The Power of Cohort Analysis

Cohort analysis is a helpful e-commerce tool to help you optimize your marketing and promotion efforts by focusing on customer experience and retention.

In simple terms, cohort analysis classifies customers into groups based on a common trait and tracks their behavior over time.

It looks at things such as the frequency with which your customers shop in your store, how long they stay, and how much they spend. Customers are typically grouped concerning their purchasing behaviors.

For example, customers can be grouped according to the type of campaign that converted them, the first product they ever bought from you, or even something as straightforward as conversion time.

What Is a Cohort and What Is Its Role in E-Commerce?

In e-commerce, a cohort is a group of customers grouped based on a commonly shared characteristic over a period of time.

Here are some specific examples:

● Customers acquired via Instagram ads.

● Customers who purchased after a free trial.

● Customers who returned over the past three months.

Essentially, people can be grouped based on similar behavior exhibited over a specific amount of time. Cohort analysis looks at the ‘customer’ not just as one single entity with similar tastes and buying habits but as a diverse group of people with varied behaviors. Once you’ve pinpointed significant patterns and trends with regard to customer behavior, you will be able to formulate marketing campaigns targeting these specific groups.

This is often a better approach than launching a singular marketing campaign targeted towards a very broad audience and wishing it would land on the right people that you can convert as customers.

Types of Cohorts

  1. Acquisition Cohorts

These are groups divided by when they signed up for your product. For example, you can break down cohorts according to their sign-up date on your e-commerce app. You’ll then be able to measure retention by how long they continue to use your app since their sign-up date.

  1. Retention Cohorts

This helps you understand the percentage of users retained on your app until a certain, defined day. User retention can also be measured by how often and how regularly or rarely they come back to use your app or browse your online store.

  1. Behavioral Cohorts

These are groups divided based on the behaviors they exhibit within your app over a certain period of time. ‘Behaviors’ can refer to any number of actions performed within an app or site, such as sharing a photo, posting something, liking something, etc. You can then look at how long these cohorts are retained after performing such actions.

Photo by Sharon McCutcheon on Unsplash

How Does Cohort Analysis Help E-Commerce Businesses?

Predicts Future Customer Behavior

Cohort analysis is very helpful in terms of predicting customer behavior. Because you have specific data that allows you to target potential customers with localized efforts (ads, promos), it allows you to convert leads to customers.

These insights allow you to understand customer behavior and anticipate their needs, thereby also increasing chances of retention.

Increases/Improves Customer Retention

Retention is king. If you lose customers, you lose sales.

Cohort analysis gives you valuable insight that will allow you to retain existing customers and maximize revenue. Studies have shown that returning customers spend at least 67% more than new customers - that’s a lot.

Not to mention that it costs significantly less to keep existing customers happy than to keep on launching campaigns to attract new ones due to poor customer retention.

Helps to Understand Your Best Products and in Turn Create Offers

Cohort analysis can give you insights into what your ‘best sellers’ are. Cohorts are often grouped according to ‘first product ordered,’ and from this alone you can see which of your products stand out the most to new customers.

You might also find which products customers buy often and repeatedly, thereby creating loyal customers. You can then highlight these products or create offers that will further improve customer engagement and retention.

Helps to Customize Your Marketing Strategies

Cohort analysis is a powerful tool that will help you customize your marketing strategies based on what works and what doesn’t. Insights from cohort analysis will let you know how exactly you need to adjust your marketing activities and what you need to focus on.

For example, cohort analysis will help you determine whether or not you need to launch or bolster your loyalty or rewards program for existing customers. Insights from cohort analysis will allow you to find out if your existing clients are satisfied even without a loyalty program.

In this case, you can just continue your best practices and may not urgently need to launch a loyalty program.

Metrics for Cohort Analysis That You Need to Know

Average Order Value

Average Order Value (AOV) measures the average total of every order placed with a merchant over a defined period of time. This is one of the most important metrics that merchants should be aware of because it drives important business decisions such as pricing, advertising budget, and store display. such as advertising spend, store layout, and product pricing.

AOV is determined by sales per order and not per customer. Even if one customer comes back multiple times to make a purchase, each order would still be factored separately into AOV. The formula for AOV is

Customer Lifetime Value

Customer lifetime value (CLV) helps to measure long-term business success as well as predict future revenue. CLV determines how much profit you can expect from a client over the course of their ‘customer lifetime’.

Depending on your margins, you can figure out how much you need to invest by estimating the ‘lifetime value’ of a customer for your business.

