Survival of the Sh*ttest: 3 Reasons Corporate Mediocrity Endures

Business is a constant process of innovation and adaptation. With business technology changing every handful of years, staying competitive means keeping abreast of trends and best practices.

If you’ve been working for at least a handful of years, then you know that the sensible thing for a business to do is to stay competitive.

If you’ve been working in the Philippines for at least a handful of years, then you know that there’s always one crap reason or another for businesses to stick to the Old Ways.

People around a bonfire in a cave
“This is fine.” (Image from Blogspot)

There’s a very good chance that the business you own or work for sucks (even just a little bit) for reasons concerning innovation. It isn’t necessarily anyone’s fault; after all, history tells us that structural change isn’t a natural inclination for our species.

Yet, like my mother would always say, “We build homes to perfect them over time, anak. So please, for God’s sake, clean your f*cking room.”

Or something to that effect.

Let’s have a look at the common organizational roadblocks to innovation–things you’d likely hear when someone shuts down the idea of opening up a new department, or signing up for a helpful new training program.

Reason 1: “If it Ain’t Broke, Don’t Fix It”

The first roadblock to innovation is the “If it Ain’t Broke, Don’t Fix It“ mentality.

You’ve heard it before: the excuse that your department or company has a perfectly fine way of doing things, so there’s never a need to shake things up.

Heck, you might even be making a lot of money sticking to the status quo, and besides: if growth is at a plateau, then it’s your own damned fault for not working harder.

To the Luddite, improvement isn’t worth the effort for so long as the status quo gets the job done.

While things could be quicker and easier for the people who need to slog through an outdated way of doing business, the current model is here to stay for as long as it isn’t impossible to work with.

Example: File Storage

Here’s a history lesson for the kids reading: before Google Drive, people had to rely on local file storage—that is, keeping all your documents and media stored on one or more computers. If people needed to access their work files from home, then they had to carry around USB flash drives or email themselves before clocking out.

They were dark times indeed, but humanity trudged through to a beautiful, cloud-hosted future.

Or, most of us did, anyway.

Some of today’s businesses continue to rely solely on local file storage.

In a handful of truly surprising cases, businesses with the funds to shift to cloud storage refuse to digitize their files at all, opting instead for clunky filing cabinets full of critical data and information (they’d better pray that there isn’t a fire hazard anywhere near).

One such business, pictured here. Probably.

If you’re dead set on introducing your workplace to a key innovation, find a way to get it into people’s hands without being insubordinate.

Find a compelling product review online, download a free trial on your personal laptop, or submit a copy of your next report made using whatever tools or formulae you’ve got your eyes on.

It’s like Prometheus bringing fire to mankind, without the punishment of being fed to ravenous birds.

Reason 2: Cost

The second possible reason why your company sucks is because it’s afraid of cost. People tend to be skeptical about the value of an investment –whether it’s in opening a new department, or adopting a new practice or technology.

Not every business can afford to upgrade every quarter, and that’s well and fine. But companies and departments with the resources to make a much-needed change are a far too common sight, much to the dismay of professionals who wish they could do more on the job.

In many cases, vital business upgrades are seen as frivolous expenses or risky gambles. The reality is that many investments like cloud hosting or a robust data infrastructure are well-known for their steep ROI.

Upon closer inspection, the cause of this line of thinking is closer to ignorance than it is to frugality.

We can understand being skeptical about the merits of today’s chatbots or even blockchain, but people continue to second-guess technology that’s been propelling businesses since 2005.

It’s rock-bashing traditionalism at its finest, and it’s holding our enterprises back.

Example: Working With Data

Every business can benefit from working with data.

That’s not a sales pitch, it’s a fact: when used properly, having a functionalthe data value chain can help an organization eliminate guesswork, predict business outcomes, and reach new heights of efficiency to the tune of over $10 in ROI for every dollar spent.

Data analytics, however, isn’t a simple matter of plug-and-play: it takes time, money, and mental bandwidth to build an infrastructure and collect the kinds of data that can be used for significant improvements.

Plenty of businesses are content with letting their data rot in filing cabinets, and any extra spending is taken as a deal-breaker. Apparently, they missed the memo that it takes money to make money.

“Like out of five stars??”

Communicating the benefits of an investment can be difficult, especially when trying to convince someone who clearly wants to remain skeptical. Our best advice is to be patient, be systematic in making the case for adopting a key innovation, and be persistent.

One way or another, you’ll either find yourself delivering the right pitch to get it through your superior’s thick skull, or find yourself killing time as you wait for your next career shift to greener pastures.

Reason 3: The Learning Curve

By its nature, innovation comes with a learning curve. For many adopters, it’s a necessary speed bump on the road to better business. For others, it’s a brick wall.

Organizations that reject innovation on account of the learning curve fall into one of three categories.

First, you have groups that can manage the challenge, but can’t be bothered to throw in any effort (the lazy).

Second, you have groups that can’t manage the challenge because they’ve hired people who simply aren’t up to par (the inept).

Finally, there are people who could learn but lack the funds or skills to hold training sessions (the unfortunate).

Example: Automation

It’s possible to automate common business tasks like managing files, organizing schedules, and even taking phone calls. If you name it, the chances are good that someone’s already taught a computer how to do it for you.

