cost of failed experiments. We see Startup can save up to $4300, Mid scale companies up to $6800 and large scale $20K in perceived value from the product.

Current, Competitor and Suggested pricing

Comparison:Analysing current Pricing:

• Currently startups are given a free plan up to 10K MAUs with some limitations. Also there is a separate startup plan which covers early stages startups to utilize the enterprise version for free.
• The price jumps sharply to $200/month if we go to growing version with 10K+ MAUs
• And enterprise version starts at $1200 for 100K+ MAUs
• If a startup is willing to move to scale version they will have to pay the price what is reasonable for the scale of the startup

What’s good:

• With current pricing MAUs as the measure metric is easy to understand for users.
• Free access to startups align to competitiveness of the market and can bring in future paid customers at early stage
• As the scale increases, per MAU price goes down and can be negotiated further

What’s bad:

• A lot of startups are free users and can move out post pricing comes
• Startup plan that give 1 year access to startups with $50,000 can be loss for company
• Enterprises does not have direct pricing shown on page

What can be improved pricing:

MAU should remain the pricing lever as it is easier to understand and aligns with product philosophy of being simple and easy to use. Division of pricing on scale of startup with names thus also make sense.
Startups:

• Pricing for startups can be free to meet the competition, number of 10K should also be okay to increase the attention of growing startups
• Experiment: Startups should be given a top up to use features that are exclusive to enterprise or scaling startups
• Experiment: Startup free plan that is run for 1 year should be shorted to 6 months and see if the users are staying post that. Post that they can switch to normal startup plan or take a top up

Scaling:

• I believe the range is again good and 10K-100k users are mid scale companies and they can afford to spend some money as they shall already be having enough revenue and for them the perceived value is also very high
• Price calculator is great to know the exact price
• Experiment: Top up can be provided as a hope of using advanced features without limitation at small cost. Top up becomes an extra source of income also keep people taking most out of the tool

Enterprise:

• Enterprise prices to be shown as to reduce the source of friction to add 1 more layer of communication
• Pricing like start at $1190/month for 100k+ users or $0.0002/MTU looks more interesting
• Price calculator can be added here also to let people know different prices exist at they drop significantly. Also, customised plans available to be written so user feel if they call they can modify the plan or reduce the cost

How to show pricing

Current Pricing Page

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Observations:

What works:

• Different plan for different team size, becomes easy to choose.
• Detailed analysis of features is good for users to analyse plans
• Free plan for startups with mention of up to $5000

What doesn’t works:

• $25 / month can be deceiving as people believe <10K MAUs should be starter plan
• Enterprise pricing is not shown and adds a friction
• No social proof or authority effect
• No stricken pricing used
• Customization is much later and hence can be missed easily

Competitor Pricing Page:

Amplitude:CleverTap:Observations:

• CleverTap uses Center stage effect and default bias
• Amplitude uses authority bias to showcase proof
• Amplitude gives clear definition of ICP
• CleverTap do not show any prices, where as Amplitude only says free and no pricing post that.

Improved Pricing Page

System Design: System 1 vs System 2

When we think about the product of B2B SaaS, decisions are taken mostly in line with system 2. As these are long term commitments, one cannot simply turn off plan tomorrow also the capital for paid plan is high.

Given that Firstly, it is B2B SaaS and secondly an involvement of dev team is needed for user to experience the core value prop, it is high switching cost decision. And hence, decision is taken with System 2 thinking, with multiple factors into consideration and only after a detailed analysis of tool and competitors.
As the decision moves to system 2 idea is to use clarity, psychological effects and details to our advantage and increase conversion by allowing people to have whatever is needed at hand.

Improvments:

• Use Social proof and authority effect to tell what similar organisations are using the tool or tool is preferred by best in the industry
• Add demo effect, add a quick link to demo if a user has not seen demo, he should see a quick video of why things are important
• Use Stricken Pricing to showcase the perceived value or have anchoring effect to show how much user gains with current free or low price tier.
• Pricing added to Enterprise to have anchoring effect and stop resistance
• Custom option given way earlier with a option to Top-up so user doesn’t feel cheated to buy enterprise for small feature added to free or growth price.

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