Marketing Metrics reports that the probability of selling your product or service to a new customer is at 5–20% whereas your chance of selling the same to a regular customer is at 60–70%. That’s such a big difference, and it drives that point that retention is indeed more cost-effective than customer procurement. Retention is a lot cheaper than acquisition.

Time Between Orders

This refers to the time between successive orders. Depending on your type of product or service, this time period could be in terms of hours, days, weeks, or even months.

This is valuable to help you determine the timing of marketing emails to remind customers to repurchase or offer promotions that will continue to keep their repeat order rate high.

Repeat Rate Per Percentage to Second Order

Repeat rate is the most telling metric in proving how successful you are in retaining your customers.

This is the share of customers who patronize your business repeatedly (as opposed to cohorts who bounce after a single purchase). 

Orders Per Customer

This is closely related to the previous metric. Simply put, the more repeat customers you have, the more orders they make each. High values in this metric likewise indicate a strong retention rate.

Image by Author - Cohort Analysis in Graphite Note

Final Thoughts

Compared to more popular analytics methods, cohort analysis tends to be more long-term and can provide slower feedback. It’s not a one-off analysis. Instead, it allows you to see patterns and insights on trends regarding customer behavior.

It takes time to observe, gather, and analyze data, which you will then translate into actual marketing and advertising strategies.

However, cohort analysis can be very rewarding in the sense that it provides real-world, reliable insights regarding customer behavior. It shows you ways to save money (ads, loyalty programs) in areas where you don’t need to spend, and also where you need to increase your investment.

It provides you with a more thorough, big-picture overview of your user journey, and creates long-term value for your company over time.


Graphite Note strives to provide businesses with automated cohort analysis models built with the help of predictive analytics to create a time-efficient, pro-tech solution to their data analysis needs. We’ve worked with real-life customers and standardized the ML models to be applicable for every customer.

New vs Returning Customers: Who’s More Important in E-commerce & Retail?

Whether you’re in e-commerce or retail, your customers are your undisputed gods. The dilemma that a lot of business owners face is: should I focus on acquiring new customers, or should I pamper my returning ones? 

There is no easy answer to this question. 

Ask any e-commerce expert and they will tell you both are important. But, one has a definitive edge over the other when it comes to your business health. 

Let’s find out more on the matter. 

Returning Customers will Always Have Your Back  

Who are returning customers?

They are the ones who have made more than one purchase from your e-commerce or retail business.

Research shows that such customers have a 27% chance of buying from you again after their first purchase. 

Truth is, retaining an existing customer will cost you five times less money and effort than it takes to acquire a new customer. You have to invest 16 times more money to make your new customers spend as much as your repeat customers. 

Here the top reasons why returning customers are invaluable: 

  1. Repeat customers pour more money into your product or service than new ones. Their spending is 300 times more compared to the latter. 
  2. You have a 60-70% chance of persuading a returning customer to make a new purchase. From a marketing perspective, investing your efforts in them makes better business sense than heavily spending on prospects. After all, only 13% of future consumers make a purchase. 
  3. Returning customers can promote your business among their peers, adding to your prospective client pool. Never underestimate the power of word-of-mouth advertising, even if it comes from happy customers. The best part? You don’t have a spend a penny on marketing yourself. 
  4. On average, 80% of a businesses’ revenue comes from 20% of its customers. Returning customers contribute to a major chunk of it. Don’t forget that how you treat your old customers also influences your ability to acquire new customers. 

Returning customers save you a lot of time, effort, and money. That’s why you cannot falter in your relationship with them. 

Image by Etiene Girardet, unsplash.com

New Customers Help in Revenue Growth

On the other hand, acquiring new customers is the only way of creating a customer base for any business. You cannot have repeat customers if you do not have prospects making their first buys. 

New customers are indispensable to your business, because: 

  1. They allow you to form the foundation of your customer base. 
  2. They help build brand recognition. 
  3. They give you insight into what is expected of you, how can improve your current offering, and what to add to your repertoire of services/products. 
  4. They can fill in the shoes of old customers who have moved on from your business. 

So, new customers are vital to your business growth. They too cannot be disregarded in any manner. 

Focus on Converting All Customers to Loyalists

Here’s a startling fact for you: 60-80% of customers who made a purchase from your business and were satisfied with your service might not return for business. One-time customers exist. It’s upon you to reconnect with them and make them repeat customers. 

What is imperative for you to understand is customer relationship management is everything. 

