The connection between life meaning and corona lockdown
It is established that serotonin, your gut’s main neurotransmitter, is the one responsible for regulating your social functioning, your ability to find meaning in life, and bowel movements.
For example, serotonergic stimulation through antidepressants or psychedelics can mislead the brain into experiencing meaningfulness in the mundane.
Life meaning, or simply meaning is the emotion that you are able to make based on serotonin. It is there to make the human find meaning in social connection. It also determines whether we care about or empathise with others, and how well we sleep at night (literally).
Following the corona lockdown, the sudden drop of social contact may change a person’s serotonin in a way that makes them lose life meaning. (paper)
Other things that make your serotonin drop:
stopping exercise
stopping exposure to outside daylight – even on a cloudy day, you get 10-30k lux outside, but on a sunny day usually no more than 200-1000 inside.
Such, it is no surprise that many of us are experiencing some level of distress due to the sudden change of habits.
How to manage the emotions and make the best of it?
First of all, the search for meaning is one of the drivers of human knowledge gaining. It has pretty useful outcomes.
This can be seen all over, where some people start to do more for each other through social help initiatives. Others focus their energies on discovering the next best thing.
However, the sudden change can also throw one in a downward spiral, so take things into your own hands and
Get some sun. Seeing the light seems to also be relevant, not just skin exposure.
Eat gut-healthy, whatever that means for you. Don’t go into extreme diets, they are usually the cause for food-related mental disorders. Instead, make small changes that you can incorporate into your routine. Google offers plenty of valid suggestions.
Connect more with people
Exercise – Again, any little bit helps.
Mentally search for good things to practice a healthy outlook
Every major website about business or work will tell you what a leader should be, how soft skills are the future, how machines cannot take over because they cannot feel…
But what does your boss being an asshole (and you wanting to take revenge) have in common with AI limitations?
Well, emotional maturity.
Being emotionally mature means having free will.
It means being able to experience human emotions while retaining your cognitive ability to decide on your next steps of action. It’s the difference between there being a you at all, or “you” being a mere reaction to something that happened.
This boils down to taking “extreme” ownership for your thoughts and actions versus seeing others responsible for your thoughts and actions (“because he did that, I did this”)
Nobody’s the asshole
Morality and fear cannot be active in the brain at the same time (see Polyvagal theory), but morality and awareness of fear can.
We don’t have good language to describe this, but being afraid and being aware of your fear are two distinct patterns of neural activity. This is because awareness of an emotion happens in a different brain part than the emotion itself.
Fear happens in the ‘reptillian brain’ (fight or flight fear response that short-circuits your brain and free will), while the other happens in your ‘attention center’ (anterior cingulate cortex, or ACC for short).
Conditioned learning (the basis of learning and memory)
Conscious experience
Error detection (koans such as “two hands clapping make a sound, what is the sound of one hand clapping” will keep this brain-bit on fire)
Spiritual awakening and enlightenment (no, seriously!)
So basically, if we are in reptile brain, we can do none of the above. That makes us a liability in an office where higher brain functions are required.
“He who fears death will never do anything worth of a man who is alive.”
– Seneca
How to not be a reptile?
We can restore ourselves to a state of safety by self-regulating or co-regulating with other safe individuals.
Co-regulation is less within our control – we can seek a more nurturing environment, but isn’t every environment going to have its reptiles?
Self-regulation however falls within the bounds of our responsibilities, and is a function of our friend, the ACC.
To sellf-regulate, (ACC-function) you need to learn (ACC-function) to pay attention (ACC-function) to yourself, find errors in your judgment (ACC-function) and take conscious (ACC-function) decisions (ACC-function) … you get the idea
So a good starting point is actually training your ACC. How do you do that? well, meditation specialises on training the ACC to the point where it becomes the dominant brain structure (in terms of activity and connectivity).
Various other buddhist methods employ things like koans to brute-force insight formation (in neurological terms, an electric braingasm of sudden understanding that precedes those “Eureka!” moments)
Introspection (also a type of meditation?) and internal conflict resolution work too.
Psychological approaches based on conditioning, self regulation, exposure to the feelings, awareness etc also work well (see cognitive behavioural therapy, DBT)
So what’s a good leader?
able to self-regulate and co-regulate others, they are ‘safe’ to be around and give others the feeling of safety.
able to decide consciously, they possess critical thinking (the ability to think objectively, which can only come through understanding of one’s self).