Of course, introducing a workplace to automation comes with an adjustment period –and the fact of that alone is enough to discourage some local businesses from literally kicking back and letting manual tasks perform themselves.

If you find yourself among the unfortunate, know that there are plenty of affordable training options for many of the latest innovations. You don’t need to cut a department to teach another how to work with automation software.

If you’re surrounded by the lazy and inept, however, we wish you all the best. There’s little to do when your managers and team members lack the initiative or mental power to stay ahead of the curve. Your best shot is to perform well, and keep your eyes open for a smarter career setting.

Case in Point: Blockbuster vs Netflix

In 2004, the American video rental service Blockbuster reported six billion dollars in revenue.

In 2010, Blockbuster declared bankruptcy.

Today, in 2019, your youngest work colleagues would scratch their heads at the very idea of renting videos only to return them to a store after some time has passed.

Conversely, there’s barely a soul alive today who hasn’t heard of Netflix, which is probably why most people cringe hard when they learn that back in the year 2000, Blockbuster declined an offer to purchase Netflix for a measly $50 million.

I’d bet my subscription that you or the people at your workplace feel for Blockbuster. Nothing was broke that needed fixing, $50 million is a hefty sum by anyone’s standards, and who the hell could have explained video streaming back in 2000 anyway?

Before you leave a flower on the grave of the world’s greatest home entertainment failure, know this: when Netflix tried selling itself to Blockbuster, the young streaming company was already raking in billions in revenue.

They knew they were sitting on gold, and the signs of continued success were plain as day. Why they even tried to sell themselves off is beyond me, but whatever motivations each party may have held, the outcome is clear: Netflix knew how to ride the tides of change.

For Blockbuster, innovation was optional, until it suddenly wasn’t.


Things aren’t all doom and gloom, and believe it or not, you can teach an old dog new tricks. In our Business Analytics Masterclass, we teach some best practices and techniques so that you can be more hands-on with your data.

We guarantee you’ll leave armed with the tools for transformative innovation.

Bonus points if you can bring any knuckle-dragging supervisors along with you—we have a thing for communicating the power of innovation to people who don’t want to hear it.

Data Scientist or Know-It-All?

The importance of domain expertise in data practice.

There’s a short yet wonderful story that perfectly encapsulates how many of today’s businesses use data. It’s a simple parable that all of us can learn from, regardless of our background, level of experience, or field of practice. In fact, if you’re already working with data, you might have a similar story to share. It goes like this:

A data scientist holds up a chart.
Everyone believes him.
End of story.

In today’s data-supercharged world, data is the law and the data practitioner is taken as the de facto expert. Ignore the fact that Ben just got hired last week—he has a MA in Statistics and a PhD in Machine Learning, so he must have all the answers, right?

(To clarify, said Ben is a hypothetical person. If you happen to know a Ben or are one, we apologize in advance. If you happen to be a woman, please don’t take our usage of a traditionally male name as a vote in favor of the patriarchy. This is purely for emphasis. We support all women, especially women in data. With everything cleared up, let’s get back to the matter at hand…)

Of course people will listen to the data guy. Numbers are compelling, especially when presented in chart form. Who are we mortals to question an interactive, multi-colored bubble chart? What power does one man hold over a regression line with an R-square well over 0.90?

In the modern boardroom, data is gospel truth. Everything else is mere conjecture.

We seldom stop to consider whether the data is flawed, or if the data guy understands the subject matter enough to draw insights or conclusions. Maybe the regression model is accurate, but what if it uses the wrong variables, or maps out the wrong features? What if the chart displays absolute figures in places where a logarithmic scale is more appropriate? What if the time series shows periods that are either too long or too short? What if the final analysis is inconsequential to the use case at hand?

When all is said and done, data can be just as flawed as the people who work with it.

Consider the infamous case of the NASA’s $125 million Mars Climate Orbiter. A simple conversion mishap—the failure to convert pound-force (lbf) to Newtons (N)—had the spacecraft flying within 37 miles of the Martian surface, dangerously below the 53-mile minimum. What followed was an epic fail of astronomic (no pun intended) proportions: Mars’ atmospheric friction burned the poor thing to a crisp before hurling its ashes deep into a cratery abyss.

Eyewitness reports allege the fire started with a contentious bar chart

Crash and burn—or, rather, burn then crash.

Mind you, this blunder happened with Lockheed Martin’s and NASA’s top brass, arguably the best domain experts in their respective fields, on the job. If even they can make mistakes like this, what makes us think we regular folk are exempt?

The next example is more down-to-earth… literally.

Applying domain knowledge could be as simple as choosing between a FIFO (first-in-first-out) and LIFO (last-in-first-out) approach, as detailed in this SuperDataScience podcast.

To explain FIFO and LIFO briefly: If element A arrives first, B second, and C last, FIFO dictates that they leave in that same order. LIFO is the complete opposite, wherein the last element, in this case C, leaves first, followed by B then A.

As you might already predict, the “right” choice varies greatly among industries.

For example, a business dealing in perishable goods like vegetables or fresh meat might prefer a FIFO approach, wherein an earlier element, say Monday’s shipment, is sent out before Tuesday’s or Wednesday’s. Conversely, a steel manufacturer may opt for convenience and use a LIFO approach wherein the steel bars at the top of the pile (i.e. the last ones in) get shipped out first. Caveat: we are experts in neither the perishable goods nor steel industries, so this is, again, purely for illustration purposes.