That way, old or new does not matter. As a business, whether online or offline, your focus should be on building a great rapport with all your customers. Make your customers feel special. 

Here a few tips on how to convert new customers into loyalists and retain returning customers: 

  1. Keep the connection alive. Customer relationship, like every other interpersonal relationship, needs your attention and effort. Be in regular contact with all your customers. Engage with them on social media outside your business needs. Understand what they expect from you and offer them your best possible service. 
  2. Give them rewards. Everyone loves to feel pampered. Don’t reserve your discounts for first-time customers, make sure your repeat customers get appreciated for their trust in you. Discounts, surprise rewards, etc. work well in keeping your consumers satisfied. 
  3. Start a loyalty program. It is never too late to jump on that bandwagon. Such programs have proven to increase Customer Lifetime Value (CLV) by 79% in a matter of just 3 months! 
Image by Author, Graphite Note Customers ML Model screenshot

The Graphite Note Edge 

With cutting-edge predictive analytics in tow, Graphite Note gets you all the data on new and returning customers. Compare absolute figures and percentages, learn how many customers you are currently retaining on a daily, weekly, or monthly basis. 

Let the best predictive analytics guide you with your marketing and customer relationship management. Decide which customers need your immediate attention. Plan your marketing and promotional activities to fit the need of the hour. 

Your customers are smart and intuitive. You should be too. 

There is a way to improve Data Literacy in Companies today

Over the last decade, data has become an important part of any organization. But over the last few years, it has become essential for the growth, productivity, and success of companies across the globe. We are gathering an immense amount of data, which provides insights into everything from consumer behavior to company finances when used in the right way. Business intelligence (BI) techniques and Predictive Analytics are undoubtedly helping organizations manage data; however, this still requires data literacy. 

The problem is that companies are lacking AI and data literacy skills. Gartner predicts that by the end of 2050, 50% of organizations will not have sufficient data literacy skills. This means that companies won't be able to achieve the necessary value. In order to gain the most from data and new technologies, companies must begin to improve data literacy.

What Is Data Literacy?

Data literacy is considered a Key Performance Indicator (KPI) for companies in today's market. Whereas not so long ago, it was limited to the ability to read and write, the skill set required by employees has now expanded.

For an employee to successfully and accurately solve problems, they must be able to read, write, and communicate data in the relevant context.

They also need to understand the sources of data, how it is constructed, and the methods used to analyze it. On top of this, it's essential to know how to explain use cases, applications, and resulting values. 

It is more than likely that most companies have begun the process of creating a data-driven organization and are already taking advantage of better decision making. They have also managed to incorporate the ethical and legal side of data protection and transparency. As much as 90% of data and analytics decision-makers are aware of making data insights in decision making a priority. Nevertheless, this is still a huge struggle for most. 

Source: Unsplash

Why Is Data Literacy a Struggle?

While everyone is aware of the importance of data, not everyone is aware of its role in their job. It can no longer be done to the data analysts to be responsible for data literacy. As data becomes more important, everybody within an organization will need to know how data impacts their work. It is also possible that data professionals don't have a clear enough understanding of their work in the business context. 

As the volume of data continues to grow, it is becoming more and more difficult for those without the technical knowledge to keep up with those who are data-savvy. Censuswide reported data literacy findings after surveying over 7,000 business decision-makers worldwide:

How to Improve Data Literacy Within Your Organization

It's much wiser to begin creating and implementing data literacy strategies now as the amount of data your company acquires is only going to grow. While the main goal is to assist the data decision-makers, it is also necessary to improve the knowledge of those employees who aren't quite so tech-savvy. Here are six ways to start improving your data literacy:

Employees companywide need to be aware of the importance of data. Being data illiterate is not just a technical problem; it is one that will, at some point, spread across the entire company. Making everyone see that it is a company issue rather than just down to the tech department will enhance employee engagement. This drive in data literacy importance needs to come from the top, so it is the executives, managers, and leaders' responsibility. To develop data-driven mindsets, employees will need to understand why data is necessary and how it is used across all business processes. 

If you wake up one morning and say, "From today, we are all going to be data literate," you run the risk of putting non-technical employees off the transition. This is a process that requires starting small. You might feel that there is a rush, but when significant changes go about too fast, mistakes are made, and this reduces employee enthusiasm. It is better to start small, allow other employees to see the success so that they are more inclined to want to be a part of it. Aside from this, there is no room for error regarding confidential data, so only those with experience should be handlining it.

