Where is the customer base expected to be in 3, 6 or 12 months?
For companies with customer-level transaction data
See where your customer base is heading, and whether today’s actions are changing its economic future.
Customer Economics recognises patterns in transaction data, the behavioural record of the market your company has captured, to forecast expected customer transitions and future gross profit. It combines this with market demand across category customers and potential category customers. Actual development can then be compared with the baseline, showing where value is strengthening, eroding or changing faster than expected.
THE EVIDENCEFROM PATTERN TO VALUE
01
Transaction patternswhat customers have actually done
02
Expected baselinehow the captured customer base is likely to develop
03
Market demandwhy customers choose you, competitors or alternatives
04
Baseline versus outcomewhether actual development is better or worse than expected
Transaction data shows what happened. Market demand helps explain why. Together they update the view of future gross profit through money, timing and risk.
01 / RECOGNISE THE FIT
Is this relevant to your company?
Having transaction data is not enough. The real opportunity appears when the company can follow customer behaviour over time and connect that movement to economics.
01
Customers can be recognised across multiple transactions.
02
Repeat purchases, renewals or an ongoing relationship matter.
03
There is enough history to see how customer behaviour changes.
04
Management acts on acquisition, frequency, contribution or survival.
COMMON STARTING POINT
Transaction data is already used to report the past, segment customers or target activity, but not to forecast whether the customer base is becoming economically stronger.
02 / THE MANAGEMENT BLIND SPOT
Revenue tells you where the business arrived. It does not show what kind of customer base today’s actions are producing.
WHAT REPORTING SHOWS
Sales, customers, campaigns and channels.
WHAT MANAGEMENT STILL NEEDS TO SEE
Whether the customer base is strengthening, eroding or becoming more concentrated and risky, and whether its development differs from what was expected.
03 / THE EXPECTED BASELINE
The missing reference point is what would probably have happened next.
Looking at change between two periods is not enough. Customer numbers, revenue and purchasing behaviour would have changed even without a new management initiative.
Pattern recognition turns customer histories into probabilities of future purchases, timing, contribution and survival. Aggregated across the customer base, these probabilities create an expected economic trajectory.
#REFERENCE CYCLESELECT A STEP TO EXPAND
Customer histories are grouped by the situations and behaviours that matter economically. The analysis learns how comparable customers previously moved between states, including when they bought again, how much they contributed and how long they remained active. These patterns provide the evidence for estimating what may happen next.
The historical patterns are translated into probabilities for each customer's future purchases, timing, contribution and survival. Aggregated across the customer base, they create a forecast of likely customer transitions and future gross profit if established patterns continue. This is the reference trajectory against which later development can be assessed.
The company records the actions intended to change acquisition, frequency, contribution or survival, together with when they begin and which customers they may affect. Choosing to maintain the current approach is also an action. Making actions explicit allows management to examine whether the customer base subsequently develops differently from the baseline.
New transaction data reveals what customers actually did after the baseline was established. The system observes who entered, purchased again, increased or reduced their contribution, became inactive or returned. This turns the next reporting period into evidence about how the economic structure of the customer base is changing.
Actual customer transitions are compared with the transitions that were expected over the same period. The comparison shows where development is stronger or weaker than anticipated, how large the divergence is and which customer movements explain it. The result indicates whether the economic trajectory is changing, but does not by itself prove that a specific action caused the difference.
The latest customer outcomes become part of the evidence used to forecast the next period. Probabilities and expected future gross profit are refreshed to reflect what has actually changed in the customer base. Management therefore gets a continuously updated view of money, timing and risk rather than a forecast that becomes obsolete after it is produced.
IMPORTANT DISTINCTION
A baseline helps assess whether actions are changing the expected trajectory. It does not, by itself, prove that a specific action caused the difference.
04 / WHAT CUSTOMER ECONOMICS CHANGES
Four levers. One economic objective.
Customer Economics starts with movement in the customer base, not with channels, campaigns or dashboards. Each movement is translated into expected future gross profit.