A good leader understands the limitations of themselves and others and that there are reasons why people decide one way or another.
Integral and honest (morality).
They empower people because they understand that this is what people need to give the best they can.
They earn respect, because they respect themselves.
They focus on helping others rather than themselves, because morality and empathy. (it’s the right thing for the team and the company)
A good leader can detect and accept an error in their own judgment.
The list could go on, but it’s basically a measure of emotional maturity. The Stoic, the Übermensch, the Buddha are good role models that allow humans to gain independence from the circumstances of their lives.
So why is AI emotionally immature?
AI is not very mature at all. But maturity is a matter of time.
We have only been able to build what we understand. We mostly don’t understand emotions, the ACC or how to be emotionally mature.
Just give us another couple of generations iterations.
In the workplace
Needless to say that a workplace that promotes emotional maturity will have a lot to reap from the improved abilities of their staff:
good communication,
improved attention and learning (1h meditation raises brain dopamine by 50%),
Honesty and morality
Admitting mistakes and doing the right thing
objective decision making
The takeaway.
If you introspect enough to understand your self, you will be able to achieve self-mastery, and most of the former obstacles in your life will become a thing of the past. Treating others kindly, connecting genuinely and honestly, taking responsibility for our behaviours.. you don’t need to be a superman to do all that, you just need to start.
And if you can’t see it this way, don’t worry, the Buddha only started at age 30. I couldn’t see it either, until I looked inside.
And don’t forget, what you need to do is to PRACTICE, not to know. Knowingly doing the wrong thing doesn’t help you become better. Reading about mindfulness is not the same as practicing mindfulness.
Have you or your company rejected talent recently because they were a freelancer? Did you challenge that decision or did you do it because “we only hire full-time?”
Many companies I’ve talked to refuse right off the bat to consider freelancers for filling a full-time position. Most of the time the reasons for this were due to a misunderstanding about who a freelancer is.
When I applied for full-time roles as a freelancer, the most common response was silence. This was very uncommon for me, having been used to a reply time <24h from my previous applications.
I did get plenty of callbacks saying that they liked my profile, would hire me as an employee, but would only hire me as a freelancer if they cannot staff the role in time for their goals. So clearly there was a big demand for my skills. Why would companies miss out on staffing a perfect skill fit?
So whenever a company replied that they don’t hire freelancers, I tried to learn from the interaction and asked why. What I sometimes got were rather hostile answers. And oftentimes they were unchallenged judgment mistakes.
Misconception 1: Freelancers are flaky.
Freelancers have the reputation of being flaky. Don’t be put off by the stereotyping – usually, people who think this haven’t actually worked with freelancers, and probably never will in that company due to policy.
Flaky and Unreliable
What is flaky in a work context?
I’ve had an employed, non-freelance colleague, that one day after someone from management insulted him, went home, resigned by email, and didn’t come in anymore. Was he being flaky? I wouldn’t say so. I’d call him a guy who knows where to draw the line. Even if it’s not nice, it’s absolutely fair.
Flaky is someone who doesn’t deliver on their responsibilities.
What makes someone deliver?
Consider this. A freelancer has less at stake than an employee. Their career path is not threatened by an uncaring manager. Their hours are not set by some slave driver. They are not working for you because they have to – they don’t have a notice period and they don’t worry about job security.
A freelancer works with you because they enjoy the work. They can likely get the same rate or even better elsewhere if they tried, but they are working with you because they want to see things through.
If an employee flakes on you, would a 3 months notice period have made it better? It didn’t for the example above. It didn’t for the various employees that I ended up replacing as a freelancer. All of them had ragequit.
Why do freelancers leave?
Think about it. Unless the freelancer really has some serious difficulty, could it be that they left because you drove them away? Could it be that an employee would have been long gone by that time, notice period and all?
Simply consider that a freelancer, due to the nature of the relationship, is less likely to be negatively affected by a difficult company culture, more motivated to do the work, and more likely to stay. It’s still a game of chance though, as conflicting values or views will slowly drive people apart.
As an example, a hiring manager who is usually very critical of others but otherwise incapable of admitting mistakes himself, told me the story of how he hired a remote freelancer.
As the freelancer delivered work, he continuously gave him feedback about how he could do better. Eventually, the freelancer just stopped replying, without even asking for payment for his work.
The hiring manager reached the conclusion the freelancer was a “stupid ass”, while the freelancer was likely just cutting his losses arguing with an insulting client.
