DATA is the tool, CHANGE is the action

Most organisations fail to gain value from data, despite having heavily invested in data initiatives. But why?

In order to be impactful, a data strategy needs to integrate the resulting organisational change.

The value chain of data goes DATA > LEARNING > CHANGE > PROFIT

A data strategy that does not include applying the learnings to produce organisational (process) change, leaves the profitability of a data initiative to chance.

Many initiatives stop at the learning stage because they are only planned to go that far to begin with. They focus on the data side but not on the change management side. Companies hire data teams but do not give them the mandate or the help to manage change.

Is this happening to you?

Yes, probably.

A good way to tell if you are not managing change is to look at the result of your data initiatives. If you have funded a data initiative in your company but are only seeing limited value, take a look where the value occurs. Does it only occur in those areas where the “change management” part is not necessary or easier, such as CRM or acquisition? (There is usually no organisation in the way of changing a marketing campaign.)

Why does this happen?

Managers are not aware of the value data chain, so they expect the specialists to know.

The specialists do know, but the specialists’ field is data and not change management, so they can only plan so far.

What can you do about it?

Use change management. I will not go into how, but on a top level, for a change-managed data initiative


  • Identify the stakeholder and their goals, and define the business case.
  • Create a model for the change together with risks and benefits and track assumption model fit and progress against it, tune when needed.
  • Communicate informatively: Why, the benefits (for you, for us, for me), the dimensions (when, who, where, how much)
  • Staff process re-training plan. When process changes make sure everyone is on board in information and action.
  • Manage employees resistance to change by aligning towards a united company goal. Provide counselling for managing fear of change.

How to fail:

  • Wrong goal (build a data thing vs change the organisation)
  • Wrong stakeholder/sponsor
  • Start working on a solution before fully understanding the problem
  • Half bake solutions and don’t validate them
  • Ignore the people involved and don’t communicate upcoming changes
  • Undefined, unclear outcomes
  • Lacking dependency management (tracking those risks)

The takeaway?

Successful data to value implementations do not stop at the data team, but in fact require the organisation to change. Data initiatives need to consider not only the staffing required but also the organisational commitment required for successful change.

Google Analytics tracking to BigQuery now for free

First, let me highlight that this is the first time that tracking and granular data access have become so free and easy. This opens the door to data usage for the smaller companies that could not previously justify the 6 figure yearly tracking cost that the alternatives bring.


A new feature released by google now allows users to push data from GA to Bigquery by simply configuring it in their accounts.

Provided you have a new property type (can tell by DATA STREAMS being present), you can add a stream to bigquery.

Under “bigquery linking” select your BQ data set (some permissions may be needed) and data will start arriving after up to 24h (you can check if you tracking works OK by checking if you get data under the current traffic view in analytics).

Once the data reaches your database you will see it comes in a sharded structure.

The data in the tables is on event level and contains the usual fields. Here is an example


data types:


I assume you want to unnest this so you can analyse it easily. Unnesting the BQ data stucture is kind of like cross joining a sub table

from `myproject***.analytics_24******8.events_20201011`,
unnest(event_params) as event_params;


  1. Granular tracking as opposed to aggregated reports allows more easily to create hypotheses based on data and verify them.
  2. Arranging data for analysis. An analytic data model typically models the data after a process (such as traversing a conversion funnel). In GA, the data is modelled for a pre-defined set of analyses about the process of browsing! (not about your process of conversion). So in order to understand the user behaviour, the data needs to be unpacked and re-arranged into meaningful tables based on the events they contain.
  3. Linking to other data points: For example, by pushing and using a logged in user-id we are able to link browsing users across devices. Or we could link those purchased products to our product information management system.
  4. Input for CRM or data science (for the kind that is useful). Knowing the browsing sequences of users allows you to detect problem items, next best action (recommendation) conversion propensity, churn propensity (now you know if they read the cancellation terms), allowing you to create personalised CRM campaigns based on browsing behaviour.