For those who aren't familiar with data or the associated technologies, it can be an overwhelming experience, especially for massive amounts of data. It is important to contextualize the data for them based on their role, experience, and background so that they are better able to effectively use the data.

Data literacy is a skill that will continue to grow. Once employees and teams master their data sets, provide access to more data so that they are able to explore and learn independently. People will need the opportunity to learn from their own mistakes by trying different strategies so that they are better prepared. That being said, certain data cannot be used for learning purposes, for example, confidential information.

Access alone is not enough. Employees to be taught how to use the data to the best advantage. After all, anyone can have access to data; this doesn't make them literate. As the statistics suggest, most employees are keen to learn essential data skills, so it is unlikely that there will be resistance. Ensure employees know how to interpret data as well as providing critical thinking training. 

Not every system or tool will be suitable for your company's needs. The system you choose needs to allow data analysts to share data across the company to relevant departments. Simultaneously, the systems you put in place need to be user-friendly for non-technical employees. If they cannot use it, the investment won't be used to its full potential. 

Graphite Data Story Notebook

Why is Graphite the Ideal Data Literacy Tool

With Graphite, it becomes easier to uncover hidden patterns, identify problems sooner, and see opportunities in your raw data. To run predictive analytics, without writing a single line of code.

And icing on the cake is - all team members can see the story of your data. 

An analyst can prepare a beautiful data story in Graphite Notebook, and share that insights and conclusions with all decision-makers and senior leaders that aren't confident to work with data and need help to understand it.

That is the Graphite Vision.

Brains are built for visuals, but hearts turn on stories

Yes, the charts are important…

For every executive who is about to pull up the first slide of a presentation in a boardroom filled with eager associates, there is usually that moment just before speaking when nerves fray, last-minute doubts fill the mind, and fear of failure can weaken the resolve. Did I choose the right image to start with? Is the text too small to read? Is the right tone conveyed throughout the presentation? The usual recommendations of how best to present data most effectively crowd into the mind — don’t clutter, choose relevant KPIs, keep it simple, choose layouts carefully, less is more, and be cautious with colors.

BusinessQ dataviz software dashboard

… but the key is an emotion

These are all helpful ideas, but none of them can hold a candle to the most important concept of all, emotion.

You see, whereas brains are built for visuals, hearts turn on stories.

Visualization might be important, but the emotion is key. If you want to sell a story, send a missile to the heart.

Don’t get me wrong, I believe in the power of graphs and charts. Stacked columns, line charts, waterfall, and scatter plots all have their place in data presentation. However, at its core, data visualization is a tool that transforms data into actionable insight by using it to tell a story.

Charts can be the start, not the end of the communication

The psychologist Jerome Bruner claims that we are 22 times more likely to remember a fact when it has been wrapped in a story. Data-driven storytelling is a powerful force as it takes stats and metrics and puts them into context through a narrative that everyone inside or outside of an organization can grasp.

Without question, human beings are, first and foremost, visual creatures. Our brains are built for visual information as the following data shows:

• 90% of the information processed by the brain is visual.

• It takes only 13 milliseconds for the human brain to process an image.

• The human brain processes images 60,000 times faster than text.

• 80% of people remember what they see, compared to ten percent what they hear and 20 percent of what they read.

• In response to a recent survey, 95% of B2B buyers said that they wanted shorter and highly visual content.

• Publishers that feature visual content grow traffic 12 times faster than those who don’t.

However, as anyone who has sat in a darkened movie theater and felt a warm tear roll down her cheek knows, it is the emotion that makes life’s moments unforgettable.

The use of emotion can make data presentation unforgettable as well

As Carl Bucher once stated, “They may forget what you said, but they will never forget how you made them feel.”

“Out of clutter, find simplicity,” said Albert Einstein. This is a great motto for presenters. Simple messages resonate deeply if extraneous items are being stripped away.

Emotions cut through the clutter like nothing else.

Before putting any design elements in place, think about the end goal. What are the most important elements that need to be showcased? Who is the audience? What is the emotional punch you want to be conveyed on the final slide? To build successful dashboards, a designer needs to put himself in the audience’s shoes.

Designers should always try to provide maximum information. Without the right context, numbers that might seem extremely obvious to one person might be perplexing to others.

All the axes should be named, and titles should be added to all charts. Comparison values should also be included. The rule of thumb here is to use the most common comparisons, for example, comparison against a set target, against a preceding period, or against a projected value.

Comparisons also add emotion as they provide benchmarks of understanding.

It is easy to recognize the emotional power of a line chart that reveals sales falling off a cliff. Or taking off into the ether.