Misconception 2: Freelancers are expensive. Market value for this work is X.
Another reason for not hiring freelancer I often heard was that freelancers are too expensive. Companies compare their rates to the average salary and leave it at that.
But just because the average salary is X doesn’t mean that the top talent you want to hire is also worth X. Just because some talented individuals are unaware of their value and sell it for X, doesn’t mean you can keep talented individuals for X.
What is a freelancer worth?
A freelancer who has been on the market for a while likely has seen a lot of things. They likely can solve a variety of problems, because they already did. They have a contract to deliver value for money, and only bill you when they work.
Let’s say you have a good senior employee in data, that you hired for a salary of 80k. This translates to about 100-110k cost to the company after adding company contributions to health, pension, hardware etc.
For someone with 30d vacation, this translates to 60/h. Add some sick days and we bring this to 65. Add management and hiring overhead divided over the employee lifespan, and you are adding at least another 10 percent, taking your total to just over 70/h.
Now, say an average senior data freelancer charges around 80-100/h with no additional cost beyond optionally office space. Considering the short notice availability, self-management skills, lower risk to the employer, and additional experiences and skills that freelancers develop due to their work style, is +10-40% not worth it?
Why the 1 to 1 comparisons are wrong
Typically, hiring manager employees don’t make these kinds of calculations, and compare their net/gross salary with the freelancer’s total cost to the company, coming to the conclusion that freelancers are not worth it.
Sometimes, employers look for a good deal on salary and discount the freelancer right off the bat.
Well, a good deal on salary is either a lose-lose situation where the employee gets lowballed and leaves upon realizing.
Or it’s a lose-lose where a junior person takes a role for a low salary, which leads to a loss on company side because 2x cheaper but 5x slower is not a good deal. Meanwhile, the junior doesn’t have an environment that facilitates talent development.
Junior talent is not cheap
A junior person should not be regarded as a cheap workhorse – they aren’t. They are, in fact, very expensive professionals. Due to their level of experience, they are content and happy doing the more trivial work, as this gives them the exposure, learning, andpractice without the pressure.
This can be good if you already have specialists because it allows them to focus on solving specialist problems, which is what in turn gives them the exposure, learning, and practice they need to be happy.
However, if you need someone who can hit the ground running, junior employees are not the right fit. They will be slow in doing simple tasks because they are still learning. This again drives up your cost per unit of work.
Who would the professionals hire?
Still not convinced? Ask any freelancer who is good at what they do – who would they hire as help? A junior, a cheap employee, or another freelancer?
They will tell you they want another freelancer or a well-compensated senior employee because they dread onboarding and management overhead for a less experienced hire and because no one wants to deal with an underpaid and therefore demotivated employee. This would likely harm client relations.
One of my former managers, a serial founder and freelancer told me their take on the above. They would always prefer to hire a freelancer for a data role because you get higher value for the spend.
Can’t I just hire many juniors?
Now you might think you could just hire several juniors to make up for the lower speed. But can you read a book faster if you have two copies?
Teams don’t scale well, so a horde of average talent quickly becomes even less performant. The rule of thumb is that beyond 5 members in a team, adding more does not improve team output due to communication overhead, lowering accountability, and creating free riders.
Of course, if you have a good process you can increase the team size by reducing cross communication overhead.
Exponential communication line growth in a growing team. Allow direct decisions to reduce communication overhead. Reference – Project management Stackoverflow question “Buying 2 of the same book does not help you read it 2x faster”
Such, you want to hire the best you can for the work you need done.
Hiring a freelancer who is more experienced, motivated and autonomous is usually a better financial deal to get the work done. He’s also a solution to bolster an already large-ish team, as the freelancer will not worry about cross-communication and will likely be able to manage the team process if required.
A freelancer gets paid for output, not communication. Hours are just a way to quantify, but at the end of the month, the hiring manager looks at the output, not hours.
So next time you as an employee or hiring manager make the judgment call that freelancers’ cost to value ratio is of concern, think again.
Misconception 3: ‘Freelancers are only for short projects’
Another mistake companies make is to assume that hiring a freelancer is a lot of effort for a short, one-time project.
Yes, freelancers are the superweapon because they are here, available now, and have the competence to get it done. However, this doesn’t mean you only use it once.
If you had a magic cannon that shot miracles worth millions/year to your company at a cost of 15k/magic, would you only buy 1 magic?
What if a complete, new data warehouse with reporting takes 2 magics? What if hiring a team takes 3 magics? Or if building a CRM continuous improvement framework takes another 3 magics?