Do you have any questions or other use case suggestions? Or do you need help with your current set-up ? Write me an email to adrian at

Pragmatism in Data

What is pragmatism, how to develop it, or how to look for it in talent?

Despite good academic qualifications of the workforce, many data initiatives fall short. This usually happens due to a lack of understanding about the operating context.

Pragmatism is about making decisions with ambiguous data by using contextual information.

Pragmatism in popular understanding

Pragmatism can mean multiple things, but in popular understanding it refers to having practical success in initiatives by keeping things “doable”.

It is often associated with keeping things simple, because simple things are more doable.

When we say someone is not pragmatic, we mean that they are doing something which in theory is doable, but in the given context is unlikely to succeed.

Basically, pragmatism means being able to understand your context and operate successfully within its bounds.

Why is pragmatism important in data?

Pragmatism concerns itself with the success of uncertain initiatives. Data concerns itself with the process of modelling and measuring uncertain initiatives.

Pragmatism and data are about making the best use of available information, in a largely uncertain universe.

Because of the large conceptual overlap of the two fields, data allows a pragmatist to shine, and pragmatism helps a data person to deliver ROI.

How do we get pragmatism in data?

Business pragmatism stems from the understanding of the business context. This means that the professionals that are expected to be pragmatic need to be well aware of the business context.

Pragmatism by team design

As with any other skill, pragmatism can be developed much better if your hires are constantly in touch with the context in which they operate.

As in management, contextual knowledge can be specialised or general.

For the specialised level, embedded analysts will be able to take pragmatic decisions in the context they are embedded in.

For the general level, data team managers should be closely integrated with the rest of management to understand department and company capabilities.

Pragmatism at hiring

To gauge the future pragmatism of your hire, you need to gauge the degree of contextual knowledge, the ability to apply it, and the ability to learn it in a new environment.

For measuring degree business understanding, ask them what your business is. They might not nail it, but they should be close to the actual business model.

For measuring the ability to apply contextual knowledge, ask them to come up with a solution to an actual business problem you have. Look for the usage of contextual knowledge and simplicity of solution – Dunning-Krueger effect dictates that someone who is not pragmatic will often underestimate efforts.

For measuring the demonstrated ability to learn a new environment, you can look at their work history to gauge if they are specialists or generalists. You can also ask about previous experiences or learning projects. You want someone who is curious, and who takes responsibility for the outcomes of their initiatives (rather than blaming it on a world they don’t understand).

The pragmatist’s tells

  • Cross domain experience
  • Business focus (studies or past experiences)
  • Previously worked closely with other teams
  • Experience in process improvement
  • Understands your business model and offers sensible solutions
  • Keeps things simple, sees the bigger picture, focuses on goals.

As a professional

To gain more understanding of the business, here are some of the things you can do:

  • Formal education
  • Gain experience by working in the sector you are optimising
  • Work closely with professional from other fields
  • Do generalist work instead of specialist work
  • Find mentorship

In conclusion

Not everybody in your team needs to be pragmatic. However, you need to have at least one person that can do “reality checks” of initiatives. This skill is something that can be created by either team formation, training, or hiring.

Survival of the (data) fittest

In an economy of plenty, many companies can maintain inefficiency and still thrive. However, being wasteful leads to business quickly losing your economic footing in a competitive market.

How does data help companies weather a crisis?


Optimising processes makes all the difference. And despite having data savvy operational employees, without the correct data, you are leaving a lot of money on the table.

Take the following example. By introducing a basic data driven optimisation we increase profit by 30% Numbers.

Such an optimisation is quite common to achieve without too much effort. Understanding how these optimisations compound, can easily lead to a 2-3x business increase.


Without data, you are ‘flying blind’. When change is occurring, it is important to understand what is happening, and to have the possibility to test new hypotheses. Without it, surviving change is a matter of blind luck.