The artist Kenneth Noland once said, “For me, context is the key — from that comes the understanding of everything.”

Without providing context, it’s impossible to know whether numbers are good or bad, typical or atypical.

Without comparison values, numbers on a dashboard are meaningless for viewers. And more importantly, users won’t know if any action is warranted.

BusinessQ software Information Dashboards

Design: Intelligence Made Visible

Dashboard design best practices concern more than just good metrics and well-thought-out charts. The second step of dashboard design is the placement of charts on a dashboard. If your dashboard is well organized visually, information can easily be found.

Poor layout forces users to think more as they try to grasp a point the presenter is trying to make. Nobody likes to look for data in a jungle of charts and a maze of tangled numbers.

The general rule is that the key information should be displayed first — on the top of the screen, upper left-hand corner. Most cultures read from left to right, then from top to bottom, which means viewers intuitively look at the upper-left part of a page first and read down from there.

“Color is a power which directly influences the soul,” states the famous Russian artist Wassily Kandinsky. Who can argue with one of the greatest painters and art theorists of the 20th century? Color is used by advertisers everywhere to convey an emotional message that resonates deeply within any audience. Without a shadow of a doubt, the use of color is one of the most important best practices in dashboard design.

Graphite Proposition

We propose data analytics to “go vertical”, so there will be much more natural, easier to explain insights and conclusions, by design.

You have a title text block, graph block, predictive analytics block, explanation text block, several graph blocks, etc.

This will allow data analysts to explain the whole process beautifully, to tell a data story, and to engage the audience (your team). And that will, in turn, supercharge decision making, because decisions are made on an emotional level, where the story hits.

Graphite Notebook for data storytelling in action

Data Storytelling

Of course, you don’t need to go all Joseph Campbell Hero of a Thousand Faces with your presentations. Charting a mythical story filled with a mixture of heroes, mentors, tricksters, shapeshifters, guardians, and shadows isn’t the goal, but recognizing how myth can turn each story point — or chart— is. Understanding the power of the story adds a subtextual level to any data presentation that, although not recognized by an audience, will pull on their emotions.

Dale Carnegie once said, “There are always three speeches for every one you actually gave. The one you practiced, the one you gave, and the one you wish you gave.” Utilizing proper data presentation techniques and wrapping emotion into the power of storytelling will help ensure that the speech you give is not separated into three but combined into one.

Above all else, it will be the speech you had wished you given and you won’t suffer pangs of regret or wishful thinking afterward.

Emotion is a tricky element to include and it must always, always, always be united with the truth. However, if you can wrap your data presentation in a compelling story, you’ll not only touch your audience’s heart but also have them eating out of the palm of your hand.

What to do about a shortage of Data Scientists Today?

Everyone needs data science…

With the volumes of data generated globally due to the advent of cloud innovation and technologies, there has been an upsurge in the need for in-house employees that can make sense of data for increased business growth and development.

Businesses, big and small, have woken up to the immense possibilities present in taking advantage of these vast data to gain insight and make profitable business decisions.

…but there is a shortage of Data Scientists

Despite the enormous data scientist job ads that go out frequently, it is unfortunate that the demand-and-supply ratio of talent acquisition has remained unbalanced.

Here are some stats for your consideration:

Data Scientist is the most fantastic job in 2019 (LinkedIn)

By 2020, openings for data scientist will rise from 364,000 to 2,720,000 (IBM)

There’s a 35% difficulty in recruiting for data science positions, resulting in a 250,000 job shortfall (IBM)

Perhaps this situation favors the handful of data science professionals positioned to quote chart-topping compensations to prospective employers.

But the same cannot be said of employers and business owners who can not compete competitively for the retention of these talents and thus miss out on the impressive ROI that comes with such talent investment.

Small and medium businesses are in trouble.

Yes, SMEs understand all the benefits of AI / ML / Data Science, but they don’t have the expertise or the funds to do it. They stay in the dark, their data is not talking to them.

Photo by Sarah Pflug from Burst

Closing Up the Shortage Gap

Several creative ways have been suggested as a workaround to the shortage of data science professionals.

Here are some of them.

● Engaging Professionals who are open to a career switch

With all the attention that data science has gathered, due primarily to the thousands of dollars that data science talents are compensated, it is an understatement that many tech and non-tech professionals are enthusiastic about switching to the field.

Several tech consulting firms train enthusiasts with data analytics background in data science business for onward recommendation to employers.

Such training takes a hell of a time and does not immediately address the shortage.