#LEVERWHAT CHANGESECONOMIC CONSEQUENCE
01AcquireWho enters the customer baseQuality of future value
02FrequencyHow often customers transactNumber and timing of future transactions
03ContributionWhat each transaction contributesGross profit per transaction
04SurvivalHow long customers remain productiveDuration of future value creation
EXPECTED FUTURE GROSS PROFIT / MONEY · TIMING · RISK
05 / FROM PREDICTION TO ACTION
A forecast matters only when it changes a decision.
The expected baseline describes what is likely if no new action is taken. Management then makes a direction bet: a declared hypothesis about which customer movement should change, why an action could influence it and what economics are at stake.
The action may come from product, pricing, marketing, sales or service. Replay closes the loop by comparing the realised outcome with the baseline, then deciding whether to scale, redesign or stop.
01
Expected baseline
What happens if we continue as we are?
02
Direction bet
What movement are we trying to change?
03
Action
What do we actually do?
04
Replay
What happened relative to baseline?
06 / CUSTOMER CREATION, NOT TRANSACTION CREATION
A first order is evidence, not yet a relationship.
A first transaction proves that a purchase occurred. It does not, by itself, prove that a valuable customer relationship has been created.
A first order may become a productive path, create valid one-time value or end in an early exit. One-time value can be perfectly sound economics. It should simply not be confused with creation of a productive customer relationship.
Customer creation is therefore an evolving economic pathway, not a label assigned automatically at first purchase.
01Productive path
Value continues through repeat purchasing, contribution and survival.
02One-time value
The first order creates sufficient value, but no ongoing relationship follows.
03Early exit
The economics end before a productive relationship develops.
THE MANAGEMENT QUESTION
The question is not only how many first orders were generated. It is what kind of future those first orders are creating.
07 / BEYOND THE CUSTOMER BASE
Transaction data shows the market you have captured. Market demand shows the customers you could still win.
Customer-level transactions provide a behavioural record of the customers already inside your business. They cannot, on their own, explain why category buyers choose someone else, which demand remains uncaptured or who may enter the category next.
Customer Economics connects this internal evidence with a market demand view: your customer base, category customers you do not currently serve and potential customers who may buy in the future. Acquisition then becomes an economic question about which future customers to win, not simply how many.
The market view is built through structured, AI-led conversations. Rather than asking mainly what people think they might do, the conversation starts with a recent category occasion and reconstructs what actually happened: what triggered it, which options and channels shaped the journey, what was chosen, rejected or postponed, and why.
CONNECTING WHAT AND WHY
This shows why your brand won, why a competitor or adjacent category won instead, and where demand remained uncaptured. Combined with transaction data, it connects the what inside your business with the why across the market.
08 / WHAT IT COULD HELP MANAGEMENT SEE
Questions that ordinary reporting leaves unanswered.
The point is not to produce another prediction score. It is to give management a forward view of the customer base and detect meaningful movement early enough to act.
Which customer transitions are most likely to determine that trajectory?
Is future gross profit expected to compound or erode?
Is actual development running above or below the expected baseline?
Where do early deviations signal an emerging opportunity or risk?
How has the economic forecast changed since management acted?
09 / THE CONVERSATION
A working conversation, not a conventional product demonstration.
I am developing Customer Economics into a practical management approach and speaking with companies that can follow customer behaviour through transaction data. I would like to explain the thinking, understand how you work today and explore where the approach could create value.
FORMAT45 to 60 minutes
01
A concise introduction to Customer Economics.
02
Questions about how your company uses transaction data today.
03
A discussion about where greater economic visibility could matter.
NO PREPARATION REQUIRED
No customer data needs to be shared beforehand.
10 / ABOUT
MARTIN HELLGREN
Built at the intersection of business, marketing and technology.
Martin Hellgren has founded and led two companies and worked across general management, technology, performance marketing and market research.
That experience has given him a broad view of the questions businesses face. Not only how marketing performs, but how customer behaviour, brand strength, commercial actions and technology combine to create economic value over time.
His background spans company building, country management for an IT consultancy, growth strategy, digital marketing, brand tracking, campaign evaluation and marketing mix modelling. The common thread is turning complex information into something management can understand, act on and measure.
Customer Economics brings those perspectives together. It connects what customers actually do with the wider market they could come from, then uses technology to identify patterns, establish credible baselines and evaluate whether management actions are building a stronger and more valuable customer base.
AN OPEN INVITATION