When goals and performance are visible, we know which areas need extra help and which are doing just fine.


Transparency is key to agile processes and brings with it increased accountability. Additionally, focusing on results instead of process, gives employees the happiness of choice.

Transparency also helps solve issues that arise from mis-alignment of goals between teams, when their individual goals contradict each other, or when they are fighting over priority over other team’s tasks.

Why the data savvy will get stronger

In the context of economic recessions, companies who were barely breaking even before, will crumble leaving a large unserved gap in the market. This market will be up for grabs by those who can service it efficiently, leading to increasing opportunities for companies with data.

Is it too late?

It’s not too late. In my time doing digital transformations, building a data stack can take a few weeks to a couple of months, and teaching the mindset and skills to the company takes more or less depending on management involvement (sometimes years, sometimes days).

Stop wasting time doing things inefficiently, and start using data.

However, building a data stack can also take years, when the goal and vision are not clear. Starting a data initiative without a clear goal is a surefire way to waste more time and resources.

In Conclusion

Start applying data as soon as possible. There are many things that can be done, but even just getting started will enable a competitive advantage.

The illusion of data literacy

The biggest misunderstanding about data is the belief that people understand data.

The current talk of numbers in the context of coronavirus exposes this fact.

Currently there isn’t any data on infection rates in the general population (unbiased samples). It’s not there on mortality, or on magnitude of outcome improvement for patients receiving treatment either.

Despite of this lack of data, the majority of the narratives are discussing the infection rates of various countries and comparing their healthcare capacities.

Basically, the majority of the narrative is founded on an incorrect understanding of how the numbers map to an actual real life process of data collection.

How is it possible, that respectable, peer reviewed expert publications include such crass mistakes?

Regardless if the incorrect narratives were produced intentionally or not, the popularity of these headlines exposes the misunderstanding of data in the general population.

On the bright side, there are ongoing efforts to collect this unbiased data. Sadly, some of them were biased in their data collection.

What does this tell us about data literacy in companies?

The fact that all these analyses can just go through reveals that the vast majority of professionals, even those working closely with data, do not have the education necessary to draw correct conclusions.

Collecting and interpreting data is no easy feat. Given that most decisions require data, this means there is a lot of data work to be done.

Many companies are finding that even when 10% of their employees are working as data professionals, hiring more still brings benefits to the bottom line.

Empowering operational employees to take better decisions and automating tasks (and decisions), has the benefit of optimizing each process in depth. Such optimisations compound, often leading to significant impacts on profit.

Stop wasting time and get on the data train.

Sure, you can build up the data stack DIY, with time. But how long does it take you to iterate through all the things before you settle on an algorithmic automation? Do you have the talent to take you straight to the finish line?

In my experience, a natural grassroots evolution only gets you so far.

If you do not focus on data in time, you will get stuck managing departments that are created just to cope with insufficiencies in technology.

Additionally, rediscovering the wheel (the flat tyre) tends to be costly and will often discourage businesses from applying data further.

Data is not just an asset, but a frame of mind, with a focus on fact and efficiency.

How much data is enough? As much as is needed for the decisions you can make (but not more).

Start with management.

Data literacy for management is key for creating organisational change.

Get them a data coach that can assess their situation and teach them any missing skills. This can be someone internal or external, depending on the company structure. Internally, this role is often filled by the embedded analysts.

Data warehouses and literacy training for the operational employees.

Operational employees consume data differently than management. Here a detailed process view requires more data and different analysis skills.

With the help of a well designed data warehouse and a little tool training, we reduce the complexity of analysis to the level of a pivot table, facilitating what is known as self-service.

Data professionals

Data professionals are there to provide data automations and help with complex analyses, to avoid any wrong conclusions. Complex experiments can be notoriously tricky to perform correctly.

Data team

You can build your own data team, or hire one externally.

This data team helps businesses develop and implement their data strategy to ensure a good return on investment.

