Bring the customer intelligence you have already built
This pill is downstream of the customer-intelligence work. The minimum run requires the consolidated SPO Customer Portfolio plus Customer ROI. RFM Detail is optional behavioural enrichment. No other source file is required to run the laboratory.
Want to understand the model first?
Load the complete Don Espadín dataset. It fills the same inputs a student would provide and immediately shows the final Output, including the R View.
The example never loads automatically.
SPO Customer Portfolio
Consolidated customer intelligence: SPO, RFM/CC, NPS, ABCD, margin, frequency, repeat rate and next-best product.
Expected model:DOC_ROI_SPO_CUSTOMER_PORTFOLIO_*.csv · matched by customer_key.Customer ROI
ROI, realised revenue, attributable cost, contribution and other customer-level economic evidence.
Expected model:DOC_ROI_CUSTOMER_ROI_BUSINESS_INTELLIGENCE.csv with customer_key, customer_roi, revenue, cost and contribution.RFM Detail
Transaction recency, frequency and monetary evidence to strengthen the transparent churn working proxy.
Expected model:DOC_ROI_RFM_SPO_READY_*.csv, matched by customer_key.Go back to the source pill and generate the CSV
The minimum input is simple: SPO Customer Portfolio + Customer ROI. Dynamic RFM is optional because the SPO portfolio already carries RFM/CC, NPS and ABCD evidence. Use these calls to action only when you need to rebuild or enrich the source data.
Build customer behaviour, Recency, Frequency, Monetary and Cognitive Category.
Open Dynamic RFM ↗Relationship and satisfaction evidence is already expected inside the SPO portfolio.
Integrated in SPO inputProduct/service relevance is already expected inside the SPO portfolio.
Integrated in SPO inputGenerate the consolidated CC × ABCD × NPS customer portfolio used as the intelligence hub.
Get SPO CSV ↗Generate customer-level ROI, revenue, cost and contribution evidence.
Get Customer ROI CSV ↗Set the economic frame before asking the model for a strategy
These are explicit working assumptions. The learner should understand what each one changes before generating the Output.
A · Economic frame
Defines the hurdle rate, time horizon and reporting currency.
WACC means Weighted Average Cost of Capital: the blended cost of equity and debt used to finance the business. In the KAI·ROI reading, CEᵢ = (ROIᵢ − WACCₜ) / WACCₜ. Therefore ROI above WACC produces positive relative Customer Equity; ROI below WACC enters the value-at-risk zone.
WACC = (E/(D+E))·Re + (D/(D+E))·Rd·(1−Tc)Periodicity matters: this field is annual. ROI and WACC must be interpreted on a comparable time basis before making a financial conclusion.
B · Portfolio Health Γ
Balances expected future value and expected retention against portfolio references.
Γ = wLTV·(LTV/LTVref) + wRET·((1−Churn)/(1−Churnref)). Γ = 1 is neutral versus the reference; Γ > 1 indicates healthier future value; Γ < 1 indicates deterioration. If references are blank, the laboratory uses the portfolio median. Unknown is never converted into zero.
C · Loyalty decision rules
Controls how much value can be reinvested and how the graph identifies extraordinary performance.
DPMO = defects per million opportunities. The values shown use the conventional long-term Six Sigma reference with the 1.5σ shift. Here they are a training anchor for tolerance and process quality. In this laboratory, a defect is explicitly an ROI operational defect: ROI below WACC. The customer is never the defect; the economic/marketing operation is what failed to clear the hurdle.
Portfolio threshold: calculated after customer ROI is loaded.
Churn and LTV are transparent operational estimates unless observed or calibrated outcomes are supplied. The selected σ rule only defines the visual extraordinary-performance boundary in the R View; it does not modify the official KAI·ROI formula.
D · Business loyalty objectives
Select the economic loyalty levers that are genuinely available in your business. The model will only recommend objectives you enable here.
These are business decisions, not an extra data source. They define which loyalty objectives the strategy is allowed to activate.
Your Loyalty Strategy
The Output converts customer evidence into a strategy, an economic loyalty objective and a concrete action for every customer.
No Output yet
Load your required files in Data Load, configure the model and press Generate Output. Or use the Don Espadín example to see the complete learning model.
See customer value and economic tolerance in one view
The chart is always the first analytical Output. Red = ROI below WACC; blue = healthy/development; green = extraordinary Customer Equity. Point symbol identifies SPO.
What should we do with this portfolio?
Strategy first: indicators explain the recommendation afterwards.
Evidence behind the decision
These measures support the strategic choice; they are not the strategic choice itself.
Which economic loyalty objective should dominate each SPO?
The SPO becomes a management policy: identify the dominant objective and the activation direction for the future Customer Area.
From portfolio diagnosis to economic activation
Revenue development and economic efficiency are the bridge between diagnosis and the concrete loyalty action.
Revenue development & preservation
Economic efficiency & future value
One strategy and one next action for every customer
This is the operational handoff: each customer is already oriented toward the economic loyalty strategy to be managed next.
| Customer | SPO | Objective | Strategy | Churn | LTV | Γ | Zone |
|---|---|---|---|---|---|---|---|
| No customer strategy calculated yet. | |||||||
Select a customer
Customer evidence will appear here.
Select a customer to inspect the decision.
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Take the customer strategy into the Loyalty Programme / Customer Area stage
Export the customer-level decisions to design journeys, benefits, triggers and personalised value propositions.