Yes, data skills can be applied to nearly every domain. However, we cannot discount the fact that data practitioners need domain expertise in order to truly be effective (or at least to avoid $125 million blunders). Data in retail can differ from data in healthcare or economics or agriculture or any other industry.

This is no different from other jobs. In the same manner we demand industry experience from management professionals and sub-specializations from doctors and engineers, we need to push for domain expertise and domain knowledge in the data practice.

What does this entail?

For the data practitioner, this means building years of experience and knowledge in a specific domain. Go deep rather than broad.

For the company looking to fill a data position, this means hiring a data practitioner with an industry background, or grooming one from the existing workforce (the second is an option we highly encourage).

For schools and institutions offering data courses, this means creating industry-specific courses and tracks, or encouraging students to pursue a minor in a field of interest.

Parting thoughts

Data does not exist in a vacuum and neither do data experts. To make data impactful, we need to encourage data practitioners to look beyond the spreadsheet and out into the real world.

Doing so might just save all of us from another epic crash and burn.

Customer Segmentation 101

Customer Segmentation 101: A Marketer’s Guide by CirroLytix

The secret to effective marketing is to know your audience inside and out. When you know what your market base wants, needs, and fears, capturing them through targeted marketing is simple.

This guide will teach the fundamentals of customer segmentation: how to sort your prospects into categories that make it possible to run targeted marketing campaigns based on shared similarities like age, purchase history, or even price sensitivity.

What is Customer Segmentation?

Before we take a deep dive into the how-to’s, we’ve got to get on the same page regarding the what is—specifically, what is customer segmentation?

As we’ve mentioned in a previous article, customer segmentation is the process of sorting leads and existing customers into useful categories.

It’s a subset of the wider process of segmentation. You can segment a lead list, and you can even segment your whole market base.

For the purposes of this article, we’ll talk about segmenting customers: the set of people who’ve already made a decision to spend on your business.

We’ll be discussing lead and market segmentation during our Business Analytics Masterclass series later this year. If you’re interested in our full playbook, you can download our primer or sign up here.

Finally, customer segmentation works for businesses of all kinds. It doesn’t matter if you offer products or services, and it works just as well for B2B marketing as it does for B2C.

Data Sources

Customer segmentation is an exercise in data analytics, meaning you’ll need to peek into the numbers behind your sales activity and customer acquisition. For basic segmentation, you won’t need a sprawling data infrastructure; basic information like age, gender, or home industry should do the trick.

One premise whenever a business wants to perform customer segmentation is that they have existing pools of data from which they can draw.

If you’re a business operating in the 21st Century, you’re more than likely to be sitting on some measure of data about your past customers, and the leads you’re currently chasing.

For example, if you’re into B2C, then you probably have data on customers including their age, sex, location, place or industry of work, and a list of their past purchases. In this case, a point of sale (POS) system or customer-relationship management (CRM) software is always a good place to start.

If you’re a B2B firm, then you probably store data on your customers’ locations, industries, purchasing power, and marketing cycles (i.e. the amount of time from capturing a lead to conversion). So long as this data is digital, you can already do basic segmentation.

Naturally, more data lends itself to better insight—-but don’t let your current situation dissuade you from exploring analytics. Learn enough, and you should already be able to make a stellar case for why your outfit should invest in data down the line.


The end product of segmentation is to create a reliable set of marketing personas: profiles of imaginary people who perfectly represent the different segments you’ll be targeting.

Personas aggregate the characteristics of your segments that matter most when it comes to actual marketing. These are things like what they need out of your product or service, what marketing tactics get their attention, and what habits they’ve expressed in their purchases.

Now, there’s a tendency among marketers to base the personas they use on gut feel and surface memory. This is a good start, but far from optimal.

Data-driven marketing expects a more precise method behind persona generation. At the very least, you should have some idea of what sex or age group lends the most to your market share, and a sense of what geographic factors work in your favor (ex. Your best customers come from X, or Your strongest location is at Y.)

You can start your segmentation efforts by assessing the personas you already use, or start with your goals and strategies, building personas as you perform segmentation.


CirroKnittix is a business that sells custom sweaters. At the broadest level, their market is, “Anyone who would buy a custom sweater.” Today, they want to take a data-driven approach and make smarter marketing decisions.

In their 4 years of doing business, they’ve noticed that their top 3 most frequent customers are the following:

  1. People who frequently travel to countries with cooler weather,
  2. People who work office jobs with a casual dress code policy, and
  3. People who put in orders for novelty sweaters to be given out as gifts.

CirroKnittix examines their data and discovers the following personas:

Persona A, a.k.a. The Traveler is a young man with a fortune in frequent flier miles. He shops at, or right before, peak travel season (May-June) and prefers to shop online. He spends an average PHP 4,000.00 per order, and is best attracted using print ads.

Persona B, a.k.a. The Employee is a young to middle-aged woman who believes in expressing herself through her fashion choices. She shops at various points throughout the year, and prefers to shop in-store. She spends an average PHP 2,000.00 per order, and is best attracted using Instagram ads.

Persona C, a.k.a. The Joker is a young man who enjoys giving gifts that leave an impression. He shops at various points throughout the year (mostly during the holidays) and prefers to shop online. He spends an average PHP 8,000.00 per order, and is best attracted using Facebook ads.