Data team provides data solutions that ensure good availability of data, and the simplicity that facilitates usage.

Get in touch at .

Data to value stream – the consumer side.

A data team turns data into insight. But to to turn that insight into value, you need consumers that are able to take the insight and make changes in their processes.

There is an entire workforce of data people ready to turn data into insight. But how that insight is used determines whether the company stands to gain any value from the initiative.

For example: let’s say that through experiment you find out that 20 percent of your current acquisitions are coming from referrals. If you are currently doing nothing to optimise referral rate, it is feasible to assume that you could be doing 2-5x better with some effort. But if you don’t have a marketing team that can create targeted referral campaigns, the insight adds no value to the company bottom line.

In the same way, if you hire a data team but do not ensure that the rest of the company is able to do something with the data, your data initiative will not pay off.

Why can’t consumers turn insight into value?

  • Lack of priority (motive)
  • Lack of resources (ability)
  • Lack of understanding (means)
  • Lack of experience (in avoiding common mistakes)

Lack of priority

This one is easy to mitigate. Make sure that company priorities are communicated objectively through goals. If the goal of each team is clearly communicated not just to them but also to the other teams, they can work together towards the same priorities.

I’ve sometimes heard managers complain that their managers don’t give them clear goals because they want to keep things flexible.

Flexibility through confusion is not a good tactic, and what helps instead is ensuring that the goals are clear, and that there will be new goals/targets/OKRs if things change.

Lack of resources

This one is as before, a matter of priorities. If there is a good understanding of goals and the impact that different initiatives bring against those goals, then budgeting accordingly should not be a problem.

However, if a company lacks the transparency and previous data-driven decision-making experience, then resources may end up diverted in a non-data driven way, and initiatives that could pay off are not implemented in favour of maintaining the status quo.

Transparency and goals (objective measurement against objectives) are foundational to resource allocation.

Think about it this way:

Suppose you sell a product for a profit of 100. Suppose some channels have a CAC of 30 and some have a CAC of 200. In a perfect world you would cut some budget on the 200 channel and assign it to the 30 channel.

However, reassigning a budget from one channel to another can have massive impact on a department. Such, there will be many excuses why data will not be used.

To prevent such cases you need to make sure that the performance of each channel is transparent to everyone, and that the goal is also openly and objectively stated.

Lack of understanding

A lack of understanding of how to implement a process change or how impactful a change could be, will cause insight to be discarded.

This one is more difficult to solve because one must know they should learn, be willing to learn and also know what to learn.

The willingness to learn typically comes from willingness to work towards targets in a more efficient way that will allow reaching them.

However, a professional who has never worked with data, might not see how it would help them. This is why typically the initiative, if it has not already come from the employee, has to come from their management. A workshop on how to do data driven optimisations focusing on their processes is a good starting point.

If the knowledge and goals exist but change isn’t happening, then the issue likely stems in how easy it is to change a process. Assigning clear ownership and giving coaching is a good way to make it easier to decide for a change.

Lack of experience

This one commonly happens when working with data, as data usage is rather new in most fields.

To solve a lack of experience without replacing talent, the focus should be on expediting learning, removing technical challenges and allowing the employees to focus on experimenting. Bringing in outside experience to offer some best practices will also help in avoiding common pitfalls.


Creating a data driven organisation is no easy feat, but it has been done successfully before. The recipe is invariably the same: Start with data driven management, because the change is systemic.

A workshop on the topic to your senior management paves the way to creating this culture.

Save yourself the years it would take this conclusion to trickle from the tears of your frustrated data team. Get in touch for training.

Hiring data people: how to interview and what to look for

Most companies that plateau early in their data initiatives do so because of a lack of understanding of who to hire, when and what for.

Data supports decisions. Start from the top.

Of course, but what does this mean for hiring data people?