Wouldn’t you get a few magics? (And yes, by that I mean hiring the freelancer repeatedly for projects or a longer time period.) Or would you instead decide to go for a student worker for 700/month that can push copy-paste Excel reports around to give you 3 splits on your revenue data?
Pictured: a Super weapon
A freelancer is a professional learner. Someone with a growth mindset.
A data freelancer is someone who makes things better, regardless of the subject at hand. They can optimize the hell out of anything, given enough freedom to operate. They might just be the ones bringing you those 2x multipliers.
Once you see the ROI of hiring a data freelancer, you’ll understand why it makes sense to work with them for longer than just for a short project.
Misconception 4: ‘We need full-time help’
For some reason, many companies think hiring a freelancer means not getting full-time help. This is not true. You can get full-time help from a freelancer.
In Germany, there is a law to stop companies from hiring people as freelancers to save on social security cost. This law lists several criteria for when it’s illegal to hire a freelancer full-time, instead of making them a regular employee.
Despite this, it’s not uncommon to see a freelancer work for 18-month stretches with one client full-time, or for years part-time. All completely legal and without breaking any laws. The law can allow for this kind of thing. It’s a matter of doing your homework.
Such, full-time IS possible. Companies who insist it’s an obstacle, even though they know better, simply might want control over an employee’s time. A relationship driven by insecurity never turns out well in the long run.
A fear-driven client does not share values with a freelancer’s growth mindset. It’s OK to have either type of thinking, but one will move you ahead while the other will not. Not everybody wants a large business though, and there are many legit reasons to want a small operation.
Misconception 5: ‘We want someone focused on working with us, not distracted by other clients’
Many companies worry that a freelancer will have other clients next to them. They worry they won’t get their full attention and capability.
On first glance, this is an understandable worry. But there are two reasons why this is a judgment error.
Freelancers are less distracted than your employees
I worked in data as an employee for 5 years. I used to interview regularly because I believe this is a fantastic way to keep in touch with the market, technologies, the approaches other teams have, and also your financial worth as an employee.
Being hired as an employee in the startup scene often means lowball salaries. So of course, the offers I received were distracting me from my job. They made me wonder if my time would be spent better in other companies.
Don’t I get just as many offers as a freelancer? Indeed I do. But I worry less about those other offers.
I’m booked more than I can take at a competitive rate. I get to choose what I work on and I am 100% focused on it when doing it because it’s my choice to be there. I don’t want to be somewhere else, and the grass is not much greener on the other side. If it was, I’d already be there.
When you rather be elsewhere… Being a “normal distraction” doesn’t make it right.
Don’t worry that a freelancer is downsizing your time to pursue something else.
I would worry more about how focused your employees are day in and day out. A freelancer is capable of running a small business him- or herself and is accustomed to responsibility and accountability. The distractions are also smaller for a freelancer, because they are already doing what interests them.
You actually profit from a freelancer having several clients
Another reason why you don’t need to worry about a freelancer having other clients next to you is that you’ll actually majorly profit from that.
Every freelance professional can tell you how there are always synergies between projects. They learn something new at one project one day and implement it the next day with another client.
The fact that your potential freelancer has other clients next to you means not only that they are a skilled professional in demand, it also means they are learning much quicker.
And your company can definitely profit off of that knowledge!
Misconception 6: ‘We want to keep the knowledge in-house’
Losing the knowledge acquired during a project is another common reason for not hiring a freelancer. Companies worry that a freelancer will leave and no one will know what was learned.
This is a really faulty approach in any case.
First of all, there’s a lot of knowledge that doesn’t even need to be retained. Anything that doesn’t lead to action, isn’t useful. Research and apply the learnings, don’t catalog them away.
Second of all, it’s neither a freelancers’ nor an employees’ task to store knowledge. That’s what analysis and documentation repositories and automated workflows are for.
Using humans as process and technology repositories is simply a sign that your processes and knowledge are not clear enough to be expressed objectively.
Don’t use people to do the work of tools. Use them to drive improvement!
Storage is for pickles. Brains are for processing.
Freelancers are perfect for driving improvement
Most employees who stay in one role for a long time, stagnate. They don’t see new things and have no reason to improve beyond the demands of the role. They will retain knowledge, but not drive improvement.
Freelancers, on the other hand, are constantly in touch with the market. They are brought in to improve things. It’s their jam!