Segmentation Characteristics

There are four types of characteristics used in profiling customers:

  1. Demographic – Who your customers are
  2. Psychographic – How your customers think
  3. Behavioral – What your customers do (and their habits)
  4. Environmental – Where your customers are
Segmentation characteristics

These characteristics vary depending on whether you’re selling to a B2B or B2C market.

At a glance, you can see how these characteristics can be useful in marketing. They give you an idea of your clientele’s needs, habits, purchasing power, and preferences.

When segmenting your customers, these characteristics will form the variables that you’ll be examining.

In the next few paragraphs, we’ll discuss two popular segmentation methods. Both of these are great starting points for marketers looking to use data for better targeting.

RFM Segmentation

One of the most straightforward ways to segment customers is to study their purchasing patterns. RFM segmentation looks at purchases according to their Recency, Frequency, and Monetary value.

Customers who purchase more recently, frequently, and with larger monetary value are usually more valuable, while those who do the opposite are usually less valuable to a brand. There are also dozens of others types in between.

Let’s use the CirroKnittix example again.

If we were to rank Personas A, B, and C according to RFM segmentation, it would look like this for the month of May, 3 being the highest score and 1 being the lowest:


Recency score

Frequency score

Monetary value score













However, the table above is just the tip of the iceberg. Once we start going deeper into the data, more detailed segments should emerge. Below are actual RFM segments we did for a retail company. These are based off of POS data, and include some recommendations for each segment.

Customer SegmentActivityActionable Tip
ChampionsBought recently, buy often and spend the most!Reward them. Can be early adopters for new products. Will promote your brand.
Loyal CustomersSpend good money with us often. Responsive to promotions.Upsell higher value products. Ask for reviews. Engage them.
Potential LoyalistRecent customers, but spent a good amount and bought more than once.Offer membership / loyalty program, recommend other products.
Recent CustomersBought most recently, but not often.Provide on-boarding support, give them early success, start building relationship.
PromisingRecent shoppers, but haven’t spent much.Create brand awareness, offer free trials
Customers Needing AttentionAbove average recency, frequency and monetary values. May not have bought very recently though.Make limited time offers, Recommend based on past purchases. Reactivate them.
About To SleepBelow average recency, frequency and monetary values. Will lose them if not reactivated.Share valuable resources, recommend popular products / renewals at discount, reconnect with them.
At RiskSpent big money and purchased often. But long time ago. Need to bring them back!Send personalized emails to reconnect, offer renewals, provide helpful resources.
Can’t Lose ThemMade biggest purchases, and often. But haven’t returned for a long time.Win them back via renewals or newer products, don’t lose them to competition, talk to them.
HibernatingLast purchase was long back, low spenders and low number of orders.Offer other relevant products and special discounts. Recreate brand value.
LostLowest recency, frequency and monetary scores.Revive interest with reach out campaign, ignore otherwise.

RFM segmentation will allow you to prioritize your marketing activity. It should help you identify your most valuable (and least valuable) segments using data so that you can budget your efforts and funding accordingly.

Correlation Clustering

While RFM segmentation analysis is a great place to start, it falls short on two counts: (1) it only looks at three factors, and (2) it’s only useful for segmenting customers that are already engaged with your brand (i.e. those with existing transaction records).

What if you want to predict purchase size based on gender, age, and location, or find out which characteristics your most valuable customers share?

Then you’ll need to cross-examine, cross-tabulate, and analyze more factors.

Correlation clustering groups data points according to their many similarities—that is, by examining all existing factors and figuring out which data points are most alike.

This is done by identifying centroids (think of these as the reference points for each cluster), and grouping data points according to their closest centroids or “nearest neighbors”. A centroid is identified by measuring the distances between data points. The point with the smallest aggregate distance (i.e. the one closest to its surrounding points is usually the centroid We illustrate this in the charts below.

Data points and centroids

Data points clustered according to nearest neighbors

Although the chart appears flat, in reality, clustering can involve multiple dimensions. As a matter of fact, many clusters involve far more dimensions than the three we’re used to seeing. It really depends how many fields you want to analyze.

Correlation clustering can tell you which factors tend to go together—and the results may surprise you. Remember beer and diapers?

A Quick Example (Segmentation Using Pivot Tables)

This isa simple customer segmentation exercise using the Pivot Table function on MS Excel. For this exercise, we’ll use the table below:

Our objectives are to find out Average Spend for the following segments:

  • Age
  • Gender
  • Age & Gender

Check the video below for a step-by-step guide.

Ready to do some analysis of your own? Download the Excel file here and let us know what you find in the comments below.


Customer segmentation is one of many tools that data-driven marketers use to monitor, predict, and optimize their work.

Once you’ve discovered and understood key similarities among your customer base through segmentation, it becomes much easier to reach your most valuable audiences with targeted ads and campaigns.

We’ll be exploring more marketing analytics techniques in the weeks leading up to our Business Analytics Masterclass this coming April. Check back regularly, or subscribe to our mailing list for live updates.

Marketing Analytics 2019: 6 Use Cases for Better ROI

Who should read this?

C-Level Executives – Why invest in marketing analytics?
Marketing Managers – How can our analytics team help with marketing?
Analytics Professionals – What marketing-related problems can I solve?
Analytics Job-Seekers – How can I improve my pitch during interviews?