It means that the company needs a goal against which performance of decisions can be measured objectively. The lack of such a goal means there is no objective use for data, as data is used to take optimal decisions for reaching a goal. Such, you simply cannot succeed at reaching a goal if that goal does not exist (or is not clear/explicit).

It also means that you need to start with the decision makers. Hiring or training your management to use data for driving decisions rather than for justifying them is essential, because if the consumer of data is unwilling to consume, there is nothing external forces can do about it (you cannot change the unwilling).

On to the team, why do we need them?

Hiring a non-specific “data person” is as random as seeing a Doctor of Philosophy about your lower back pain. They might be able to help you accept the pain, but that wasn’t why you called.

Once you have clear goals and managers that know how to overcome personal bias, they will tell you what they try to achieve. Different problems can require vastly different skillsets, so in order to judge fit, be specific during the interview and test for ability to solve the particular problem. Are you looking for someone to maintain web scrapers, or are you looking for a pragmatic analyst with good algorithmic knowledge? Or perhaps a database expert? If you don’t know, listen to the candidates – they will tell you if you ask.

The first data hire.

Disclaimer: Nobody’s perfect, and you should adapt your hiring requirements to what the market has to offer and the talent you can access. That being said…

Thinking short term is the wrong tactic here. There is little to be gained from hiring someone who can only solve your right-now problem, because that candidate won’t know how to deliver something that can be taken further. Experienced data professionals can smell a mess through the job description, and will not even apply if you started off with the wrong tech, causing further progress delays.

It is key to start with someone experienced and capable. Technical challenges that can typically be overcome by an experienced data engineer in a month or two (such as building a data warehouse) can bog down companies for years until they change their approach.

“Everything should be as simple as it can be, but not simpler”

– a scientist’s defense of art and knowledge. Attributed to Albert Einstein.

Key traits to look for:

  • Honesty and self awareness. Overcoming personal bias is a requirement to taking data driven decisions consistently.
  • Learner and teacher. Can this person learn something new for the interview? Can they teach you how to approach a problem you are facing? Do they show a history of learning?
  • Pragmatism: A marker of seniority is knowing what caliber guns to use.

What to avoid:

  • Confidence that is not backed up by learning. “When all you have is a hammer, the world is your nail”. They already know the best way, and incidentally it’s the only way they know.
  • Fast talkers. There are many frauds applying for management positions that can convince a manager. Repeat the question if you have to. Poke their analytical knowledge and don’t be easily satisfied – they are not too senior to answer a simple methodological question such as “how do you choose sample size” respectfully. (anecdote: worst answer I heard was a firm “30”, from a guy that had on his profile over 5 years of data leadership and consulting in corporations. He got angry when I asked him why 30 and not 3000.).

Don’t look at the wrong things.

Remember, data driven decisions are taken on objective measures. Someone’s shyness or clothing style is not an objective measure of their job potential. I have witnessed an excellent candidate (young math phd applying as data scientist) get rejected for being shy. Don’t let the bigotry of your interviewer be a selection criteria. Value objective reason above subjective opinion. (be data driven…)

Another common misconception is the belief that everybody needs to like the person. B players will rarely accept an A player.

Transparency on process performance can also make a lot of people uncomfortable.

The data engineer

Base skills to look for: Databases, programming, grit, learning

A data engineer is someone who does not give up until the problem is solved.

-grit, the common denominator of successful data engineers

Basically, data engineers typically have a couple of duties:
1. Keep stuff running
2. Make more stuff run

The keeping stuff running needs knowledge of some best practices and experience.

The making of more stuff run requires a good learning ability, because you are always interfacing with new and ever changing services.

Grit is also key because with the exception of the major vendors apis, API documentations are different from reality, so persisting until stuff works (regardless of existing docs, etc) is just necessary.

Who can fill the role?

With grit and learning, anyone can make it. But the hire-able candidate will likely need to have foundational understanding of databases and the concepts of programming. Specific languages are not required (but desired), because like anything else they can be learned.