So if you use a data freelancer for R&D, you still retain the research – but save the time of discovering it yourself. In some cases, a freelancer might not even have to R&D your particular topic, as chances are they already bring a lot from former projects.
In conclusion: Don’t lose out on good talent
Are freelancers the perfect fit for any situation? Probably not.
But the contract type or common misconceptions about freelancers should never stop you from hiring good talent that is worth it for your company.
If at this point you’re still not sure if a data freelancer is the right choice for your particular case, don’t hesitate to drop me a line through my contact form. I’ll be happy to help.
The Produce Many Customers method. Actually, the (tech) Product, Marketing and Customer lifecycle management method. Give it whatever gimmickly name you will, but it’s the cornerstone to user base growth in a tech startup.
This method is particularly relevant for subscription models, but it applies just as well to e-commerce. It’s about leveraging your communication channels with the customer to acquire the market.
Now, it’s clear to everyone why Marketing is typically in the lead of any user acquisition strategy. However, it might not be clear to everyone what Product and CLM have to do with it.
How to acquire the market with Marketing without going broke?
tl;dr for profit: CAC <= Gross Profit (only sell at profit) tl;dr for growth: Profit after CAC = Constant (reinvest everything).
If your startup has much more investor money than it has scale, then you are probably not too worried about cost of customer acquisition (CAC). However, this is not the case for most startups, and their CAC becomes a limiting factor. Typically, you would budget a percentage of your margin to marketing to be able to make the sale.
For example, if you are selling a basket worth 80 with a 25% repeat purchase rate, your customer is worth 100. Assume goods cost of 40, and you will probably want your average CAC to be <= 60, so you are able to at least break even on every sale.
For a subscription example, assume you charge 10/month for services costing 4, for 10 months average lifespan (half churn at first month, the rest stay avg 19 months).
However, not all CAC is built equally and the average CAC hides a distribution. For the sake of example, let’s assume your CAC goes up by 1 with each new member and starts from 1 for the first member. You will only be able to acquire members profitably up to the terminal (max) CAC of 60 (60 members, of which the last bringing 0 profit). Usually, the slope gets very steep very fast when you have already reached most of your target market.
Now, if those members were more profitable, it might look different.
Your maximum profit is reached when your max(CAC) = CLV
How does product pay a role?
tl;dr: Customer success or CR increase resulting in small CLV increases leads to large member base increases.
Product is responsible for assisting to convert visitors and tentative customers into successful customers. In the context of subscriptions where the user has to get repeated value, or of repeat purchases where the customer comes back because he had a good experience, it is vital to consider customer success.
While specific to your product, do whatever it takes to ensure your customer gets value from the start. If you are running a subscription model, make sure your customers are using the product they paid for proficiently, and that they are fully onboarded. If you are selling goods, make sure the shopping experience is easy, the order tracked, communicated, delivered on time.
If we manage to get that repeat order rate from 25% to 38%, (or that first month retention rate from 50% to 56%, then we increase the CLV by approx 10%.
Now, your max profit is 2145, up 21% from where we started with 66 customers, up 10%. Or, if we are optimising only on customers, we would be able to reach a whopping 93 (+55%!!!) customers while keeping profit the same as in the first scenario.
Small improvements on product make all the difference. As long as the strategy is executed coherently, a small nudge from Product to increase the CLV by 10%, leveraged by Marketing, becomes a +55% avalanche of users.
In a subscription model, your total members is a measure of customers and lifetime duration. Such, in the example above, we lowered first month churn, increasing avg lifetime of the user from 10 months to 11. Such, our total members over time goes up an additional 10%, leading to a whopping +65% total member base increase.
In the wild, your cost curve will look different. If you have already reached most of your market, your cost curve is harsh. However, most startups are still startups because they have not reached the entire market, and such have milder CAC curves.
So how does CLM play a role?
tl;d: for profit More lifespan(+60%) = more CLV(+60%) = more money = more customers (+75%) = more money (+212%)
tl;dr for expansion: More lifespan(+60%) = more CLV(+60%) = more customers (+220%) = more user base (+460%)
CLM makes good members better. Customer lifecycle management, which is often reduced to CRM in e-commerce, can easily account for a +60% increase in your sales from your existing customers. A standard for e-commerce is likely closer to +25-40%, while for subscription models it usually starts at +50% (that’s why your phone company keeps making you those offers when you are on a prepaid card)
So let’s assume a +60% increase in customer lifetime, which leads to the same increase in CLV. How does this play out in our scenario? We can maximise our profit at 5523 (+212%), with 105(+75%) customers, or we can maximise our customers while keeping profit constant, reaching 192 (+220%) customers.