The old saying, “nobody counts the number of ads you run; they just remember the impression you make” is truer now than it has ever been.

Although marketers run more ads today than ever before, many continue to struggle to reach their respective markets. As a result, the average marketer burns through their budget for minimal returns.

The reason?

People hate ads. Specifically, people hate ads that suck—and the same can be said about any kind of marketing activity.

When a field is as saturated as digital marketing, you’re bound to have your digital space polluted with crappy content. There are plenty of people who claim to know what the market wants, but only a handful can walk the talk.

Rather than letting loose generic ads upon an undefined audience (which is a great way to waste cash btw), marketers should aim to understand their customers on a deeper level.

Making the right impression takes intelligent decision making, which in turn depends on having access to the kinds of facts and evidence that only a foothold in data analytics can provide.

This article will outline six use-cases that illustrate how working with data can yield greater returns. If you’re sick of feeding your budget to the black holes of guesswork and intuition, read on.

1. Customer Segmentation

Customer segmentation is the practice of splitting your customer base into groups that share common characteristics that matter when it comes to marketing.

Think of customer segmentation as a magnifying glass that allows marketers see unique groups within a broader audience. Segments might be based on factors like age, gender, interests, or spending habits—anything that helps you tap into the specific demands of your target market.

In the UK, skincare brand NIVEA Sun was able to divide their market based on demographic and attitudinal characteristics. This allowed the brand to grow its portfolio to over 40 products, increase its market share by an average of 6.4% per year, and expand into new markets such as men’s facial care, aftershave, and deodorants.

Customer segmentation is a marketing analytics use-case that can answer important questions you may have about your market such as:

  • Which customers generate the highest lifetime value for my brand?
  • What features does each customer group look for in my brand?
  • Which customers provide the highest upside?
  • Who should I target for a discount campaign?

Once you can identify groups within your larger audience base, you can easily set up campaigns that speak directly to each of them and create content that generates genuine interest instead of passive disgust.

The data says all our customers hate our products

Looking to learn customer segmentation?

Visit our Customer Segmentation 101 blog for some hands-on exercises—we also attached a downloadable dataset you can work with.

2. Market Basket Analysis

If customer segmentation is about finding people who share similar traits, market basket analysis is about finding patterns in what people buy.

The underlying theory behind market basket analysis is this: if you buy a certain set of items, you are more (or less) likely to buy another set of items.

For example, let’s say people who buy underwear are more likely to buy socks during the same shopping trip. A department store that knows this might choose to place their sock aisle closer to other undergarments, or offer a bundle featuring both products.

Market basket analysis was first applied in supermarkets and groceries to predict which items were likely to go together in a single cart or basket (hence, “market basket”). Today, it’s applied to all forms of retail, including consumer goods, fashion, and others.

One of the best known applications of market basket analysis is in the fast food industry. Restaurants like McDonald’s and Burger King know that burger buyers are highly likely to order fries and soda on the side, which is why they upsell nearly every burger purchase. In fact, we’re at a point where the upsell has become the default.

This simple bundling practice has made fast food chains billions—and it’s doubly impressive because they make far better margins on the upsell than on the initial purchase!

3. Marketing Mix Modeling

One of the biggest questions marketers face is, finding an optimal way to allocate their budget and effort across a slate of different marketing channels.

Marketing mix modeling is a marketing analytics technique that predicts the ROI of each marketing channel, and suggests the best combination of marketing channels given a certain budget.

It helps marketers know how best to spend, and can be used to predict sales, track campaign effectiveness, and create data-driven budget plans.

Any marketer that can apply marketing mix modeling will have a breeze with budget approval, effectively waving goodbye to awkward conversations about wasting company money.

4. Propensity-to-Purchase Modeling

As the name implies, propensity to purchase modeling (a.k.a. lead scoring or likelihood-to-buy modeling) is method used to determine the likelihood of a customer to buy a product or perform a predefined action.

Marketing Analyst on a computer

These graphs are too upward sloping. They must be true.

Most models under this use-case classify prospects on a spectrum of “highly likely” to “highly unlikely”—the more similar a prospect is to previous buyers, the higher the likelihood of a purchase (or action), and vice versa.

Propensity to purchase modeling is especially helpful for calibrating prospects. In practice, you’d be able to direct your ads to customers who are more likely to convert, while deprioritizing those who are less likely to make a purchase.

This can potentially save you tens of thousands in ad expenses, increase your conversion rates, and boost ROI. If you ask us, that’s a pretty good reason to go data-driven.

5. Sentiment Analysis

In our social media-driven world, it pays to know exactly how people feel about your brand: Are they happy with your product? Is your service enjoyable? Are your sales reps leaving a positive impression on your customers?

Sentiment analysis (also called opinion mining) is defined as the practice of extracting insights about the emotions within a series of words.

It uses a method called natural language processing (NLP) to help machines uncover meanings behind certain words or groups of words.

Unlike the above use-cases that measure probability and magnitude, sentiment analysis attempts to measure human emotion (e.g. happy, sad, angry, excited, etc.) by looking at unstructured data in the form of social media posts, reviews, blogs, and the like.

Popular applications of this use-case include: social listening, intent analysis, and contextual search.