A data engineer who is not a good learner will struggle.

There are some cases that require very specific qualifications (particular technologies, CI), but most will get by fine on python and sql.

If they are building the data warehouse, make sure they understand the basics of dimensional modelling. (the key word here is ‘business intelligence’, as the domain includes a paradigm about how to prepare data for consumption)

The analyst

Different problems require different skill sets.

“Business understanding”

First, to define the most important skill, business understanding. In the context of data, this means

  • Knowing the objectives of the business (like profit, growth, specific objective measures, or any specific limitations)
  • Ability to map data point to real life events, understanding how the process is tracked in the data and how altering the process could impact the data.
  • Ability to estimate effort and potential result of various initiatives and choosing according to added business value (pragmatism).

Reporting analysts

Sometimes called a business analyst. A reporting analyst is someone who produces reports ad-hoc. This is typically a transitional position for junior analysts, but some find their passion in it and specialise.

Reporting analysts must understand the business and be able to map a data point to a process. This is the absolute minimum starting point. Useful skills are sql, tool usage, and programming. Also useful is objective decisonmaking, but since they will not be making the decisions, it may backfire and hurt their ambition. This is often what triggers them to move into areas where they can take more ownership.

You will not be interviewing them for their current skill, but for their business understanding, interest and potential.

Data analysts

A data analyst can utilise a variety of tools and techniques to answer more complex questions and maybe run some simulations or predictions. They don’t necessarily have a formal quantitative background, but they are more experienced in generalising and finding simple answers to complex questions.

They typically want to make positive changes to processes in order to optimise things, and will provide the necessary analyses to back things up.

Necessary skills: business understanding, programming, analyst experience.

Nice to haves: Statistics

Data scientists

A data scientist possess knowledge of algorithms and they use it to bring about business improvements either through analyses, or through data products such as recommenders, similarity searches etc.

They should have little trouble programming, and often have an academic background.

There are many ‘wannabes’ in the applications, so make sure they actually possess the hard skills. But that’s just a pre-requisite.

The most important thing to look for is pragmatism. I like to use the following task as an example:
1. Download today’s temperature for our city from a weather api (eg,
2. How would you approach forecasting tomorrow’s temperature? Optionally implement and evaluate.

A pragmatic, simple answer is to download that too from the weather api, as the prediction is done by qualified meteorologists and easily available.

A pragmatic but less senior answer would be something like taking the same temperature as yesterday, as you will be right most of the times.

A less than pragmatic answer includes complex algorithms used to produce a prediction based on past data.

You obviously should not exclude someone on a single question, so get creative and come up with something perhaps more relevant to your business. You are looking to filter candidates in, so make sure that your testing approach allows for alternatives in case someone feels put on the spot.

But unless the role is on the bleeding edge of AI, or working on an already optimised algorithm, the data scientist will not need to know all the latest algorithms. Pareto principle, the basics will go a long way.

So who do you need to hire?

The difficulty about knowing how to build a data team is that you need to know ahead of time what can be done with data and what can be done within a company.

“What you see is all there is”

– the reason you are still not using data (Daniel Kahneman, Thinking, fast and slow)

Knowing what can be done with data requires some degree of data experience.

Knowing what can be done in the company requires open communication with management.

Often, the company is not able to consume data at the rate that the data team produces.

This usually happens because the there is a gap of experience on the consumer side in how to transform insight into value.

This is something that can be remedied with training (data driven decision making workshop kind, not 30 minutes of tool usage).

If the consumer however is just not motivated to take action based on data, then their management needs to set better goals and expectations.

In both of these cases (lack of training or motivation), the change cannot come from the data team, because they cannot teach the unwilling.

Such, it is key to start with an experienced data person who has previously seen the applications of data, and work closely with them to implement changes in the organisation. Otherwise, they will be inefficient on their own.

With this experienced data person, and with knowledge of the current and future limits of your company’s ability to consume data, you are able to plan the data team roles as well.