● Retraining Current Employees

Some organizations searching for data scientists take the search initiative inward by screening through their technical skill workforce for suitable candidates that can be trained for the job.

The challenge in this is the risk of such full-blossomed talents taking a hike for a better opportunity.

● Broadening the Search Scope

As you may already know, there’s an unbelievable disparity in the supply of data science talents based on geography.

We sure can’t compare the supply of data scientists in the developed world to other parts of the world.

But with remote and flexible work arrangements becoming the future of work, there is an increased probability that candidate pools from which corporates find suitable talent will become widened.

However, concerns about data security and protection that’s globally making rounds might make it problematic to remotely engage talents across continents due to data migration’s inevitability.

Graphite - a model results

But what about AI automation as a solution?

What if there is a better, viable, and cheaper solution for addressing this talent shortage?

Why should advanced data analysis always have to be strictly handled by data scientists?

In Graphite, our thoughts and activities are ongoing in developing AI-enabled tools that help businesses sense and leverage data in their operations without specifically-skilled data science professionals.

With our SaaS tool, businesses of varying types and sizes can assign average data science tasks to virtually anyone in the organization — powered by Graphite’s AI automation and Machine Learning ready-to-go templates.

Graphite Data Storytelling with notebook
Graphite - a notebook for data storytelling

Moreso, by design, Graphite promotes data literacy through the data storytelling capabilities that simplify and ease up data understanding and explain insights.

With our unique “dashboards go vertical” approach, Graphite allows you to explain your data, your process, and insights beautifully and simply, ensuring adequate data democracy, transparency, and widespread usage.

Let's rethink the whole data analytics space. Together.

Let's rethink the whole data analytics and business intelligence space. Together.

Why Graphite?

With Graphite, we are solving 2 major problems in business intelligence and data analytics today. 

  1. traditional business intelligence tools can not provide answers to business questions that can be answered only with machine learning or advanced statistical models (What is my revenue forecast?)
  2. The Chart is not and can not be the end of communication! There are no data storytelling features available. Without narrative, business people are usually lost in (although often beautiful) dashboards.

Let's dive deeper...

How it all started - Graphite vision

We have been in the business intelligence and data visualization space for almost 20 years.  

In the last couple of years, many of our clients started to ask business questions which can be answered only with the power of AI algorithms. AI is not a buzzword anymore, people know (at least in general) what it is, what business value it can bring to their business. They know they want it and need it.

So that gave us an idea.

Artificial intelligence is one of the most impactful trends in business today and more than 80% of businesses say AI is a strategic priority. 

Still, data scientists are very expensive so SMEs simply can’t afford it. Plus, they don’t have the in-house expertise to do it.

We think it’s time for all companies, for SMEs too, to benefit from the AI and predictive analytics revolution as well.

That’s why we came up with Graphite.

Graphite screenshot: a dataset summary

Problem 1: Predictive Analytics

No matter how small or big your business is, you will benefit from predictive analytics. Don't you want to know what is your Customer Lifetime Value, based on future sales prediction for every one of your customers? Don't you need to know your sales and product quantity forecast to adjust and optimize your stock? And what about marketing, don't you need machine learning to segment your customers so that you can target your segments appropriately?

Wouldn't it be cool that there was a platform that allows you to run Predictive Analytics algorithms, on your data, without writing a single line of code? 

Well, now there is! 🙂 Meet Graphite!

Graphite screenshot: a timeseries forecast model results

Problem 2: Data Storytelling

Traditional business intelligence tools are talking about data storytelling, but what they mean is just a sequence of charts. Charts that human beings so often have trouble interpreting and understanding!

We know from our experience that sometimes it is very hard for business people to understand even (to us) a simple chart.

Data visualization expert Stephen Few said, “Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” The problem is, today's BI tools don't have the means to give you that voice.

We realized that the chart or dashboard you have just created can not be the end of the communication. There is often so much more needed, a context, explanation, conclusion, a direction on how to interpret that slope of the chart, and so on... 

That is especially the case with the results of predictive analytics.

Consequently, it’s very important to have a way to combine visuals and narrative to share insights with your team. 

That is data storytelling.

Graphite screenshot: a data story notebook

Graphite is rethinking the whole business intelligence space by combining 3 essential elements: your data and traditional analytics, predictive analytics algorithms, and human communication.


Plus, it is suitable for SMEs too, so they won't stay in the dark anymore.

Don't be left behind, join us in the natural evolution of data analytics.

We would love to hear your experience and your thoughts on this!