What does this look like for that subscription model? Well, increasing customer lifetime directly increases total user base. Adding to our previous scenario, we reach a total user base 5.6 times larger.
How to take advantage of both this and product, to bring them to the point where they win the market?
With data of course. You are unable to acquire accurately without visibility and clear attribution.
You need data to improve product – you are unable to optimise product without well designed experiments. (and most product managers already do it right)
You need data to improve CLM – you need it to understand your customers and to send them personalised relevant communication at the right time. You need it to measure the effect of what you do, to make sure you aren’t doing more harm than good.
In conclusion
To sum up, you leveraging CLM and Product together with Marketing allow you to leverage the same resources (assuming salaries are only a fraction of marketing budget) to achieve vastly different results.
In our example we can see how a coordinated strategy helps in reaching a significant 5.6x increase in total user base for the expansionist on a budget, or 3x profits and 1.75x members if optimising for profit. And how does this scenario work with salaries? well, assuming the above scenario is happening daily, you could easily give up some conversions in favour of profits to pay your staff.
The numbers above are more or less arbitrary chosen for the sake of easy representation. Your mileage may vary.
Due to technological progress in the last years, the success of a data project is less likely to hinge on tech, but much more likely to depend on deployment and adoption.
We measure success of a data project in terms of ROI. ROI happens when people use data to make data driven decisions. Such, we need to make sure not only that we have data, but also that this data is used. In most cases, data availability is not an issue, but even when it is, the reasons are often usage related.
A Data project manager, or data usage ambassador, or data application ambassador, is a person who ensures that there is consistency between data products built by the data team, and data consumption and adoption by the stakeholder teams.
Why do I need a Data project manager?
1. We already solved tech (mostly)
Having been in the data industry for some time, I have been witness to progressive change in our ability to integrate and prepare data. It used to be that most ETL was done with tools, and random snippets of various languages would calculate various reports.
Nowadays it’s easy sailing and clear sky. There’s a decently documented API for everything, often with a Python wrapper. There are managed big data solutions that make data size a challenge of the past for the typical business. There are multiple flavors of open source ETL frameworks and workflow engines that make logging, alerting and dependencies no longer a challenge.
Now we do everything with Python on Docker, and everything is abstracted away, allowing us to focus on the business logic. Connections, credentials, temporary file storage, logging … those used to be managed by hand. All the jobs are idempotent and the code is all deployed and tested with ease in the new CI-CD setup.
Yes, there are still many potholes on our way, and they will probably not go away soon. Of those, I would name test and QA environments. Currently most companies still struggle due to the complexity of maintaining either an expensive and slow clone, a selective load, or simulated (eg, Postgres to validate redshift code) or all of the above.
2. Using data proficiently is a complex process that requires follow-through.
The reason data usage is a challenge is because it’s a pull process. The team must need to use data in order to be motivated to use it proficiently.
Typically, most departments are too busy working to worry about working better. It takes a significant amount of time (hours) to familiarize yourself with a data model of a business. It takes understanding of how the real process works and at the same time how it maps into data.
This understanding is what makes someone data driven. This understanding takes hours to develop while actively working with the data and analyzing the contents. This cannot be learned in a quick meeting about the state of data or from a dashboard.
Such, it is ideal to have someone available to explain data usage and advocate possible use cases, while being focused on ROI rather than on the analysis itself, keeping implementation simple.
3. Bad usage breaks tech.
Vanity reports. Any report that does not serve the purpose of data driven optimization is out of scope for BI. However, it doesn’t mean we won’t build them.
When you as a manager check those reports, you get a dopamine hit and it makes you happy, so any yes-men will be quick to provide. Typically these reports are high maintenance due to hardcoding and provide low ROI. But we can’t blame you for asking, since you haven’t been educated on the topic.
Such, a neutral party can often distinguish trash from gold, and take the pressure off the developers.
The requester in this situation is not to blame, because the expectation of seeing a good number raises dopamine and can be addictive. Such, it can easily mislead the brain into thinking it’s doing something useful. Sadly, half the numbers will be below average and will send your dopamine down as well.
The developer just does what the requester asks, without arguing if it makes sense or not, since he often will not have full visibility over usage.