Once you have the key problem areas identified, you can plan the roles that you need to fill for each case. Sometimes, it will be machine learning or engineering heavy, sometimes it will be more about teaching, mentorship and communication.

Do you need all data team members to drive organisational change?

In fact no, because they cannot drive organisational change. Organisational change is driven by management. The data team’s role is to support data driven organisational change.


If you need help with your team creation and candidate assessment process, do not hesitate to get in touch.

Preventing churn with data. A practical guide.

Any successful business must have a working customer acquisition method. But the ones that stay successful are the ones that keep their customers for the long run.

Despite this fact, many succesful companies do not have a customer retention strategy.

Churn is especially relevant for businesses such as services, subscription models, or for lifestyle choices that involve ongoing expenses (diet, fashion, pets etc), for whom the customer lifetime value happens over longer stretches of time.

Targeting churn

There are many reasons people could churn.

  • They did not see the value of a repeat purchase.
  • They could be disengaged.
  • They might have found cheaper alternatives.
  • An event changed their ability to use your product/service
  • Service disruption.

And just as many things we could do about it.

  • We could do customer success campaigns
  • We could do engagement campaigns (or
  • We could make competitively discounted offers.
  • We could recommend a new product/service fitted to their needs
  • We can apologise with a small gift for service disruptions.

But what happens if we give the wrong message to the wrong customer? We would fail at achieving our communication goal and thus waste the opportunity, your time and theirs. Give them a wrong offer, and your communication is at best costly and ineffective. At worst, contacting low-risk users it can lead to more undesirable behaviours such as more churn (if it ain’t broke, don’t fix it)

It is only when our action solves a need of the customer, that they are likely to respond positively to it.

Such, understanding not only the risk to churn but also the reason is the only way to be effective at reducing it.

Collecting the data

Collect the likely churn reasons: Ask your customers, ask your customer support team, ask your common sense. It’s usually one or a handful of reasons that end up covering most of the cases. Once you have this understanding, you are able to define actual datapoints that may be useful in a prediction.

For instance, your data points could look like :

Customer might be price sensitive and looking for a cheaper alternative:

  • % discounted purchase ratio
  • % bottom shelf purchase ratio
  • Bargain/sales participation rate
  • average basket value

Customer might be unsuccessful at extracting value from your proposition:

  • Customer success funnel percent complete
  • Last action (eg, abandoned cart, payment due, purchase, return, cancellation, customer support call)

Customer is disengaged from your proposition:

  • Last action days ago
  • avg time between orders
  • order cycles missed (time since last order / avg time between orders)
  • Subscription cycles unused
  • Depth of action (some actions are more relevant, like referring a friend or sharing your posts)

Customer needs something else now.

  • As this is an external reason for a customer’s need changing (eg, pregnancy ending, moving city), there is probably not much you can do about it. But you can try leading them to a service that suits their new needs, if applicable.

Customer is dissatisfied:

  • Recent actions (eg, returns, support calls, complaints)
  • Customer feedback (calls, complaints, comments)
  • Return rate (% products returned after purchase)
  • Usage rate (where measurable)
  • NPS survey
  • User received crappy products (poor general feedback)
  • Was active user during service outage

After checking the relationship between the features used for prediction and the churn events, predict the likelyhood or churn and the reason. (or let us do it for you)

Assess the business impact

Not all types of churn can be prevented, so focus on the main preventable reasons and create targeted campaigns. Before going overboard, make sure that the expected improvement is large enough to justify the cost of taking action (enough users, enough value).

Make sure you measure performance against your goal correctly.

But they churn even faster!??

– my reaction to a churn prevention campaign after a few days

Users with a high risk of churn are like mimosas – if you have any touchpoints, they might cancel right away.