Good marketing is about listening as much as it is about broadcasting ideas. Using sentiment analysis, you can get better and faster feedback so that you can meet your market where it needs you.

6. Time-to-Next-Purchase Modeling

Time-to-next purchase modeling draws its inspiration from survival analysis or time-to-event analysis. However, instead of solving traditional problems like mortality, morbidity, and half-life, it is used in marketing analytics to forecast the time to a purchase or conversion.

This could be the time it takes for a driver to change car tires, for an organic restaurant to restock on fresh produce, or for the average woman to shop for more makeup.

Using time-to-next purchase modeling, you can anticipate your customer’s needs ahead of time and plan accordingly.

Let’s say you work for an optical shop and know that it takes roughly two months until contact lenses need to be replaced. A month and a half after a customer purchases a set of lenses from your store, you can send them an email reminder to restock and offer them free delivery while you’re at it.

According to Listrak, replenishment emails have the highest conversion rate (28.59%) of all post-purchase emails. That’s an easy return for very minimal investment.


As we said from the start, getting positive returns from your marketing activity isn’t a matter of running the most ads or having the widest reach; it’s about hitting the right notes to appeal to your target market.

These six use-cases illustrate how data-driven marketing does precisely that. By cross-checking the numbers, you can make reliable conclusions based on past encounters and experiences.

Intelligent guessing is no longer the intelligent approach to marketing. From here on out, it’s data or bust.


Over the next few months, we’ll be dedicating an article each for the six marketing analytics use-cases. These will include how-to’s on MS Excel and Google Analytics so that you can begin using data to drive real-life marketing strategies and campaigns.

Like us on 
Facebook, and you’ll be the first to know when a new article drops…or, if you’re after more than just snippets from our playbook, you can sign up for our upcoming Marketing Analytics Masterclass and leave with entire pages’ worth of strategies and techniques.


HR Analytics: 5 Use-Cases for Better Productivity

The workplace is broken.

Research by Gallup shows that, of the 1 billion workers worldwide, only 15-percent consider themselves engaged—and we’re not just talking third-world sweatshops. Even advanced countries like the United States have their fair share of workplace woes.

How might companies address this?

HR analytics (also called people analytics) is shedding light on why people behave the way they do in the workplace. It makes use of data (e.g. KPIs, employee surveys, productivity reports, resumés, etc.) to answer questions on productivity, job satisfaction, employee turnover, and all things people.

Infographic of People Who Consider People Analytics Important
Borrowed from Deloitte

BAs a result, we’re seeing companies of all shapes and sizes invest in data-driven human resource management. Here are some common use cases:

Employee Segmentation

Who are my employees?

Not all employees are cut from the same cloth. Some prefer people-facing jobs, and others work better with machines. Some are motivated by benefits, and others respond to recognition. The major challenge HR managers face is figuring out who belongs where.

Employee segmentation is the practice of grouping employees into well-defined buckets based on demographics (e.g age, gender, race, and civil status) and behavior (e.g. skills, personality type, values, and spending habits). It often leads us to questions such as: Who are our top performers? Are our highest paid staff also the most productive?

One of the most popular examples of employee segmentation is Google’s Project Aristotle. The project sought to understand team dynamics—that is, what makes a successful team?—by studying hundreds of teams across Google’s 51,000 employees (spoiler alert: the answers were conversational turn-taking and empathy).

Employee segmentation can also lead to better hiring. An American call center operator called NOVO 1 was able to identify common traits among its top performers, and decided to hire more people like them. The effort paid off as it allowed them to shorten job interviews from one hour to twelve minutes, cut call times by a minute, and reduce attrition by 39-percent.

Employee Engagement

Are my employees engaged at work?

Companies spend billions on employee engagement—but it doesn’t always translate. Research by Dale Carnegie shows that 22-percent of employees think their organizations spend too much time and money trying to engage employees, and 26-percent say efforts to engage employees are a distraction from getting real work done.

Why the disconnect?

Many companies that invest in employee engagement ignore a crucial step. They put money in expensive engagement activities without first figuring out what makes their employees tick.

For example, an employee motivated by health benefits may not enjoy team building activities; and one who responds to sales bonuses may not respond to recognition.

Employers need to understand that each company is unique—so is each team and each employee. The next time someone decides to spend on engagement, look at the data and find out what drives it all the way down to the employee level. Often, you’ll be pleasantly surprised that the cheapest initiatives can lead to the most significant results. Your managers will be thrilled.

In Bank of America, simply switching from solo breaks to group breaks increased productivity by 23-percent and reduced employee stress levels by an average of 19-percent. No fancy gimmick required.

If you have no employee data yet, consider regular employee surveys or tracking activity using badges and IDs—or, if you can afford to be more sophisticated, investing in continuous listening will allow you to collect millions of data points per employee and perform advanced people analytics.

Managing employee engagement can contribute to higher job satisfaction, better work-life balance, and stronger personal development in the workplace—all of which add to productivity and improve your bottom-line.

Performance Management

How do I track employee performance?

Performance is always at the top of every manager’s mind. After all, that’s how people are measured in the workplace (and how bonuses are computed at the end of every quarter). It’s no longer enough to say Mary is doing a good job John isn’t. We need to be able to quantify and qualify what constitutes a “good job” and what doesn’t. In the words of the late-great Peter Drucker: What gets measured gets managed.

