Marketing
Customer acquisition analytics
Customer acquisition analytics seeks to establish how effective you are at acquiring new customers, including how effective you are at pinching customers from your competitors.
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Customer acquisition analytics vs Customer engagement analytics
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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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Use this when.
Use it to locate failure across marketing, product, delivery, sales and ordering. Complete a broad review at least annually and monitor campaign economics continuously or whenever the process changes. If radio prices rise or response falls, a business should discover the change immediately rather than after six months of unprofitable spend.
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.
Extracted signals
Strengths, limits, and pitfalls.
Customer acquisition analytics
- Calculate cost per lead and cost per qualified lead separately for every campaign, then compare them with downstream conversion and customer value.
- Define the acquisition event, lookback window, eligible spend and channel attribution first.
- Cost per lead (CPL) divides acquisition spend by captured leads and can act as a revenue indicator.
Watch for
- Do not pool unlike initiatives into one acquisition cost. Differences in audience, offer, timing and attribution can conceal an unprofitable campaign behind a blended average.
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.
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Related templates.
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