However, silence is not the answer. If you do nothing, they all cancel. If you reach out, Some will cancel sooner, but some will come back to being an active customer. But 10% of users cancelling 10% sooner means a 1% CLV loss, while a 10% reactivation rate with a 50% repurchase value is a 5% CLV gain.

Despite some of them churning faster, the overall cohort will churn less.

… so actually we reduce churn

Measure what you really care. In some cases companies want to maximize profitability, in which case CLV optimisation would be crucial. In other cases, it’s a market share grab and all that matters are subscribed customers. Make sure your campaigns are performing against goals, and don’t worry about non-goals.

Why do this at all?

On paper it might look like a lot of effort for a small CLV boost, but how does it look in practice? In practice, we have multiple processes we optimise with data, all with compounding results.

Assume a 20% CLV increase due to targeted CRM treatments.

This 20% CLV increase can translate into +50% or more max CAC (what you can spend on a customer while still being profitable, assuming your CAC is 30-60% of Revenue)

This max CAC change now allows you to purchase another cohort of users (at a price between previous maxCAC and current maxCAC), say another 15%.

But your current users being more loyal also translates to an increased (longer-living) user base, say 15%

And all the extra profitable users, and all the extra profit from existing users can easily translate to another 30-50% to your revenues.

So while at first it may seem small, from a modest CRM performance of +20% we end up with +30% user base and +40% profit. This is the difference between a business slowly dwindling, or winning the market. And all the numbers above are rather conservative.


Of course, if you need help with any of the above, don’t hesitate to get in touch.

A checklist for data driven CRM

Any business owner who wants their enterprise to be profitable needs to make sure their CRM team works the best way possible. The only way to make sure your CRM team can thrive and hugely increase profitability for your business is to make sure they are using data.

Essentially, CRM is about reaching out to the customer with various communication and sales campaigns, with the goal of increasing their lifetime value. So where does data fit into it?

Data driven campaign optimisation

The first step to optimise your CRM with data is data driven campaign optimisation.

Data driven campaign optimisation refers to always measuring campaign performance and testing as many improvement hypotheses as possible. Before doing anything else, make sure you have the points below checked, because without them your strategy is to try and pray.

The checklist:

  • You track your campaign performance against campaign goals (eg, purchase)
  • You check necessary sample sizes before emailing, and perform as many relevant experiments as the data volume allows
  • You verify the statistical validity of your test results. (check your p values)
  • You perform as many A/B tests as possible with your volume of data

Data driven segmentation

Once you have the basics of data driven campaign optimisation nailed down, it’s time to look at data driven segmentation.

Segmentation refers to grouping users together by specific attributes (gender, age, marketing channel, purchased items etc). Such, it is vital to keeping the number of campaigns manageable, but still relevant for the user.

The checklist:

  • You know the customer acquisition details (acquisition channel, time, season)
  • You know the customer behaviour (currently active/inactive, purchases, interactions etc)
  • You leverage unsupervised learning (data science) to classify behavioural profiles in the data and group users in manageable similar clusters

Data driven personalisation

Data driven personalisation is where data science becomes necessary. Creating relevant, specialised, personalised communications for your users is the best way to optimise goals such as purchases, reactivation, referral etc. All of the above require some form of statistics or data science.

The checklist:

  • You leverage predictions of the conversion reasons
  • You use the most likely to convert recommendation for the user.
  • You tackle churn by identifying churn risk and reasons of each individual user
  • You leverage the performance data of your campaigns correlated with the profiled data to identify which campaigns work best for which users

How to get there

To check off the entire checklist you will not only need a data driven CRM team, but also a data team.

At minimum, you will want to build some automation pipelines to provide a customer view into your CRM system.

But in order to leverage the majority of the advantages in the long run, you will need a data warehouse and a business savvy data scientist to run the numbers on the experiments and provide valuable support in CLV optimisation.

However, improving CRM campaigns can start well before having everything. In fact, the sooner you start, the sooner you can start testing improvement hypotheses. And let me know if you need a hand.