People have been tracking performance for ages, some say as far back as the Chinese Wei dynasty during the third century. However, the KPI-based framework we know today has only been around since the 90s, when Dr. Robert Kaplan and Dr. David Norton introduced the balanced scorecard.

Borrowed from Tools Hero

Today, companies are tracking thousands of metrics, some as esoteric as team dynamics or company culture, or even as mundane as the average number of bathroom breaks or trips to the pantry. This explosion in employee data has helped companies find discover many factors that contribute to performance. Of course, there are some instances where data collection can feel oppressive to the employee…but that’s a topic for another blog.

Employee Turnover Prediction

Who are most likely to leave the company?

Losing employees is expensive. Apart from the obvious costs of losing a new hire, the void that an employee leaves behind can disrupt an otherwise smooth workflow. Of course, turnover is inevitable; there will always be people leaving companies for better pay, new opportunities, and many other reasons.

The best thing a company can do is understand why people leave.

We mentioned earlier that people leave for different reasons. The key to successful attrition management is to identify which people leave and for which reasons. This is a use case called Flight Risk Scoring or, in layman’s terms, employee turnover prediction.

Attrition Correlation Analysis - Factors That Contribute To Employee Attrition
Borrowed from R Tutt Insights

The goal of this use case is to differentiate employees who are likely to leave from those who are likely to stay. The findings can be interesting such as: people in sales are more likely to leave than those in IT (perhaps IT people are better compensated), or employees who work under Boss X are more likely to leave than those under Boss Y (maybe Boss X is the problem).

Once you figure out why certain employees leave, it becomes much easier to take action and keep those employees from leaving.

Time-to-Attrition Modeling

How long does the typical employee last in a job?

If employee turnover prediction identifies who in the company is likely to leave, time-to-attrition modeling identifies when a particular employee is likely to leave.

Microsoft has gotten this down to a science. They’ve been able to use predictive modelling to tag flight risks ahead of time, and make the necessary adjustments, either by proactively looking for new talent or by offering better incentives. This has allowed them to reduce the time it takes to fill new positions and mitigate the costs associated with attrition.

Employee Survival Rate
Borrowed from Predictive Analytics World

What’s Next?

Employee data is possibly HR’s most important asset. The more you know about your employees, the better can manage them and treat them.

If you find managing people a challenge, stay tuned for our upcoming articles. We’ll be guiding you through the above use cases one by one, including some instructionals on MS Excel.

Who Killed Analytics?

Solve the data mystery.

Picture that you are part of a big data investigative team formed by your company’s CEO and you have one mission: discover who has killed the analytics in your firm.

Just a few months ago everything seemed on track in Company X. The CEO had announced that your company had embarked on several ambitious projects to bring the company’s operation to the digital age.

Several initiatives had been launched: consolidating data in a data warehouse, implementing business intelligence and predictive analytics tools, hiring new staff and consultants to train and evangelize everyone in the company of the merits and advantages of using data and analytics in operations and decisions. In the span of a few months, business analytics, financial forecasts, recommendation engines, and data mining activities were all in full swing. Company X was going to conquer the industry, using data as a weapon for competitive advantage.

Then suddenly, information ceased to flow in Company X. Reports stopped being produced, data was now impossible to extract, and no new business insights could be generated. Business was grinding to a halt and the CEO and board were furious.

As part of the big data investigations team, you round up the list of people who were recently involved in the flurry of big data and analytics initiatives in the company. In the tradition of the renowned board game Cluedo – by process of elimination you now need to interview and identify how analytics died in Company X from among the suspects.

Fortunately a few individual dossiers exist and you now begin the tedious task of piecing together the mystery of the missing information.

Dysfunctional Analytics

How do you know your analytics is failing?

How do you know if your company’s analytics is failing? And if you find out, how do you fix the problem? Dysfunctional analytics can either be an obvious or latent condition depending on the situation, but executives should be aware when symptoms of dysfunction emerge and move quickly to deal with it. The cost of mis-informed decisions can be greater than the opportunity loss of having no information to begin with.

5 Dysfunctions of a Team Book
5 Dysfunctions of a Team

One management book I love re-reading is Patrick Lencioni’s Five Dysfunctions Of A Team. The book details five common causes of team dysfunction, the likely behavioral root causes, and ways to deal with them. I’ve personally used the framework in effectively managing small and large teams, and I also find them useful taken in the context of analytics.

Symptoms of dysfunction

  • Inattention to Results – Company reports, data and figures are prone to errors, missed deadlines, and failure to tie up with past numbers.
  • Avoidance of Accountability – no one is taking responsibility for reporting errors and delays, teams point fingers at each other, lengthy email trails – mostly about assigning blame rather than resolving issues.
  • Lack of Commitment – memos and directives on report errors and problems are filed and go unheeded, diagnoses and solutions to the reporting problems are superficial, status quo prevails. Critical issues make a big splash one moment and then die down for a few periods only to get resurrected under a different name.
  • Fear of Conflict – despite self-evident flaws in reporting and analytics, everyone acts as if everything is moving along, business-as-usual. Inquiries are often answered by half-declarations veiled in ambiguity (“they’d rather not say”). No root causes to the problem are uncovered. There is an artificial harmony prevalent among the analytics teams. Analytical investigation and data discovery is a minimal to nil practice.
  • Absence of Trust – no authority on analytics is mutually recognized by all teams and team members – often defensive of each other’s outputs. Multiple versions of the truth exist and there is either general disagreement about analytics strategy or no strategy at all. Senior management refuses to rely on any analytics in decision making.