Such, a data project manager must navigate this minefield with care and spare the time of the developers, lest there is nothing left for useful things.
4. Follow-up
Once something is built, feedback is required so further development can happen as soon as possible. Ideally, while the topic is still fresh with the developer. A user who finds the report they asked for to not be useful will often not follow up, or put it off until they reallllly need it (when the boss asks for the numbers). At that point, it will be too late to deliver something good, and the cycle will repeat.
5. Because what you see is all there is. If you don’t know it exists, you cannot imagine using it.
Being data driven means making decisions based on data. It means optimizing business processes and answering business questions. Typically those questions have a lot of hidden complexity, just like a real business.
Mapping that question to data is never a 1:1 process, but rather an approximation that can be calculated with the data you have. For example: How would buying a competitor affect our acquisition? We look at their acquisition channels, we look at the intersection, we look at the impression share for shared ads. But we first need to know these kinds of things exist.
Lack of interest in the topic and expectation that ‘data will build it for you’ is what cost of opportunity is made of.
Such, the best ideas will come from those that are intimately familiar with both the data and the stakeholders, but of those the stakeholders hold the higher complexity.
6. Data doesn’t tell me what to do.
Data tells you where opportunities exist in a process, to give you visibility over mitigating those issues. Data does not tell you how to mitigate them. You can use data to A/B test hypotheses – but you have to have hypotheses, budget for testing, and cooperation from the teams.
Such, data departments by themselves are limited in scope if not empowered to research. Data is like R&D, not like magic mirror on the wall. It’s the trip, not the ticket.
You need a person to push these agendas along, in order to make progress. Most developers are surprisingly better at software development than they are at stakeholder management, so that’s why you need a people person.
7. Because Ambassador is too esoteric.
We need an ambassador to represent the connection between data availability and correct data usage.
We need an ambassador who must make sure what is built is used, or quickly adapted to fit the needs.
We need an ambassador that aligns management KPIs with process KPIs to give teams clear steering through numbers.
We need an ambassador to explain the importance of dimensional questions to identify issues, ownership over KPI to create hypotheses, and data for A/B testing.
Finally, we need those ambassadors to exist. Luckily they already do, in the form of a project manager. Let’s just slap Data on there and welcome them among our data crews. They will be invaluable to the company by optimizing the ROI on data itself.
Having worked across multiple teams in data and having exchanged experiences, I have come across some recurring themes that lead to companies being unhappy with their ROI on data. This is not about the problems of data teams but about the problems companies have regarding data usage.
1. Pain Point: No ROI reporting
Due to the diversity of marketing channels, as well as attribution tracking, it is difficult to always have a complete view of the spend and revenue of each marketing channel while maintaining high granularity in the data.
Maintaining granularity means to have your data in a still non-aggregated way, such as keyword level for SEA campaigns, billboard address level for out of home, etc.
Why do we need ROI reporting?
In an ideal world, deciding what we are willing to spend on advertising is determined by our return. You look at what money you make per sale, your margin. And that’s what you can afford to spend to make a sale. This means that anything up to your margin is an acceptable cost, as anything that you bring in at this value will make you some ( >0 ) money. This also means that if you spend more than what your margin is, you lose money. This part of auction theory.
So the key to understanding what we can afford to spend on marketing is understanding our margin.
For example, if our margin per sale is 20 euro, it would be silly to only spend 3 euro per sale in acquisition cost. Sure, get those customers that you can for 3 euro, but also get the ones that cost you 19.99, because some money is better than no money.
Enable your data team to do complete ROI reporting
In order to have proper attribution, we must design our data collection correctly – how you collect and attribute sales for online campaigns is different to how you collect data for emailing, post campaigns, flyers, or billboards.
Additionally, accurate records must be kept of cost data, to be able to understand what each sale is costing us in terms of cost of acquisition.
The solution to this is straightforward: Involve the data team from the start to have accurate tracking and attribution. Also support them in their data collection with good tracking solutions. Once the attribution is clear, cost is only a matter of importing data (integrations) you already have next to your other data.
Where possible, it’s important to automate data centralization through API integrations. This will keep your latency, time consumption, and error rates low, and your marketing employee satisfaction high.
2. Pain Point: No direction in the data team
Many data teams do not have initiative or direction. As a result, they are merely reacting to requests but never producing anything of their own initiative. There can be a couple of reasons for this.
Lack of empowerment
The first reason causing this data pain point is a lack of empowerment. The data team is treated as a service that supports other teams and is not given the chance to participate in key project planning.
