Marketing
Customer engagement analytics
Customer engagement analytics is a highly evolving field at the moment where businesses are trying to map the entire customer interactive journey on- and offline.
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Customer engagement analytics vs Customer profitability analytics
These models cover closely related ground. Compare their purpose, scope, practical guidance, and supporting resources to choose the better fit.
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Finance
Customer profitability analytics
Customer profitability analytics is the process of identifying which of your customers are actually making you money.
Read articleChoice logic
Use this when.
Review the full model at least every six months and monitor leading signals between reviews. Reassess it when a product, service or system changes the experience.
Use this analysis continuously to understand where customer value is being created, and give it particular attention when revenue is falling, costs are rising or margins are under pressure. It can reveal whether the problem comes from customer mix, acquisition channels, service demands, pricing or operational cost.
Extracted signals
Strengths, limits, and pitfalls.
Customer engagement analytics
- Identify the few experiences most strongly associated with emotional attachment and repeat behaviour, then test improvements against a baseline rather than trying to please every customer in every way.
- Define engagement for the business and separate emotional state, rational loyalty and behaviour.
- Measure perception through Quantitative Surveys and Qualitative Surveys; use Social Media Analytics for relevant participation and advocacy.
Watch for
- Do not wait for a perfect composite score or treat the first score as truth. Start with a documented model, validate it, monitor drift and refine it without changing definitions so often that trends become meaningless.
Customer profitability analytics
- Assess profitability across the customer’s complete relationship and expected lifetime. When systems treat a person who buys five products as five unrelated customers, each product view can look weak even though the combined relationship is highly profitable. Resolve customer identities before acting on the result.
- The method also applies where “profit” means efficient use of a constrained budget. In one NHS project, only 5 per cent of patients accounted for more than 200 accident and emergency visits. Identifying these super users allowed the organisation to address their underlying needs through different support while freeing resources for other patients.
- The same logic helps broadband providers identify customers whose use of an unlimited plan makes the relationship uneconomic. Regression analysis (Regression Analysis), correlation analysis (Correlation Analysis) and data mining (Data Mining) can help identify the characteristics associated with different profitability groups.
Watch for
- Do not leave customer profitability inside the finance function. Finance understands cost allocation, while sales, service and operations understand the behaviours that create those costs. Their combined interpretation is what turns the metric into sound pricing, service and marketing decisions.
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Application bridge
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Distribution asset inventoryA practical template / form addressing this question: Which channels, customer relationships, defaults, integrations, and communities can support new growth?Pricing readiness checklistA practical template / form addressing this question: Have product, finance, legal, operations, billing, sales, and customer implications of a price been addressed?Metric definition cardDefine a metric's decision question, formula, population, source, owner, cadence, segments, target, caveats, and counter-metric.Benefits Realization ReportBenefits Realization Report