Behavioral causes

  • Status and ego – teams or team members do not collaborate with each other in generating analytics and report figures only minding their own patch – leading to inconsistencies in data handling, transformation, preparation which in turn lead to rework and delays. Teams seek to discredit each other’s numbers rather than working together to align them.
  • Low standards – responsibility is easily deflected due to low performance standards set by management (if at all). No one feels threatened by bad performance on their part and conversely no one aspires to perform better.
  • Ambiguity – with no clear direction, reporting teams are rabidly territorial, there is no buy-in on the ground for decisions made on top and the issues are often considered too complex for any single party to correct. Any queries are bogged down by over-analysis without clear outcomes.
  • Artificial harmony – teams prefer the security of harmony over any form of conflict and bona-fide resolution of a core issue. Any remotely incisive query on reporting and analytics output is seen as destructive and teams would rather accept the status quo, no matter how jaded and imperfect, as the way things should always be.
  • Invulnerability – teams and managers are more bent on protecting their reputation rather than admit any weakness. Gut-decisions persist even in the face of contradicting data and evidence.

Any management framework like Patrick’s 5 dysfunctions is only as good as the users of the framework. The key is not just proactively spotting the symptoms early enough, but also moving quickly and decisively to address them. Of the symptoms, reporting errors and delays are the first things to spot, but they don’t necessarily denote any real dysfunction since these things will happen over the course of time. What should cause concern is if the problems in the way a company generates information and insight keep coming back or no clear course of action to resolve them seems to be evident. Worst of all: if no one appears to care about it.

Team dysfunctions hinder analytical competitiveness

Competing on Analytics Book
Competing on Analytics

Continued prevalence of dysfunction may also indicate a problem in how analytics is viewed by the company. In Competing On Analytics, Tom Davenport and Jeanne Harris shared four (4) clear pillars common among analytically competitive companies. If and when symptoms of dysfunctional analytics emerge, I find it useful to start asking questions around these pillars to help spot the underlying root-causes of the problem:

  • Senior Management Commitment – is analytics supported by the prevailing culture of senior management? Is fact-based decision-making a priority? Is data gathering and accuracy a key priority ahead of decisions?
  • Analytics as a distinctive capability – does the company view its analytics as a key differentiator in its market or against peers or just another management toy to play with? Is analytics aligned to the company’s vision, mission, and objectives? Is analytics viewed as a competitive advantage or a new chore dictated by the management?
  • Large-scale ambitions – does the company aspire to dominate its industry through analytics? Does analytics support clear measures which define success for the company?
  • Enterprise-wide analytics – is responsibility for analytical output entrusted to all business units, or just the role of one team? Is data and insight intended to be siloed in the hands of few players or made available to all levels of the organization?

Although based on Harris and Davenport’s research, the presence of all four pillars above indicate a company that is analytically competitive – I also believe when one or more of the pillars above do not yet exist in a company – that absence can be the breeding ground for the dysfunctions to occur.

Analytical leadership

Going back to Lencioni’s framework, there are also five paths to resolving dysfunction. They appear simple, but also taken against the four pillars above, they are very straightforward ways to achieve competitive advantage through analytics:

  • Collective Outcomes – teams should be encouraged to share in collective goals to avoid unnecessary siloes and territories. Make the end output of any report the joint responsibility of those extracting, transforming, and preparing the data. Encourage integration and reuse of data whenever possible – rather than teams keeping multiple copies of the same information independently.
  • Call out Accountability – apart from the numbers generated by analytics and reports, measures and processes should also be set in place to monitor the analytical outputs themselves like reporting service levels (SLAs) and some basic key performance indicators (KPIs) such as error rates – and teams should be both rewarded for achieving them as well as penalized for repeat failures.
  • Clarity of Objectives – establish clear strategies and outcomes not just for the production of analytics but also the rationale for it to exist (i.e. the company’s competitive advantage). Ensure that all teams are aware of this to enforce adoption.
  • Constructive Debate – create adequate and ideological tension between teams to keep them on their toes. Encourage members and teams to challenge each other’s reports and data with the aim of improving quality of analytics output, performance, and drive better insights. Data discovery and investigation is encouraged to unearth insights outside of the usual coverage of standard reports.
  • Leaders Take Initiative – leadership should take the first steps to expose themselves to criticism, resolve issues, and expose weaknesses in order to generate trust and positive buy-in from all team members and stakeholders. Even low-ranking members are encouraged to speak up to voice out insights, new ways of doing things, and calling out problems as they see it. Leaders also encourage accurate and timely analytics by being the first to drive fact-based decision making not just about company-level decisions but also the way analytics is utilized and delivered.
Happy team in front of a laptop
Happy teams produce better work

At the core of it, the symptoms, root causes, and solutions to dysfunctional analytics is the same as that of dysfunctional teams. On the other hand, a healthy team culture also reinforces a healthy analytical culture and vice-versa. As companies move forward in this era of big data they will have more to gain from driving better analytics, and also a lot to lose by not encouraging it.