This causes the data team to always be outside of the loop and late to the party. “Can you check the performance of this campaign you did?” – “Uhm, no, you did not track anything, you should have said something BEFORE starting!”
Also due to lack of empowerment and being kept outside of the loop, the data team becomes disconnected from the business. This makes them unable to spot opportunities for improvement.
A data team can only act on information they have, so bringing data in early on key projects is very relevant to the team’s potential to add value.
Lack of experience
The second reason causing this data pain point is a lack of experience. Let’s not put it on creativity, logical intelligence, etc. Most of these things are learned skills. The naked truth is that if you do not know of a technology that solves a problem, it is unlikely that you will manage to invent this technology from scratch. An experienced team has worked in many environments, on a variety of problems, and has an easy time re-using past ideas or learnings.
Giving your data team direction
If the lack of direction is due to lack of empowerment, start bringing your data team (representative) into your ‘heads meeting’, into key project planning at the conception phase, and into your budget planning.
Chances are that the team will have valuable input on all these things, and it will keep the team focused on optimizing the business, not on optimizing loading times into the DWH. That billboard campaign you wanna run? Data team probably has some ability to extrapolate location based ROI, design tracking, and offer follow up improvement hypotheses for future campaigns.
Hire expertise & invest in learning
If the lack of direction is due to the lack of experience, this is easy to solve. In the short term, bring in someone who has the experience to assess the situation and offer some actionables. You can find these people in the form of team managers in companies that attract talent, or in the form of freelancers.
In the long term, encourage your team to allocate time to learning. Encourage them to meet other BI people at meetups by counting it towards their work time. Create a learning/development plan for them and track their progress against it. Always pay for books and courses, and conferences if you can afford it.
3. Pain Point: Inconsistent metrics
Between home brewed department-only metrics and missing definitions, different numbers will tell different stories about the same events. This leads to a lot of back and forth and frustration between teams as they try to align, and can even lead to incorrect decisions being made. Measuring different things (customers vs orders) or at different times (ex: order date vs delivery date) is a typical example.
A good way to go about defining KPIs is to map the team’s work process and identify the measurable steps across the process. Decide what you want to optimize, and create the right metrics for it. Make sure they are applied consistently throughout the whole team.
If a KPI cannot be acted upon to change the performance of a process, then we should remove it, to avoid any confusion or dilution of information.
4. Pain Point: Show data in the right place
A typical pain is that many employees end up having to do a lot of manual work to be able to use the data available to them. Log in here, copy this, open that, paste here, sum up this…
In the spirit of automation and encouraging data consumption, we need to make this data available in the right place.
For data-driven decision making, the information needed has to be easily available to the decision maker.
What does this mean?
It means that if a salesman wants to call his least spending clients, this spend data should be available in his CRM, integrated into his workflow.
If a manager wants a quick peek at top level performance, it should be in an easily accessible place such as an email or dashboard.
If a customer support agent wants to know how much time he should allocate to this client, he needs to easily see his call/email queue size, to be able to prioritize. A convenient way to do this is a screen on the wall.
If a marketer needs to do budgeting, the performance and ROI information should be available to them in the same place where they calculate their budgets.
If we need to tell our operations to restock a product, the data should be accessible within their workflow of ordering.
You get the idea. Dashboards are not everything. The real value of data is when it becomes directly usable for your team.
5. Pain Point: The right time
A lot of companies focus on having real-time data available. But does this really make sense for you?
Can you change your actions in a matter of hours from observing an event? Typically, once an anomaly is noticed, it takes quite a while to analyse the cause, come up with a solution, and put it into practice. Unless you are delivering real-time relevant services like recommendations, bidding, perishable merchandise, stock-keeping, etc, it is unlikely anything you can do in a few hours would have any significant impact.
Averting disasters like “What if checkout doesn’t work and nobody notices for a day” is what monitoring and alerting is for. This is not the task of large data analytical pipelines.
The complexity of building data streaming pipelines and their associated maintenance cost are significantly higher than in a batch-jobs scenario. The latter are the ones where data is refreshed at regular intervals, like hourly, daily etc. In fact, in the wild, many near-real-time pipelines are just batch jobs that start up as soon as they have finished, to give the lowest possible latency.
Such, the question is reduced to how quickly you can react to data, which is company specific. If you cannot react in real time, don’t focus on having real-time data.
What typical pain points do you experience, and what works best for you to resolve them?
