A local consumer business wanted to grow across stores and digital channels without mistaking channel movement for incremental value. Marketways found that customer journeys, assortment, promotion and loyalty decisions were interacting across the same demand. We structured the engagement to distinguish substitution from growth and connect customer action to portfolio economics.
The engagement objective
Through the initial discovery, Marketways defined the objective: coordinate customers, channels, assortment and marketing around incremental value.
How Marketways translated the problem
We began with a practical question: How do digital and physical channels change total customer behaviour? The first analysis used customer identity, transactions, clickstream, store visits, media and returns.
That evidence could not be read in isolation. Customers move between discovery, purchase, fulfilment and returns across channels. Segments must be stable, understandable and actionable rather than statistically interesting alone.
Digital growth could reflect customers moving between channels rather than new demand. We distinguished substitution from incremental value before attributing performance to the channel intervention.
We did not judge each component by its isolated KPI. We examined how people, assets, decisions and constraints affected one another, then used the evidence to test whether an apparent improvement would strengthen the complete system or merely move cost, pressure or risk elsewhere.
How the engagement developed
The initial work on omnichannel customer and cannibalisation analysis exposed dependencies with customer segmentation, loyalty and churn, assortment, price and pack architecture, promotion and media incrementality. Treating them as separate recommendations would have left the operating trade-offs unresolved.
- Omnichannel customer and cannibalisation analysis: Measure channel contribution and design coordinated journeys.
- Customer segmentation, loyalty and churn: Design relevant service, product and retention choices.
- Assortment, price and pack architecture: Improve customer choice and portfolio economics together.
- Promotion and media incrementality: Shift spend towards interventions that create profitable behaviour.
Evidence we examined
- Customer identity, transactions, clickstream, store visits, media and returns.
- Transactions, products, channels, responses, complaints and customer attributes.
- SKU sales, price, promotion, availability, customer and competitor data.
- Exposure, sales, price, promotion, inventory, customer and market context.
Industry conditions we accounted for
- Customers move between discovery, purchase, fulfilment and returns across channels.
- Segments must be stable, understandable and actionable rather than statistically interesting alone.
- Observed sales reflect availability, placement and promotion as well as preference.
- Promotion timing, seasonality, competitor activity and stock availability confound simple comparisons.
How our engagement contributed to business impact
We connected every method to a decision and a business measure. The organisation could assess the engagement through operating results as well as model performance.
- Omnichannel customer and cannibalisation analysis
- Method: Statistics & Econometrics, Machine Learning & Predictive Analytics.
- Evidence: Customer identity, transactions, clickstream, store visits, media and returns.
- Decision supported: Measure channel contribution and design coordinated journeys.
- Impact measure: Incremental sales, customer value, fulfilment and marketing efficiency.
- Customer segmentation, loyalty and churn
- Method: Machine Learning & Predictive Analytics, Market, Customer & Behavioural Analytics.
- Evidence: Transactions, products, channels, responses, complaints and customer attributes.
- Decision supported: Design relevant service, product and retention choices.
- Impact measure: Incremental retention, share of wallet, response and segment actionability.
- Assortment, price and pack architecture
- Method: Statistics & Econometrics, Market, Customer & Behavioural Analytics.
- Evidence: SKU sales, price, promotion, availability, customer and competitor data.
- Decision supported: Improve customer choice and portfolio economics together.
- Impact measure: Incremental margin, substitution, availability and customer value.
- Promotion and media incrementality
- Method: Statistics & Econometrics, Research & Evidence Collection.
- Evidence: Exposure, sales, price, promotion, inventory, customer and market context.
- Decision supported: Shift spend towards interventions that create profitable behaviour.
- Impact measure: Incremental margin, new buyers, repeat and payback.
Implementation
The engagement was structured as diagnostic followed by controlled commercial trials. We connected the analysis to the decisions, operating constraints and measures that the organisation would continue to use.
How success was assessed
The overall assessment considered incremental margin, customer value and channel economics. The supporting measures included:
- Incremental sales, customer value, fulfilment and marketing efficiency.
- Incremental retention, share of wallet, response and segment actionability.
- Incremental margin, substitution, availability and customer value.
- Incremental margin, new buyers, repeat and payback.
Services and methods used
Services: Operational Performance Diagnostic, Customer Satisfaction & Experience Research, Product & Concept Testing, Decision Assurance, Sales Forecasting & Demand Planning.
Methods: Statistics & Econometrics, Machine Learning & Predictive Analytics, Market, Customer & Behavioural Analytics, Research & Evidence Collection.